Tag Archives: Amazon Kinesis Firehose

Your guide to Amazon Kinesis sessions, chalk talks, and workshops at AWS re:Invent 2018

Post Syndicated from Larry Heathcote original https://aws.amazon.com/blogs/big-data/your-guide-to-amazon-kinesis-sessions-chalk-talks-and-workshops-at-aws-reinvent-2018/

AWS re:Invent 2018 is almost here! This post includes a list of Amazon Kinesis sessions, chalk talks, and workshops at AWS re:Invent 2018. You can choose the link next to each session description for the session schedule. Use the information to help schedule your conference week in Las Vegas to learn more about Amazon Kinesis.

Sessions

ANT208 – Serverless Video Ingestion & Analytics with Amazon Kinesis Video Streams

Amazon Kinesis Video Streams makes it easy to capture live video, play it back, and store it for real-time and batch-oriented ML-driven analytics. In this session, we first dive deep on the top five best practices for getting started and scaling with Amazon Kinesis Video Streams. Next, we demonstrate a streaming video from a standard USB camera connected to a laptop, and we perform a live playback on a standard browser within minutes. We also have on stage members of Amazon Go, who are building the next generation of physical retail store experiences powered by their “just walk out” technology. They walk through the technical details of their integration with Kinesis Video Streams and highlight their successes and difficulties along the way.

ANT310 – Architecting for Real-Time Insights with Amazon Kinesis

Amazon Kinesis makes it easy to speed up the time it takes for you to get valuable, real-time insights from your streaming data. In this session, we walk through the most popular applications that customers implement using Amazon Kinesis, including streaming extract-transform-load, continuous metric generation, and responsive analytics. Our customer Autodesk joins us to describe how they created real-time metrics generation and analytics using Amazon Kinesis and Amazon Elasticsearch Service. They walk us through their architecture and the best practices they learned in building and deploying their real-time analytics solution.

ANT322-R – High Performance Data Streaming with Amazon Kinesis: Best Practices

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we dive deep into best practices for Kinesis Data Streams and Kinesis Data Firehose to get the most performance out of your data streaming applications. Our customer NICE inContact joins us to discuss how they utilize Amazon Kinesis Data Streams to make real-time decisions on customer contact routing and agent assignments for its Call Center as a Service (CCaaS) Platform. NICE inContact walks through their architecture and requirements for low-latency, accurate processing to be as responsive as possible to changes.

ANT322-R1 – High Performance Data Streaming with Amazon Kinesis: Best Practices

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we dive deep into best practices for Kinesis Data Streams and Kinesis Data Firehose to get the most performance out of your data streaming applications. Comcast uses Amazon Kinesis Data Streams to build a Streaming Data Platform that centralizes data exchanges. It is foundational to the way our data analysts and data scientists derive real-time insights from the data. In the second part of this talk, Comcast zooms into how to properly scale a Kinesis stream. We first list the factors to consider to avoid scaling issues with standard Kinesis stream consumption, and then we see how the new fan-out feature changes these scaling considerations.

SRV316-R & SRV316-R1 – Serverless Stream Processing Pipeline Best Practices

Real-time analytics has traditionally been analyzed using batch processing in DWH/Hadoop environments. Common use cases use data lakes, data science, and machine learning (ML). Creating serverless data-driven architecture and serverless streaming solutions with services like Amazon Kinesis, AWS Lambda, and Amazon Athena can solve real-time ingestion, storage, and analytics challenges, and help you focus on application logic without managing infrastructure. In this session, we introduce design patterns, best practices, and share customer journeys from batch to real-time insights in building modern serverless data-driven architecture applications. Hear how Intel built the Intel Pharma Analytics Platform using a serverless architecture. This AI cloud-based offering enables remote monitoring of patients using an array of sensors, wearable devices, and ML algorithms to objectively quantify the impact of interventions and power clinical studies in various therapeutics conditions.

SEC402-R – AWS, I Choose You: Pokemon’s Battle against the Bots

Join us for this advanced-level talk to learn about Pokemon’s journey defending against DDoS attacks and bad bots with AWS WAF, AWS Shield, and other AWS services. We go through their initial challenges and the evolution of their bot mitigation solution, which includes offline log analysis and dynamic updates of badbot IPs along with rate-based rules. This is an advanced talk and assumes some knowledge of Amazon DynamoDB, Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, AWS Firewall Manager, AWS Shield, and AWS WAF.

Chalk Talks

ANT358 – Serverless Stream Processing Tips & Tricks

Streaming data ingestion and near real-time analysis gives you immediate insights into your data. By using AWS Lambda with Amazon Kinesis, you can obtain these insights without the need to manage servers. But are you doing this in the most optimal way? In this interactive session, we review the best practices for using Lambda with Kinesis, and how to avoid common pitfalls.

ANT359 – Considerations for Building Your First Streaming Application

Do you want to increase your knowledge of AWS big data web services and launch your first big data application on the cloud? In this chalk talk, we provide an overview of many of the AWS analytics services, including Amazon EMR, Amazon Kinesis, Amazon Athena, and Amazon Redshift. We discuss how they are architected together to solve common big data problems, such as ingestion, ETL, and real-time analytics.

ANT360 – Don’t Wait Until Tomorrow: From Batch to Streaming

In recent years, there has been explosive growth in the number of connected devices and real-time data sources. Data is being produced continuously and its production rate is accelerating. Businesses can no longer wait for hours or days to use this data. To gain the most valuable insights, they must use this data immediately so they can react quickly to new information. In this chalk talk, we discuss how to take advantage of streaming data sources to analyze and react in near-real time. In addition, we present different options for how to solve a real-world scenario and walk through those solutions.

ANT361 – Using Amazon Kinesis Data Streams as a Low-Latency Message Bus

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this chalk talk, we dive deep into best practices for Kinesis Data Streams and how to optimize for low-latency, multi-consumer solutions.

BAP328-R & BAP328-R1 – Architectures for Gaining Data Insights into Your Contact Center Experience

Join us for a deep dive into using Amazon Kinesis Data Analytics for insight into what’s happening with the contacts and agents in your Amazon Connect contact center. Learn how to leverage AWS analytics and ML services to inspect, transform, and gain insight into the customer’s journey through your contact center. We also show you how to use Alexa for Business to receive timely voice-activated business intelligence on your contact center’s performance.

Workshops

Before starting a workshop, you should have a basic understanding of Amazon Kinesis. Please bring your laptop and power supply to the workshop.

ANT213-R – Build Your First Big Data Application on AWS

Do you want to increase your knowledge of AWS big data web services and launch your first big data application on the cloud? In this session, we walk you through simplifying big data processing as a data bus comprising ingest, store, process, and visualize. You will build a big data application using AWS managed services, including Amazon Athena, Amazon Kinesis, Amazon DynamoDB, and Amazon S3. Along the way, we review architecture design patterns for big data applications and give you access to a take-home lab so you can rebuild and customize the application yourself.

ANT213-R1 – Build Your First Big Data Application on AWS

Do you want to increase your knowledge of AWS big data web services and launch your first big data application on the cloud? In this session, we walk you through simplifying big data processing as a data bus comprising ingest, store, process, and visualize. You will build a big data application using AWS managed services, including Amazon Athena, Amazon Kinesis, Amazon DynamoDB, and Amazon S3. Along the way, we review architecture design patterns for big data applications and give you access to a take-home lab so you can rebuild and customize the application yourself.

ANT357 – Stream Video, Analyze It in Real Time, and Share It in Real Time

Video is ‘big data.’ Image sensors—in our smartphones, smart home devices, and traffic cameras—are getting Internet-connected. Massive streams of video data are generated, but currently not mined for real-time insights to drive businesses forward. In this workshop, learn to capture, process, and analyze video streams. Build and configure your camera device’s media pipeline to start streaming video into the AWS Cloud using Amazon Kinesis Video Streams. Next, build and deploy your own machine learning (ML) model in Amazon SageMaker to generate inferences about objects or activities in your video stream. Finally, build a browser-based web player to view the video in Live and On-Demand modes, including the analyzed video stream. In this workshop, you use Amazon Kinesis Video Streams, Amazon SageMaker, Amazon Rekognition Video, and Amazon ECS.

ANT362 – Use Streaming Data to Gain Real-Time Insights into Your Business

In recent years, there has been an explosive growth in the number of connected devices and real-time data sources. Because of this, data is being continuously produced, and its production rate is accelerating. Businesses can no longer wait for hours or days to use this data. To gain the most valuable insights, they must use this data immediately so they can react quickly to new information. In this workshop, you will learn how to take advantage of streaming data sources to analyze and react in near real time. We provide several requirements for a real-world streaming data scenario, and you’re tasked with creating a solution that successfully satisfies the requirements using services such as Amazon Kinesis, AWS Lambda, and Amazon SNS.

ANT318-R – Build, Deploy and Serve Machine learning models on streaming data using Amazon SageMaker, Apache Spark on Amazon EMR and Amazon Kinesis

As data exponentially grows in organizations, there is an increasing need to use machine learning (ML) to gather insights from this data at scale and to use those insights to perform real-time predictions on incoming data. In this workshop, we walk you through how to train an Apache Spark model using Amazon SageMaker that points to Apache Livy and running on an Amazon EMR Spark cluster. We also show you how to host the Spark model on Amazon SageMaker to serve a RESTful inference API. Finally, we show you how to use the RESTful API to serve real-time predictions on streaming data from Amazon Kinesis Data Streams.

ANT318-R1 – Build, Deploy and Serve Machine learning models on streaming data using Amazon SageMaker, Apache Spark on Amazon EMR and Amazon Kinesis

As data exponentially grows in organizations, there is an increasing need to use machine learning (ML) to gather insights from this data at scale and to use those insights to perform real-time predictions on incoming data. In this workshop, we walk you through how to train an Apache Spark model using Amazon SageMaker that points to Apache Livy and running on an Amazon EMR Spark cluster. We also show you how to host the Spark model on Amazon SageMaker to serve a RESTful inference API. Finally, we show you how to use the RESTful API to serve real-time predictions on streaming data from Amazon Kinesis Data Streams.

GPSWS406 – Advanced Serverless Data Processing

In this hands-on workshop, you learn best practices and architectural patterns for building streaming data processing pipelines without servers. Using Amazon Kinesis, AWS Lambda, and other services, you have the opportunity to build, deploy, and monitor an application to ingest and process high-velocity data at scale. This advanced workshop assumes that you have experience writing Lambda functions and understand the basics of the AWS serverless platform, so come ready to dive into the deep end. Bring your laptop with a full keyboard. We provide a sandbox AWS account for you to use during the workshop.

MAE309 – Build an AWS Analytics Solution to Monitor the Video Streaming Experience

In this workshop, we build and deploy an end-to-end analytics solution for monitoring the video streaming experience. We integrate an open source video player with Amazon Kinesis Data Streams to capture events in real time. We explore the data available for capture and a variety of use cases: from generating alerts on poor experience to content recommendations based on user behavior. We also show you how this real-time data can be archived in a data lake and further used to generate reports of aggregate performance and experience across a number of dimensions.

ADT401 – Real-Time Web Analytics with Amazon Kinesis Data Analytics

Knowing what users are doing on your websites in real time provides insights you can act on without waiting for delayed batch processing of clickstream data. Watching the immediate impact on user behavior after new releases, detecting and responding to anomalies, situational awareness, and evaluating trends are all benefits of real-time website analytics. In this workshop, we build a cost-optimized platform to capture web beacon traffic, analyze it for interesting metrics, and display it on a customized dashboard. We start by deploying the Web Analytics Solution Accelerator, then once the core is complete, we extend their solution to capture new and interesting metrics, process those with Amazon Kinesis Data Analytics, and display new graphs on their custom dashboard. Participants come away with a fully functional system for capturing, analyzing, and displaying valuable website metrics in real time.

GAM305 – Dynamic Encounters for Veteran Players Using Machine Learning

Are you trying to keep your game fresh for long-time players but don’t have the resources to keep building new handcrafted content? Join this session to learn how to launch dynamic content for groups of players without relying on static techniques like instancing or spawn points. We dive into Amazon Kinesis and Amazon Kinesis Data Analytics for real-time data collection and hotspot detection, and machine learning for encounter-building based on observed player behavior.

Conclusion

We look forward to seeing you at AWS re:Invent 2018 in Las Vegas. In addition to the sessions described in this blog post, please stop by the Analytics booth during Expo hours to learn more about Amazon Kinesis.

 


About the Author

Larry Heathcote is a Principal Product Marketing Manager at Amazon Web Services. Larry is passionate about seeing the results of data-driven insights on business outcomes. He enjoys family time, home projects, grilling out, and classic barbeque.

 

 

Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift

Post Syndicated from Roy Hasson original https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/

Amazon Kinesis Data Firehose is the easiest way to capture and stream data into a data lake built on Amazon S3. This data can be anything—from AWS service logs like AWS CloudTrail log files, Amazon VPC Flow Logs, Application Load Balancer logs, and others. It can also be IoT events, game events, and much more. To efficiently query this data, a time-consuming ETL (extract, transform, and load) process is required to massage and convert the data to an optimal file format, which increases the time to insight. This situation is less than ideal, especially for real-time data that loses its value over time.

To solve this common challenge, Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

Amazon Connect is a simple-to-use, cloud-based contact center service that makes it easy for any business to provide a great customer experience at a lower cost than common alternatives. Its open platform design enables easy integration with other systems. One of those systems is Amazon Kinesis—in particular, Kinesis Data Streams and Kinesis Data Firehose.

What’s really exciting is that you can now save events from Amazon Connect to S3 in Apache Parquet format. You can then perform analytics using Amazon Athena and Amazon Redshift Spectrum in real time, taking advantage of this key performance and cost optimization. Of course, Amazon Connect is only one example. This new capability opens the door for a great deal of opportunity, especially as organizations continue to build their data lakes.

Amazon Connect includes an array of analytics views in the Administrator dashboard. But you might want to run other types of analysis. In this post, I describe how to set up a data stream from Amazon Connect through Kinesis Data Streams and Kinesis Data Firehose and out to S3, and then perform analytics using Athena and Amazon Redshift Spectrum. I focus primarily on the Kinesis Data Firehose support for Parquet and its integration with the AWS Glue Data Catalog, Amazon Athena, and Amazon Redshift.

Solution overview

Here is how the solution is laid out:

 

 

The following sections walk you through each of these steps to set up the pipeline.

1. Define the schema

When Kinesis Data Firehose processes incoming events and converts the data to Parquet, it needs to know which schema to apply. The reason is that many times, incoming events contain all or some of the expected fields based on which values the producers are advertising. A typical process is to normalize the schema during a batch ETL job so that you end up with a consistent schema that can easily be understood and queried. Doing this introduces latency due to the nature of the batch process. To overcome this issue, Kinesis Data Firehose requires the schema to be defined in advance.

To see the available columns and structures, see Amazon Connect Agent Event Streams. For the purpose of simplicity, I opted to make all the columns of type String rather than create the nested structures. But you can definitely do that if you want.

The simplest way to define the schema is to create a table in the Amazon Athena console. Open the Athena console, and paste the following create table statement, substituting your own S3 bucket and prefix for where your event data will be stored. A Data Catalog database is a logical container that holds the different tables that you can create. The default database name shown here should already exist. If it doesn’t, you can create it or use another database that you’ve already created.

CREATE EXTERNAL TABLE default.kfhconnectblog (
  awsaccountid string,
  agentarn string,
  currentagentsnapshot string,
  eventid string,
  eventtimestamp string,
  eventtype string,
  instancearn string,
  previousagentsnapshot string,
  version string
)
STORED AS parquet
LOCATION 's3://your_bucket/kfhconnectblog/'
TBLPROPERTIES ("parquet.compression"="SNAPPY")

That’s all you have to do to prepare the schema for Kinesis Data Firehose.

2. Define the data streams

Next, you need to define the Kinesis data streams that will be used to stream the Amazon Connect events.  Open the Kinesis Data Streams console and create two streams.  You can configure them with only one shard each because you don’t have a lot of data right now.

3. Define the Kinesis Data Firehose delivery stream for Parquet

Let’s configure the Data Firehose delivery stream using the data stream as the source and Amazon S3 as the output. Start by opening the Kinesis Data Firehose console and creating a new data delivery stream. Give it a name, and associate it with the Kinesis data stream that you created in Step 2.

As shown in the following screenshot, enable Record format conversion (1) and choose Apache Parquet (2). As you can see, Apache ORC is also supported. Scroll down and provide the AWS Glue Data Catalog database name (3) and table names (4) that you created in Step 1. Choose Next.

To make things easier, the output S3 bucket and prefix fields are automatically populated using the values that you defined in the LOCATION parameter of the create table statement from Step 1. Pretty cool. Additionally, you have the option to save the raw events into another location as defined in the Source record S3 backup section. Don’t forget to add a trailing forward slash “ / “ so that Data Firehose creates the date partitions inside that prefix.

On the next page, in the S3 buffer conditions section, there is a note about configuring a large buffer size. The Parquet file format is highly efficient in how it stores and compresses data. Increasing the buffer size allows you to pack more rows into each output file, which is preferred and gives you the most benefit from Parquet.

Compression using Snappy is automatically enabled for both Parquet and ORC. You can modify the compression algorithm by using the Kinesis Data Firehose API and update the OutputFormatConfiguration.

Be sure to also enable Amazon CloudWatch Logs so that you can debug any issues that you might run into.

Lastly, finalize the creation of the Firehose delivery stream, and continue on to the next section.

4. Set up the Amazon Connect contact center

After setting up the Kinesis pipeline, you now need to set up a simple contact center in Amazon Connect. The Getting Started page provides clear instructions on how to set up your environment, acquire a phone number, and create an agent to accept calls.

After setting up the contact center, in the Amazon Connect console, choose your Instance Alias, and then choose Data Streaming. Under Agent Event, choose the Kinesis data stream that you created in Step 2, and then choose Save.

At this point, your pipeline is complete.  Agent events from Amazon Connect are generated as agents go about their day. Events are sent via Kinesis Data Streams to Kinesis Data Firehose, which converts the event data from JSON to Parquet and stores it in S3. Athena and Amazon Redshift Spectrum can simply query the data without any additional work.

So let’s generate some data. Go back into the Administrator console for your Amazon Connect contact center, and create an agent to handle incoming calls. In this example, I creatively named mine Agent One. After it is created, Agent One can get to work and log into their console and set their availability to Available so that they are ready to receive calls.

To make the data a bit more interesting, I also created a second agent, Agent Two. I then made some incoming and outgoing calls and caused some failures to occur, so I now have enough data available to analyze.

5. Analyze the data with Athena

Let’s open the Athena console and run some queries. One thing you’ll notice is that when we created the schema for the dataset, we defined some of the fields as Strings even though in the documentation they were complex structures.  The reason for doing that was simply to show some of the flexibility of Athena to be able to parse JSON data. However, you can define nested structures in your table schema so that Kinesis Data Firehose applies the appropriate schema to the Parquet file.

Let’s run the first query to see which agents have logged into the system.

The query might look complex, but it’s fairly straightforward:

WITH dataset AS (
  SELECT 
    from_iso8601_timestamp(eventtimestamp) AS event_ts,
    eventtype,
    -- CURRENT STATE
    json_extract_scalar(
      currentagentsnapshot,
      '$.agentstatus.name') AS current_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        currentagentsnapshot,
        '$.agentstatus.starttimestamp')) AS current_starttimestamp,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.firstname') AS current_firstname,
    json_extract_scalar(
      currentagentsnapshot,
      '$.configuration.lastname') AS current_lastname,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.username') AS current_username,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') AS               current_outboundqueue,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as current_inboundqueue,
    -- PREVIOUS STATE
    json_extract_scalar(
      previousagentsnapshot, 
      '$.agentstatus.name') as prev_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        previousagentsnapshot, 
       '$.agentstatus.starttimestamp')) as prev_starttimestamp,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.firstname') as prev_firstname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.lastname') as prev_lastname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.username') as prev_username,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') as current_outboundqueue,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as prev_inboundqueue
  from kfhconnectblog
  where eventtype <> 'HEART_BEAT'
)
SELECT
  current_status as status,
  current_username as username,
  event_ts
FROM dataset
WHERE eventtype = 'LOGIN' AND current_username <> ''
ORDER BY event_ts DESC

The query output looks something like this:

Here is another query that shows the sessions each of the agents engaged with. It tells us where they were incoming or outgoing, if they were completed, and where there were missed or failed calls.

WITH src AS (
  SELECT
     eventid,
     json_extract_scalar(currentagentsnapshot, '$.configuration.username') as username,
     cast(json_extract(currentagentsnapshot, '$.contacts') AS ARRAY(JSON)) as c,
     cast(json_extract(previousagentsnapshot, '$.contacts') AS ARRAY(JSON)) as p
  from kfhconnectblog
),
src2 AS (
  SELECT *
  FROM src CROSS JOIN UNNEST (c, p) AS contacts(c_item, p_item)
),
dataset AS (
SELECT 
  eventid,
  username,
  json_extract_scalar(c_item, '$.contactid') as c_contactid,
  json_extract_scalar(c_item, '$.channel') as c_channel,
  json_extract_scalar(c_item, '$.initiationmethod') as c_direction,
  json_extract_scalar(c_item, '$.queue.name') as c_queue,
  json_extract_scalar(c_item, '$.state') as c_state,
  from_iso8601_timestamp(json_extract_scalar(c_item, '$.statestarttimestamp')) as c_ts,
  
  json_extract_scalar(p_item, '$.contactid') as p_contactid,
  json_extract_scalar(p_item, '$.channel') as p_channel,
  json_extract_scalar(p_item, '$.initiationmethod') as p_direction,
  json_extract_scalar(p_item, '$.queue.name') as p_queue,
  json_extract_scalar(p_item, '$.state') as p_state,
  from_iso8601_timestamp(json_extract_scalar(p_item, '$.statestarttimestamp')) as p_ts
FROM src2
)
SELECT 
  username,
  c_channel as channel,
  c_direction as direction,
  p_state as prev_state,
  c_state as current_state,
  c_ts as current_ts,
  c_contactid as id
FROM dataset
WHERE c_contactid = p_contactid
ORDER BY id DESC, current_ts ASC

The query output looks similar to the following:

6. Analyze the data with Amazon Redshift Spectrum

With Amazon Redshift Spectrum, you can query data directly in S3 using your existing Amazon Redshift data warehouse cluster. Because the data is already in Parquet format, Redshift Spectrum gets the same great benefits that Athena does.

Here is a simple query to show querying the same data from Amazon Redshift. Note that to do this, you need to first create an external schema in Amazon Redshift that points to the AWS Glue Data Catalog.

SELECT 
  eventtype,
  json_extract_path_text(currentagentsnapshot,'agentstatus','name') AS current_status,
  json_extract_path_text(currentagentsnapshot, 'configuration','firstname') AS current_firstname,
  json_extract_path_text(currentagentsnapshot, 'configuration','lastname') AS current_lastname,
  json_extract_path_text(
    currentagentsnapshot,
    'configuration','routingprofile','defaultoutboundqueue','name') AS current_outboundqueue,
FROM default_schema.kfhconnectblog

The following shows the query output:

Summary

In this post, I showed you how to use Kinesis Data Firehose to ingest and convert data to columnar file format, enabling real-time analysis using Athena and Amazon Redshift. This great feature enables a level of optimization in both cost and performance that you need when storing and analyzing large amounts of data. This feature is equally important if you are investing in building data lakes on AWS.

 


Additional Reading

If you found this post useful, be sure to check out Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight and Work with partitioned data in AWS Glue.


About the Author

Roy Hasson is a Global Business Development Manager for AWS Analytics. He works with customers around the globe to design solutions to meet their data processing, analytics and business intelligence needs. Roy is big Manchester United fan cheering his team on and hanging out with his family.

 

 

 

How I built a data warehouse using Amazon Redshift and AWS services in record time

Post Syndicated from Stephen Borg original https://aws.amazon.com/blogs/big-data/how-i-built-a-data-warehouse-using-amazon-redshift-and-aws-services-in-record-time/

This is a customer post by Stephen Borg, the Head of Big Data and BI at Cerberus Technologies.

Cerberus Technologies, in their own words: Cerberus is a company founded in 2017 by a team of visionary iGaming veterans. Our mission is simple – to offer the best tech solutions through a data-driven and a customer-first approach, delivering innovative solutions that go against traditional forms of working and process. This mission is based on the solid foundations of reliability, flexibility and security, and we intend to fundamentally change the way iGaming and other industries interact with technology.

Over the years, I have developed and created a number of data warehouses from scratch. Recently, I built a data warehouse for the iGaming industry single-handedly. To do it, I used the power and flexibility of Amazon Redshift and the wider AWS data management ecosystem. In this post, I explain how I was able to build a robust and scalable data warehouse without the large team of experts typically needed.

In two of my recent projects, I ran into challenges when scaling our data warehouse using on-premises infrastructure. Data was growing at many tens of gigabytes per day, and query performance was suffering. Scaling required major capital investment for hardware and software licenses, and also significant operational costs for maintenance and technical staff to keep it running and performing well. Unfortunately, I couldn’t get the resources needed to scale the infrastructure with data growth, and these projects were abandoned. Thanks to cloud data warehousing, the bottleneck of infrastructure resources, capital expense, and operational costs have been significantly reduced or have totally gone away. There is no more excuse for allowing obstacles of the past to delay delivering timely insights to decision makers, no matter how much data you have.

With Amazon Redshift and AWS, I delivered a cloud data warehouse to the business very quickly, and with a small team: me. I didn’t have to order hardware or software, and I no longer needed to install, configure, tune, or keep up with patches and version updates. Instead, I easily set up a robust data processing pipeline and we were quickly ingesting and analyzing data. Now, my data warehouse team can be extremely lean, and focus more time on bringing in new data and delivering insights. In this post, I show you the AWS services and the architecture that I used.

Handling data feeds

I have several different data sources that provide everything needed to run the business. The data includes activity from our iGaming platform, social media posts, clickstream data, marketing and campaign performance, and customer support engagements.

To handle the diversity of data feeds, I developed abstract integration applications using Docker that run on Amazon EC2 Container Service (Amazon ECS) and feed data to Amazon Kinesis Data Streams. These data streams can be used for real time analytics. In my system, each record in Kinesis is preprocessed by an AWS Lambda function to cleanse and aggregate information. My system then routes it to be stored where I need on Amazon S3 by Amazon Kinesis Data Firehose. Suppose that you used an on-premises architecture to accomplish the same task. A team of data engineers would be required to maintain and monitor a Kafka cluster, develop applications to stream data, and maintain a Hadoop cluster and the infrastructure underneath it for data storage. With my stream processing architecture, there are no servers to manage, no disk drives to replace, and no service monitoring to write.

Setting up a Kinesis stream can be done with a few clicks, and the same for Kinesis Firehose. Firehose can be configured to automatically consume data from a Kinesis Data Stream, and then write compressed data every N minutes to Amazon S3. When I want to process a Kinesis data stream, it’s very easy to set up a Lambda function to be executed on each message received. I can just set a trigger from the AWS Lambda Management Console, as shown following.

I also monitor the duration of function execution using Amazon CloudWatch and AWS X-Ray.

Regardless of the format I receive the data from our partners, I can send it to Kinesis as JSON data using my own formatters. After Firehose writes this to Amazon S3, I have everything in nearly the same structure I received but compressed, encrypted, and optimized for reading.

This data is automatically crawled by AWS Glue and placed into the AWS Glue Data Catalog. This means that I can immediately query the data directly on S3 using Amazon Athena or through Amazon Redshift Spectrum. Previously, I used Amazon EMR and an Amazon RDS–based metastore in Apache Hive for catalog management. Now I can avoid the complexity of maintaining Hive Metastore catalogs. Glue takes care of high availability and the operations side so that I know that end users can always be productive.

Working with Amazon Athena and Amazon Redshift for analysis

I found Amazon Athena extremely useful out of the box for ad hoc analysis. Our engineers (me) use Athena to understand new datasets that we receive and to understand what transformations will be needed for long-term query efficiency.

For our data analysts and data scientists, we’ve selected Amazon Redshift. Amazon Redshift has proven to be the right tool for us over and over again. It easily processes 20+ million transactions per day, regardless of the footprint of the tables and the type of analytics required by the business. Latency is low and query performance expectations have been more than met. We use Redshift Spectrum for long-term data retention, which enables me to extend the analytic power of Amazon Redshift beyond local data to anything stored in S3, and without requiring me to load any data. Redshift Spectrum gives me the freedom to store data where I want, in the format I want, and have it available for processing when I need it.

To load data directly into Amazon Redshift, I use AWS Data Pipeline to orchestrate data workflows. I create Amazon EMR clusters on an intra-day basis, which I can easily adjust to run more or less frequently as needed throughout the day. EMR clusters are used together with Amazon RDS, Apache Spark 2.0, and S3 storage. The data pipeline application loads ETL configurations from Spring RESTful services hosted on AWS Elastic Beanstalk. The application then loads data from S3 into memory, aggregates and cleans the data, and then writes the final version of the data to Amazon Redshift. This data is then ready to use for analysis. Spark on EMR also helps with recommendations and personalization use cases for various business users, and I find this easy to set up and deliver what users want. Finally, business users use Amazon QuickSight for self-service BI to slice, dice, and visualize the data depending on their requirements.

Each AWS service in this architecture plays its part in saving precious time that’s crucial for delivery and getting different departments in the business on board. I found the services easy to set up and use, and all have proven to be highly reliable for our use as our production environments. When the architecture was in place, scaling out was either completely handled by the service, or a matter of a simple API call, and crucially doesn’t require me to change one line of code. Increasing shards for Kinesis can be done in a minute by editing a stream. Increasing capacity for Lambda functions can be accomplished by editing the megabytes allocated for processing, and concurrency is handled automatically. EMR cluster capacity can easily be increased by changing the master and slave node types in Data Pipeline, or by using Auto Scaling. Lastly, RDS and Amazon Redshift can be easily upgraded without any major tasks to be performed by our team (again, me).

In the end, using AWS services including Kinesis, Lambda, Data Pipeline, and Amazon Redshift allows me to keep my team lean and highly productive. I eliminated the cost and delays of capital infrastructure, as well as the late night and weekend calls for support. I can now give maximum value to the business while keeping operational costs down. My team pushed out an agile and highly responsive data warehouse solution in record time and we can handle changing business requirements rapidly, and quickly adapt to new data and new user requests.


Additional Reading

If you found this post useful, be sure to check out Deploy a Data Warehouse Quickly with Amazon Redshift, Amazon RDS for PostgreSQL and Tableau Server and Top 8 Best Practices for High-Performance ETL Processing Using Amazon Redshift.


About the Author

Stephen Borg is the Head of Big Data and BI at Cerberus Technologies. He has a background in platform software engineering, and first became involved in data warehousing using the typical RDBMS, SQL, ETL, and BI tools. He quickly became passionate about providing insight to help others optimize the business and add personalization to products. He is now the Head of Big Data and BI at Cerberus Technologies.

 

 

 

Optimize Delivery of Trending, Personalized News Using Amazon Kinesis and Related Services

Post Syndicated from Yukinori Koide original https://aws.amazon.com/blogs/big-data/optimize-delivery-of-trending-personalized-news-using-amazon-kinesis-and-related-services/

This is a guest post by Yukinori Koide, an the head of development for the Newspass department at Gunosy.

Gunosy is a news curation application that covers a wide range of topics, such as entertainment, sports, politics, and gourmet news. The application has been installed more than 20 million times.

Gunosy aims to provide people with the content they want without the stress of dealing with a large influx of information. We analyze user attributes, such as gender and age, and past activity logs like click-through rate (CTR). We combine this information with article attributes to provide trending, personalized news articles to users.

In this post, I show you how to process user activity logs in real time using Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and related AWS services.

Why does Gunosy need real-time processing?

Users need fresh and personalized news. There are two constraints to consider when delivering appropriate articles:

  • Time: Articles have freshness—that is, they lose value over time. New articles need to reach users as soon as possible.
  • Frequency (volume): Only a limited number of articles can be shown. It’s unreasonable to display all articles in the application, and users can’t read all of them anyway.

To deliver fresh articles with a high probability that the user is interested in them, it’s necessary to include not only past user activity logs and some feature values of articles, but also the most recent (real-time) user activity logs.

We optimize the delivery of articles with these two steps.

  1. Personalization: Deliver articles based on each user’s attributes, past activity logs, and feature values of each article—to account for each user’s interests.
  2. Trends analysis/identification: Optimize delivering articles using recent (real-time) user activity logs—to incorporate the latest trends from all users.

Optimizing the delivery of articles is always a cold start. Initially, we deliver articles based on past logs. We then use real-time data to optimize as quickly as possible. In addition, news has a short freshness time. Specifically, day-old news is past news, and even the news that is three hours old is past news. Therefore, shortening the time between step 1 and step 2 is important.

To tackle this issue, we chose AWS for processing streaming data because of its fully managed services, cost-effectiveness, and so on.

Solution

The following diagrams depict the architecture for optimizing article delivery by processing real-time user activity logs

There are three processing flows:

  1. Process real-time user activity logs.
  2. Store and process all user-based and article-based logs.
  3. Execute ad hoc or heavy queries.

In this post, I focus on the first processing flow and explain how it works.

Process real-time user activity logs

The following are the steps for processing user activity logs in real time using Kinesis Data Streams and Kinesis Data Analytics.

  1. The Fluentd server sends the following user activity logs to Kinesis Data Streams:
{"article_id": 12345, "user_id": 12345, "action": "click"}
{"article_id": 12345, "user_id": 12345, "action": "impression"}
...
  1. Map rows of logs to columns in Kinesis Data Analytics.

  1. Set the reference data to Kinesis Data Analytics from Amazon S3.

a. Gunosy has user attributes such as gender, age, and segment. Prepare the following CSV file (user_id, gender, segment_id) and put it in Amazon S3:

101,female,1
102,male,2
103,female,3
...

b. Add the application reference data source to Kinesis Data Analytics using the AWS CLI:

$ aws kinesisanalytics add-application-reference-data-source \
  --application-name <my-application-name> \
  --current-application-version-id <version-id> \
  --reference-data-source '{
  "TableName": "REFERENCE_DATA_SOURCE",
  "S3ReferenceDataSource": {
    "BucketARN": "arn:aws:s3:::<my-bucket-name>",
    "FileKey": "mydata.csv",
    "ReferenceRoleARN": "arn:aws:iam::<account-id>:role/..."
  },
  "ReferenceSchema": {
    "RecordFormat": {
      "RecordFormatType": "CSV",
      "MappingParameters": {
        "CSVMappingParameters": {"RecordRowDelimiter": "\n", "RecordColumnDelimiter": ","}
      }
    },
    "RecordEncoding": "UTF-8",
    "RecordColumns": [
      {"Name": "USER_ID", "Mapping": "0", "SqlType": "INTEGER"},
      {"Name": "GENDER",  "Mapping": "1", "SqlType": "VARCHAR(32)"},
      {"Name": "SEGMENT_ID", "Mapping": "2", "SqlType": "INTEGER"}
    ]
  }
}'

This application reference data source can be referred on Kinesis Data Analytics.

  1. Run a query against the source data stream on Kinesis Data Analytics with the application reference data source.

a. Define the temporary stream named TMP_SQL_STREAM.

CREATE OR REPLACE STREAM "TMP_SQL_STREAM" (
  GENDER VARCHAR(32), SEGMENT_ID INTEGER, ARTICLE_ID INTEGER
);

b. Insert the joined source stream and application reference data source into the temporary stream.

CREATE OR REPLACE PUMP "TMP_PUMP" AS
INSERT INTO "TMP_SQL_STREAM"
SELECT STREAM
  R.GENDER, R.SEGMENT_ID, S.ARTICLE_ID, S.ACTION
FROM      "SOURCE_SQL_STREAM_001" S
LEFT JOIN "REFERENCE_DATA_SOURCE" R
  ON S.USER_ID = R.USER_ID;

c. Define the destination stream named DESTINATION_SQL_STREAM.

CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
  TIME TIMESTAMP, GENDER VARCHAR(32), SEGMENT_ID INTEGER, ARTICLE_ID INTEGER, 
  IMPRESSION INTEGER, CLICK INTEGER
);

d. Insert the processed temporary stream, using a tumbling window, into the destination stream per minute.

CREATE OR REPLACE PUMP "STREAM_PUMP" AS
INSERT INTO "DESTINATION_SQL_STREAM"
SELECT STREAM
  ROW_TIME AS TIME,
  GENDER, SEGMENT_ID, ARTICLE_ID,
  SUM(CASE ACTION WHEN 'impression' THEN 1 ELSE 0 END) AS IMPRESSION,
  SUM(CASE ACTION WHEN 'click' THEN 1 ELSE 0 END) AS CLICK
FROM "TMP_SQL_STREAM"
GROUP BY
  GENDER, SEGMENT_ID, ARTICLE_ID,
  FLOOR("TMP_SQL_STREAM".ROWTIME TO MINUTE);

The results look like the following:

  1. Insert the results into Amazon Elasticsearch Service (Amazon ES).
  2. Batch servers get results from Amazon ES every minute. They then optimize delivering articles with other data sources using a proprietary optimization algorithm.

How to connect a stream to another stream in another AWS Region

When we built the solution, Kinesis Data Analytics was not available in the Asia Pacific (Tokyo) Region, so we used the US West (Oregon) Region. The following shows how we connected a data stream to another data stream in the other Region.

There is no need to continue containing all components in a single AWS Region, unless you have a situation where a response difference at the millisecond level is critical to the service.

Benefits

The solution provides benefits for both our company and for our users. Benefits for the company are cost savings—including development costs, operational costs, and infrastructure costs—and reducing delivery time. Users can now find articles of interest more quickly. The solution can process more than 500,000 records per minute, and it enables fast and personalized news curating for our users.

Conclusion

In this post, I showed you how we optimize trending user activities to personalize news using Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and related AWS services in Gunosy.

AWS gives us a quick and economical solution and a good experience.

If you have questions or suggestions, please comment below.


Additional Reading

If you found this post useful, be sure to check out Implement Serverless Log Analytics Using Amazon Kinesis Analytics and Joining and Enriching Streaming Data on Amazon Kinesis.


About the Authors

Yukinori Koide is the head of development for the Newspass department at Gunosy. He is working on standardization of provisioning and deployment flow, promoting the utilization of serverless and containers for machine learning and AI services. His favorite AWS services are DynamoDB, Lambda, Kinesis, and ECS.

 

 

 

Akihiro Tsukada is a start-up solutions architect with AWS. He supports start-up companies in Japan technically at many levels, ranging from seed to later-stage.

 

 

 

 

Yuta Ishii is a solutions architect with AWS. He works with our customers to provide architectural guidance for building media & entertainment services, helping them improve the value of their services when using AWS.

 

 

 

 

 

Power data ingestion into Splunk using Amazon Kinesis Data Firehose

Post Syndicated from Tarik Makota original https://aws.amazon.com/blogs/big-data/power-data-ingestion-into-splunk-using-amazon-kinesis-data-firehose/

In late September, during the annual Splunk .conf, Splunk and Amazon Web Services (AWS) jointly announced that Amazon Kinesis Data Firehose now supports Splunk Enterprise and Splunk Cloud as a delivery destination. This native integration between Splunk Enterprise, Splunk Cloud, and Amazon Kinesis Data Firehose is designed to make AWS data ingestion setup seamless, while offering a secure and fault-tolerant delivery mechanism. We want to enable customers to monitor and analyze machine data from any source and use it to deliver operational intelligence and optimize IT, security, and business performance.

With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose.

Push vs. Pull data ingestion

Presently, customers use a combination of two ingestion patterns, primarily based on data source and volume, in addition to existing company infrastructure and expertise:

  1. Pull-based approach: Using dedicated pollers running the popular Splunk Add-on for AWS to pull data from various AWS services such as Amazon CloudWatch or Amazon S3.
  2. Push-based approach: Streaming data directly from AWS to Splunk HTTP Event Collector (HEC) by using AWS Lambda. Examples of applicable data sources include CloudWatch Logs and Amazon Kinesis Data Streams.

The pull-based approach offers data delivery guarantees such as retries and checkpointing out of the box. However, it requires more ops to manage and orchestrate the dedicated pollers, which are commonly running on Amazon EC2 instances. With this setup, you pay for the infrastructure even when it’s idle.

On the other hand, the push-based approach offers a low-latency scalable data pipeline made up of serverless resources like AWS Lambda sending directly to Splunk indexers (by using Splunk HEC). This approach translates into lower operational complexity and cost. However, if you need guaranteed data delivery then you have to design your solution to handle issues such as a Splunk connection failure or Lambda execution failure. To do so, you might use, for example, AWS Lambda Dead Letter Queues.

How about getting the best of both worlds?

Let’s go over the new integration’s end-to-end solution and examine how Kinesis Data Firehose and Splunk together expand the push-based approach into a native AWS solution for applicable data sources.

By using a managed service like Kinesis Data Firehose for data ingestion into Splunk, we provide out-of-the-box reliability and scalability. One of the pain points of the old approach was the overhead of managing the data collection nodes (Splunk heavy forwarders). With the new Kinesis Data Firehose to Splunk integration, there are no forwarders to manage or set up. Data producers (1) are configured through the AWS Management Console to drop data into Kinesis Data Firehose.

You can also create your own data producers. For example, you can drop data into a Firehose delivery stream by using Amazon Kinesis Agent, or by using the Firehose API (PutRecord(), PutRecordBatch()), or by writing to a Kinesis Data Stream configured to be the data source of a Firehose delivery stream. For more details, refer to Sending Data to an Amazon Kinesis Data Firehose Delivery Stream.

You might need to transform the data before it goes into Splunk for analysis. For example, you might want to enrich it or filter or anonymize sensitive data. You can do so using AWS Lambda. In this scenario, Kinesis Data Firehose buffers data from the incoming source data, sends it to the specified Lambda function (2), and then rebuffers the transformed data to the Splunk Cluster. Kinesis Data Firehose provides the Lambda blueprints that you can use to create a Lambda function for data transformation.

Systems fail all the time. Let’s see how this integration handles outside failures to guarantee data durability. In cases when Kinesis Data Firehose can’t deliver data to the Splunk Cluster, data is automatically backed up to an S3 bucket. You can configure this feature while creating the Firehose delivery stream (3). You can choose to back up all data or only the data that’s failed during delivery to Splunk.

In addition to using S3 for data backup, this Firehose integration with Splunk supports Splunk Indexer Acknowledgments to guarantee event delivery. This feature is configured on Splunk’s HTTP Event Collector (HEC) (4). It ensures that HEC returns an acknowledgment to Kinesis Data Firehose only after data has been indexed and is available in the Splunk cluster (5).

Now let’s look at a hands-on exercise that shows how to forward VPC flow logs to Splunk.

How-to guide

To process VPC flow logs, we implement the following architecture.

Amazon Virtual Private Cloud (Amazon VPC) delivers flow log files into an Amazon CloudWatch Logs group. Using a CloudWatch Logs subscription filter, we set up real-time delivery of CloudWatch Logs to an Kinesis Data Firehose stream.

Data coming from CloudWatch Logs is compressed with gzip compression. To work with this compression, we need to configure a Lambda-based data transformation in Kinesis Data Firehose to decompress the data and deposit it back into the stream. Firehose then delivers the raw logs to the Splunk Http Event Collector (HEC).

If delivery to the Splunk HEC fails, Firehose deposits the logs into an Amazon S3 bucket. You can then ingest the events from S3 using an alternate mechanism such as a Lambda function.

When data reaches Splunk (Enterprise or Cloud), Splunk parsing configurations (packaged in the Splunk Add-on for Kinesis Data Firehose) extract and parse all fields. They make data ready for querying and visualization using Splunk Enterprise and Splunk Cloud.

Walkthrough

Install the Splunk Add-on for Amazon Kinesis Data Firehose

The Splunk Add-on for Amazon Kinesis Data Firehose enables Splunk (be it Splunk Enterprise, Splunk App for AWS, or Splunk Enterprise Security) to use data ingested from Amazon Kinesis Data Firehose. Install the Add-on on all the indexers with an HTTP Event Collector (HEC). The Add-on is available for download from Splunkbase.

HTTP Event Collector (HEC)

Before you can use Kinesis Data Firehose to deliver data to Splunk, set up the Splunk HEC to receive the data. From Splunk web, go to the Setting menu, choose Data Inputs, and choose HTTP Event Collector. Choose Global Settings, ensure All tokens is enabled, and then choose Save. Then choose New Token to create a new HEC endpoint and token. When you create a new token, make sure that Enable indexer acknowledgment is checked.

When prompted to select a source type, select aws:cloudwatch:vpcflow.

Create an S3 backsplash bucket

To provide for situations in which Kinesis Data Firehose can’t deliver data to the Splunk Cluster, we use an S3 bucket to back up the data. You can configure this feature to back up all data or only the data that’s failed during delivery to Splunk.

Note: Bucket names are unique. Thus, you can’t use tmak-backsplash-bucket.

aws s3 create-bucket --bucket tmak-backsplash-bucket --create-bucket-configuration LocationConstraint=ap-northeast-1

Create an IAM role for the Lambda transform function

Firehose triggers an AWS Lambda function that transforms the data in the delivery stream. Let’s first create a role for the Lambda function called LambdaBasicRole.

Note: You can also set this role up when creating your Lambda function.

$ aws iam create-role --role-name LambdaBasicRole --assume-role-policy-document file://TrustPolicyForLambda.json

Here is TrustPolicyForLambda.json.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

 

After the role is created, attach the managed Lambda basic execution policy to it.

$ aws iam attach-role-policy 
  --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole 
  --role-name LambdaBasicRole

 

Create a Firehose Stream

On the AWS console, open the Amazon Kinesis service, go to the Firehose console, and choose Create Delivery Stream.

In the next section, you can specify whether you want to use an inline Lambda function for transformation. Because incoming CloudWatch Logs are gzip compressed, choose Enabled for Record transformation, and then choose Create new.

From the list of the available blueprint functions, choose Kinesis Data Firehose CloudWatch Logs Processor. This function unzips data and place it back into the Firehose stream in compliance with the record transformation output model.

Enter a name for the Lambda function, choose Choose an existing role, and then choose the role you created earlier. Then choose Create Function.

Go back to the Firehose Stream wizard, choose the Lambda function you just created, and then choose Next.

Select Splunk as the destination, and enter your Splunk Http Event Collector information.

Note: Amazon Kinesis Data Firehose requires the Splunk HTTP Event Collector (HEC) endpoint to be terminated with a valid CA-signed certificate matching the DNS hostname used to connect to your HEC endpoint. You receive delivery errors if you are using a self-signed certificate.

In this example, we only back up logs that fail during delivery.

To monitor your Firehose delivery stream, enable error logging. Doing this means that you can monitor record delivery errors.

Create an IAM role for the Firehose stream by choosing Create new, or Choose. Doing this brings you to a new screen. Choose Create a new IAM role, give the role a name, and then choose Allow.

If you look at the policy document, you can see that the role gives Kinesis Data Firehose permission to publish error logs to CloudWatch, execute your Lambda function, and put records into your S3 backup bucket.

You now get a chance to review and adjust the Firehose stream settings. When you are satisfied, choose Create Stream. You get a confirmation once the stream is created and active.

Create a VPC Flow Log

To send events from Amazon VPC, you need to set up a VPC flow log. If you already have a VPC flow log you want to use, you can skip to the “Publish CloudWatch to Kinesis Data Firehose” section.

On the AWS console, open the Amazon VPC service. Then choose VPC, Your VPC, and choose the VPC you want to send flow logs from. Choose Flow Logs, and then choose Create Flow Log. If you don’t have an IAM role that allows your VPC to publish logs to CloudWatch, choose Set Up Permissions and Create new role. Use the defaults when presented with the screen to create the new IAM role.

Once active, your VPC flow log should look like the following.

Publish CloudWatch to Kinesis Data Firehose

When you generate traffic to or from your VPC, the log group is created in Amazon CloudWatch. The new log group has no subscription filter, so set up a subscription filter. Setting this up establishes a real-time data feed from the log group to your Firehose delivery stream.

At present, you have to use the AWS Command Line Interface (AWS CLI) to create a CloudWatch Logs subscription to a Kinesis Data Firehose stream. However, you can use the AWS console to create subscriptions to Lambda and Amazon Elasticsearch Service.

To allow CloudWatch to publish to your Firehose stream, you need to give it permissions.

$ aws iam create-role --role-name CWLtoKinesisFirehoseRole --assume-role-policy-document file://TrustPolicyForCWLToFireHose.json


Here is the content for TrustPolicyForCWLToFireHose.json.

{
  "Statement": {
    "Effect": "Allow",
    "Principal": { "Service": "logs.us-east-1.amazonaws.com" },
    "Action": "sts:AssumeRole"
  }
}

 

Attach the policy to the newly created role.

$ aws iam put-role-policy 
    --role-name CWLtoKinesisFirehoseRole 
    --policy-name Permissions-Policy-For-CWL 
    --policy-document file://PermissionPolicyForCWLToFireHose.json

Here is the content for PermissionPolicyForCWLToFireHose.json.

{
    "Statement":[
      {
        "Effect":"Allow",
        "Action":["firehose:*"],
        "Resource":["arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/ FirehoseSplunkDeliveryStream"]
      },
      {
        "Effect":"Allow",
        "Action":["iam:PassRole"],
        "Resource":["arn:aws:iam::YOUR-AWS-ACCT-NUM:role/CWLtoKinesisFirehoseRole"]
      }
    ]
}

Finally, create a subscription filter.

$ aws logs put-subscription-filter 
   --log-group-name " /vpc/flowlog/FirehoseSplunkDemo" 
   --filter-name "Destination" 
   --filter-pattern "" 
   --destination-arn "arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/FirehoseSplunkDeliveryStream" 
   --role-arn "arn:aws:iam::YOUR-AWS-ACCT-NUM:role/CWLtoKinesisFirehoseRole"

When you run the AWS CLI command preceding, you don’t get any acknowledgment. To validate that your CloudWatch Log Group is subscribed to your Firehose stream, check the CloudWatch console.

As soon as the subscription filter is created, the real-time log data from the log group goes into your Firehose delivery stream. Your stream then delivers it to your Splunk Enterprise or Splunk Cloud environment for querying and visualization. The screenshot following is from Splunk Enterprise.

In addition, you can monitor and view metrics associated with your delivery stream using the AWS console.

Conclusion

Although our walkthrough uses VPC Flow Logs, the pattern can be used in many other scenarios. These include ingesting data from AWS IoT, other CloudWatch logs and events, Kinesis Streams or other data sources using the Kinesis Agent or Kinesis Producer Library. We also used Lambda blueprint Kinesis Data Firehose CloudWatch Logs Processor to transform streaming records from Kinesis Data Firehose. However, you might need to use a different Lambda blueprint or disable record transformation entirely depending on your use case. For an additional use case using Kinesis Data Firehose, check out This is My Architecture Video, which discusses how to securely centralize cross-account data analytics using Kinesis and Splunk.

 


Additional Reading

If you found this post useful, be sure to check out Integrating Splunk with Amazon Kinesis Streams and Using Amazon EMR and Hunk for Rapid Response Log Analysis and Review.


About the Authors

Tarik Makota is a solutions architect with the Amazon Web Services Partner Network. He provides technical guidance, design advice and thought leadership to AWS’ most strategic software partners. His career includes work in an extremely broad software development and architecture roles across ERP, financial printing, benefit delivery and administration and financial services. He holds an M.S. in Software Development and Management from Rochester Institute of Technology.

 

 

 

Roy Arsan is a solutions architect in the Splunk Partner Integrations team. He has a background in product development, cloud architecture, and building consumer and enterprise cloud applications. More recently, he has architected Splunk solutions on major cloud providers, including an AWS Quick Start for Splunk that enables AWS users to easily deploy distributed Splunk Enterprise straight from their AWS console. He’s also the co-author of the AWS Lambda blueprints for Splunk. He holds an M.S. in Computer Science Engineering from the University of Michigan.

 

 

 

Using Amazon Redshift Spectrum, Amazon Athena, and AWS Glue with Node.js in Production

Post Syndicated from Rafi Ton original https://aws.amazon.com/blogs/big-data/using-amazon-redshift-spectrum-amazon-athena-and-aws-glue-with-node-js-in-production/

This is a guest post by Rafi Ton, founder and CEO of NUVIAD. NUVIAD is, in their own words, “a mobile marketing platform providing professional marketers, agencies and local businesses state of the art tools to promote their products and services through hyper targeting, big data analytics and advanced machine learning tools.”

At NUVIAD, we’ve been using Amazon Redshift as our main data warehouse solution for more than 3 years.

We store massive amounts of ad transaction data that our users and partners analyze to determine ad campaign strategies. When running real-time bidding (RTB) campaigns in large scale, data freshness is critical so that our users can respond rapidly to changes in campaign performance. We chose Amazon Redshift because of its simplicity, scalability, performance, and ability to load new data in near real time.

Over the past three years, our customer base grew significantly and so did our data. We saw our Amazon Redshift cluster grow from three nodes to 65 nodes. To balance cost and analytics performance, we looked for a way to store large amounts of less-frequently analyzed data at a lower cost. Yet, we still wanted to have the data immediately available for user queries and to meet their expectations for fast performance. We turned to Amazon Redshift Spectrum.

In this post, I explain the reasons why we extended Amazon Redshift with Redshift Spectrum as our modern data warehouse. I cover how our data growth and the need to balance cost and performance led us to adopt Redshift Spectrum. I also share key performance metrics in our environment, and discuss the additional AWS services that provide a scalable and fast environment, with data available for immediate querying by our growing user base.

Amazon Redshift as our foundation

The ability to provide fresh, up-to-the-minute data to our customers and partners was always a main goal with our platform. We saw other solutions provide data that was a few hours old, but this was not good enough for us. We insisted on providing the freshest data possible. For us, that meant loading Amazon Redshift in frequent micro batches and allowing our customers to query Amazon Redshift directly to get results in near real time.

The benefits were immediately evident. Our customers could see how their campaigns performed faster than with other solutions, and react sooner to the ever-changing media supply pricing and availability. They were very happy.

However, this approach required Amazon Redshift to store a lot of data for long periods, and our data grew substantially. In our peak, we maintained a cluster running 65 DC1.large nodes. The impact on our Amazon Redshift cluster was evident, and we saw our CPU utilization grow to 90%.

Why we extended Amazon Redshift to Redshift Spectrum

Redshift Spectrum gives us the ability to run SQL queries using the powerful Amazon Redshift query engine against data stored in Amazon S3, without needing to load the data. With Redshift Spectrum, we store data where we want, at the cost that we want. We have the data available for analytics when our users need it with the performance they expect.

Seamless scalability, high performance, and unlimited concurrency

Scaling Redshift Spectrum is a simple process. First, it allows us to leverage Amazon S3 as the storage engine and get practically unlimited data capacity.

Second, if we need more compute power, we can leverage Redshift Spectrum’s distributed compute engine over thousands of nodes to provide superior performance – perfect for complex queries running against massive amounts of data.

Third, all Redshift Spectrum clusters access the same data catalog so that we don’t have to worry about data migration at all, making scaling effortless and seamless.

Lastly, since Redshift Spectrum distributes queries across potentially thousands of nodes, they are not affected by other queries, providing much more stable performance and unlimited concurrency.

Keeping it SQL

Redshift Spectrum uses the same query engine as Amazon Redshift. This means that we did not need to change our BI tools or query syntax, whether we used complex queries across a single table or joins across multiple tables.

An interesting capability introduced recently is the ability to create a view that spans both Amazon Redshift and Redshift Spectrum external tables. With this feature, you can query frequently accessed data in your Amazon Redshift cluster and less-frequently accessed data in Amazon S3, using a single view.

Leveraging Parquet for higher performance

Parquet is a columnar data format that provides superior performance and allows Redshift Spectrum (or Amazon Athena) to scan significantly less data. With less I/O, queries run faster and we pay less per query. You can read all about Parquet at https://parquet.apache.org/ or https://en.wikipedia.org/wiki/Apache_Parquet.

Lower cost

From a cost perspective, we pay standard rates for our data in Amazon S3, and only small amounts per query to analyze data with Redshift Spectrum. Using the Parquet format, we can significantly reduce the amount of data scanned. Our costs are now lower, and our users get fast results even for large complex queries.

What we learned about Amazon Redshift vs. Redshift Spectrum performance

When we first started looking at Redshift Spectrum, we wanted to put it to the test. We wanted to know how it would compare to Amazon Redshift, so we looked at two key questions:

  1. What is the performance difference between Amazon Redshift and Redshift Spectrum on simple and complex queries?
  2. Does the data format impact performance?

During the migration phase, we had our dataset stored in Amazon Redshift and S3 as CSV/GZIP and as Parquet file formats. We tested three configurations:

  • Amazon Redshift cluster with 28 DC1.large nodes
  • Redshift Spectrum using CSV/GZIP
  • Redshift Spectrum using Parquet

We performed benchmarks for simple and complex queries on one month’s worth of data. We tested how much time it took to perform the query, and how consistent the results were when running the same query multiple times. The data we used for the tests was already partitioned by date and hour. Properly partitioning the data improves performance significantly and reduces query times.

Simple query

First, we tested a simple query aggregating billing data across a month:

SELECT 
  user_id, 
  count(*) AS impressions, 
  SUM(billing)::decimal /1000000 AS billing 
FROM <table_name> 
WHERE 
  date >= '2017-08-01' AND 
  date <= '2017-08-31'  
GROUP BY 
  user_id;

We ran the same query seven times and measured the response times (red marking the longest time and green the shortest time):

Execution Time (seconds)
  Amazon Redshift Redshift Spectrum
CSV
Redshift Spectrum Parquet
Run #1 39.65 45.11 11.92
Run #2 15.26 43.13 12.05
Run #3 15.27 46.47 13.38
Run #4 21.22 51.02 12.74
Run #5 17.27 43.35 11.76
Run #6 16.67 44.23 13.67
Run #7 25.37 40.39 12.75
Average 21.53  44.82 12.61

For simple queries, Amazon Redshift performed better than Redshift Spectrum, as we thought, because the data is local to Amazon Redshift.

What was surprising was that using Parquet data format in Redshift Spectrum significantly beat ‘traditional’ Amazon Redshift performance. For our queries, using Parquet data format with Redshift Spectrum delivered an average 40% performance gain over traditional Amazon Redshift. Furthermore, Redshift Spectrum showed high consistency in execution time with a smaller difference between the slowest run and the fastest run.

Comparing the amount of data scanned when using CSV/GZIP and Parquet, the difference was also significant:

Data Scanned (GB)
CSV (Gzip) 135.49
Parquet 2.83

Because we pay only for the data scanned by Redshift Spectrum, the cost saving of using Parquet is evident and substantial.

Complex query

Next, we compared the same three configurations with a complex query.

Execution Time (seconds)
  Amazon Redshift Redshift Spectrum CSV Redshift Spectrum Parquet
Run #1 329.80 84.20 42.40
Run #2 167.60 65.30 35.10
Run #3 165.20 62.20 23.90
Run #4 273.90 74.90 55.90
Run #5 167.70 69.00 58.40
Average 220.84 71.12 43.14

This time, Redshift Spectrum using Parquet cut the average query time by 80% compared to traditional Amazon Redshift!

Bottom line: For complex queries, Redshift Spectrum provided a 67% performance gain over Amazon Redshift. Using the Parquet data format, Redshift Spectrum delivered an 80% performance improvement over Amazon Redshift. For us, this was substantial.

Optimizing the data structure for different workloads

Because the cost of S3 is relatively inexpensive and we pay only for the data scanned by each query, we believe that it makes sense to keep our data in different formats for different workloads and different analytics engines. It is important to note that we can have any number of tables pointing to the same data on S3. It all depends on how we partition the data and update the table partitions.

Data permutations

For example, we have a process that runs every minute and generates statistics for the last minute of data collected. With Amazon Redshift, this would be done by running the query on the table with something as follows:

SELECT 
  user, 
  COUNT(*) 
FROM 
  events_table 
WHERE 
  ts BETWEEN ‘2017-08-01 14:00:00’ AND ‘2017-08-01 14:00:59’ 
GROUP BY 
  user;

(Assuming ‘ts’ is your column storing the time stamp for each event.)

With Redshift Spectrum, we pay for the data scanned in each query. If the data is partitioned by the minute instead of the hour, a query looking at one minute would be 1/60th the cost. If we use a temporary table that points only to the data of the last minute, we save that unnecessary cost.

Creating Parquet data efficiently

On the average, we have 800 instances that process our traffic. Each instance sends events that are eventually loaded into Amazon Redshift. When we started three years ago, we would offload data from each server to S3 and then perform a periodic copy command from S3 to Amazon Redshift.

Recently, Amazon Kinesis Firehose added the capability to offload data directly to Amazon Redshift. While this is now a viable option, we kept the same collection process that worked flawlessly and efficiently for three years.

This changed, however, when we incorporated Redshift Spectrum. With Redshift Spectrum, we needed to find a way to:

  • Collect the event data from the instances.
  • Save the data in Parquet format.
  • Partition the data effectively.

To accomplish this, we save the data as CSV and then transform it to Parquet. The most effective method to generate the Parquet files is to:

  1. Send the data in one-minute intervals from the instances to Kinesis Firehose with an S3 temporary bucket as the destination.
  2. Aggregate hourly data and convert it to Parquet using AWS Lambda and AWS Glue.
  3. Add the Parquet data to S3 by updating the table partitions.

With this new process, we had to give more attention to validating the data before we sent it to Kinesis Firehose, because a single corrupted record in a partition fails queries on that partition.

Data validation

To store our click data in a table, we considered the following SQL create table command:

create external TABLE spectrum.blog_clicks (
    user_id varchar(50),
    campaign_id varchar(50),
    os varchar(50),
    ua varchar(255),
    ts bigint,
    billing float
)
partitioned by (date date, hour smallint)  
stored as parquet
location 's3://nuviad-temp/blog/clicks/';

The above statement defines a new external table (all Redshift Spectrum tables are external tables) with a few attributes. We stored ‘ts’ as a Unix time stamp and not as Timestamp, and billing data is stored as float and not decimal (more on that later). We also said that the data is partitioned by date and hour, and then stored as Parquet on S3.

First, we need to get the table definitions. This can be achieved by running the following query:

SELECT 
  * 
FROM 
  svv_external_columns 
WHERE 
  tablename = 'blog_clicks';

This query lists all the columns in the table with their respective definitions:

schemaname tablename columnname external_type columnnum part_key
spectrum blog_clicks user_id varchar(50) 1 0
spectrum blog_clicks campaign_id varchar(50) 2 0
spectrum blog_clicks os varchar(50) 3 0
spectrum blog_clicks ua varchar(255) 4 0
spectrum blog_clicks ts bigint 5 0
spectrum blog_clicks billing double 6 0
spectrum blog_clicks date date 7 1
spectrum blog_clicks hour smallint 8 2

Now we can use this data to create a validation schema for our data:

const rtb_request_schema = {
    "name": "clicks",
    "items": {
        "user_id": {
            "type": "string",
            "max_length": 100
        },
        "campaign_id": {
            "type": "string",
            "max_length": 50
        },
        "os": {
            "type": "string",
            "max_length": 50            
        },
        "ua": {
            "type": "string",
            "max_length": 255            
        },
        "ts": {
            "type": "integer",
            "min_value": 0,
            "max_value": 9999999999999
        },
        "billing": {
            "type": "float",
            "min_value": 0,
            "max_value": 9999999999999
        }
    }
};

Next, we create a function that uses this schema to validate data:

function valueIsValid(value, item_schema) {
    if (schema.type == 'string') {
        return (typeof value == 'string' && value.length <= schema.max_length);
    }
    else if (schema.type == 'integer') {
        return (typeof value == 'number' && value >= schema.min_value && value <= schema.max_value);
    }
    else if (schema.type == 'float' || schema.type == 'double') {
        return (typeof value == 'number' && value >= schema.min_value && value <= schema.max_value);
    }
    else if (schema.type == 'boolean') {
        return typeof value == 'boolean';
    }
    else if (schema.type == 'timestamp') {
        return (new Date(value)).getTime() > 0;
    }
    else {
        return true;
    }
}

Near real-time data loading with Kinesis Firehose

On Kinesis Firehose, we created a new delivery stream to handle the events as follows:

Delivery stream name: events
Source: Direct PUT
S3 bucket: nuviad-events
S3 prefix: rtb/
IAM role: firehose_delivery_role_1
Data transformation: Disabled
Source record backup: Disabled
S3 buffer size (MB): 100
S3 buffer interval (sec): 60
S3 Compression: GZIP
S3 Encryption: No Encryption
Status: ACTIVE
Error logging: Enabled

This delivery stream aggregates event data every minute, or up to 100 MB, and writes the data to an S3 bucket as a CSV/GZIP compressed file. Next, after we have the data validated, we can safely send it to our Kinesis Firehose API:

if (validated) {
    let itemString = item.join('|')+'\n'; //Sending csv delimited by pipe and adding new line

    let params = {
        DeliveryStreamName: 'events',
        Record: {
            Data: itemString
        }
    };

    firehose.putRecord(params, function(err, data) {
        if (err) {
            console.error(err, err.stack);        
        }
        else {
            // Continue to your next step 
        }
    });
}

Now, we have a single CSV file representing one minute of event data stored in S3. The files are named automatically by Kinesis Firehose by adding a UTC time prefix in the format YYYY/MM/DD/HH before writing objects to S3. Because we use the date and hour as partitions, we need to change the file naming and location to fit our Redshift Spectrum schema.

Automating data distribution using AWS Lambda

We created a simple Lambda function triggered by an S3 put event that copies the file to a different location (or locations), while renaming it to fit our data structure and processing flow. As mentioned before, the files generated by Kinesis Firehose are structured in a pre-defined hierarchy, such as:

S3://your-bucket/your-prefix/2017/08/01/20/events-4-2017-08-01-20-06-06-536f5c40-6893-4ee4-907d-81e4d3b09455.gz

All we need to do is parse the object name and restructure it as we see fit. In our case, we did the following (the event is an object received in the Lambda function with all the data about the object written to S3):

/*
	object key structure in the event object:
your-prefix/2017/08/01/20/event-4-2017-08-01-20-06-06-536f5c40-6893-4ee4-907d-81e4d3b09455.gz
	*/

let key_parts = event.Records[0].s3.object.key.split('/'); 

let event_type = key_parts[0];
let date = key_parts[1] + '-' + key_parts[2] + '-' + key_parts[3];
let hour = key_parts[4];
if (hour.indexOf('0') == 0) {
 		hour = parseInt(hour, 10) + '';
}
    
let parts1 = key_parts[5].split('-');
let minute = parts1[7];
if (minute.indexOf('0') == 0) {
        minute = parseInt(minute, 10) + '';
}

Now, we can redistribute the file to the two destinations we need—one for the minute processing task and the other for hourly aggregation:

    copyObjectToHourlyFolder(event, date, hour, minute)
        .then(copyObjectToMinuteFolder.bind(null, event, date, hour, minute))
        .then(addPartitionToSpectrum.bind(null, event, date, hour, minute))
        .then(deleteOldMinuteObjects.bind(null, event))
        .then(deleteStreamObject.bind(null, event))        
        .then(result => {
            callback(null, { message: 'done' });            
        })
        .catch(err => {
            console.error(err);
            callback(null, { message: err });            
        }); 

Kinesis Firehose stores the data in a temporary folder. We copy the object to another folder that holds the data for the last processed minute. This folder is connected to a small Redshift Spectrum table where the data is being processed without needing to scan a much larger dataset. We also copy the data to a folder that holds the data for the entire hour, to be later aggregated and converted to Parquet.

Because we partition the data by date and hour, we created a new partition on the Redshift Spectrum table if the processed minute is the first minute in the hour (that is, minute 0). We ran the following:

ALTER TABLE 
  spectrum.events 
ADD partition
  (date='2017-08-01', hour=0) 
  LOCATION 's3://nuviad-temp/events/2017-08-01/0/';

After the data is processed and added to the table, we delete the processed data from the temporary Kinesis Firehose storage and from the minute storage folder.

Migrating CSV to Parquet using AWS Glue and Amazon EMR

The simplest way we found to run an hourly job converting our CSV data to Parquet is using Lambda and AWS Glue (and thanks to the awesome AWS Big Data team for their help with this).

Creating AWS Glue jobs

What this simple AWS Glue script does:

  • Gets parameters for the job, date, and hour to be processed
  • Creates a Spark EMR context allowing us to run Spark code
  • Reads CSV data into a DataFrame
  • Writes the data as Parquet to the destination S3 bucket
  • Adds or modifies the Redshift Spectrum / Amazon Athena table partition for the table
import sys
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
import boto3

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME','day_partition_key', 'hour_partition_key', 'day_partition_value', 'hour_partition_value' ])

#day_partition_key = "partition_0"
#hour_partition_key = "partition_1"
#day_partition_value = "2017-08-01"
#hour_partition_value = "0"

day_partition_key = args['day_partition_key']
hour_partition_key = args['hour_partition_key']
day_partition_value = args['day_partition_value']
hour_partition_value = args['hour_partition_value']

print("Running for " + day_partition_value + "/" + hour_partition_value)

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

df = spark.read.option("delimiter","|").csv("s3://nuviad-temp/events/"+day_partition_value+"/"+hour_partition_value)
df.registerTempTable("data")

df1 = spark.sql("select _c0 as user_id, _c1 as campaign_id, _c2 as os, _c3 as ua, cast(_c4 as bigint) as ts, cast(_c5 as double) as billing from data")

df1.repartition(1).write.mode("overwrite").parquet("s3://nuviad-temp/parquet/"+day_partition_value+"/hour="+hour_partition_value)

client = boto3.client('athena', region_name='us-east-1')

response = client.start_query_execution(
    QueryString='alter table parquet_events add if not exists partition(' + day_partition_key + '=\'' + day_partition_value + '\',' + hour_partition_key + '=' + hour_partition_value + ')  location \'s3://nuviad-temp/parquet/' + day_partition_value + '/hour=' + hour_partition_value + '\'' ,
    QueryExecutionContext={
        'Database': 'spectrumdb'
    },
    ResultConfiguration={
        'OutputLocation': 's3://nuviad-temp/convertresults'
    }
)

response = client.start_query_execution(
    QueryString='alter table parquet_events partition(' + day_partition_key + '=\'' + day_partition_value + '\',' + hour_partition_key + '=' + hour_partition_value + ') set location \'s3://nuviad-temp/parquet/' + day_partition_value + '/hour=' + hour_partition_value + '\'' ,
    QueryExecutionContext={
        'Database': 'spectrumdb'
    },
    ResultConfiguration={
        'OutputLocation': 's3://nuviad-temp/convertresults'
    }
)

job.commit()

Note: Because Redshift Spectrum and Athena both use the AWS Glue Data Catalog, we could use the Athena client to add the partition to the table.

Here are a few words about float, decimal, and double. Using decimal proved to be more challenging than we expected, as it seems that Redshift Spectrum and Spark use them differently. Whenever we used decimal in Redshift Spectrum and in Spark, we kept getting errors, such as:

S3 Query Exception (Fetch). Task failed due to an internal error. File 'https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parquet has an incompatible Parquet schema for column 's3://nuviad-events/events.lat'. Column type: DECIMAL(18, 8), Parquet schema:\noptional float lat [i:4 d:1 r:0]\n (https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parq

We had to experiment with a few floating-point formats until we found that the only combination that worked was to define the column as double in the Spark code and float in Spectrum. This is the reason you see billing defined as float in Spectrum and double in the Spark code.

Creating a Lambda function to trigger conversion

Next, we created a simple Lambda function to trigger the AWS Glue script hourly using a simple Python code:

import boto3
import json
from datetime import datetime, timedelta
 
client = boto3.client('glue')
 
def lambda_handler(event, context):
    last_hour_date_time = datetime.now() - timedelta(hours = 1)
    day_partition_value = last_hour_date_time.strftime("%Y-%m-%d") 
    hour_partition_value = last_hour_date_time.strftime("%-H") 
    response = client.start_job_run(
    JobName='convertEventsParquetHourly',
    Arguments={
         '--day_partition_key': 'date',
         '--hour_partition_key': 'hour',
         '--day_partition_value': day_partition_value,
         '--hour_partition_value': hour_partition_value
         }
    )

Using Amazon CloudWatch Events, we trigger this function hourly. This function triggers an AWS Glue job named ‘convertEventsParquetHourly’ and runs it for the previous hour, passing job names and values of the partitions to process to AWS Glue.

Redshift Spectrum and Node.js

Our development stack is based on Node.js, which is well-suited for high-speed, light servers that need to process a huge number of transactions. However, a few limitations of the Node.js environment required us to create workarounds and use other tools to complete the process.

Node.js and Parquet

The lack of Parquet modules for Node.js required us to implement an AWS Glue/Amazon EMR process to effectively migrate data from CSV to Parquet. We would rather save directly to Parquet, but we couldn’t find an effective way to do it.

One interesting project in the works is the development of a Parquet NPM by Marc Vertes called node-parquet (https://www.npmjs.com/package/node-parquet). It is not in a production state yet, but we think it would be well worth following the progress of this package.

Timestamp data type

According to the Parquet documentation, Timestamp data are stored in Parquet as 64-bit integers. However, JavaScript does not support 64-bit integers, because the native number type is a 64-bit double, giving only 53 bits of integer range.

The result is that you cannot store Timestamp correctly in Parquet using Node.js. The solution is to store Timestamp as string and cast the type to Timestamp in the query. Using this method, we did not witness any performance degradation whatsoever.

Lessons learned

You can benefit from our trial-and-error experience.

Lesson #1: Data validation is critical

As mentioned earlier, a single corrupt entry in a partition can fail queries running against this partition, especially when using Parquet, which is harder to edit than a simple CSV file. Make sure that you validate your data before scanning it with Redshift Spectrum.

Lesson #2: Structure and partition data effectively

One of the biggest benefits of using Redshift Spectrum (or Athena for that matter) is that you don’t need to keep nodes up and running all the time. You pay only for the queries you perform and only for the data scanned per query.

Keeping different permutations of your data for different queries makes a lot of sense in this case. For example, you can partition your data by date and hour to run time-based queries, and also have another set partitioned by user_id and date to run user-based queries. This results in faster and more efficient performance of your data warehouse.

Storing data in the right format

Use Parquet whenever you can. The benefits of Parquet are substantial. Faster performance, less data to scan, and much more efficient columnar format. However, it is not supported out-of-the-box by Kinesis Firehose, so you need to implement your own ETL. AWS Glue is a great option.

Creating small tables for frequent tasks

When we started using Redshift Spectrum, we saw our Amazon Redshift costs jump by hundreds of dollars per day. Then we realized that we were unnecessarily scanning a full day’s worth of data every minute. Take advantage of the ability to define multiple tables on the same S3 bucket or folder, and create temporary and small tables for frequent queries.

Lesson #3: Combine Athena and Redshift Spectrum for optimal performance

Moving to Redshift Spectrum also allowed us to take advantage of Athena as both use the AWS Glue Data Catalog. Run fast and simple queries using Athena while taking advantage of the advanced Amazon Redshift query engine for complex queries using Redshift Spectrum.

Redshift Spectrum excels when running complex queries. It can push many compute-intensive tasks, such as predicate filtering and aggregation, down to the Redshift Spectrum layer, so that queries use much less of your cluster’s processing capacity.

Lesson #4: Sort your Parquet data within the partition

We achieved another performance improvement by sorting data within the partition using sortWithinPartitions(sort_field). For example:

df.repartition(1).sortWithinPartitions("campaign_id")…

Conclusion

We were extremely pleased with using Amazon Redshift as our core data warehouse for over three years. But as our client base and volume of data grew substantially, we extended Amazon Redshift to take advantage of scalability, performance, and cost with Redshift Spectrum.

Redshift Spectrum lets us scale to virtually unlimited storage, scale compute transparently, and deliver super-fast results for our users. With Redshift Spectrum, we store data where we want at the cost we want, and have the data available for analytics when our users need it with the performance they expect.


About the Author

With 7 years of experience in the AdTech industry and 15 years in leading technology companies, Rafi Ton is the founder and CEO of NUVIAD. He enjoys exploring new technologies and putting them to use in cutting edge products and services, in the real world generating real money. Being an experienced entrepreneur, Rafi believes in practical-programming and fast adaptation of new technologies to achieve a significant market advantage.

 

 

Event-Driven Computing with Amazon SNS and AWS Compute, Storage, Database, and Networking Services

Post Syndicated from Christie Gifrin original https://aws.amazon.com/blogs/compute/event-driven-computing-with-amazon-sns-compute-storage-database-and-networking-services/

Contributed by Otavio Ferreira, Manager, Software Development, AWS Messaging

Like other developers around the world, you may be tackling increasingly complex business problems. A key success factor, in that case, is the ability to break down a large project scope into smaller, more manageable components. A service-oriented architecture guides you toward designing systems as a collection of loosely coupled, independently scaled, and highly reusable services. Microservices take this even further. To improve performance and scalability, they promote fine-grained interfaces and lightweight protocols.

However, the communication among isolated microservices can be challenging. Services are often deployed onto independent servers and don’t share any compute or storage resources. Also, you should avoid hard dependencies among microservices, to preserve maintainability and reusability.

If you apply the pub/sub design pattern, you can effortlessly decouple and independently scale out your microservices and serverless architectures. A pub/sub messaging service, such as Amazon SNS, promotes event-driven computing that statically decouples event publishers from subscribers, while dynamically allowing for the exchange of messages between them. An event-driven architecture also introduces the responsiveness needed to deal with complex problems, which are often unpredictable and asynchronous.

What is event-driven computing?

Given the context of microservices, event-driven computing is a model in which subscriber services automatically perform work in response to events triggered by publisher services. This paradigm can be applied to automate workflows while decoupling the services that collectively and independently work to fulfil these workflows. Amazon SNS is an event-driven computing hub, in the AWS Cloud, that has native integration with several AWS publisher and subscriber services.

Which AWS services publish events to SNS natively?

Several AWS services have been integrated as SNS publishers and, therefore, can natively trigger event-driven computing for a variety of use cases. In this post, I specifically cover AWS compute, storage, database, and networking services, as depicted below.

Compute services

  • Auto Scaling: Helps you ensure that you have the correct number of Amazon EC2 instances available to handle the load for your application. You can configure Auto Scaling lifecycle hooks to trigger events, as Auto Scaling resizes your EC2 cluster.As an example, you may want to warm up the local cache store on newly launched EC2 instances, and also download log files from other EC2 instances that are about to be terminated. To make this happen, set an SNS topic as your Auto Scaling group’s notification target, then subscribe two Lambda functions to this SNS topic. The first function is responsible for handling scale-out events (to warm up cache upon provisioning), whereas the second is in charge of handling scale-in events (to download logs upon termination).

  • AWS Elastic Beanstalk: An easy-to-use service for deploying and scaling web applications and web services developed in a number of programming languages. You can configure event notifications for your Elastic Beanstalk environment so that notable events can be automatically published to an SNS topic, then pushed to topic subscribers.As an example, you may use this event-driven architecture to coordinate your continuous integration pipeline (such as Jenkins CI). That way, whenever an environment is created, Elastic Beanstalk publishes this event to an SNS topic, which triggers a subscribing Lambda function, which then kicks off a CI job against your newly created Elastic Beanstalk environment.

  • Elastic Load Balancing: Automatically distributes incoming application traffic across Amazon EC2 instances, containers, or other resources identified by IP addresses.You can configure CloudWatch alarms on Elastic Load Balancing metrics, to automate the handling of events derived from Classic Load Balancers. As an example, you may leverage this event-driven design to automate latency profiling in an Amazon ECS cluster behind a Classic Load Balancer. In this example, whenever your ECS cluster breaches your load balancer latency threshold, an event is posted by CloudWatch to an SNS topic, which then triggers a subscribing Lambda function. This function runs a task on your ECS cluster to trigger a latency profiling tool, hosted on the cluster itself. This can enhance your latency troubleshooting exercise by making it timely.

Storage services

  • Amazon S3: Object storage built to store and retrieve any amount of data.You can enable S3 event notifications, and automatically get them posted to SNS topics, to automate a variety of workflows. For instance, imagine that you have an S3 bucket to store incoming resumes from candidates, and a fleet of EC2 instances to encode these resumes from their original format (such as Word or text) into a portable format (such as PDF).In this example, whenever new files are uploaded to your input bucket, S3 publishes these events to an SNS topic, which in turn pushes these messages into subscribing SQS queues. Then, encoding workers running on EC2 instances poll these messages from the SQS queues; retrieve the original files from the input S3 bucket; encode them into PDF; and finally store them in an output S3 bucket.

  • Amazon EFS: Provides simple and scalable file storage, for use with Amazon EC2 instances, in the AWS Cloud.You can configure CloudWatch alarms on EFS metrics, to automate the management of your EFS systems. For example, consider a highly parallelized genomics analysis application that runs against an EFS system. By default, this file system is instantiated on the “General Purpose” performance mode. Although this performance mode allows for lower latency, it might eventually impose a scaling bottleneck. Therefore, you may leverage an event-driven design to handle it automatically.Basically, as soon as the EFS metric “Percent I/O Limit” breaches 95%, CloudWatch could post this event to an SNS topic, which in turn would push this message into a subscribing Lambda function. This function automatically creates a new file system, this time on the “Max I/O” performance mode, then switches the genomics analysis application to this new file system. As a result, your application starts experiencing higher I/O throughput rates.

  • Amazon Glacier: A secure, durable, and low-cost cloud storage service for data archiving and long-term backup.You can set a notification configuration on an Amazon Glacier vault so that when a job completes, a message is published to an SNS topic. Retrieving an archive from Amazon Glacier is a two-step asynchronous operation, in which you first initiate a job, and then download the output after the job completes. Therefore, SNS helps you eliminate polling your Amazon Glacier vault to check whether your job has been completed, or not. As usual, you may subscribe SQS queues, Lambda functions, and HTTP endpoints to your SNS topic, to be notified when your Amazon Glacier job is done.

  • AWS Snowball: A petabyte-scale data transport solution that uses secure appliances to transfer large amounts of data.You can leverage Snowball notifications to automate workflows related to importing data into and exporting data from AWS. More specifically, whenever your Snowball job status changes, Snowball can publish this event to an SNS topic, which in turn can broadcast the event to all its subscribers.As an example, imagine a Geographic Information System (GIS) that distributes high-resolution satellite images to users via Web browser. In this example, the GIS vendor could capture up to 80 TB of satellite images; create a Snowball job to import these files from an on-premises system to an S3 bucket; and provide an SNS topic ARN to be notified upon job status changes in Snowball. After Snowball changes the job status from “Importing” to “Completed”, Snowball publishes this event to the specified SNS topic, which delivers this message to a subscribing Lambda function, which finally creates a CloudFront web distribution for the target S3 bucket, to serve the images to end users.

Database services

  • Amazon RDS: Makes it easy to set up, operate, and scale a relational database in the cloud.RDS leverages SNS to broadcast notifications when RDS events occur. As usual, these notifications can be delivered via any protocol supported by SNS, including SQS queues, Lambda functions, and HTTP endpoints.As an example, imagine that you own a social network website that has experienced organic growth, and needs to scale its compute and database resources on demand. In this case, you could provide an SNS topic to listen to RDS DB instance events. When the “Low Storage” event is published to the topic, SNS pushes this event to a subscribing Lambda function, which in turn leverages the RDS API to increase the storage capacity allocated to your DB instance. The provisioning itself takes place within the specified DB maintenance window.

  • Amazon ElastiCache: A web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud.ElastiCache can publish messages using Amazon SNS when significant events happen on your cache cluster. This feature can be used to refresh the list of servers on client machines connected to individual cache node endpoints of a cache cluster. For instance, an ecommerce website fetches product details from a cache cluster, with the goal of offloading a relational database and speeding up page load times. Ideally, you want to make sure that each web server always has an updated list of cache servers to which to connect.To automate this node discovery process, you can get your ElastiCache cluster to publish events to an SNS topic. Thus, when ElastiCache event “AddCacheNodeComplete” is published, your topic then pushes this event to all subscribing HTTP endpoints that serve your ecommerce website, so that these HTTP servers can update their list of cache nodes.

  • Amazon Redshift: A fully managed data warehouse that makes it simple to analyze data using standard SQL and BI (Business Intelligence) tools.Amazon Redshift uses SNS to broadcast relevant events so that data warehouse workflows can be automated. As an example, imagine a news website that sends clickstream data to a Kinesis Firehose stream, which then loads the data into Amazon Redshift, so that popular news and reading preferences might be surfaced on a BI tool. At some point though, this Amazon Redshift cluster might need to be resized, and the cluster enters a ready-only mode. Hence, this Amazon Redshift event is published to an SNS topic, which delivers this event to a subscribing Lambda function, which finally deletes the corresponding Kinesis Firehose delivery stream, so that clickstream data uploads can be put on hold.At a later point, after Amazon Redshift publishes the event that the maintenance window has been closed, SNS notifies a subscribing Lambda function accordingly, so that this function can re-create the Kinesis Firehose delivery stream, and resume clickstream data uploads to Amazon Redshift.

  • AWS DMS: Helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database.DMS also uses SNS to provide notifications when DMS events occur, which can automate database migration workflows. As an example, you might create data replication tasks to migrate an on-premises MS SQL database, composed of multiple tables, to MySQL. Thus, if replication tasks fail due to incompatible data encoding in the source tables, these events can be published to an SNS topic, which can push these messages into a subscribing SQS queue. Then, encoders running on EC2 can poll these messages from the SQS queue, encode the source tables into a compatible character set, and restart the corresponding replication tasks in DMS. This is an event-driven approach to a self-healing database migration process.

Networking services

  • Amazon Route 53: A highly available and scalable cloud-based DNS (Domain Name System). Route 53 health checks monitor the health and performance of your web applications, web servers, and other resources.You can set CloudWatch alarms and get automated Amazon SNS notifications when the status of your Route 53 health check changes. As an example, imagine an online payment gateway that reports the health of its platform to merchants worldwide, via a status page. This page is hosted on EC2 and fetches platform health data from DynamoDB. In this case, you could configure a CloudWatch alarm for your Route 53 health check, so that when the alarm threshold is breached, and the payment gateway is no longer considered healthy, then CloudWatch publishes this event to an SNS topic, which pushes this message to a subscribing Lambda function, which finally updates the DynamoDB table that populates the status page. This event-driven approach avoids any kind of manual update to the status page visited by merchants.

  • AWS Direct Connect (AWS DX): Makes it easy to establish a dedicated network connection from your premises to AWS, which can reduce your network costs, increase bandwidth throughput, and provide a more consistent network experience than Internet-based connections.You can monitor physical DX connections using CloudWatch alarms, and send SNS messages when alarms change their status. As an example, when a DX connection state shifts to 0 (zero), indicating that the connection is down, this event can be published to an SNS topic, which can fan out this message to impacted servers through HTTP endpoints, so that they might reroute their traffic through a different connection instead. This is an event-driven approach to connectivity resilience.

More event-driven computing on AWS

In addition to SNS, event-driven computing is also addressed by Amazon CloudWatch Events, which delivers a near real-time stream of system events that describe changes in AWS resources. With CloudWatch Events, you can route each event type to one or more targets, including:

Many AWS services publish events to CloudWatch. As an example, you can get CloudWatch Events to capture events on your ETL (Extract, Transform, Load) jobs running on AWS Glue and push failed ones to an SQS queue, so that you can retry them later.

Conclusion

Amazon SNS is a pub/sub messaging service that can be used as an event-driven computing hub to AWS customers worldwide. By capturing events natively triggered by AWS services, such as EC2, S3 and RDS, you can automate and optimize all kinds of workflows, namely scaling, testing, encoding, profiling, broadcasting, discovery, failover, and much more. Business use cases presented in this post ranged from recruiting websites, to scientific research, geographic systems, social networks, retail websites, and news portals.

Start now by visiting Amazon SNS in the AWS Management Console, or by trying the AWS 10-Minute Tutorial, Send Fan-out Event Notifications with Amazon SNS and Amazon SQS.

 

Just in Case You Missed It: Catching Up on Some Recent AWS Launches

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/just-in-case-you-missed-it-catching-up-on-some-recent-aws-launches/

So many launches and cloud innovations, that you simply may not believe.  In order to catch up on some service launches and features, this post will be a round-up of some cool releases that happened this summer and through the end of September.

The launches and features I want to share with you today are:

  • AWS IAM for Authenticating Database Users for RDS MySQL and Amazon Aurora
  • Amazon SES Reputation Dashboard
  • Amazon SES Open and Click Tracking Metrics
  • Serverless Image Handler by the Solutions Builder Team
  • AWS Ops Automator by the Solutions Builder Team

Let’s dive in, shall we!

AWS IAM for Authenticating Database Users for RDS MySQL and Amazon Aurora

Wished you could manage access to your Amazon RDS database instances and clusters using AWS IAM? Well, wish no longer. Amazon RDS has launched the ability for you to use IAM to manage database access for Amazon RDS for MySQL and Amazon Aurora DB.

What I like most about this new service feature is, it’s very easy to get started.  To enable database user authentication using IAM, you would select a checkbox Enable IAM DB Authentication when creating, modifying, or restoring your DB instance or cluster. You can enable IAM access using the RDS console, the AWS CLI, and/or the Amazon RDS API.

After configuring the database for IAM authentication, client applications authenticate to the database engine by providing temporary security credentials generated by the IAM Security Token Service. These credentials can be used instead of providing a password to the database engine.

You can learn more about using IAM to provide targeted permissions and authentication to MySQL and Aurora by reviewing the Amazon RDS user guide.

Amazon SES Reputation Dashboard

In order to aid Amazon Simple Email Service customers’ in utilizing best practice guidelines for sending email, I am thrilled to announce we launched the Reputation Dashboard to provide comprehensive reporting on email sending health. To aid in proactively managing emails being sent, customers now have visibility into overall account health, sending metrics, and compliance or enforcement status.

The Reputation Dashboard will provide the following information:

  • Account status: A description of your account health status.
    • Healthy – No issues currently impacting your account.
    • Probation – Account is on probation; Issues causing probation must be resolved to prevent suspension
    • Pending end of probation decision – Your account is on probation. Amazon SES team member must review your account prior to action.
    • Shutdown – Your account has been shut down. No email will be able to be sent using Amazon SES.
    • Pending shutdown – Your account is on probation and issues causing probation are unresolved.
  • Bounce Rate: Percentage of emails sent that have bounced and bounce rate status messages.
  • Complaint Rate: Percentage of emails sent that recipients have reported as spam and complaint rate status messages.
  • Notifications: Messages about other account reputation issues.

Amazon SES Open and Click Tracking Metrics

Another exciting feature recently added to Amazon SES is support for Email Open and Click Tracking Metrics. With Email Open and Click Tracking Metrics feature, SES customers can now track when email they’ve sent has been opened and track when links within the email have been clicked.  Using this SES feature will allow you to better track email campaign engagement and effectiveness.

How does this work?

When using the email open tracking feature, SES will add a transparent, miniature image into the emails that you choose to track. When the email is opened, the mail application client will load the aforementioned tracking which triggers an open track event with Amazon SES. For the email click (link) tracking, links in email and/or email templates are replaced with a custom link.  When the custom link is clicked, a click event is recorded in SES and the custom link will redirect the email user to the link destination of the original email.

You can take advantage of the new open tracking and click tracking features by creating a new configuration set or altering an existing configuration set within SES. After choosing either; Amazon SNS, Amazon CloudWatch, or Amazon Kinesis Firehose as the AWS service to receive the open and click metrics, you would only need to select a new configuration set to successfully enable these new features for any emails you want to send.

AWS Solutions: Serverless Image Handler & AWS Ops Automator

The AWS Solution Builder team has been hard at work helping to make it easier for you all to find answers to common architectural questions to aid in building and running applications on AWS. You can find these solutions on the AWS Answers page. Two new solutions released earlier this fall on AWS Answers are  Serverless Image Handler and the AWS Ops Automator.
Serverless Image Handler was developed to provide a solution to help customers dynamically process, manipulate, and optimize the handling of images on the AWS Cloud. The solution combines Amazon CloudFront for caching, AWS Lambda to dynamically retrieve images and make image modifications, and Amazon S3 bucket to store images. Additionally, the Serverless Image Handler leverages the open source image-processing suite, Thumbor, for additional image manipulation, processing, and optimization.

AWS Ops Automator solution helps you to automate manual tasks using time-based or event-based triggers to automatically such as snapshot scheduling by providing a framework for automated tasks and includes task audit trails, logging, resource selection, scaling, concurrency handling, task completion handing, and API request retries. The solution includes the following AWS services:

  • AWS CloudFormation: a templates to launches the core framework of microservices and solution generated task configurations
  • Amazon DynamoDB: a table which stores task configuration data to defines the event triggers, resources, and saves the results of the action and the errors.
  • Amazon CloudWatch Logs: provides logging to track warning and error messages
  • Amazon SNS: topic to send messages to a subscribed email address to which to send the logging information from the solution

Have fun exploring and coding.

Tara

Amazon Elasticsearch Service now supports VPC

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/amazon-elasticsearch-service-now-supports-vpc/

Starting today, you can connect to your Amazon Elasticsearch Service domains from within an Amazon VPC without the need for NAT instances or Internet gateways. VPC support for Amazon ES is easy to configure, reliable, and offers an extra layer of security. With VPC support, traffic between other services and Amazon ES stays entirely within the AWS network, isolated from the public Internet. You can manage network access using existing VPC security groups, and you can use AWS Identity and Access Management (IAM) policies for additional protection. VPC support for Amazon ES domains is available at no additional charge.

Getting Started

Creating an Amazon Elasticsearch Service domain in your VPC is easy. Follow all the steps you would normally follow to create your cluster and then select “VPC access”.

That’s it. There are no additional steps. You can now access your domain from within your VPC!

Things To Know

To support VPCs, Amazon ES places an endpoint into at least one subnet of your VPC. Amazon ES places an Elastic Network Interface (ENI) into the VPC for each data node in the cluster. Each ENI uses a private IP address from the IPv4 range of your subnet and receives a public DNS hostname. If you enable zone awareness, Amazon ES creates endpoints in two subnets in different availability zones, which provides greater data durability.

You need to set aside three times the number of IP addresses as the number of nodes in your cluster. You can divide that number by two if Zone Awareness is enabled. Ideally, you would create separate subnets just for Amazon ES.

A few notes:

  • Currently, you cannot move existing domains to a VPC or vice-versa. To take advantage of VPC support, you must create a new domain and migrate your data.
  • Currently, Amazon ES does not support Amazon Kinesis Firehose integration for domains inside a VPC.

To learn more, see the Amazon ES documentation.

Randall

Perform Near Real-time Analytics on Streaming Data with Amazon Kinesis and Amazon Elasticsearch Service

Post Syndicated from Tristan Li original https://aws.amazon.com/blogs/big-data/perform-near-real-time-analytics-on-streaming-data-with-amazon-kinesis-and-amazon-elasticsearch-service/

Nowadays, streaming data is seen and used everywhere—from social networks, to mobile and web applications, IoT devices, instrumentation in data centers, and many other sources. As the speed and volume of this type of data increases, the need to perform data analysis in real time with machine learning algorithms and extract a deeper understanding from the data becomes ever more important. For example, you might want a continuous monitoring system to detect sentiment changes in a social media feed so that you can react to the sentiment in near real time.

In this post, we use Amazon Kinesis Streams to collect and store streaming data. We then use Amazon Kinesis Analytics to process and analyze the streaming data continuously. Specifically, we use the Kinesis Analytics built-in RANDOM_CUT_FOREST function, a machine learning algorithm, to detect anomalies in the streaming data. Finally, we use Amazon Kinesis Firehose to export the anomalies data to Amazon Elasticsearch Service (Amazon ES). We then build a simple dashboard in the open source tool Kibana to visualize the result.

Solution overview

The following diagram depicts a high-level overview of this solution.

Amazon Kinesis Streams

You can use Amazon Kinesis Streams to build your own streaming application. This application can process and analyze streaming data by continuously capturing and storing terabytes of data per hour from hundreds of thousands of sources.

Amazon Kinesis Analytics

Kinesis Analytics provides an easy and familiar standard SQL language to analyze streaming data in real time. One of its most powerful features is that there are no new languages, processing frameworks, or complex machine learning algorithms that you need to learn.

Amazon Kinesis Firehose

Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service.

Amazon Elasticsearch Service

Amazon ES is a fully managed service that makes it easy to deploy, operate, and scale Elasticsearch for log analytics, full text search, application monitoring, and more.

Solution summary

The following is a quick walkthrough of the solution that’s presented in the diagram:

  1. IoT sensors send streaming data into Kinesis Streams. In this post, you use a Python script to simulate an IoT temperature sensor device that sends the streaming data.
  2. By using the built-in RANDOM_CUT_FOREST function in Kinesis Analytics, you can detect anomalies in real time with the sensor data that is stored in Kinesis Streams. RANDOM_CUT_FOREST is also an appropriate algorithm for many other kinds of anomaly-detection use cases—for example, the media sentiment example mentioned earlier in this post.
  3. The processed anomaly data is then loaded into the Kinesis Firehose delivery stream.
  4. By using the built-in integration that Kinesis Firehose has with Amazon ES, you can easily export the processed anomaly data into the service and visualize it with Kibana.

Implementation steps

The following sections walk through the implementation steps in detail.

Creating the delivery stream

  1. Open the Amazon Kinesis Streams console.
  2. Create a new Kinesis stream. Give it a name that indicates it’s for raw incoming stream data—for example, RawStreamData. For Number of shards, type 1.
  3. The Python code provided below simulates a streaming application, such as an IoT device, and generates random data and anomalies into a Kinesis stream. The code generates two temperature ranges, where the first range is the hypothetical sensor’s normal operating temperature range (10–20), and the second is the anomaly temperature range (100–120).Make sure to change the stream name on line 16 and 20 and the Region on line 6 to match your configuration. Alternatively, you can download the Amazon Kinesis Data Generator from this repository and use it to generate the data.
    import json
    import datetime
    import random
    import testdata
    from boto import kinesis
    
    kinesis = kinesis.connect_to_region("us-east-1")
    
    def getData(iotName, lowVal, highVal):
       data = {}
       data["iotName"] = iotName
       data["iotValue"] = random.randint(lowVal, highVal) 
       return data
    
    while 1:
       rnd = random.random()
       if (rnd < 0.01):
          data = json.dumps(getData("DemoSensor", 100, 120))  
          kinesis.put_record("RawStreamData", data, "DemoSensor")
          print '***************************** anomaly ************************* ' + data
       else:
          data = json.dumps(getData("DemoSensor", 10, 20))  
          kinesis.put_record("RawStreamData", data, "DemoSensor")
          print data

  4. Open the Amazon Elasticsearch Service console and create a new domain.
    1. Give the domain a unique name. In the Configure cluster screen, use the default settings.
    2. In the Set up access policy screen, in the Set the domain access policy list, choose Allow access to the domain from specific IP(s).
    3. Enter the public IP address of your computer.
      Note: If you’re working behind a proxy or firewall, see the “Use a proxy to simplify request signing” section in this AWS Database blog post to learn how to work with a proxy. For additional information about securing access to your Amazon ES domain, see How to Control Access to Your Amazon Elasticsearch Domain in the AWS Security Blog.
  5. After the Amazon ES domain is up and running, you can set up and configure Kinesis Firehose to export results to Amazon ES:
    1. Open the Amazon Kinesis Firehose console and choose Create Delivery Stream.
    2. In the Destination dropdown list, choose Amazon Elasticsearch Service.
    3. Type a stream name, and choose the Amazon ES domain that you created in Step 4.
    4. Provide an index name and ES type. In the S3 bucket dropdown list, choose Create New S3 bucket. Choose Next.
    5. In the configuration, change the Elasticsearch Buffer size to 1 MB and the Buffer interval to 60s. Use the default settings for all other fields. This shortens the time for the data to reach the ES cluster.
    6. Under IAM Role, choose Create/Update existing IAM role.
      The best practice is to create a new role every time. Otherwise, the console keeps adding policy documents to the same role. Eventually the size of the attached policies causes IAM to reject the role, but it does it in a non-obvious way, where the console basically quits functioning.
    7. Choose Next to move to the Review page.
  6. Review the configuration, and then choose Create Delivery Stream.
  7. Run the Python file for 1–2 minutes, and then press Ctrl+C to stop the execution. This loads some data into the stream for you to visualize in the next step.

Analyzing the data

Now it’s time to analyze the IoT streaming data using Amazon Kinesis Analytics.

  1. Open the Amazon Kinesis Analytics console and create a new application. Give the application a name, and then choose Create Application.
  2. On the next screen, choose Connect to a source. Choose the raw incoming data stream that you created earlier. (Note the stream name Source_SQL_STREAM_001 because you will need it later.)
  3. Use the default settings for everything else. When the schema discovery process is complete, it displays a success message with the formatted stream sample in a table as shown in the following screenshot. Review the data, and then choose Save and continue.
  4. Next, choose Go to SQL editor. When prompted, choose Yes, start application.
  5. Copy the following SQL code and paste it into the SQL editor window.
    CREATE OR REPLACE STREAM "TEMP_STREAM" (
       "iotName"        varchar (40),
       "iotValue"   integer,
       "ANOMALY_SCORE"  DOUBLE);
    -- Creates an output stream and defines a schema
    CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
       "iotName"       varchar(40),
       "iotValue"       integer,
       "ANOMALY_SCORE"  DOUBLE,
       "created" TimeStamp);
     
    -- Compute an anomaly score for each record in the source stream
    -- using Random Cut Forest
    CREATE OR REPLACE PUMP "STREAM_PUMP_1" AS INSERT INTO "TEMP_STREAM"
    SELECT STREAM "iotName", "iotValue", ANOMALY_SCORE FROM
      TABLE(RANDOM_CUT_FOREST(
        CURSOR(SELECT STREAM * FROM "SOURCE_SQL_STREAM_001")
      )
    );
    
    -- Sort records by descending anomaly score, insert into output stream
    CREATE OR REPLACE PUMP "OUTPUT_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM"
    SELECT STREAM "iotName", "iotValue", ANOMALY_SCORE, ROWTIME FROM "TEMP_STREAM"
    ORDER BY FLOOR("TEMP_STREAM".ROWTIME TO SECOND), ANOMALY_SCORE DESC;

 

  1. Choose Save and run SQL.
    As the application is running, it displays the results as stream data arrives. If you don’t see any data coming in, run the Python script again to generate some fresh data. When there is data, it appears in a grid as shown in the following screenshot.Note that you are selecting data from the source stream name Source_SQL_STREAM_001 that you created previously. Also note the ANOMALY_SCORE column. This is the value that the Random_Cut_Forest function calculates based on the temperature ranges provided by the Python script. Higher (anomaly) temperature ranges have a higher score.Looking at the SQL code, note that the first two blocks of code create two new streams to store temporary data and the final result. The third block of code analyzes the raw source data (Stream_Pump_1) using the Random_Cut_Forest function. It calculates an anomaly score (ANOMALY_SCORE) and inserts it into the TEMP_STREAM stream. The final code block loads the result stored in the TEMP_STREAM into DESTINATION_SQL_STREAM.
  2. Choose Exit (done editing) next to the Save and run SQL button to return to the application configuration page.

Load processed data into the Kinesis Firehose delivery stream

Now, you can export the result from DESTINATION_SQL_STREAM into the Amazon Kinesis Firehose stream that you created previously.

  1. On the application configuration page, choose Connect to a destination.
  2. Choose the stream name that you created earlier, and use the default settings for everything else. Then choose Save and Continue.
  3. On the application configuration page, choose Exit to Kinesis Analytics applications to return to the Amazon Kinesis Analytics console.
  4. Run the Python script again for 4–5 minutes to generate enough data to flow through Amazon Kinesis Streams, Kinesis Analytics, Kinesis Firehose, and finally into the Amazon ES domain.
  5. Open the Kinesis Firehose console, choose the stream, and then choose the Monitoring
  6. As the processed data flows into Kinesis Firehose and Amazon ES, the metrics appear on the Delivery Stream metrics page. Keep in mind that the metrics page takes a few minutes to refresh with the latest data.
  7. Open the Amazon Elasticsearch Service dashboard in the AWS Management Console. The count in the Searchable documents column increases as shown in the following screenshot. In addition, the domain shows a cluster health of Yellow. This is because, by default, it needs two instances to deploy redundant copies of the index. To fix this, you can deploy two instances instead of one.

Visualize the data using Kibana

Now it’s time to launch Kibana and visualize the data.

  1. Use the ES domain link to go to the cluster detail page, and then choose the Kibana link as shown in the following screenshot.

    If you’re working behind a proxy or firewall, see the “Use a proxy to simplify request signing” section in this blog post to learn how to work with a proxy.
  2. In the Kibana dashboard, choose the Discover tab to perform a query.
  3. You can also visualize the data using the different types of charts offered by Kibana. For example, by going to the Visualize tab, you can quickly create a split bar chart that aggregates by ANOMALY_SCORE per minute.


Conclusion

In this post, you learned how to use Amazon Kinesis to collect, process, and analyze real-time streaming data, and then export the results to Amazon ES for analysis and visualization with Kibana. If you have comments about this post, add them to the “Comments” section below. If you have questions or issues with implementing this solution, please open a new thread on the Amazon Kinesis or Amazon ES discussion forums.


Next Steps

Take your skills to the next level. Learn real-time clickstream anomaly detection with Amazon Kinesis Analytics.

 


About the Author

Tristan Li is a Solutions Architect with Amazon Web Services. He works with enterprise customers in the US, helping them adopt cloud technology to build scalable and secure solutions on AWS.

 

 

 

 

Analysis of Top-N DynamoDB Objects using Amazon Athena and Amazon QuickSight

Post Syndicated from Rendy Oka original https://aws.amazon.com/blogs/big-data/analysis-of-top-n-dynamodb-objects-using-amazon-athena-and-amazon-quicksight/

If you run an operation that continuously generates a large amount of data, you may want to know what kind of data is being inserted by your application. The ability to analyze data intake quickly can be very valuable for business units, such as operations and marketing. For many operations, it’s important to see what is driving the business at any particular moment. For retail companies, for example, understanding which products are currently popular can aid in planning for future growth. Similarly, for PR companies, understanding the impact of an advertising campaign can help them market their products more effectively.

This post covers an architecture that helps you analyze your streaming data. You’ll build a solution using Amazon DynamoDB Streams, AWS Lambda, Amazon Kinesis Firehose, and Amazon Athena to analyze data intake at a frequency that you choose. And because this is a serverless architecture, you can use all of the services here without the need to provision or manage servers.

The data source

You’ll collect a random sampling of tweets via Twitter’s API and store a variety of attributes in your DynamoDB table, such as: Twitter handle, tweet ID, hashtags, location, and Time-To-Live (TTL) value.

In DynamoDB, the primary key is used as an input to an internal hash function. The output from this function determines the partition in which the data will be stored. When using a combination of primary key and sort key as a DynamoDB schema, you need to make sure that no single partition key contains many more objects than the other partition keys because this can cause partition level throttling. For the demonstration in this blog, the Twitter handle will be the primary key and the tweet ID will be the sort key. This allows you to group and sort tweets from each user.

To help you get started, I have written a script that pulls a live Twitter stream that you can use to generate your data. All you need to do is provide your own Twitter Apps credentials, and it should generate the data immediately. Alternatively, I have also provided a script that you can use to generate random Tweets with little effort.

You can find both scripts in the Github repository:

https://github.com/awslabs/aws-blog-dynamodb-analysis

There are some modules that you may need to install to run these scripts. You can find them in Python’s module repository:

To get your own Twitter credentials, go to https://www.twitter.com/ and sign up for a free account, if you don’t already have one. After your account is set up, go to https://apps.twitter.com/. On the main landing page, choose the Create New App button. After the application is created, go to Keys and Access Tokens to get your credentials to use the Twitter API. You’ll need to generate Customer Tokens/Secret and Access Token/Secret. All four keys will be used to authenticate your request.

Architecture overview

Before we begin, let’s take a look at the overall flow of information will look like, from data ingestion into DynamoDB to visualization of results in Amazon QuickSight.

As illustrated in the architecture diagram above, any changes made to the items in DynamoDB will be captured and processed using DynamoDB Streams. Next, a Lambda function will be invoked by a trigger that is configured to respond to events in DynamoDB Streams. The Lambda function processes the data prior to pushing to Amazon Kinesis Firehose, which will output to Amazon S3. Finally, you use Amazon Athena to analyze the streaming data landing in Amazon S3. The result can be explored and visualized in Amazon QuickSight for your company’s business analytics.

You’ll need to implement your custom Lambda function to help transform the raw <key, value> data stored in DynamoDB to a JSON format for Athena to digest, but I can help you with a sample code that you are free to modify.

Implementation

In the following sections, I’ll walk through how you can set up the architecture discussed earlier.

Create your DynamoDB table

First, let’s create a DynamoDB table and enable DynamoDB Streams. This will enable data to be copied out of this table. From the console, use the user_id as the partition key and tweet_id as the sort key:

After the table is ready, you can enable DynamoDB Streams. This process operates asynchronously, so there is no performance impact on the table when you enable this feature. The easiest way to manage DynamoDB Streams is also through the DynamoDB console.

In the Overview tab of your newly created table, click Manage Stream. In the window, choose the information that will be written to the stream whenever data in the table is added or modified. In this example, you can choose either New image or New and old images.

For more details on this process, check out our documentation:

http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Streams.html

Configure Kinesis Firehose

Before creating the Lambda function, you need to configure Kinesis Firehose delivery stream so that it’s ready to accept data from Lambda. Open the Firehose console and choose Create Firehose Delivery Stream. From here, choose S3 as the destination and use the following to information to configure the resource. Note the Delivery stream name because you will use it in the next step.

For more details on this process, check out our documentation:

http://docs.aws.amazon.com/firehose/latest/dev/basic-create.html#console-to-s3

Create your Lambda function

Now that Kinesis Firehose is ready to accept data, you can create your Lambda function.

From the AWS Lambda console, choose the Create a Lambda function button and use the Blank Function. Enter a name and description, and choose Python 2.7 as the Runtime. Note your Lambda function name because you’ll need it in the next step.

In the Lambda function code field, you can paste the script that I have written for this purpose. All this function needs is the name of your Firehose stream name set as an environment variable.

import boto3
import json
import os

# Initiate Firehose client
firehose_client = boto3.client('firehose')

def lambda_handler(event, context):
    records = []
    batch   = []
    try :
        for record in event['Records']:
            tweet = {}
            t_stats = '{ "table_name":"%s", "user_id":"%s", "tweet_id":"%s", "approx_post_time":"%d" }\n' \
                      % ( record['eventSourceARN'].split('/')[1], \
                          record['dynamodb']['Keys']['user_id']['S'], \
                          record['dynamodb']['Keys']['tweet_id']['N'], \
                          int(record['dynamodb']['ApproximateCreationDateTime']) )
            tweet["Data"] = t_stats
            records.append(tweet)
        batch.append(records)
        res = firehose_client.put_record_batch(
            DeliveryStreamName = os.environ['firehose_stream_name'],
            Records = batch[0]
        )
        return 'Successfully processed {} records.'.format(len(event['Records']))
    except Exception :
        pass

The handler should be set to lambda_function.lambda_handler and you can use the existing lambda_dynamodb_streams role that’s been created by default.

Enable DynamoDB trigger and start collecting data

Everything is ready to go. Open your table using the DynamoDB console and go to the Triggers tab. Select the Create trigger drop down list and choose Existing Lambda function. In the pop-up window, select the function that you just created, and choose the Create button.

At this point, you can start collecting data with the Python script that I’ve provided. The first one will create a script that will pull public Twitter data and the other will generate fake tweets using Lorem Ipsum text.

Configure Amazon Athena to read the data

Next, you will configure Amazon Athena so that it can read the data Kinesis Firehose outputs to Amazon S3 and allow you to analyze the data as needed. You can connect to Athena directly from the Athena console, and you can establish a connection using JDBC or the Athena API. In this example, I’m going to demonstrate what this looks like on the Athena console.

First, create a new database and a new table. You can do this by running the following two queries. The first query creates a new database:

CREATE DATABASE IF NOT EXISTS ddbtablestats

And the second query creates a new table:

CREATE EXTERNAL TABLE IF NOT EXISTS ddbtablestats.twitterfeed (
    `table_name` string,
    `user_id` string,
    `tweet_id` bigint,
    `approx_post_time` timestamp 
) PARTITIONED BY (
    year string,
    month string,
    day string,
    hour string 
)
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES ('serialization.format' = '1')
LOCATION 's3://myBucket/dynamodb/streams/transactions/'

Note that this table is created using partitions. Partitioning separates your data into logical parts based on certain criteria, such as date, location, language, etc. This allows Athena to selectively pull your data without needing to process the entire data set. This effectively minimizes the query execution time, and it also allows you to have greater control over the data that you want to query.

After the query has completed, you should be able to see the table in the left side pane of the Athena dashboard.

After the database and table have been created, execute the ALTER TABLE query to populate the partitions in your table. Replace the date with the current date when the script was executed.

ALTER TABLE ddbtablestats.TwitterFeed ADD IF NOT EXISTS
PARTITION (year='2017',month='05',day='17',hour='01') location 's3://myBucket/dynamodb/streams/transactions/2017/05/17/01/'

Using the Athena console, you’ll need to manually populate each partition for each additional partition that you’d like to analyze, however you can programmatically automate this process by using the JDBC driver or any AWS SDK of your choice.

For more information on partitioning in Athena, check out our documentation:

http://docs.aws.amazon.com/athena/latest/ug/partitions.html

Querying the data in Amazon Athena

This is it! Let’s run this query to see the top 10 most active Twitter users in the last 24 hours. You can do this from the Athena console:

SELECT user_id, COUNT(DISTINCT tweet_id) tweets FROM ddbTableStats.TwitterFeed
WHERE year='2017' AND month='05' AND day='17'
GROUP BY user_id
ORDER BY tweets DESC
LIMIT 10

The result should look similar to the following:

Linking Athena to Amazon QuickSight

Finally, to make this data available to a larger audience, let’s visualize this data in Amazon QuickSight. Amazon QuickSight provides native connectivity to AWS data sources such as Amazon Redshift, Amazon RDS, and Amazon Athena. Amazon QuickSight can also connect to on-premises databases, Excel, or CSV files, and it can connect to cloud data sources such as Salesforce.com. For this solution, we will connect Amazon QuickSight to the Athena table we just created.

Amazon QuickSight has a free tier that provides 1 user and 1GB of SPICE (Superfast Parallel In-memory Calculated Engine) capacity free. So you can sign up and use QuickSight free of charge.

When you are signing up for Amazon QuickSight, ensure that you grant permissions for QuickSight to connect to Athena and the S3 bucket where the data is stored.

After you’ve signed up, navigate to the new analysis button, and choose new data set, and then select the Athena data source option. Create a new name for your data source and proceed to the next prompt. At this point, you should see the Athena table you created earlier.

Choose the option to import the data to SPICE for a quicker analysis. SPICE is an in-memory optimized calculation engine that is designed for quick data visualization through parallel processing. SPICE also enables you to refresh your data sets at a regular interval or on-demand as you want.

In the dialog box, confirm this data set creation, and you’ll arrive on the landing page where you can start building your graph. The X-axis will represent the user_id and the Value will be used to represent the SUM total of the tweets from each user.

The Amazon QuickSight report looks like this:

Through this visualization, I can easily see that there are 3 users that tweeted over 20 times that day and that the majority of the users have fewer than 10 tweets that day. I can also set up a scheduled refresh of my SPICE dataset so that I have a dashboard that is regularly updated with the latest data.

Closing thoughts

Here are the benefits that you can gain from using this architecture:

  1. You can optimize the design of your DynamoDB schema that follows AWS best practice recommendations.
  1. You can run analysis and data intelligence in order to understand the current customer demands for your business.
  1. You can store incremental backup for future auditing.

The flexibility of our AWS services invites you to create and design the ideal workflow for your production at any scale, and, as always, if you ever need some guidance, don’t hesitate to reach out to us.I  hope this has been helpful to you! Please leave any questions and comments below.

 


Additional Reading

Learn how to analyze VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight.


About the Author

Rendy Oka is a Big Data Support Engineer for Amazon Web Services. He provides consultations and architectural designs and partners with the TAMs, Solution Architects, and AWS product teams to help develop solutions for our customers. He is also a team lead for the big data support team in Seattle. Rendy has traveled to dozens of countries around the world and takes every opportunity to experience the local culture wherever he goes

 

 

 

 

Visualize and Monitor Amazon EC2 Events with Amazon CloudWatch Events and Amazon Kinesis Firehose

Post Syndicated from Karan Desai original https://aws.amazon.com/blogs/big-data/visualize-and-monitor-amazon-ec2-events-with-amazon-cloudwatch-events-and-amazon-kinesis-firehose/

Monitoring your AWS environment is important for security, performance, and cost control purposes. For example, by monitoring and analyzing API calls made to your Amazon EC2 instances, you can trace security incidents and gain insights into administrative behaviors and access patterns. The kinds of events you might monitor include console logins, Amazon EBS snapshot creation/deletion/modification, VPC creation/deletion/modification, and instance reboots, etc.

In this post, I show you how to build a near real-time API monitoring solution for EC2 events using Amazon CloudWatch Events and Amazon Kinesis Firehose. Please be sure to have Amazon CloudTrail enabled in your account.

  • CloudWatch Events offers a near real-time stream of system events that describe changes in AWS resources. CloudWatch Events now supports Kinesis Firehose as a target.
  • Kinesis Firehose is a fully managed service for continuously capturing, transforming, and delivering data in minutes to storage and analytics destinations such as Amazon S3, Amazon Kinesis Analytics, Amazon Redshift, and Amazon Elasticsearch Service.

Walkthrough

For this walkthrough, you create a CloudWatch event rule that matches specific EC2 events such as:

  • Starting, stopping, and terminating an instance
  • Creating and deleting VPC route tables
  • Creating and deleting a security group
  • Creating, deleting, and modifying instance volumes and snapshots

Your CloudWatch event target is a Kinesis Firehose delivery stream that delivers this data to an Elasticsearch cluster, where you set up Kibana for visualization. Using this solution, you can easily load and visualize EC2 events in minutes without setting up complicated data pipelines.

Set up the Elasticsearch cluster

Create the Amazon ES domain in the Amazon ES console, or by using the create-elasticsearch-domain command in the AWS CLI.

This example uses the following configuration:

  • Domain Name: esLogSearch
  • Elasticsearch Version: 1
  • Instance Count: 2
  • Instance type:elasticsearch
  • Enable dedicated master: true
  • Enable zone awareness: true
  • Restrict Amazon ES to an IP-based access policy

Other settings are left as the defaults.

Create a Kinesis Firehose delivery stream

In the Kinesis Firehose console, create a new delivery stream with Amazon ES as the destination. For detailed steps, see Create a Kinesis Firehose Delivery Stream to Amazon Elasticsearch Service.

Set up CloudWatch Events

Create a rule, and configure the event source and target. You can choose to configure multiple event sources with several AWS resources, along with options to specify specific or multiple event types.

In the CloudWatch console, choose Events.

For Service Name, choose EC2.

In Event Pattern Preview, choose Edit and copy the pattern below. For this walkthrough, I selected events that are specific to the EC2 API, but you can modify it to include events for any of your AWS resources.

 

{
	"source": [
		"aws.ec2"
	],
	"detail-type": [
		"AWS API Call via CloudTrail"
	],
	"detail": {
		"eventSource": [
			"ec2.amazonaws.com"
		],
		"eventName": [
			"RunInstances",
			"StopInstances",
			"StartInstances",
			"CreateFlowLogs",
			"CreateImage",
			"CreateNatGateway",
			"CreateVpc",
			"DeleteKeyPair",
			"DeleteNatGateway",
			"DeleteRoute",
			"DeleteRouteTable",
"CreateSnapshot",
"DeleteSnapshot",
			"DeleteVpc",
			"DeleteVpcEndpoints",
			"DeleteSecurityGroup",
			"ModifyVolume",
			"ModifyVpcEndpoint",
			"TerminateInstances"
		]
	}
}

The following screenshot shows what your event looks like in the console.

Next, choose Add target and select the delivery stream that you just created.

Set up Kibana on the Elasticsearch cluster

Amazon ES provides a default installation of Kibana with every Amazon ES domain. You can find the Kibana endpoint on your domain dashboard in the Amazon ES console. You can restrict Amazon ES access to an IP-based access policy.

In the Kibana console, for Index name or pattern, type log. This is the name of the Elasticsearch index.

For Time-field name, choose @time.

To view the events, choose Discover.

The following chart demonstrates the API operations and the number of times that they have been triggered in the past 12 hours.

Summary

In this post, you created a continuous, near real-time solution to monitor various EC2 events such as starting and shutting down instances, creating VPCs, etc. Likewise, you can build a continuous monitoring solution for all the API operations that are relevant to your daily AWS operations and resources.

With Kinesis Firehose as a new target for CloudWatch Events, you can retrieve, transform, and load system events to the storage and analytics destination of your choice in minutes, without setting up complicated data pipelines.

If you have any questions or suggestions, please comment below.


Additional Reading

Learn how to build a serverless architecture to analyze Amazon CloudFront access logs using AWS Lambda, Amazon Athena, and Amazon Kinesis Analytics

 

 

 

Build a Serverless Architecture to Analyze Amazon CloudFront Access Logs Using AWS Lambda, Amazon Athena, and Amazon Kinesis Analytics

Post Syndicated from Rajeev Srinivasan original https://aws.amazon.com/blogs/big-data/build-a-serverless-architecture-to-analyze-amazon-cloudfront-access-logs-using-aws-lambda-amazon-athena-and-amazon-kinesis-analytics/

Nowadays, it’s common for a web server to be fronted by a global content delivery service, like Amazon CloudFront. This type of front end accelerates delivery of websites, APIs, media content, and other web assets to provide a better experience to users across the globe.

The insights gained by analysis of Amazon CloudFront access logs helps improve website availability through bot detection and mitigation, optimizing web content based on the devices and browser used to view your webpages, reducing perceived latency by caching of popular object closer to its viewer, and so on. This results in a significant improvement in the overall perceived experience for the user.

This blog post provides a way to build a serverless architecture to generate some of these insights. To do so, we analyze Amazon CloudFront access logs both at rest and in transit through the stream. This serverless architecture uses Amazon Athena to analyze large volumes of CloudFront access logs (on the scale of terabytes per day), and Amazon Kinesis Analytics for streaming analysis.

The analytic queries in this blog post focus on three common use cases:

  1. Detection of common bots using the user agent string
  2. Calculation of current bandwidth usage per Amazon CloudFront distribution per edge location
  3. Determination of the current top 50 viewers

However, you can easily extend the architecture described to power dashboards for monitoring, reporting, and trigger alarms based on deeper insights gained by processing and analyzing the logs. Some examples are dashboards for cache performance, usage and viewer patterns, and so on.

Following we show a diagram of this architecture.

Prerequisites

Before you set up this architecture, install the AWS Command Line Interface (AWS CLI) tool on your local machine, if you don’t have it already.

Setup summary

The following steps are involved in setting up the serverless architecture on the AWS platform:

  1. Create an Amazon S3 bucket for your Amazon CloudFront access logs to be delivered to and stored in.
  2. Create a second Amazon S3 bucket to receive processed logs and store the partitioned data for interactive analysis.
  3. Create an Amazon Kinesis Firehose delivery stream to batch, compress, and deliver the preprocessed logs for analysis.
  4. Create an AWS Lambda function to preprocess the logs for analysis.
  5. Configure Amazon S3 event notification on the CloudFront access logs bucket, which contains the raw logs, to trigger the Lambda preprocessing function.
  6. Create an Amazon DynamoDB table to look up partition details, such as partition specification and partition location.
  7. Create an Amazon Athena table for interactive analysis.
  8. Create a second AWS Lambda function to add new partitions to the Athena table based on the log delivered to the processed logs bucket.
  9. Configure Amazon S3 event notification on the processed logs bucket to trigger the Lambda partitioning function.
  10. Configure Amazon Kinesis Analytics application for analysis of the logs directly from the stream.

ETL and preprocessing

In this section, we parse the CloudFront access logs as they are delivered, which occurs multiple times in an hour. We filter out commented records and use the user agent string to decipher the browser name, the name of the operating system, and whether the request has been made by a bot. For more details on how to decipher the preceding information based on the user agent string, see user-agents 1.1.0 in the Python documentation.

We use the Lambda preprocessing function to perform these tasks on individual rows of the access log. On successful completion, the rows are pushed to an Amazon Kinesis Firehose delivery stream to be persistently stored in an Amazon S3 bucket, the processed logs bucket.

To create a Firehose delivery stream with a new or existing S3 bucket as the destination, follow the steps described in Create a Firehose Delivery Stream to Amazon S3 in the S3 documentation. Keep most of the default settings, but select an AWS Identity and Access Management (IAM) role that has write access to your S3 bucket and specify GZIP compression. Name the delivery stream CloudFrontLogsToS3.

Another pre-requisite for this setup is to create an IAM role that provides the necessary permissions our AWS Lambda function to get the data from S3, process it, and deliver it to the CloudFrontLogsToS3 delivery stream.

Let’s use the AWS CLI to create the IAM role using the following the steps:

  1. Create the IAM policy (lambda-exec-policy) for the Lambda execution role to use.
  2. Create the Lambda execution role (lambda-cflogs-exec-role) and assign the service to use this role.
  3. Attach the policy created in step 1 to the Lambda execution role.

To download the policy document to your local machine, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/preprocessiong-lambda/lambda-exec-policy.json  <path_on_your_local_machine>

To download the assume policy document to your local machine, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/preprocessiong-lambda/assume-lambda-policy.json  <path_on_your_local_machine>

Following is the lambda-exec-policy.json file, which is the IAM policy used by the Lambda execution role.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "CloudWatchAccess",
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": "arn:aws:logs:*:*:*"
        },
        {
            "Sid": "S3Access",
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::*"
            ]
        },
        {
            "Sid": "FirehoseAccess",
            "Effect": "Allow",
            "Action": [
                "firehose:ListDeliveryStreams",
                "firehose:PutRecord",
                "firehose:PutRecordBatch"
            ],
            "Resource": [
                "arn:aws:firehose:*:*:deliverystream/CloudFrontLogsToS3"
            ]
        }
    ]
}

To create the IAM policy used by Lambda execution role, type the following command.

aws iam create-policy --policy-name lambda-exec-policy --policy-document file://<path>/lambda-exec-policy.json

To create the AWS Lambda execution role and assign the service to use this role, type the following command.

aws iam create-role --role-name lambda-cflogs-exec-role --assume-role-policy-document file://<path>/assume-lambda-policy.json

Following is the assume-lambda-policy.json file, to grant Lambda permission to assume a role.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

To attach the policy (lambda-exec-policy) created to the AWS Lambda execution role (lambda-cflogs-exec-role), type the following command.

aws iam attach-role-policy --role-name lambda-cflogs-exec-role --policy-arn arn:aws:iam::<your-account-id>:policy/lambda-exec-policy

Now that we have created the CloudFrontLogsToS3 Firehose delivery stream and the lambda-cflogs-exec-role IAM role for Lambda, the next step is to create a Lambda preprocessing function.

This Lambda preprocessing function parses the CloudFront access logs delivered into the S3 bucket and performs a few transformation and mapping operations on the data. The Lambda function adds descriptive information, such as the browser and the operating system that were used to make this request based on the user agent string found in the logs. The Lambda function also adds information about the web distribution to support scenarios where CloudFront access logs are delivered to a centralized S3 bucket from multiple distributions. With the solution in this blog post, you can get insights across distributions and their edge locations.

Use the Lambda Management Console to create a new Lambda function with a Python 2.7 runtime and the s3-get-object-python blueprint. Open the console, and on the Configure triggers page, choose the name of the S3 bucket where the CloudFront access logs are delivered. Choose Put for Event type. For Prefix, type the name of the prefix, if any, for the folder where CloudFront access logs are delivered, for example cloudfront-logs/. To invoke Lambda to retrieve the logs from the S3 bucket as they are delivered, select Enable trigger.

Choose Next and provide a function name to identify this Lambda preprocessing function.

For Code entry type, choose Upload a file from Amazon S3. For S3 link URL, type https.amazonaws.com//preprocessing-lambda/pre-data.zip. In the section, also create an environment variable with the key KINESIS_FIREHOSE_STREAM and a value with the name of the Firehose delivery stream as CloudFrontLogsToS3.

Choose lambda-cflogs-exec-role as the IAM role for the Lambda function, and type prep-data.lambda_handler for the value for Handler.

Choose Next, and then choose Create Lambda.

Table creation in Amazon Athena

In this step, we will build the Athena table. Use the Athena console in the same region and create the table using the query editor.

CREATE EXTERNAL TABLE IF NOT EXISTS cf_logs (
  logdate date,
  logtime string,
  location string,
  bytes bigint,
  requestip string,
  method string,
  host string,
  uri string,
  status bigint,
  referrer string,
  useragent string,
  uriquery string,
  cookie string,
  resulttype string,
  requestid string,
  header string,
  csprotocol string,
  csbytes string,
  timetaken bigint,
  forwardedfor string,
  sslprotocol string,
  sslcipher string,
  responseresulttype string,
  protocolversion string,
  browserfamily string,
  osfamily string,
  isbot string,
  filename string,
  distribution string
)
PARTITIONED BY(year string, month string, day string, hour string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
LOCATION 's3://<pre-processing-log-bucket>/prefix/';

Creation of the Athena partition

A popular website with millions of requests each day routed using Amazon CloudFront can generate a large volume of logs, on the order of a few terabytes a day. We strongly recommend that you partition your data to effectively restrict the amount of data scanned by each query. Partitioning significantly improves query performance and substantially reduces cost. The Lambda partitioning function adds the partition information to the Athena table for the data delivered to the preprocessed logs bucket.

Before delivering the preprocessed Amazon CloudFront logs file into the preprocessed logs bucket, Amazon Kinesis Firehose adds a UTC time prefix in the format YYYY/MM/DD/HH. This approach supports multilevel partitioning of the data by year, month, date, and hour. You can invoke the Lambda partitioning function every time a new processed Amazon CloudFront log is delivered to the preprocessed logs bucket. To do so, configure the Lambda partitioning function to be triggered by an S3 Put event.

For a website with millions of requests, a large number of preprocessed logs can be delivered multiple times in an hour—for example, at the interval of one each second. To avoid querying the Athena table for partition information every time a preprocessed log file is delivered, you can create an Amazon DynamoDB table for fast lookup.

Based on the year, month, data and hour in the prefix of the delivered log, the Lambda partitioning function checks if the partition specification exists in the Amazon DynamoDB table. If it doesn’t, it’s added to the table using an atomic operation, and then the Athena table is updated.

Type the following command to create the Amazon DynamoDB table.

aws dynamodb create-table --table-name athenapartitiondetails \
--attribute-definitions AttributeName=PartitionSpec,AttributeType=S \
--key-schema AttributeName=PartitionSpec,KeyType=HASH \
--provisioned-throughput ReadCapacityUnits=100,WriteCapacityUnits=100

Here the following is true:

  • PartitionSpec is the hash key and is a representation of the partition signature—for example, year=”2017”; month=”05”; day=”15”; hour=”10”.
  • Depending on the rate at which the processed log files are delivered to the processed log bucket, you might have to increase the ReadCapacityUnits and WriteCapacityUnits values, if these are throttled.

The other attributes besides PartitionSpec are the following:

  • PartitionPath – The S3 path associated with the partition.
  • PartitionType – The type of partition used (Hour, Month, Date, Year, or ALL). In this case, ALL is used.

Next step is to create the IAM role to provide permissions for the Lambda partitioning function. You require permissions to do the following:

  1. Look up and write partition information to DynamoDB.
  2. Alter the Athena table with new partition information.
  3. Perform Amazon CloudWatch logs operations.
  4. Perform Amazon S3 operations.

To download the policy document to your local machine, type following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/partitioning-lambda/lambda-partition-function-execution-policy.json  <path_on_your_local_machine>

To download the assume policy document to your local machine, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/partitioning-lambda/assume-lambda-policy.json <path_on_your_local_machine>

To create the Lambda execution role and assign the service to use this role, type the following command.

aws iam create-role --role-name lambda-cflogs-exec-role --assume-role-policy-document file://<path>/assume-lambda-policy.json

Let’s use the AWS CLI to create the IAM role using the following three steps:

  1. Create the IAM policy(lambda-partition-exec-policy) used by the Lambda execution role.
  2. Create the Lambda execution role (lambda-partition-execution-role)and assign the service to use this role.
  3. Attach the policy created in step 1 to the Lambda execution role.

To create the IAM policy used by Lambda execution role, type the following command.

aws iam create-policy --policy-name lambda-partition-exec-policy --policy-document file://<path>/lambda-partition-function-execution-policy.json

To create the Lambda execution role and assign the service to use this role, type the following command.

aws iam create-role --role-name lambda-partition-execution-role --assume-role-policy-document file://<path>/assume-lambda-policy.json

To attach the policy (lambda-partition-exec-policy) created to the AWS Lambda execution role (lambda-partition-execution-role), type the following command.

aws iam attach-role-policy --role-name lambda-partition-execution-role --policy-arn arn:aws:iam::<your-account-id>:policy/lambda-partition-exec-policy

Following is the lambda-partition-function-execution-policy.json file, which is the IAM policy used by the Lambda execution role.

{
    "Version": "2012-10-17",
    "Statement": [
      	{
            	"Sid": "DDBTableAccess",
            	"Effect": "Allow",
            	"Action": "dynamodb:PutItem"
            	"Resource": "arn:aws:dynamodb*:*:table/athenapartitiondetails"
        	},
        	{
            	"Sid": "S3Access",
            	"Effect": "Allow",
            	"Action": [
                		"s3:GetBucketLocation",
                		"s3:GetObject",
                		"s3:ListBucket",
                		"s3:ListBucketMultipartUploads",
                		"s3:ListMultipartUploadParts",
                		"s3:AbortMultipartUpload",
                		"s3:PutObject"
            	],
          		"Resource":"arn:aws:s3:::*"
		},
	              {
		      "Sid": "AthenaAccess",
      		"Effect": "Allow",
      		"Action": [ "athena:*" ],
      		"Resource": [ "*" ]
	      },
        	{
            	"Sid": "CloudWatchLogsAccess",
            	"Effect": "Allow",
            	"Action": [
                		"logs:CreateLogGroup",
                		"logs:CreateLogStream",
             	   	"logs:PutLogEvents"
            	],
            	"Resource": "arn:aws:logs:*:*:*"
        	}
    ]
}

Download the .jar file containing the Java deployment package to your local machine.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/partitioning-lambda/aws-lambda-athena-1.0.0.jar <path_on_your_local_machine>

From the AWS Management Console, create a new Lambda function with Java8 as the runtime. Select the Blank Function blueprint.

On the Configure triggers page, choose the name of the S3 bucket where the preprocessed logs are delivered. Choose Put for the Event Type. For Prefix, type the name of the prefix folder, if any, where preprocessed logs are delivered by Firehose—for example, out/. For Suffix, type the name of the compression format that the Firehose stream (CloudFrontLogToS3) delivers the preprocessed logs —for example, gz. To invoke Lambda to retrieve the logs from the S3 bucket as they are delivered, select Enable Trigger.

Choose Next and provide a function name to identify this Lambda partitioning function.

Choose Java8 for Runtime for the AWS Lambda function. Choose Upload a .ZIP or .JAR file for the Code entry type, and choose Upload to upload the downloaded aws-lambda-athena-1.0.0.jar file.

Next, create the following environment variables for the Lambda function:

  • TABLE_NAME – The name of the Athena table (for example, cf_logs).
  • PARTITION_TYPE – The partition to be created based on the Athena table for the logs delivered to the sub folders in S3 bucket based on Year, Month, Date, Hour, or Set this to ALL to use Year, Month, Date, and Hour.
  • DDB_TABLE_NAME – The name of the DynamoDB table holding partition information (for example, athenapartitiondetails).
  • ATHENA_REGION – The current AWS Region for the Athena table to construct the JDBC connection string.
  • S3_STAGING_DIR – The Amazon S3 location where your query output is written. The JDBC driver asks Athena to read the results and provide rows of data back to the user (for example, s3://<bucketname>/<folder>/).

To configure the function handler and IAM, for Handler copy and paste the name of the handler: com.amazonaws.services.lambda.CreateAthenaPartitionsBasedOnS3EventWithDDB::handleRequest. Choose the existing IAM role, lambda-partition-execution-role.

Choose Next and then Create Lambda.

Interactive analysis using Amazon Athena

In this section, we analyze the historical data that’s been collected since we added the partitions to the Amazon Athena table for data delivered to the preprocessing logs bucket.

Scenario 1 is robot traffic by edge location.

SELECT COUNT(*) AS ct, requestip, location FROM cf_logs
WHERE isbot='True'
GROUP BY requestip, location
ORDER BY ct DESC;

Scenario 2 is total bytes transferred per distribution for each edge location for your website.

SELECT distribution, location, SUM(bytes) as totalBytes
FROM cf_logs
GROUP BY location, distribution;

Scenario 3 is the top 50 viewers of your website.

SELECT requestip, COUNT(*) AS ct  FROM cf_logs
GROUP BY requestip
ORDER BY ct DESC;

Streaming analysis using Amazon Kinesis Analytics

In this section, you deploy a stream processing application using Amazon Kinesis Analytics to analyze the preprocessed Amazon CloudFront log streams. This application analyzes directly from the Amazon Kinesis Stream as it is delivered to the preprocessing logs bucket. The stream queries in section are focused on gaining the following insights:

  • The IP address of the bot, identified by its Amazon CloudFront edge location, that is currently sending requests to your website. The query also includes the total bytes transferred as part of the response.
  • The total bytes served per distribution per population for your website.
  • The top 10 viewers of your website.

To download the firehose-access-policy.json file, type the following.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/kinesisanalytics/firehose-access-policy.json  <path_on_your_local_machine>

To download the kinesisanalytics-policy.json file, type the following.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/kinesisanalytics/assume-kinesisanalytics-policy.json <path_on_your_local_machine>

Before we create the Amazon Kinesis Analytics application, we need to create the IAM role to provide permission for the analytics application to access Amazon Kinesis Firehose stream.

Let’s use the AWS CLI to create the IAM role using the following three steps:

  1. Create the IAM policy(firehose-access-policy) for the Lambda execution role to use.
  2. Create the Lambda execution role (ka-execution-role) and assign the service to use this role.
  3. Attach the policy created in step 1 to the Lambda execution role.

Following is the firehose-access-policy.json file, which is the IAM policy used by Kinesis Analytics to read Firehose delivery stream.

{
    "Version": "2012-10-17",
    "Statement": [
      	{
    	"Sid": "AmazonFirehoseAccess",
    	"Effect": "Allow",
    	"Action": [
       	"firehose:DescribeDeliveryStream",
        	"firehose:Get*"
    	],
    	"Resource": [
              "arn:aws:firehose:*:*:deliverystream/CloudFrontLogsToS3”
       ]
     }
}

Following is the assume-kinesisanalytics-policy.json file, to grant Amazon Kinesis Analytics permissions to assume a role.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "kinesisanalytics.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

To create the IAM policy used by Analytics access role, type the following command.

aws iam create-policy --policy-name firehose-access-policy --policy-document file://<path>/firehose-access-policy.json

To create the Analytics execution role and assign the service to use this role, type the following command.

aws iam attach-role-policy --role-name ka-execution-role --policy-arn arn:aws:iam::<your-account-id>:policy/firehose-access-policy

To attach the policy (irehose-access-policy) created to the Analytics execution role (ka-execution-role), type the following command.

aws iam attach-role-policy --role-name ka-execution-role --policy-arn arn:aws:iam::<your-account-id>:policy/firehose-access-policy

To deploy the Analytics application, first download the configuration file and then modify ResourceARN and RoleARN for the Amazon Kinesis Firehose input configuration.

"KinesisFirehoseInput": { 
    "ResourceARN": "arn:aws:firehose:<region>:<account-id>:deliverystream/CloudFrontLogsToS3", 
    "RoleARN": "arn:aws:iam:<account-id>:role/ka-execution-role"
}

To download the Analytics application configuration file, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis//kinesisanalytics/kinesis-analytics-app-configuration.json <path_on_your_local_machine>

To deploy the application, type the following command.

aws kinesisanalytics create-application --application-name "cf-log-analysis" --cli-input-json file://<path>/kinesis-analytics-app-configuration.json

To start the application, type the following command.

aws kinesisanalytics start-application --application-name "cf-log-analysis" --input-configuration Id="1.1",InputStartingPositionConfiguration={InputStartingPosition="NOW"}

SQL queries using Amazon Kinesis Analytics

Scenario 1 is a query for detecting bots for sending request to your website detection for your website.

-- Create output stream, which can be used to send to a destination
CREATE OR REPLACE STREAM "BOT_DETECTION" (requesttime TIME, destribution VARCHAR(16), requestip VARCHAR(64), edgelocation VARCHAR(64), totalBytes BIGINT);
-- Create pump to insert into output 
CREATE OR REPLACE PUMP "BOT_DETECTION_PUMP" AS INSERT INTO "BOT_DETECTION"
--
SELECT STREAM 
    STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND) as requesttime,
    "distribution_name" as distribution,
    "request_ip" as requestip, 
    "edge_location" as edgelocation, 
    SUM("bytes") as totalBytes
FROM "CF_LOG_STREAM_001"
WHERE "is_bot" = true
GROUP BY "request_ip", "edge_location", "distribution_name",
STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND),
STEP("CF_LOG_STREAM_001".ROWTIME BY INTERVAL '1' SECOND);

Scenario 2 is a query for total bytes transferred per distribution for each edge location for your website.

-- Create output stream, which can be used to send to a destination
CREATE OR REPLACE STREAM "BYTES_TRANSFFERED" (requesttime TIME, destribution VARCHAR(16), edgelocation VARCHAR(64), totalBytes BIGINT);
-- Create pump to insert into output 
CREATE OR REPLACE PUMP "BYTES_TRANSFFERED_PUMP" AS INSERT INTO "BYTES_TRANSFFERED"
-- Bytes Transffered per second per web destribution by edge location
SELECT STREAM 
    STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND) as requesttime,
    "distribution_name" as distribution,
    "edge_location" as edgelocation, 
    SUM("bytes") as totalBytes
FROM "CF_LOG_STREAM_001"
GROUP BY "distribution_name", "edge_location", "request_date",
STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND),
STEP("CF_LOG_STREAM_001".ROWTIME BY INTERVAL '1' SECOND);

Scenario 3 is a query for the top 50 viewers for your website.

-- Create output stream, which can be used to send to a destination
CREATE OR REPLACE STREAM "TOP_TALKERS" (requestip VARCHAR(64), requestcount DOUBLE);
-- Create pump to insert into output 
CREATE OR REPLACE PUMP "TOP_TALKERS_PUMP" AS INSERT INTO "TOP_TALKERS"
-- Top Ten Talker
SELECT STREAM ITEM as requestip, ITEM_COUNT as requestcount FROM TABLE(TOP_K_ITEMS_TUMBLING(
  CURSOR(SELECT STREAM * FROM "CF_LOG_STREAM_001"),
  'request_ip', -- name of column in single quotes
  50, -- number of top items
  60 -- tumbling window size in seconds
  )
);

Conclusion

Following the steps in this blog post, you just built an end-to-end serverless architecture to analyze Amazon CloudFront access logs. You analyzed these both in interactive and streaming mode, using Amazon Athena and Amazon Kinesis Analytics respectively.

By creating a partition in Athena for the logs delivered to a centralized bucket, this architecture is optimized for performance and cost when analyzing large volumes of logs for popular websites that receive millions of requests. Here, we have focused on just three common use cases for analysis, sharing the analytic queries as part of the post. However, you can extend this architecture to gain deeper insights and generate usage reports to reduce latency and increase availability. This way, you can provide a better experience on your websites fronted with Amazon CloudFront.

In this blog post, we focused on building serverless architecture to analyze Amazon CloudFront access logs. Our plan is to extend the solution to provide rich visualization as part of our next blog post.


About the Authors

Rajeev Srinivasan is a Senior Solution Architect for AWS. He works very close with our customers to provide big data and NoSQL solution leveraging the AWS platform and enjoys coding . In his spare time he enjoys riding his motorcycle and reading books.

 

Sai Sriparasa is a consultant with AWS Professional Services. He works with our customers to provide strategic and tactical big data solutions with an emphasis on automation, operations & security on AWS. In his spare time, he follows sports and current affairs.

 

 


Related

Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight

Build a Visualization and Monitoring Dashboard for IoT Data with Amazon Kinesis Analytics and Amazon QuickSight

Post Syndicated from Karan Desai original https://aws.amazon.com/blogs/big-data/build-a-visualization-and-monitoring-dashboard-for-iot-data-with-amazon-kinesis-analytics-and-amazon-quicksight/

Customers across the world are increasingly building innovative Internet of Things (IoT) workloads on AWS. With AWS, they can handle the constant stream of data coming from millions of new, internet-connected devices. This data can be a valuable source of information if it can be processed, analyzed, and visualized quickly in a scalable, cost-efficient manner. Engineers and developers can monitor performance and troubleshoot issues while sales and marketing can track usage patterns and statistics to base business decisions.

In this post, I demonstrate a sample solution to build a quick and easy monitoring and visualization dashboard for your IoT data using AWS serverless and managed services. There’s no need for purchasing any additional software or hardware. If you are already using AWS IoT, you can build this dashboard to tap into your existing device data. If you are new to AWS IoT, you can be up and running in minutes using sample data. Later, you can customize it to your needs, as your business grows to millions of devices and messages.

Architecture

The following is a high-level architecture diagram showing the serverless setup to configure.

 

AWS service overview

AWS IoT is a managed cloud platform that lets connected devices interact easily and securely with cloud applications and other devices. AWS IoT can process and route billions of messages to AWS endpoints and to other devices reliably and securely.

Amazon Kinesis Firehose is the easiest way to capture, transform, and load streaming data continuously into AWS from thousands of data sources, such as IoT devices. It is a fully managed service that automatically scales to match the throughput of your data and requires no ongoing administration.

Amazon Kinesis Analytics allows you to process streaming data coming from IoT devices in real time with standard SQL, without having to learn new programming languages or processing frameworks, providing actionable insights promptly.

The processed data is fed into Amazon QuickSight, which is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from the data.

The most popular way for Internet-connected devices to send data is using MQTT messages. The AWS IoT gateway receives these messages from registered IoT devices. The solution in this post uses device data from AWS Simple Beer Service (SBS), a series of internet-connected kegerators sending sensor outputs such as temperature, humidity, and sound levels in a JSON payload. You can use any existing IoT data source that you may have.

The AWS IoT rules engine allows selecting data from message payloads, processing it, and sending it to other services. You forward the data to a Firehose delivery stream to consolidate the continuous data stream into batches for further processing. The batched data is also stored temporarily in an Amazon S3 bucket for later retrieval and can be set for deletion after a specified time using S3 Lifecycle Management rules.

The incoming data from the Firehose delivery stream is fed into an Analytics application that provides an easy way to process the data in real time using standard SQL queries. Analytics allows writing standard SQL queries to extract specific components from the incoming data stream and perform real-time ETL on it. In this post, you use this feature to aggregate minimum and maximum temperature values from the sensors per minute. You load it in Amazon QuickSight to create a monitoring dashboard and check if the devices are over-heating or cooling down during use. You also extract every device’s location, parameters such as temperature, sound levels, humidity, and the time stamp in Analytics to use on the visualization dashboard.

The processed data from the two queries is fed into two Firehose delivery streams, both of which batch the data into CSV files every minute and store it in S3. The batching time interval is configurable between 1 and 15 minutes in 1-second intervals.

Finally, you use Amazon QuickSight to ingest the processed CSV files from S3 as a data source to build visualizations. Amazon QuickSight’s super-fast, parallel, in-memory, calculation engine (SPICE) parses the ingested data and allows you to create a variety of visualizations with different graph types. You can also use the Amazon QuickSight built-in Story feature to combine visualizations into business dashboards that can be shared in a secure manner.

Implementation

AWS IoT, Amazon Kinesis, and Amazon QuickSight are all fully managed services, which means you can complete the entire setup in just a few steps using the AWS Management Console. Don’t worry about setting up any underlying hardware or installing any additional software. So, get started.

Step 1. Set up your AWS IoT data source

Do you currently use AWS IoT? If you have an existing IoT thing set up and running on AWS IoT, you can skip to Step 2.

If you have an AWS IoT button or other IoT devices that can publish MQTT messages and would like to use that for the setup, follow the Getting Started with AWS IoT topic to connect your thing to AWS IoT. Continue to Step 2.

If you do not have an existing IoT device, you can generate simulated device data using a script on your local machine and have it publish to AWS IoT. The following script lets you set up your AWS IoT environment and publish simulated data that mimics device data from Simple Beer Service.

Generate sample Data

Running the sbs.py Python script generates fictitious AWS IoT messages from multiple SBS devices. The IoT rule sends the message to Firehose for further processing.

The script requires access to AWS CLI credentials and boto3 installation on the machine running the script. Download and run the following Python script:

https://github.com/awslabs/sbs-iot-data-generator/blob/master/sbs.py

The script generates random data that looks like the following:

{"deviceParameter": "Temperature", "deviceValue": 33, "deviceId": "SBS01", "dateTime": "2017-02-03 11:29:37"}
{"deviceParameter": "Sound", "deviceValue": 140, "deviceId": "SBS03", "dateTime": "2017-02-03 11:29:38"}
{"deviceParameter": "Humidity", "deviceValue": 63, "deviceId": "SBS01", "dateTime": "2017-02-03 11:29:39"}
{"deviceParameter": "Flow", "deviceValue": 80, "deviceId": "SBS04", "dateTime": "2017-02-03 11:29:41"}

Run the script and keep it running for the duration of the project to generate sufficient data.

Tip: If you encounter any issues running the script from your local machine, launch an EC2 instance and run the script there as a root user. Remember to assign an appropriate IAM role to your instance at the time of launch that allows it to access AWS IoT.

Step 2. Create three Firehose delivery streams

For this post, you require three Firehose delivery streams:  one to batch raw data from AWS IoT, and two to batch output device data and aggregated data from Analytics.

  1. In the console, choose Firehose.
  2. Create all three Firehose delivery streams using the following field values.

Delivery stream 1:

Name IoT-Source-Stream
S3 bucket <your unique name>-kinesis
S3 prefix source/

Delivery stream 2:

Name IoT-Destination-Data-Stream
S3 bucket <your unique name>-kinesis
S3 prefix data/

Delivery stream 3:

Name IoT-Destination-Aggregate-Stream
S3 bucket <your unique name>-kinesis
S3 prefix aggregate/

Step 3. Set up AWS IoT to receive and forward incoming data

  1. In the console, choose IoT.
  2. Create a new AWS IoT rule with the following field values.
Name IoT_to_Firehose
Attribute *
Topic Filter /sbs/devicedata/#
Add Action Send messages to an Amazon Kinesis Firehose stream (select IoT-Source-Stream from dropdown)
Select Separator “\n (newline)”

A quick check before proceeding further: make sure that you have run the script to generate simulated IoT data or that your IoT Thing is running and delivering data. If not, set it up now. The Amazon Kinesis Analytics application you set up in the next step needs the data to process it further.

Step 4: Create an Analytics application to process data

  1. In the console, choose Kinesis.
  2. Create a new application.
  3. Enter a name of your choice, for example, SBS-IoT-Data.
  4. For the source, choose IoT-Source-Stream.

Analytics auto-discovers the schema on the data by sampling records from the input stream. It also includes an in-built SQL editor that allows you to write standard SQL queries to transform incoming data.

Tip: If Analytics is unable to discover your incoming data, it may be missing the appropriate IAM permissions. In the IAM console, select the role that you assigned to your IoT rule in Step 3. Make sure that it has the ARN of the IoT-Source-Data Firehose stream listed in the firehose:putRecord section.

Here is a sample SQL query that generates two output streams:

  • DESTINATION_SQL_BASIC_STREAM contains the device ID, device parameter, its value, and the time stamp from the incoming stream.
  • DESTINATION_SQL_AGGREGATE_STREAM aggregates the maximum and minimum values of temperatures from the sensors over a one-minute period from the incoming data.
-- Create an output stream with four columns, which is used to send IoT data to the destination
CREATE OR REPLACE STREAM "DESTINATION_SQL_BASIC_STREAM" (dateTime TIMESTAMP, deviceId VARCHAR(8), deviceParameter VARCHAR(16), deviceValue INTEGER);

-- Create a pump that continuously selects from the source stream and inserts it into the output data stream
CREATE OR REPLACE PUMP "STREAM_PUMP_1" AS INSERT INTO "DESTINATION_SQL_BASIC_STREAM"

-- Filter specific columns from the source stream
SELECT STREAM "dateTime", "deviceId", "deviceParameter", "deviceValue" FROM "SOURCE_SQL_STREAM_001";

-- Create a second output stream with three columns, which is used to send aggregated min/max data to the destination
CREATE OR REPLACE STREAM "DESTINATION_SQL_AGGREGATE_STREAM" (dateTime TIMESTAMP, highestTemp SMALLINT, lowestTemp SMALLINT);

-- Create a pump that continuously selects from a source stream 
CREATE OR REPLACE PUMP "STREAM_PUMP_2" AS INSERT INTO "DESTINATION_SQL_AGGREGATE_STREAM"

-- Extract time in minutes, plus the highest and lowest value of device temperature in that minute, into the destination aggregate stream, aggregated per minute
SELECT STREAM FLOOR("SOURCE_SQL_STREAM_001".ROWTIME TO MINUTE) AS "dateTime", MAX("deviceValue") AS "highestTemp", MIN("deviceValue") AS "lowestTemp" FROM "SOURCE_SQL_STREAM_001" WHERE "deviceParameter"='Temperature' GROUP BY FLOOR("SOURCE_SQL_STREAM_001".ROWTIME TO MINUTE);

Real-time analytics shows the results of the SQL query. If everything is working correctly, you see three streams listed, similar to the following screenshot.

Step 5: Connect the Analytics application to output Firehose delivery streams

You create two destinations for the two delivery streams that you created in the previous step. A single Analytics application can have multiple destinations defined; however, this needs to be set up using the AWS CLI, not from the console. If you do not already have it, install the AWS CLI on your local machine and configure it with your credentials.

Tip: If you are running the IoT script from an EC2 instance, it comes pre-installed with the AWS CLI.

Create the first destination delivery stream 

The AWS CLI command to create a new output Firehose delivery stream is as follows:

aws kinesisanalytics add-application-output --application-name <Name of Analytics Application> --current-application-version-id <number> --application-output 'Name=DESTINATION_SQL_BASIC_STREAM,KinesisFirehoseOutput={ResourceARN=<ARN of IoT-Data-Stream>,RoleARN=<ARN of Analytics application>,DestinationSchema={RecordFormatType=CSV}'

Do not copy this into the CLI just yet! Before entering this command, make the following four changes to personalize it:

  • For Name of Analytics Application, enter the value from Step 4, or from the Analytics console.
  • For current-application-version-ID, run the following command:
aws kinesisanalytics describe-application --application-name <application name from above>; | grep ApplicationVersionId
  • For ResourceARN, run the following command:
aws firehose describe-delivery-stream --delivery-stream-name IoT-Destination-Data-Stream | grep DeliveryStreamARN
  • For RoleARN, run the following command:
aws kinesisanalytics describe-application --application-name <application name from above>; | grep RoleARN

Now, paste the complete command in the AWS CLI and press Enter. If there are any errors, the response provides details. If everything goes well, a new destination delivery stream is created to send the first query (DESTINATION_SQL_BASIC_STREAM) to IoT-Destination-Data-Stream.

Create the second destination delivery stream

Following similar steps as above, create a second destination Firehose delivery stream with the following changes:

  • For Name of Analytics Application, enter the same name as the first delivery stream.
  • For current-application-version-ID, increment by 1 from the previous value (unless you made other changes in between these steps). If unsure, run the same command as above to get it again.
  • For ResourceARN, get the value by running the following CLI command:
aws firehose describe-delivery-stream --delivery-stream-name IoT-Destination-Aggregate-Stream | grep DeliveryStreamARN
  • For RoleArn, enter the same value as the first stream.

Run the aws kinesisanalytics CLI command, similar to the previous step but with the new parameters substituted. This creates the second output Firehose destination delivery stream.

Update the IAM role for Analytics to allow writing to both output streams.

  1. In the console, choose IAM, Roles.
  2. Select the role that you created with Analytics in Step 4.
  3. Choose Policy, JSON, and Edit.
  4. Find “Sid”: “WriteOutputFirehose” in the JSON document, go to the “Resource” section and make sure that it includes Resource ARNs of both streams that you found in the previous step.
  5. If it has only one ARN, add the second ARN and choose Save.

This completes the Amazon Kinesis setup. The incoming IoT data is processed by Analytics and delivered, using two output delivery streams, to two separate folders in your S3 bucket.

Step 6: Set up Amazon QuickSight to analyze the data

To build the visualization dashboard, ingest the processed CSV files from the S3 bucket into Amazon QuickSight.

  1. In the console, choose QuickSight.
  2. If this is your first time using Amazon QuickSight, you are asked to create a new account. Follow the prompts.
  3. When you are logged in to your account, choose New Analysis and enter a name of your choice.
  4. Choose New data set for the analysis or, if you have previously imported your data set, select one from the available data sets.
  5. You import two data sets: one with general device parameters information, and the other with aggregates of maximum and minimum temperatures for monitoring. For the first data set, choose S3 from the list of available data sources and enter a name, for example, IoT Device Data.
  6. The location of the S3 bucket and the objects to use are provided to Amazon QuickSight as a manifest file. Create a new manifest file following the supported formats for Amazon S3 manifest files.
  7. In the URIPrefixes section, provide your appropriate S3 bucket and folder location for the general device data. Hint: it should include <your unique name>-kinesis/data/.

Your manifest file should look similar to the following:

{ 
    "fileLocations": [                                                    
              {"URIPrefixes": ["https://s3.amazonaws.com/<YOUR_BUCKET_NAME>/data/<YEAR>/<MONTH>/<DATE>/<HOUR>/"]}
     ],
     "globalUploadSettings": { 
     "format": "CSV",  
     "delimiter": ","
    }
}

Amazon QuickSight imports and parses the data set, and provides available data fields that can be used for making graphs. The Edit/Preview data button allows you to format and transform the data, change data types, and filter or join your data. Make sure that the columns have the correct titles. If not, you can edit them and then save.

Tip: choose the downward arrow on the top right and unselect Files include headers to give each column appropriate headers. Choose Save. This takes you back to the data sets page.

Follow the same steps as above to import the second data set. This time, your manifest should include your aggregate data set folder on S3, which is named <your unique name>-kinesis/aggregate/. Update headers if necessary and choose Save & visualize.

Build an analysis

The visualization screen shows the data set that you last imported, which in this case is the aggregate data. To include the general device data as well, for Fields on the top left, choose Edit analysis data sets. Choose Add data set and select the other data set that you saved earlier.

Now both data sets are available on the analysis screen. For Visual Types at bottom left, select the type of graph to make. For Fields, select the fields to visualize. For example, drag Device ID, Device Parameter, and Value to Field wells, as shown in the screenshot below, to generate a visualization of average parameter values compared across devices.

You can create another visual by choose +Add. This time, select a line graph to show monitoring of the maximum temperature values of the sensors in any minute, from the aggregate data set.

If you would like to create an interactive story to present to your team or organization, you can choose the Story option on the left panel. Create a dashboard with multiple visualizations, to save and share securely with the intended audience. An example of a story is shown below.

Conclusion

Any data is valuable only when it can be actually put to use. In this post, you’ve seen how it’s possible to quickly build a simple Analytics application to ingest, process, and visualize IoT data in near real time entirely using AWS managed services. This solution is scalable and reliable, and costs a fraction of other business intelligence solutions. It is easy enough that anyone with an AWS account can build and use it without any special training.

If you have any questions or suggestions, please comment below.


About the Author

Karan Desai is a Solutions Architect with Amazon Web Services. He works with startups and small businesses in the US, helping them adopt cloud technology to build scalable and secure solutions using AWS. In his spare time, he likes to build personal IoT projects, travel to offbeat places and write about it.

 

 


Related

Visualize Big Data with Amazon QuickSight, Presto, and Apache Spark on Amazon EMR

 

 

 

 

 

 

 

Test Your Streaming Data Solution with the New Amazon Kinesis Data Generator

Post Syndicated from Allan MacInnis original https://aws.amazon.com/blogs/big-data/test-your-streaming-data-solution-with-the-new-amazon-kinesis-data-generator/

When building a streaming data solution, most customers want to test it with data that is similar to their production data. Creating this data and streaming it to your solution can often be the most tedious task in testing the solution.

Amazon Kinesis Streams and Amazon Kinesis Firehose enable you to continuously capture and store terabytes of data per hour from hundreds of thousands of sources. Amazon Kinesis Analytics gives you the ability to use standard SQL to analyze and aggregate this data in real-time. It’s easy to create an Amazon Kinesis stream or Firehose delivery stream with just a few clicks in the AWS Management Console (or a few commands using the AWS CLI or Amazon Kinesis API). However, to generate a continuous stream of test data, you must write a custom process or script that runs continuously, using the AWS SDK or CLI to send test records to Amazon Kinesis. Although this task is necessary to adequately test your solution, it means more complexity and longer development and testing times.

Wouldn’t it be great if there were a user-friendly tool to generate test data and send it to Amazon Kinesis? Well, now there is—the Amazon Kinesis Data Generator (KDG).

KDG overview

The KDG simplifies the task of generating data and sending it to Amazon Kinesis. The tool provides a user-friendly UI that runs directly in your browser. With the KDG, you can do the following:

  • Create templates that represent records for your specific use cases
  • Populate the templates with fixed data or random data
  • Save the templates for future use
  • Continuously send thousands of records per second to your Amazon Kinesis stream or Firehose delivery stream

The KDG is open source, and you can find the source code on the Amazon Kinesis Data Generator repo in GitHub. Because the tool is a collection of static HTML and JavaScript files that run directly in your browser, you can start using it immediately without downloading or cloning the project. It is enabled as a static site in GitHub, and we created a short URL to access it.

To get started immediately, check it out at http://amzn.to/datagen.

Using the KDG

Getting started with the KDG requires only three short steps:

  1. Create an Amazon Cognito user in your AWS account (first-time only).
  2. Use this user’s credentials to log in to the KDG.
  3. Create a record template for your data.

When you’ve completed these steps, you can then send data to Streams or Firehose.

Create an Amazon Cognito user

The KDG is a great example of a mobile application that uses Amazon Cognito for a user repository and user authentication, and the AWS JavaScript SDK to communicate with AWS services directly from your browser. For information about how to build your own JavaScript application that uses Amazon Cognito, see Use Amazon Cognito in your website for simple AWS authentication on the AWS Mobile Blog.

Before you can start sending data to your Amazon Kinesis stream, you must create an Amazon Cognito user in your account who can write to Streams and Firehose. When you create the user, you create a username and password for that user. You use those credentials to sign in to the KDG. To simplify creating the Amazon Cognito user in your account, we created a Lambda function and a CloudFormation template. For more information about creating the Amazon Cognito user in your AWS account, see Configure Your AWS Account.

Note:  It’s important that you use the URL provided by the output of the CloudFormation stack the first time that you access the KDG. This URL contains parameters needed by the KDG. The KDG stores the values of these parameters locally, so you can then access the tool using the short URL, http://amzn.to/datagen.

Log in to the KDG

After you create an Amazon Cognito user in your account, the next step is to log in to the KDG. To do this, provide the username and password that you created earlier.

On the main page, you can configure your data templates and send data to an Amazon Kinesis stream or Firehose delivery stream.

The basic configuration is simple enough. All fields on the page are required:

  • Region: Choose the AWS Region that contains the Amazon Kinesis stream or Firehose delivery stream to receive your streaming data.
  • Stream/firehose name: Choose the name of the stream or delivery stream to receive your streaming data.
  • Records per second: Enter the number of records to send to your stream or delivery stream each second.
  • Record template: Enter the raw data, or a template that represents your data structure, to be used for each record sent by the KDG. For information about creating templates for your data, see the “Creating Record Templates” section, later in this post.

When you set the Records per second value, consider that the KDG isn’t intended to be a data producer for load-testing your application. However, it can easily send several thousand records per second from a single tab in your browser, which is plenty of data for most applications. In testing, the KDG has produced 80,000 records per second to a single Amazon Kinesis stream, but your mileage may vary. The maximum rate at which it produces records depends on your computer’s specs and the complexity of your record template.

Ensure that your stream or delivery stream is scaled appropriately:

  • 1,000 records/second or 1 MB/second to an Amazon Kinesis stream
  • 5,000 records/second or 5 MB/second to a Firehose delivery stream

Otherwise, Amazon Kinesis may reject records, and you won’t achieve your desired throughput. For more information about adding capacity to a stream by adding more shards, see Resharding a Stream. For information about increasing the capacity of a delivery stream, see Amazon Kinesis Firehose Limits.

Create record templates

The Record Template field is a free-text field where you can enter any text that represents a single streaming data record. You can create a single line of static data, so that each record sent to Amazon Kinesis is identical. Or, you can format the text as a template.

In this case, the KDG substitutes portions of the template with fake or random data before sending the record. This lets you introduce randomness or variability in each record that is sent in your data stream. The KDG uses Faker.js, an open source library, to generate fake data. For more information, see the faker.js project page in GitHub. The easiest way to see how this works is to review an example.

To simulate records being sent from a weather sensor Internet of Things (IoT) device, you want each record to be formatted in JSON. The following is an example of what a final record must look like:

{
	"sensorId": 40,
	"currentTemperature": 76,
	"status": "OK"
} 

For this use case, you want to simulate sending data from one of 50 sensors, so the sensorID field can be an integer between 1 and 50. The temperature value can range between 10 and 150, so the currentTemperature field should contain a value in this range. Finally, the status value can be one of three possible values: OK, FAIL, and WARN. The KDG template format uses moustache syntax (double curly-braces) to enclose items that should be replaced before the record is sent to Amazon Kinesis. To model the record, the template looks like this:

{
    "sensorId": {{random.number(50)}},
    "currentTemperature": {{random.number(
        {
            "min":10,
            "max":150
        }
    )}},
    "status": "{{random.arrayElement(
        ["OK","FAIL","WARN"]
    )}}"
}

Take a look at one more example, simulating a stream of records that represent rows from an Apache access log. A single Apache access log entry might look like this:

76.0.56.179 - - [29/Apr/2017:16:32:11 -05:00] "GET /wp-admin" 200 8233 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_0 rv:6.0; CY) AppleWebKit/535.0.0 (KHTML, like Gecko) Version/4.0.3 Safari/535.0.0"

The following example shows how to create a template for the Apache access log:

{{internet.ip}} - - [{{date.now("DD/MMM/YYYY:HH:mm:ss Z")}}] "{{random.weightedArrayElement({"weights":[0.6,0.1,0.1,0.2],"data":["GET","POST","DELETE","PUT"]})}} {{random.arrayElement(["/list","/wp-content","/wp-admin","/explore","/search/tag/list","/app/main/posts","/posts/posts/explore"])}}" {{random.weightedArrayElement({"weights": [0.9,0.04,0.02,0.04], "data":["200","404","500","301"]})}} {{random.number(10000)}} "-" "{{internet.userAgent}}"

For more information about creating your own templates, see the Record Template section of the KDG documentation.

The KDG saves the templates that you create in your local browser storage. As long as you use the same browser on the same computer, you can reuse up to five templates.

Summary

Testing your streaming data solution has never been easier. Get started today by visiting the KDG hosted UI or its Amazon Kinesis Data Generator page in GitHub. The project is licensed under the Apache 2.0 license, so feel free to clone and modify it for your own use as necessary. And of course, please submit any issues or pull requests via GitHub.

If you have any questions or suggestions, please add them below.

 


About the Author

Allan MacInnis is a Solutions Architect at Amazon Web Services. He works with our customers to help them build streaming data solutions using Amazon Kinesis. In his spare time, he enjoys mountain biking and spending time with his family.

 

 


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