Tag Archives: Dyn

Migrating .NET Classic Applications to Amazon ECS Using Windows Containers

Post Syndicated from Sundar Narasiman original https://aws.amazon.com/blogs/compute/migrating-net-classic-applications-to-amazon-ecs-using-windows-containers/

This post contributed by Sundar Narasiman, Arun Kannan, and Thomas Fuller.

AWS recently announced the general availability of Windows container management for Amazon Elastic Container Service (Amazon ECS). Docker containers and Amazon ECS make it easy to run and scale applications on a virtual machine by abstracting the complex cluster management and setup needed.

Classic .NET applications are developed with .NET Framework 4.7.1 or older and can run only on a Windows platform. These include Windows Communication Foundation (WCF), ASP.NET Web Forms, and an ASP.NET MVC web app or web API.

Why classic ASP.NET?

ASP.NET MVC 4.6 and older versions of ASP.NET occupy a significant footprint in the enterprise web application space. As enterprises move towards microservices for new or existing applications, containers are one of the stepping stones for migrating from monolithic to microservices architectures. Additionally, the support for Windows containers in Windows 10, Windows Server 2016, and Visual Studio Tooling support for Docker simplifies the containerization of ASP.NET MVC apps.

Getting started

In this post, you pick an ASP.NET 4.6.2 MVC application and get step-by-step instructions for migrating to ECS using Windows containers. The detailed steps, AWS CloudFormation template, Microsoft Visual Studio solution, ECS service definition, and ECS task definition are available in the aws-ecs-windows-aspnet GitHub repository.

To help you getting started running Windows containers, here is the reference architecture for Windows containers on GitHub: ecs-refarch-cloudformation-windows. This reference architecture is the layered CloudFormation stack, in that it calls the other stacks to create the environment. The CloudFormation YAML template in this reference architecture is referenced to create a single JSON CloudFormation stack, which is used in the steps for the migration.

Steps for Migration

The code and templates to implement this migration can be found on GitHub: https://github.com/aws-samples/aws-ecs-windows-aspnet.

  1. Your development environment needs to have the latest version and updates for Visual Studio 2017, Windows 10, and Docker for Windows Stable.
  2. Next, containerize the ASP.NET application and test it locally. The size of Windows container application images is generally larger compared to Linux containers. This is because the base image of the Windows container itself is large in size, typically greater than 9 GB.
  3. After the application is containerized, the container image needs to be pushed to Amazon Elastic Container Registry (Amazon ECR). Images stored in ECR are compressed to improve pull times and reduce storage costs. In this case, you can see that ECR compresses the image to around 1 GB, for an optimization factor of 90%.
  4. Create a CloudFormation stack using the template in the ‘CloudFormation template’ folder. This creates an ECS service, task definition (referring the containerized ASP.NET application), and other related components mentioned in the ECS reference architecture for Windows containers.
  5. After the stack is created, verify the successful creation of the ECS service, ECS instances, running tasks (with the threshold mentioned in the task definition), and the Application Load Balancer’s successful health check against running containers.
  6. Navigate to the Application Load Balancer URL and see the successful rendering of the containerized ASP.NET MVC app in the browser.

Key Notes

  • Generally, Windows container images occupy large amount of space (in the order of few GBs).
  • All the task definition parameters for Linux containers are not available for Windows containers. For more information, see Windows Task Definitions.
  • An Application Load Balancer can be configured to route requests to one or more ports on each container instance in a cluster. The dynamic port mapping allows you to have multiple tasks from a single service on the same container instance.
  • IAM roles for Windows tasks require extra configuration. For more information, see Windows IAM Roles for Tasks. For this post, configuration was handled by the CloudFormation template.
  • The ECS container agent log file can be accessed for troubleshooting Windows containers: C:\ProgramData\Amazon\ECS\log\ecs-agent.log

Summary

In this post, you migrated an ASP.NET MVC application to ECS using Windows containers.

The logical next step is to automate the activities for migration to ECS and build a fully automated continuous integration/continuous deployment (CI/CD) pipeline for Windows containers. This can be orchestrated by leveraging services such as AWS CodeCommit, AWS CodePipeline, AWS CodeBuild, Amazon ECR, and Amazon ECS. You can learn more about how this is done in the Set Up a Continuous Delivery Pipeline for Containers Using AWS CodePipeline and Amazon ECS post.

If you have questions or suggestions, please comment below.

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.

 

 

 

 

 

New AWS Auto Scaling – Unified Scaling For Your Cloud Applications

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-auto-scaling-unified-scaling-for-your-cloud-applications/

I’ve been talking about scalability for servers and other cloud resources for a very long time! Back in 2006, I wrote “This is the new world of scalable, on-demand web services. Pay for what you need and use, and not a byte more.” Shortly after we launched Amazon Elastic Compute Cloud (EC2), we made it easy for you to do this with the simultaneous launch of Elastic Load Balancing, EC2 Auto Scaling, and Amazon CloudWatch. Since then we have added Auto Scaling to other AWS services including ECS, Spot Fleets, DynamoDB, Aurora, AppStream 2.0, and EMR. We have also added features such as target tracking to make it easier for you to scale based on the metric that is most appropriate for your application.

Introducing AWS Auto Scaling
Today we are making it easier for you to use the Auto Scaling features of multiple AWS services from a single user interface with the introduction of AWS Auto Scaling. This new service unifies and builds on our existing, service-specific, scaling features. It operates on any desired EC2 Auto Scaling groups, EC2 Spot Fleets, ECS tasks, DynamoDB tables, DynamoDB Global Secondary Indexes, and Aurora Replicas that are part of your application, as described by an AWS CloudFormation stack or in AWS Elastic Beanstalk (we’re also exploring some other ways to flag a set of resources as an application for use with AWS Auto Scaling).

You no longer need to set up alarms and scaling actions for each resource and each service. Instead, you simply point AWS Auto Scaling at your application and select the services and resources of interest. Then you select the desired scaling option for each one, and AWS Auto Scaling will do the rest, helping you to discover the scalable resources and then creating a scaling plan that addresses the resources of interest.

If you have tried to use any of our Auto Scaling options in the past, you undoubtedly understand the trade-offs involved in choosing scaling thresholds. AWS Auto Scaling gives you a variety of scaling options: You can optimize for availability, keeping plenty of resources in reserve in order to meet sudden spikes in demand. You can optimize for costs, running close to the line and accepting the possibility that you will tax your resources if that spike arrives. Alternatively, you can aim for the middle, with a generous but not excessive level of spare capacity. In addition to optimizing for availability, cost, or a blend of both, you can also set a custom scaling threshold. In each case, AWS Auto Scaling will create scaling policies on your behalf, including appropriate upper and lower bounds for each resource.

AWS Auto Scaling in Action
I will use AWS Auto Scaling on a simple CloudFormation stack consisting of an Auto Scaling group of EC2 instances and a pair of DynamoDB tables. I start by removing the existing Scaling Policies from my Auto Scaling group:

Then I open up the new Auto Scaling Console and selecting the stack:

Behind the scenes, Elastic Beanstalk applications are always launched via a CloudFormation stack. In the screen shot above, awseb-e-sdwttqizbp-stack is an Elastic Beanstalk application that I launched.

I can click on any stack to learn more about it before proceeding:

I select the desired stack and click on Next to proceed. Then I enter a name for my scaling plan and choose the resources that I’d like it to include:

I choose the scaling strategy for each type of resource:

After I have selected the desired strategies, I click Next to proceed. Then I review the proposed scaling plan, and click Create scaling plan to move ahead:

The scaling plan is created and in effect within a few minutes:

I can click on the plan to learn more:

I can also inspect each scaling policy:

I tested my new policy by applying a load to the initial EC2 instance, and watched the scale out activity take place:

I also took a look at the CloudWatch metrics for the EC2 Auto Scaling group:

Available Now
We are launching AWS Auto Scaling today in the US East (Northern Virginia), US East (Ohio), US West (Oregon), EU (Ireland), and Asia Pacific (Singapore) Regions today, with more to follow. There’s no charge for AWS Auto Scaling; you pay only for the CloudWatch Alarms that it creates and any AWS resources that you consume.

As is often the case with our new services, this is just the first step on what we hope to be a long and interesting journey! We have a long roadmap, and we’ll be adding new features and options throughout 2018 in response to your feedback.

Jeff;

Scale Your Web Application — One Step at a Time

Post Syndicated from Saurabh Shrivastava original https://aws.amazon.com/blogs/architecture/scale-your-web-application-one-step-at-a-time/

I often encounter people experiencing frustration as they attempt to scale their e-commerce or WordPress site—particularly around the cost and complexity related to scaling. When I talk to customers about their scaling plans, they often mention phrases such as horizontal scaling and microservices, but usually people aren’t sure about how to dive in and effectively scale their sites.

Now let’s talk about different scaling options. For instance if your current workload is in a traditional data center, you can leverage the cloud for your on-premises solution. This way you can scale to achieve greater efficiency with less cost. It’s not necessary to set up a whole powerhouse to light a few bulbs. If your workload is already in the cloud, you can use one of the available out-of-the-box options.

Designing your API in microservices and adding horizontal scaling might seem like the best choice, unless your web application is already running in an on-premises environment and you’ll need to quickly scale it because of unexpected large spikes in web traffic.

So how to handle this situation? Take things one step at a time when scaling and you may find horizontal scaling isn’t the right choice, after all.

For example, assume you have a tech news website where you did an early-look review of an upcoming—and highly-anticipated—smartphone launch, which went viral. The review, a blog post on your website, includes both video and pictures. Comments are enabled for the post and readers can also rate it. For example, if your website is hosted on a traditional Linux with a LAMP stack, you may find yourself with immediate scaling problems.

Let’s get more details on the current scenario and dig out more:

  • Where are images and videos stored?
  • How many read/write requests are received per second? Per minute?
  • What is the level of security required?
  • Are these synchronous or asynchronous requests?

We’ll also want to consider the following if your website has a transactional load like e-commerce or banking:

How is the website handling sessions?

  • Do you have any compliance requests—like the Payment Card Industry Data Security Standard (PCI DSS compliance) —if your website is using its own payment gateway?
  • How are you recording customer behavior data and fulfilling your analytics needs?
  • What are your loading balancing considerations (scaling, caching, session maintenance, etc.)?

So, if we take this one step at a time:

Step 1: Ease server load. We need to quickly handle spikes in traffic, generated by activity on the blog post, so let’s reduce server load by moving image and video to some third -party content delivery network (CDN). AWS provides Amazon CloudFront as a CDN solution, which is highly scalable with built-in security to verify origin access identity and handle any DDoS attacks. CloudFront can direct traffic to your on-premises or cloud-hosted server with its 113 Points of Presence (102 Edge Locations and 11 Regional Edge Caches) in 56 cities across 24 countries, which provides efficient caching.
Step 2: Reduce read load by adding more read replicas. MySQL provides a nice mirror replication for databases. Oracle has its own Oracle plug for replication and AWS RDS provide up to five read replicas, which can span across the region and even the Amazon database Amazon Aurora can have 15 read replicas with Amazon Aurora autoscaling support. If a workload is highly variable, you should consider Amazon Aurora Serverless database  to achieve high efficiency and reduced cost. While most mirror technologies do asynchronous replication, AWS RDS can provide synchronous multi-AZ replication, which is good for disaster recovery but not for scalability. Asynchronous replication to mirror instance means replication data can sometimes be stale if network bandwidth is low, so you need to plan and design your application accordingly.

I recommend that you always use a read replica for any reporting needs and try to move non-critical GET services to read replica and reduce the load on the master database. In this case, loading comments associated with a blog can be fetched from a read replica—as it can handle some delay—in case there is any issue with asynchronous reflection.

Step 3: Reduce write requests. This can be achieved by introducing queue to process the asynchronous message. Amazon Simple Queue Service (Amazon SQS) is a highly-scalable queue, which can handle any kind of work-message load. You can process data, like rating and review; or calculate Deal Quality Score (DQS) using batch processing via an SQS queue. If your workload is in AWS, I recommend using a job-observer pattern by setting up Auto Scaling to automatically increase or decrease the number of batch servers, using the number of SQS messages, with Amazon CloudWatch, as the trigger.  For on-premises workloads, you can use SQS SDK to create an Amazon SQS queue that holds messages until they’re processed by your stack. Or you can use Amazon SNS  to fan out your message processing in parallel for different purposes like adding a watermark in an image, generating a thumbnail, etc.

Step 4: Introduce a more robust caching engine. You can use Amazon Elastic Cache for Memcached or Redis to reduce write requests. Memcached and Redis have different use cases so if you can afford to lose and recover your cache from your database, use Memcached. If you are looking for more robust data persistence and complex data structure, use Redis. In AWS, these are managed services, which means AWS takes care of the workload for you and you can also deploy them in your on-premises instances or use a hybrid approach.

Step 5: Scale your server. If there are still issues, it’s time to scale your server.  For the greatest cost-effectiveness and unlimited scalability, I suggest always using horizontal scaling. However, use cases like database vertical scaling may be a better choice until you are good with sharding; or use Amazon Aurora Serverless for variable workloads. It will be wise to use Auto Scaling to manage your workload effectively for horizontal scaling. Also, to achieve that, you need to persist the session. Amazon DynamoDB can handle session persistence across instances.

If your server is on premises, consider creating a multisite architecture, which will help you achieve quick scalability as required and provide a good disaster recovery solution.  You can pick and choose individual services like Amazon Route 53, AWS CloudFormation, Amazon SQS, Amazon SNS, Amazon RDS, etc. depending on your needs.

Your multisite architecture will look like the following diagram:

In this architecture, you can run your regular workload on premises, and use your AWS workload as required for scalability and disaster recovery. Using Route 53, you can direct a precise percentage of users to an AWS workload.

If you decide to move all of your workloads to AWS, the recommended multi-AZ architecture would look like the following:

In this architecture, you are using a multi-AZ distributed workload for high availability. You can have a multi-region setup and use Route53 to distribute your workload between AWS Regions. CloudFront helps you to scale and distribute static content via an S3 bucket and DynamoDB, maintaining your application state so that Auto Scaling can apply horizontal scaling without loss of session data. At the database layer, RDS with multi-AZ standby provides high availability and read replica helps achieve scalability.

This is a high-level strategy to help you think through the scalability of your workload by using AWS even if your workload in on premises and not in the cloud…yet.

I highly recommend creating a hybrid, multisite model by placing your on-premises environment replica in the public cloud like AWS Cloud, and using Amazon Route53 DNS Service and Elastic Load Balancing to route traffic between on-premises and cloud environments. AWS now supports load balancing between AWS and on-premises environments to help you scale your cloud environment quickly, whenever required, and reduce it further by applying Amazon auto-scaling and placing a threshold on your on-premises traffic using Route 53.

AWS Glue Now Supports Scala Scripts

Post Syndicated from Mehul Shah original https://aws.amazon.com/blogs/big-data/aws-glue-now-supports-scala-scripts/

We are excited to announce AWS Glue support for running ETL (extract, transform, and load) scripts in Scala. Scala lovers can rejoice because they now have one more powerful tool in their arsenal. Scala is the native language for Apache Spark, the underlying engine that AWS Glue offers for performing data transformations.

Beyond its elegant language features, writing Scala scripts for AWS Glue has two main advantages over writing scripts in Python. First, Scala is faster for custom transformations that do a lot of heavy lifting because there is no need to shovel data between Python and Apache Spark’s Scala runtime (that is, the Java virtual machine, or JVM). You can build your own transformations or invoke functions in third-party libraries. Second, it’s simpler to call functions in external Java class libraries from Scala because Scala is designed to be Java-compatible. It compiles to the same bytecode, and its data structures don’t need to be converted.

To illustrate these benefits, we walk through an example that analyzes a recent sample of the GitHub public timeline available from the GitHub archive. This site is an archive of public requests to the GitHub service, recording more than 35 event types ranging from commits and forks to issues and comments.

This post shows how to build an example Scala script that identifies highly negative issues in the timeline. It pulls out issue events in the timeline sample, analyzes their titles using the sentiment prediction functions from the Stanford CoreNLP libraries, and surfaces the most negative issues.

Getting started

Before we start writing scripts, we use AWS Glue crawlers to get a sense of the data—its structure and characteristics. We also set up a development endpoint and attach an Apache Zeppelin notebook, so we can interactively explore the data and author the script.

Crawl the data

The dataset used in this example was downloaded from the GitHub archive website into our sample dataset bucket in Amazon S3, and copied to the following locations:

s3://aws-glue-datasets-<region>/examples/scala-blog/githubarchive/data/

Choose the best folder by replacing <region> with the region that you’re working in, for example, us-east-1. Crawl this folder, and put the results into a database named githubarchive in the AWS Glue Data Catalog, as described in the AWS Glue Developer Guide. This folder contains 12 hours of the timeline from January 22, 2017, and is organized hierarchically (that is, partitioned) by year, month, and day.

When finished, use the AWS Glue console to navigate to the table named data in the githubarchive database. Notice that this data has eight top-level columns, which are common to each event type, and three partition columns that correspond to year, month, and day.

Choose the payload column, and you will notice that it has a complex schema—one that reflects the union of the payloads of event types that appear in the crawled data. Also note that the schema that crawlers generate is a subset of the true schema because they sample only a subset of the data.

Set up the library, development endpoint, and notebook

Next, you need to download and set up the libraries that estimate the sentiment in a snippet of text. The Stanford CoreNLP libraries contain a number of human language processing tools, including sentiment prediction.

Download the Stanford CoreNLP libraries. Unzip the .zip file, and you’ll see a directory full of jar files. For this example, the following jars are required:

  • stanford-corenlp-3.8.0.jar
  • stanford-corenlp-3.8.0-models.jar
  • ejml-0.23.jar

Upload these files to an Amazon S3 path that is accessible to AWS Glue so that it can load these libraries when needed. For this example, they are in s3://glue-sample-other/corenlp/.

Development endpoints are static Spark-based environments that can serve as the backend for data exploration. You can attach notebooks to these endpoints to interactively send commands and explore and analyze your data. These endpoints have the same configuration as that of AWS Glue’s job execution system. So, commands and scripts that work there also work the same when registered and run as jobs in AWS Glue.

To set up an endpoint and a Zeppelin notebook to work with that endpoint, follow the instructions in the AWS Glue Developer Guide. When you are creating an endpoint, be sure to specify the locations of the previously mentioned jars in the Dependent jars path as a comma-separated list. Otherwise, the libraries will not be loaded.

After you set up the notebook server, go to the Zeppelin notebook by choosing Dev Endpoints in the left navigation pane on the AWS Glue console. Choose the endpoint that you created. Next, choose the Notebook Server URL, which takes you to the Zeppelin server. Log in using the notebook user name and password that you specified when creating the notebook. Finally, create a new note to try out this example.

Each notebook is a collection of paragraphs, and each paragraph contains a sequence of commands and the output for that command. Moreover, each notebook includes a number of interpreters. If you set up the Zeppelin server using the console, the (Python-based) pyspark and (Scala-based) spark interpreters are already connected to your new development endpoint, with pyspark as the default. Therefore, throughout this example, you need to prepend %spark at the top of your paragraphs. In this example, we omit these for brevity.

Working with the data

In this section, we use AWS Glue extensions to Spark to work with the dataset. We look at the actual schema of the data and filter out the interesting event types for our analysis.

Start with some boilerplate code to import libraries that you need:

%spark

import com.amazonaws.services.glue.DynamicRecord
import com.amazonaws.services.glue.GlueContext
import com.amazonaws.services.glue.util.GlueArgParser
import com.amazonaws.services.glue.util.Job
import com.amazonaws.services.glue.util.JsonOptions
import com.amazonaws.services.glue.types._
import org.apache.spark.SparkContext

Then, create the Spark and AWS Glue contexts needed for working with the data:

@transient val spark: SparkContext = SparkContext.getOrCreate()
val glueContext: GlueContext = new GlueContext(spark)

You need the transient decorator on the SparkContext when working in Zeppelin; otherwise, you will run into a serialization error when executing commands.

Dynamic frames

This section shows how to create a dynamic frame that contains the GitHub records in the table that you crawled earlier. A dynamic frame is the basic data structure in AWS Glue scripts. It is like an Apache Spark data frame, except that it is designed and optimized for data cleaning and transformation workloads. A dynamic frame is well-suited for representing semi-structured datasets like the GitHub timeline.

A dynamic frame is a collection of dynamic records. In Spark lingo, it is an RDD (resilient distributed dataset) of DynamicRecords. A dynamic record is a self-describing record. Each record encodes its columns and types, so every record can have a schema that is unique from all others in the dynamic frame. This is convenient and often more efficient for datasets like the GitHub timeline, where payloads can vary drastically from one event type to another.

The following creates a dynamic frame, github_events, from your table:

val github_events = glueContext
                    .getCatalogSource(database = "githubarchive", tableName = "data")
                    .getDynamicFrame()

The getCatalogSource() method returns a DataSource, which represents a particular table in the Data Catalog. The getDynamicFrame() method returns a dynamic frame from the source.

Recall that the crawler created a schema from only a sample of the data. You can scan the entire dataset, count the rows, and print the complete schema as follows:

github_events.count
github_events.printSchema()

The result looks like the following:

The data has 414,826 records. As before, notice that there are eight top-level columns, and three partition columns. If you scroll down, you’ll also notice that the payload is the most complex column.

Run functions and filter records

This section describes how you can create your own functions and invoke them seamlessly to filter records. Unlike filtering with Python lambdas, Scala scripts do not need to convert records from one language representation to another, thereby reducing overhead and running much faster.

Let’s create a function that picks only the IssuesEvents from the GitHub timeline. These events are generated whenever someone posts an issue for a particular repository. Each GitHub event record has a field, “type”, that indicates the kind of event it is. The issueFilter() function returns true for records that are IssuesEvents.

def issueFilter(rec: DynamicRecord): Boolean = { 
    rec.getField("type").exists(_ == "IssuesEvent") 
}

Note that the getField() method returns an Option[Any] type, so you first need to check that it exists before checking the type.

You pass this function to the filter transformation, which applies the function on each record and returns a dynamic frame of those records that pass.

val issue_events =  github_events.filter(issueFilter)

Now, let’s look at the size and schema of issue_events.

issue_events.count
issue_events.printSchema()

It’s much smaller (14,063 records), and the payload schema is less complex, reflecting only the schema for issues. Keep a few essential columns for your analysis, and drop the rest using the ApplyMapping() transform:

val issue_titles = issue_events.applyMapping(Seq(("id", "string", "id", "string"),
                                                 ("actor.login", "string", "actor", "string"), 
                                                 ("repo.name", "string", "repo", "string"),
                                                 ("payload.action", "string", "action", "string"),
                                                 ("payload.issue.title", "string", "title", "string")))
issue_titles.show()

The ApplyMapping() transform is quite handy for renaming columns, casting types, and restructuring records. The preceding code snippet tells the transform to select the fields (or columns) that are enumerated in the left half of the tuples and map them to the fields and types in the right half.

Estimating sentiment using Stanford CoreNLP

To focus on the most pressing issues, you might want to isolate the records with the most negative sentiments. The Stanford CoreNLP libraries are Java-based and offer sentiment-prediction functions. Accessing these functions through Python is possible, but quite cumbersome. It requires creating Python surrogate classes and objects for those found on the Java side. Instead, with Scala support, you can use those classes and objects directly and invoke their methods. Let’s see how.

First, import the libraries needed for the analysis:

import java.util.Properties
import edu.stanford.nlp.ling.CoreAnnotations
import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations
import edu.stanford.nlp.pipeline.{Annotation, StanfordCoreNLP}
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations
import scala.collection.convert.wrapAll._

The Stanford CoreNLP libraries have a main driver that orchestrates all of their analysis. The driver setup is heavyweight, setting up threads and data structures that are shared across analyses. Apache Spark runs on a cluster with a main driver process and a collection of backend executor processes that do most of the heavy sifting of the data.

The Stanford CoreNLP shared objects are not serializable, so they cannot be distributed easily across a cluster. Instead, you need to initialize them once for every backend executor process that might need them. Here is how to accomplish that:

val props = new Properties()
props.setProperty("annotators", "tokenize, ssplit, parse, sentiment")
props.setProperty("parse.maxlen", "70")

object myNLP {
    lazy val coreNLP = new StanfordCoreNLP(props)
}

The properties tell the libraries which annotators to execute and how many words to process. The preceding code creates an object, myNLP, with a field coreNLP that is lazily evaluated. This field is initialized only when it is needed, and only once. So, when the backend executors start processing the records, each executor initializes the driver for the Stanford CoreNLP libraries only one time.

Next is a function that estimates the sentiment of a text string. It first calls Stanford CoreNLP to annotate the text. Then, it pulls out the sentences and takes the average sentiment across all the sentences. The sentiment is a double, from 0.0 as the most negative to 4.0 as the most positive.

def estimatedSentiment(text: String): Double = {
    if ((text == null) || (!text.nonEmpty)) { return Double.NaN }
    val annotations = myNLP.coreNLP.process(text)
    val sentences = annotations.get(classOf[CoreAnnotations.SentencesAnnotation])
    sentences.foldLeft(0.0)( (csum, x) => { 
        csum + RNNCoreAnnotations.getPredictedClass(x.get(classOf[SentimentCoreAnnotations.SentimentAnnotatedTree])) 
    }) / sentences.length
}

Now, let’s estimate the sentiment of the issue titles and add that computed field as part of the records. You can accomplish this with the map() method on dynamic frames:

val issue_sentiments = issue_titles.map((rec: DynamicRecord) => { 
    val mbody = rec.getField("title")
    mbody match {
        case Some(mval: String) => { 
            rec.addField("sentiment", ScalarNode(estimatedSentiment(mval)))
            rec }
        case _ => rec
    }
})

The map() method applies the user-provided function on every record. The function takes a DynamicRecord as an argument and returns a DynamicRecord. The code above computes the sentiment, adds it in a top-level field, sentiment, to the record, and returns the record.

Count the records with sentiment and show the schema. This takes a few minutes because Spark must initialize the library and run the sentiment analysis, which can be involved.

issue_sentiments.count
issue_sentiments.printSchema()

Notice that all records were processed (14,063), and the sentiment value was added to the schema.

Finally, let’s pick out the titles that have the lowest sentiment (less than 1.5). Count them and print out a sample to see what some of the titles look like.

val pressing_issues = issue_sentiments.filter(_.getField("sentiment").exists(_.asInstanceOf[Double] < 1.5))
pressing_issues.count
pressing_issues.show(10)

Next, write them all to a file so that you can handle them later. (You’ll need to replace the output path with your own.)

glueContext.getSinkWithFormat(connectionType = "s3", 
                              options = JsonOptions("""{"path": "s3://<bucket>/out/path/"}"""), 
                              format = "json")
            .writeDynamicFrame(pressing_issues)

Take a look in the output path, and you can see the output files.

Putting it all together

Now, let’s create a job from the preceding interactive session. The following script combines all the commands from earlier. It processes the GitHub archive files and writes out the highly negative issues:

import com.amazonaws.services.glue.DynamicRecord
import com.amazonaws.services.glue.GlueContext
import com.amazonaws.services.glue.util.GlueArgParser
import com.amazonaws.services.glue.util.Job
import com.amazonaws.services.glue.util.JsonOptions
import com.amazonaws.services.glue.types._
import org.apache.spark.SparkContext
import java.util.Properties
import edu.stanford.nlp.ling.CoreAnnotations
import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations
import edu.stanford.nlp.pipeline.{Annotation, StanfordCoreNLP}
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations
import scala.collection.convert.wrapAll._

object GlueApp {

    object myNLP {
        val props = new Properties()
        props.setProperty("annotators", "tokenize, ssplit, parse, sentiment")
        props.setProperty("parse.maxlen", "70")

        lazy val coreNLP = new StanfordCoreNLP(props)
    }

    def estimatedSentiment(text: String): Double = {
        if ((text == null) || (!text.nonEmpty)) { return Double.NaN }
        val annotations = myNLP.coreNLP.process(text)
        val sentences = annotations.get(classOf[CoreAnnotations.SentencesAnnotation])
        sentences.foldLeft(0.0)( (csum, x) => { 
            csum + RNNCoreAnnotations.getPredictedClass(x.get(classOf[SentimentCoreAnnotations.SentimentAnnotatedTree])) 
        }) / sentences.length
    }

    def main(sysArgs: Array[String]) {
        val spark: SparkContext = SparkContext.getOrCreate()
        val glueContext: GlueContext = new GlueContext(spark)

        val dbname = "githubarchive"
        val tblname = "data"
        val outpath = "s3://<bucket>/out/path/"

        val github_events = glueContext
                            .getCatalogSource(database = dbname, tableName = tblname)
                            .getDynamicFrame()

        val issue_events =  github_events.filter((rec: DynamicRecord) => {
            rec.getField("type").exists(_ == "IssuesEvent")
        })

        val issue_titles = issue_events.applyMapping(Seq(("id", "string", "id", "string"),
                                                         ("actor.login", "string", "actor", "string"), 
                                                         ("repo.name", "string", "repo", "string"),
                                                         ("payload.action", "string", "action", "string"),
                                                         ("payload.issue.title", "string", "title", "string")))

        val issue_sentiments = issue_titles.map((rec: DynamicRecord) => { 
            val mbody = rec.getField("title")
            mbody match {
                case Some(mval: String) => { 
                    rec.addField("sentiment", ScalarNode(estimatedSentiment(mval)))
                    rec }
                case _ => rec
            }
        })

        val pressing_issues = issue_sentiments.filter(_.getField("sentiment").exists(_.asInstanceOf[Double] < 1.5))

        glueContext.getSinkWithFormat(connectionType = "s3", 
                              options = JsonOptions(s"""{"path": "$outpath"}"""), 
                              format = "json")
                    .writeDynamicFrame(pressing_issues)
    }
}

Notice that the script is enclosed in a top-level object called GlueApp, which serves as the script’s entry point for the job. (You’ll need to replace the output path with your own.) Upload the script to an Amazon S3 location so that AWS Glue can load it when needed.

To create the job, open the AWS Glue console. Choose Jobs in the left navigation pane, and then choose Add job. Create a name for the job, and specify a role with permissions to access the data. Choose An existing script that you provide, and choose Scala as the language.

For the Scala class name, type GlueApp to indicate the script’s entry point. Specify the Amazon S3 location of the script.

Choose Script libraries and job parameters. In the Dependent jars path field, enter the Amazon S3 locations of the Stanford CoreNLP libraries from earlier as a comma-separated list (without spaces). Then choose Next.

No connections are needed for this job, so choose Next again. Review the job properties, and choose Finish. Finally, choose Run job to execute the job.

You can simply edit the script’s input table and output path to run this job on whatever GitHub timeline datasets that you might have.

Conclusion

In this post, we showed how to write AWS Glue ETL scripts in Scala via notebooks and how to run them as jobs. Scala has the advantage that it is the native language for the Spark runtime. With Scala, it is easier to call Scala or Java functions and third-party libraries for analyses. Moreover, data processing is faster in Scala because there’s no need to convert records from one language runtime to another.

You can find more example of Scala scripts in our GitHub examples repository: https://github.com/awslabs/aws-glue-samples. We encourage you to experiment with Scala scripts and let us know about any interesting ETL flows that you want to share.

Happy Glue-ing!

 


Additional Reading

If you found this post useful, be sure to check out Simplify Querying Nested JSON with the AWS Glue Relationalize Transform and Genomic Analysis with Hail on Amazon EMR and Amazon Athena.

 


About the Authors

Mehul Shah is a senior software manager for AWS Glue. His passion is leveraging the cloud to build smarter, more efficient, and easier to use data systems. He has three girls, and, therefore, he has no spare time.

 

 

 

Ben Sowell is a software development engineer at AWS Glue.

 

 

 

 
Vinay Vivili is a software development engineer for AWS Glue.

 

 

 

Fix Your Crawler

Post Syndicated from Bozho original https://techblog.bozho.net/fix-your-crawler/

Every now and then I open the admin panel of my blog hosting and ban a few IPs (after I’ve tried messaging their abuse email, if I find one). It is always IPs that are generating tons of requests (and traffic) – most likely running some home-made crawler. In some cases the IPs belong to an actual service that captures and provides content, in other cases it’s just a scraper for unknown reasons.

I don’t want to ban IPs, especially because that same IP may be reassigned to a legitimate user (or network) in the future. But they are increasing my hosting usage, which in turn leads to the hosting provider suggesting an upgrade in the plan. And this is not about me, I’m just an example – tons of requests to millions of sites are … useless.

My advice (and plea) is this – please fix your crawlers. Or scrapers. Or whatever you prefer to call that thing that programmatically goes on websites and gets their content.

How? First, reuse an existing crawler. No need to make something new (unless there’s a very specific use-case). A good intro and comparison can be seen here.

Second, make your crawler “polite” (the “politeness” property in the article above). Here’s a good overview on how to be polite, including respect for robots.txt. Existing implementations most likely have politeness options, but you may have to configure them.

Here I’d suggest another option – set a dynamic crawl rate per website that depends on how often the content is updated. My blog updates 3 times a month – no need to crawl it more than once or twice a day. TechCrunch updates many times a day; it’s probably a good idea to crawl it way more often. I don’t have a formula, but you can come up with one that ends up crawling different sites with periods between 2 minutes and 1 day.

Third, don’t “scrape” the content if a better protocol is supported. Many content websites have RSS – use that instead of the HTML of the page. If not, make use of sitemaps. If the WebSub protocol gains traction, you can avoid the crawling/scraping entirely and get notified on new content.

Finally, make sure your crawler/scraper is identifiable by the UserAgent. You can supply your service name or web address in it to make it easier for website owners to find you and complain in case you’ve misconfigured something.

I guess it makes sense to see if using a service like import.io, ScrapingHub, WrapAPI or GetData makes sense for your usecase, instead of reinventing the wheel.

No matter what your use case or approach is, please make sure you don’t put unnecessary pressure on others’ websites.

The post Fix Your Crawler appeared first on Bozho's tech blog.

RuTracker Reveals Innovative Plan For Users to Subvert ISP Blocking

Post Syndicated from Andy original https://torrentfreak.com/rutracker-reveals-innovative-plan-for-users-to-subvert-isp-blocking-180110/

As Russia’s largest torrent site and one that earned itself a mention in TF’s list of most popular torrent sites 2018, RuTracker is continuously under fire.

The site has an extremely dedicated following but Russia’s telecoms watchdog, spurred on by copyright holders brandishing court rulings, does everything in its power to ensure that people can’t access the site easily.

As a result, RuTracker’s main domains are blocked by all ISPs, meaning that people have to resort to VPNs or the many dozens of proxy and mirror sites that have been set up to facilitate access to the popular tracker.

While all of these methods used to work just fine, new legislation that came into force during October means that mirror and proxy sites can be added to block lists without copyright holders having to return to court. And, following legislation introduced in November, local VPN services are forbidden from providing access to blocked sites.

While RuTracker has always insisted that web blockades have little effect on the numbers of people sharing content, direct traffic to their main domains has definitely suffered. To solve this problem and go some way towards mitigating VPN and proxy bans, the site has just come up with a new plan to keep the torrents flowing.

The scheme was quietly announced, not on RuTracker’s main forum, but to a smaller set of users on local site Leprosorium. The idea was that a quieter launch there would allow for controlled testing before a release to the masses. The project is called My.RuTracker and here’s how it works.

Instead of blocked users fruitlessly trying to find public circumvention methods that once seen are immediately blocked, they are invited to register their own domains. These can be single use, for the person who registers them, but it’s envisioned that they’ll be shared out between friends, family, and online groups, to better make use of the resource.

Once domains are registered, users are invited to contact a special user account on the RuTracker site (operated by the site’s operators) which will provide them with precise technical details on how to set up their domain (.ru domains are not allowed) to gain access to RuTracker.

“In response, after a while (usually every other day), a list of NS-addresses will be sent to the registrar’s domain settings. Under this scheme, the user domain will be redirected to the RuTracker site via a dynamic IP address: this will avoid blocking the torrent tracker for a particular IP address,” the scheme envisages.

According to local news resource Tjournal, 62 personal mirrors were launched following the initial appeal, with the operators of RuTracker now planning to publicly announce the project to their community. As more are added, the site will keep track of traffic from each of the personal “mirrors” for balancing the load on the site.

At least in theory, this seems like a pretty innovative scheme. Currently, the authorities rely on the scale and public awareness of a particular proxy or mirror in order to earmark it for blocking. This much more decentralized plan, in which only small numbers of people should know each domain, seems like a much more robust system – at least until the authorities and indeed the law catches up.

And so the cat-and-mouse game continues.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Wanted: Datacenter Technician

Post Syndicated from Yev original https://www.backblaze.com/blog/wanted-datacenter-technician/

As we shoot way past 400 Petabytes of data under management we need some help scaling up our datacenters! We’re on the lookout for some datacenter technicians that can help us. This role is located near the Sacramento, California area. If you want to join a dynamic team that helps keep our almost 90,000+ hard drives spinning, this might be the job for you!

Responsibilities

  • Work as Backblaze’s physical presence in Sacramento area datacenter(s).
  • Help maintain physical infrastructure including racking equipment, replacing hard drives and other system components.
  • Repair and troubleshoot defective equipment with minimal supervision.
  • Support datacenter’s 24×7 staff to install new equipment, handle after hours emergencies and other tasks.
  • Help manage onsite inventory of hard drives, cables, rails and other spare parts.
  • RMA defective components.
  • Setup, test and activate new equipment via the Linux command line.
  • Help train new Datacenter Technicians as needed.
  • Help with projects to install new systems and services as time allows.
  • Follow and improve Datacenter best practices and documentation.
  • Maintain a clean and well organized work environment.
  • On-call responsibilities require being within an hour of the SunGard’s Rancho Cordova/Roseville facility and occasional trips onsite 24×7 to resolve issues that can’t be handled remotely.
  • Work days may include Saturday and/or Sunday (e.g. working Tuesday – Saturday).

Requirements

  • Excellent communication, time management, problem solving and organizational skills.
  • Ability to learn quickly.
  • Ability to lift/move 50-75 lbs and work down near the floor on a daily basis.
  • Position based near Sacramento, California and may require periodic visits to the corporate office in San Mateo.
  • May require travel to other Datacenters to provide coverage and/or to assist
    with new site set-up.

Backblaze Employees Have:

  • Good attitude and willingness to do whatever it takes to get the job done.
  • Strong desire to work for a small, fast-paced company.
  • Desire to learn and adapt to rapidly changing technologies and work environment.
  • Comfortable with well-behaved pets in the office.
  • This position is located near Sacramento, California.

Backblaze is an Equal Opportunity Employer and we offer competitive salary and benefits, including our no policy vacation policy.

If This Sounds Like You:
Send an email to [email protected] with:

  1. Datacenter Tech in the subject line
  2. Your resume attached
  3. An overview of your relevant experience

The post Wanted: Datacenter Technician appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Combine Transactional and Analytical Data Using Amazon Aurora and Amazon Redshift

Post Syndicated from Re Alvarez-Parmar original https://aws.amazon.com/blogs/big-data/combine-transactional-and-analytical-data-using-amazon-aurora-and-amazon-redshift/

A few months ago, we published a blog post about capturing data changes in an Amazon Aurora database and sending it to Amazon Athena and Amazon QuickSight for fast analysis and visualization. In this post, I want to demonstrate how easy it can be to take the data in Aurora and combine it with data in Amazon Redshift using Amazon Redshift Spectrum.

With Amazon Redshift, you can build petabyte-scale data warehouses that unify data from a variety of internal and external sources. Because Amazon Redshift is optimized for complex queries (often involving multiple joins) across large tables, it can handle large volumes of retail, inventory, and financial data without breaking a sweat.

In this post, we describe how to combine data in Aurora in Amazon Redshift. Here’s an overview of the solution:

  • Use AWS Lambda functions with Amazon Aurora to capture data changes in a table.
  • Save data in an Amazon S3
  • Query data using Amazon Redshift Spectrum.

We use the following services:

Serverless architecture for capturing and analyzing Aurora data changes

Consider a scenario in which an e-commerce web application uses Amazon Aurora for a transactional database layer. The company has a sales table that captures every single sale, along with a few corresponding data items. This information is stored as immutable data in a table. Business users want to monitor the sales data and then analyze and visualize it.

In this example, you take the changes in data in an Aurora database table and save it in Amazon S3. After the data is captured in Amazon S3, you combine it with data in your existing Amazon Redshift cluster for analysis.

By the end of this post, you will understand how to capture data events in an Aurora table and push them out to other AWS services using AWS Lambda.

The following diagram shows the flow of data as it occurs in this tutorial:

The starting point in this architecture is a database insert operation in Amazon Aurora. When the insert statement is executed, a custom trigger calls a Lambda function and forwards the inserted data. Lambda writes the data that it received from Amazon Aurora to a Kinesis data delivery stream. Kinesis Data Firehose writes the data to an Amazon S3 bucket. Once the data is in an Amazon S3 bucket, it is queried in place using Amazon Redshift Spectrum.

Creating an Aurora database

First, create a database by following these steps in the Amazon RDS console:

  1. Sign in to the AWS Management Console, and open the Amazon RDS console.
  2. Choose Launch a DB instance, and choose Next.
  3. For Engine, choose Amazon Aurora.
  4. Choose a DB instance class. This example uses a small, since this is not a production database.
  5. In Multi-AZ deployment, choose No.
  6. Configure DB instance identifier, Master username, and Master password.
  7. Launch the DB instance.

After you create the database, use MySQL Workbench to connect to the database using the CNAME from the console. For information about connecting to an Aurora database, see Connecting to an Amazon Aurora DB Cluster.

The following screenshot shows the MySQL Workbench configuration:

Next, create a table in the database by running the following SQL statement:

Create Table
CREATE TABLE Sales (
InvoiceID int NOT NULL AUTO_INCREMENT,
ItemID int NOT NULL,
Category varchar(255),
Price double(10,2), 
Quantity int not NULL,
OrderDate timestamp,
DestinationState varchar(2),
ShippingType varchar(255),
Referral varchar(255),
PRIMARY KEY (InvoiceID)
)

You can now populate the table with some sample data. To generate sample data in your table, copy and run the following script. Ensure that the highlighted (bold) variables are replaced with appropriate values.

#!/usr/bin/python
import MySQLdb
import random
import datetime

db = MySQLdb.connect(host="AURORA_CNAME",
                     user="DBUSER",
                     passwd="DBPASSWORD",
                     db="DB")

states = ("AL","AK","AZ","AR","CA","CO","CT","DE","FL","GA","HI","ID","IL","IN",
"IA","KS","KY","LA","ME","MD","MA","MI","MN","MS","MO","MT","NE","NV","NH","NJ",
"NM","NY","NC","ND","OH","OK","OR","PA","RI","SC","SD","TN","TX","UT","VT","VA",
"WA","WV","WI","WY")

shipping_types = ("Free", "3-Day", "2-Day")

product_categories = ("Garden", "Kitchen", "Office", "Household")
referrals = ("Other", "Friend/Colleague", "Repeat Customer", "Online Ad")

for i in range(0,10):
    item_id = random.randint(1,100)
    state = states[random.randint(0,len(states)-1)]
    shipping_type = shipping_types[random.randint(0,len(shipping_types)-1)]
    product_category = product_categories[random.randint(0,len(product_categories)-1)]
    quantity = random.randint(1,4)
    referral = referrals[random.randint(0,len(referrals)-1)]
    price = random.randint(1,100)
    order_date = datetime.date(2016,random.randint(1,12),random.randint(1,30)).isoformat()

    data_order = (item_id, product_category, price, quantity, order_date, state,
    shipping_type, referral)

    add_order = ("INSERT INTO Sales "
                   "(ItemID, Category, Price, Quantity, OrderDate, DestinationState, \
                   ShippingType, Referral) "
                   "VALUES (%s, %s, %s, %s, %s, %s, %s, %s)")

    cursor = db.cursor()
    cursor.execute(add_order, data_order)

    db.commit()

cursor.close()
db.close() 

The following screenshot shows how the table appears with the sample data:

Sending data from Amazon Aurora to Amazon S3

There are two methods available to send data from Amazon Aurora to Amazon S3:

  • Using a Lambda function
  • Using SELECT INTO OUTFILE S3

To demonstrate the ease of setting up integration between multiple AWS services, we use a Lambda function to send data to Amazon S3 using Amazon Kinesis Data Firehose.

Alternatively, you can use a SELECT INTO OUTFILE S3 statement to query data from an Amazon Aurora DB cluster and save it directly in text files that are stored in an Amazon S3 bucket. However, with this method, there is a delay between the time that the database transaction occurs and the time that the data is exported to Amazon S3 because the default file size threshold is 6 GB.

Creating a Kinesis data delivery stream

The next step is to create a Kinesis data delivery stream, since it’s a dependency of the Lambda function.

To create a delivery stream:

  1. Open the Kinesis Data Firehose console
  2. Choose Create delivery stream.
  3. For Delivery stream name, type AuroraChangesToS3.
  4. For Source, choose Direct PUT.
  5. For Record transformation, choose Disabled.
  6. For Destination, choose Amazon S3.
  7. In the S3 bucket drop-down list, choose an existing bucket, or create a new one.
  8. Enter a prefix if needed, and choose Next.
  9. For Data compression, choose GZIP.
  10. In IAM role, choose either an existing role that has access to write to Amazon S3, or choose to generate one automatically. Choose Next.
  11. Review all the details on the screen, and choose Create delivery stream when you’re finished.

 

Creating a Lambda function

Now you can create a Lambda function that is called every time there is a change that needs to be tracked in the database table. This Lambda function passes the data to the Kinesis data delivery stream that you created earlier.

To create the Lambda function:

  1. Open the AWS Lambda console.
  2. Ensure that you are in the AWS Region where your Amazon Aurora database is located.
  3. If you have no Lambda functions yet, choose Get started now. Otherwise, choose Create function.
  4. Choose Author from scratch.
  5. Give your function a name and select Python 3.6 for Runtime
  6. Choose and existing or create a new Role, the role would need to have access to call firehose:PutRecord
  7. Choose Next on the trigger selection screen.
  8. Paste the following code in the code window. Change the stream_name variable to the Kinesis data delivery stream that you created in the previous step.
  9. Choose File -> Save in the code editor and then choose Save.
import boto3
import json

firehose = boto3.client('firehose')
stream_name = ‘AuroraChangesToS3’


def Kinesis_publish_message(event, context):
    
    firehose_data = (("%s,%s,%s,%s,%s,%s,%s,%s\n") %(event['ItemID'], 
    event['Category'], event['Price'], event['Quantity'],
    event['OrderDate'], event['DestinationState'], event['ShippingType'], 
    event['Referral']))
    
    firehose_data = {'Data': str(firehose_data)}
    print(firehose_data)
    
    firehose.put_record(DeliveryStreamName=stream_name,
    Record=firehose_data)

Note the Amazon Resource Name (ARN) of this Lambda function.

Giving Aurora permissions to invoke a Lambda function

To give Amazon Aurora permissions to invoke a Lambda function, you must attach an IAM role with appropriate permissions to the cluster. For more information, see Invoking a Lambda Function from an Amazon Aurora DB Cluster.

Once you are finished, the Amazon Aurora database has access to invoke a Lambda function.

Creating a stored procedure and a trigger in Amazon Aurora

Now, go back to MySQL Workbench, and run the following command to create a new stored procedure. When this stored procedure is called, it invokes the Lambda function you created. Change the ARN in the following code to your Lambda function’s ARN.

DROP PROCEDURE IF EXISTS CDC_TO_FIREHOSE;
DELIMITER ;;
CREATE PROCEDURE CDC_TO_FIREHOSE (IN ItemID VARCHAR(255), 
									IN Category varchar(255), 
									IN Price double(10,2),
                                    IN Quantity int(11),
                                    IN OrderDate timestamp,
                                    IN DestinationState varchar(2),
                                    IN ShippingType varchar(255),
                                    IN Referral  varchar(255)) LANGUAGE SQL 
BEGIN
  CALL mysql.lambda_async('arn:aws:lambda:us-east-1:XXXXXXXXXXXXX:function:CDCFromAuroraToKinesis', 
     CONCAT('{ "ItemID" : "', ItemID, 
            '", "Category" : "', Category,
            '", "Price" : "', Price,
            '", "Quantity" : "', Quantity, 
            '", "OrderDate" : "', OrderDate, 
            '", "DestinationState" : "', DestinationState, 
            '", "ShippingType" : "', ShippingType, 
            '", "Referral" : "', Referral, '"}')
     );
END
;;
DELIMITER ;

Create a trigger TR_Sales_CDC on the Sales table. When a new record is inserted, this trigger calls the CDC_TO_FIREHOSE stored procedure.

DROP TRIGGER IF EXISTS TR_Sales_CDC;
 
DELIMITER ;;
CREATE TRIGGER TR_Sales_CDC
  AFTER INSERT ON Sales
  FOR EACH ROW
BEGIN
  SELECT  NEW.ItemID , NEW.Category, New.Price, New.Quantity, New.OrderDate
  , New.DestinationState, New.ShippingType, New.Referral
  INTO @ItemID , @Category, @Price, @Quantity, @OrderDate
  , @DestinationState, @ShippingType, @Referral;
  CALL  CDC_TO_FIREHOSE(@ItemID , @Category, @Price, @Quantity, @OrderDate
  , @DestinationState, @ShippingType, @Referral);
END
;;
DELIMITER ;

If a new row is inserted in the Sales table, the Lambda function that is mentioned in the stored procedure is invoked.

Verify that data is being sent from the Lambda function to Kinesis Data Firehose to Amazon S3 successfully. You might have to insert a few records, depending on the size of your data, before new records appear in Amazon S3. This is due to Kinesis Data Firehose buffering. To learn more about Kinesis Data Firehose buffering, see the “Amazon S3” section in Amazon Kinesis Data Firehose Data Delivery.

Every time a new record is inserted in the sales table, a stored procedure is called, and it updates data in Amazon S3.

Querying data in Amazon Redshift

In this section, you use the data you produced from Amazon Aurora and consume it as-is in Amazon Redshift. In order to allow you to process your data as-is, where it is, while taking advantage of the power and flexibility of Amazon Redshift, you use Amazon Redshift Spectrum. You can use Redshift Spectrum to run complex queries on data stored in Amazon S3, with no need for loading or other data prep.

Just create a data source and issue your queries to your Amazon Redshift cluster as usual. Behind the scenes, Redshift Spectrum scales to thousands of instances on a per-query basis, ensuring that you get fast, consistent performance even as your dataset grows to beyond an exabyte! Being able to query data that is stored in Amazon S3 means that you can scale your compute and your storage independently. You have the full power of the Amazon Redshift query model and all the reporting and business intelligence tools at your disposal. Your queries can reference any combination of data stored in Amazon Redshift tables and in Amazon S3.

Redshift Spectrum supports open, common data types, including CSV/TSV, Apache Parquet, SequenceFile, and RCFile. Files can be compressed using gzip or Snappy, with other data types and compression methods in the works.

First, create an Amazon Redshift cluster. Follow the steps in Launch a Sample Amazon Redshift Cluster.

Next, create an IAM role that has access to Amazon S3 and Athena. By default, Amazon Redshift Spectrum uses the Amazon Athena data catalog. Your cluster needs authorization to access your external data catalog in AWS Glue or Athena and your data files in Amazon S3.

In the demo setup, I attached AmazonS3FullAccess and AmazonAthenaFullAccess. In a production environment, the IAM roles should follow the standard security of granting least privilege. For more information, see IAM Policies for Amazon Redshift Spectrum.

Attach the newly created role to the Amazon Redshift cluster. For more information, see Associate the IAM Role with Your Cluster.

Next, connect to the Amazon Redshift cluster, and create an external schema and database:

create external schema if not exists spectrum_schema
from data catalog 
database 'spectrum_db' 
region 'us-east-1'
IAM_ROLE 'arn:aws:iam::XXXXXXXXXXXX:role/RedshiftSpectrumRole'
create external database if not exists;

Don’t forget to replace the IAM role in the statement.

Then create an external table within the database:

 CREATE EXTERNAL TABLE IF NOT EXISTS spectrum_schema.ecommerce_sales(
  ItemID int,
  Category varchar,
  Price DOUBLE PRECISION,
  Quantity int,
  OrderDate TIMESTAMP,
  DestinationState varchar,
  ShippingType varchar,
  Referral varchar)
ROW FORMAT DELIMITED
      FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
LOCATION 's3://{BUCKET_NAME}/CDC/'

Query the table, and it should contain data. This is a fact table.

select top 10 * from spectrum_schema.ecommerce_sales

 

Next, create a dimension table. For this example, we create a date/time dimension table. Create the table:

CREATE TABLE date_dimension (
  d_datekey           integer       not null sortkey,
  d_dayofmonth        integer       not null,
  d_monthnum          integer       not null,
  d_dayofweek                varchar(10)   not null,
  d_prettydate        date       not null,
  d_quarter           integer       not null,
  d_half              integer       not null,
  d_year              integer       not null,
  d_season            varchar(10)   not null,
  d_fiscalyear        integer       not null)
diststyle all;

Populate the table with data:

copy date_dimension from 's3://reparmar-lab/2016dates' 
iam_role 'arn:aws:iam::XXXXXXXXXXXX:role/redshiftspectrum'
DELIMITER ','
dateformat 'auto';

The date dimension table should look like the following:

Querying data in local and external tables using Amazon Redshift

Now that you have the fact and dimension table populated with data, you can combine the two and run analysis. For example, if you want to query the total sales amount by weekday, you can run the following:

select sum(quantity*price) as total_sales, date_dimension.d_season
from spectrum_schema.ecommerce_sales 
join date_dimension on spectrum_schema.ecommerce_sales.orderdate = date_dimension.d_prettydate 
group by date_dimension.d_season

You get the following results:

Similarly, you can replace d_season with d_dayofweek to get sales figures by weekday:

With Amazon Redshift Spectrum, you pay only for the queries you run against the data that you actually scan. We encourage you to use file partitioning, columnar data formats, and data compression to significantly minimize the amount of data scanned in Amazon S3. This is important for data warehousing because it dramatically improves query performance and reduces cost.

Partitioning your data in Amazon S3 by date, time, or any other custom keys enables Amazon Redshift Spectrum to dynamically prune nonrelevant partitions to minimize the amount of data processed. If you store data in a columnar format, such as Parquet, Amazon Redshift Spectrum scans only the columns needed by your query, rather than processing entire rows. Similarly, if you compress your data using one of the supported compression algorithms in Amazon Redshift Spectrum, less data is scanned.

Analyzing and visualizing Amazon Redshift data in Amazon QuickSight

Modify the Amazon Redshift security group to allow an Amazon QuickSight connection. For more information, see Authorizing Connections from Amazon QuickSight to Amazon Redshift Clusters.

After modifying the Amazon Redshift security group, go to Amazon QuickSight. Create a new analysis, and choose Amazon Redshift as the data source.

Enter the database connection details, validate the connection, and create the data source.

Choose the schema to be analyzed. In this case, choose spectrum_schema, and then choose the ecommerce_sales table.

Next, we add a custom field for Total Sales = Price*Quantity. In the drop-down list for the ecommerce_sales table, choose Edit analysis data sets.

On the next screen, choose Edit.

In the data prep screen, choose New Field. Add a new calculated field Total Sales $, which is the product of the Price*Quantity fields. Then choose Create. Save and visualize it.

Next, to visualize total sales figures by month, create a graph with Total Sales on the x-axis and Order Data formatted as month on the y-axis.

After you’ve finished, you can use Amazon QuickSight to add different columns from your Amazon Redshift tables and perform different types of visualizations. You can build operational dashboards that continuously monitor your transactional and analytical data. You can publish these dashboards and share them with others.

Final notes

Amazon QuickSight can also read data in Amazon S3 directly. However, with the method demonstrated in this post, you have the option to manipulate, filter, and combine data from multiple sources or Amazon Redshift tables before visualizing it in Amazon QuickSight.

In this example, we dealt with data being inserted, but triggers can be activated in response to an INSERT, UPDATE, or DELETE trigger.

Keep the following in mind:

  • Be careful when invoking a Lambda function from triggers on tables that experience high write traffic. This would result in a large number of calls to your Lambda function. Although calls to the lambda_async procedure are asynchronous, triggers are synchronous.
  • A statement that results in a large number of trigger activations does not wait for the call to the AWS Lambda function to complete. But it does wait for the triggers to complete before returning control to the client.
  • Similarly, you must account for Amazon Kinesis Data Firehose limits. By default, Kinesis Data Firehose is limited to a maximum of 5,000 records/second. For more information, see Monitoring Amazon Kinesis Data Firehose.

In certain cases, it may be optimal to use AWS Database Migration Service (AWS DMS) to capture data changes in Aurora and use Amazon S3 as a target. For example, AWS DMS might be a good option if you don’t need to transform data from Amazon Aurora. The method used in this post gives you the flexibility to transform data from Aurora using Lambda before sending it to Amazon S3. Additionally, the architecture has the benefits of being serverless, whereas AWS DMS requires an Amazon EC2 instance for replication.

For design considerations while using Redshift Spectrum, see Using Amazon Redshift Spectrum to Query External Data.

If you have questions or suggestions, please comment below.


Additional Reading

If you found this post useful, be sure to check out Capturing Data Changes in Amazon Aurora Using AWS Lambda and 10 Best Practices for Amazon Redshift Spectrum


About the Authors

Re Alvarez-Parmar is a solutions architect for Amazon Web Services. He helps enterprises achieve success through technical guidance and thought leadership. In his spare time, he enjoys spending time with his two kids and exploring outdoors.

 

 

 

Instrumenting Web Apps Using AWS X-Ray

Post Syndicated from Bharath Kumar original https://aws.amazon.com/blogs/devops/instrumenting-web-apps-using-aws-x-ray/

This post was written by James Bowman, Software Development Engineer, AWS X-Ray

AWS X-Ray helps developers analyze and debug distributed applications and underlying services in production. You can identify and analyze root-causes of performance issues and errors, understand customer impact, and extract statistical aggregations (such as histograms) for optimization.

In this blog post, I will provide a step-by-step walkthrough for enabling X-Ray tracing in the Go programming language. You can use these steps to add X-Ray tracing to any distributed application.

Revel: A web framework for the Go language

This section will assist you with designing a guestbook application. Skip to “Instrumenting with AWS X-Ray” section below if you already have a Go language application.

Revel is a web framework for the Go language. It facilitates the rapid development of web applications by providing a predefined framework for controllers, views, routes, filters, and more.

To get started with Revel, run revel new github.com/jamesdbowman/guestbook. A project base is then copied to $GOPATH/src/github.com/jamesdbowman/guestbook.

$ tree -L 2
.
├── README.md
├── app
│ ├── controllers
│ ├── init.go
│ ├── routes
│ ├── tmp
│ └── views
├── conf
│ ├── app.conf
│ └── routes
├── messages
│ └── sample.en
├── public
│ ├── css
│ ├── fonts
│ ├── img
│ └── js
└── tests
└── apptest.go

Writing a guestbook application

A basic guestbook application can consist of just two routes: one to sign the guestbook and another to list all entries.
Let’s set up these routes by adding a Book controller, which can be routed to by modifying ./conf/routes.

./app/controllers/book.go:
package controllers

import (
    "math/rand"
    "time"

    "github.com/aws/aws-sdk-go/aws"
    "github.com/aws/aws-sdk-go/aws/endpoints"
    "github.com/aws/aws-sdk-go/aws/session"
    "github.com/aws/aws-sdk-go/service/dynamodb"
    "github.com/aws/aws-sdk-go/service/dynamodb/dynamodbattribute"
    "github.com/revel/revel"
)

const TABLE_NAME = "guestbook"
const SUCCESS = "Success.\n"
const DAY = 86400

var letters = []rune("ABCDEFGHIJKLMNOPQRSTUVWXYZ")

func init() {
    rand.Seed(time.Now().UnixNano())
}

// randString returns a random string of len n, used for DynamoDB Hash key.
func randString(n int) string {
    b := make([]rune, n)
    for i := range b {
        b[i] = letters[rand.Intn(len(letters))]
    }
    return string(b)
}

// Book controls interactions with the guestbook.
type Book struct {
    *revel.Controller
    ddbClient *dynamodb.DynamoDB
}

// Signature represents a user's signature.
type Signature struct {
    Message string
    Epoch   int64
    ID      string
}

// ddb returns the controller's DynamoDB client, instatiating a new client if necessary.
func (c Book) ddb() *dynamodb.DynamoDB {
    if c.ddbClient == nil {
        sess := session.Must(session.NewSession(&aws.Config{
            Region: aws.String(endpoints.UsWest2RegionID),
        }))
        c.ddbClient = dynamodb.New(sess)
    }
    return c.ddbClient
}

// Sign allows users to sign the book.
// The message is to be passed as application/json typed content, listed under the "message" top level key.
func (c Book) Sign() revel.Result {
    var s Signature

    err := c.Params.BindJSON(&s)
    if err != nil {
        return c.RenderError(err)
    }
    now := time.Now()
    s.Epoch = now.Unix()
    s.ID = randString(20)

    item, err := dynamodbattribute.MarshalMap(s)
    if err != nil {
        return c.RenderError(err)
    }

    putItemInput := &dynamodb.PutItemInput{
        TableName: aws.String(TABLE_NAME),
        Item:      item,
    }
    _, err = c.ddb().PutItem(putItemInput)
    if err != nil {
        return c.RenderError(err)
    }

    return c.RenderText(SUCCESS)
}

// List allows users to list all signatures in the book.
func (c Book) List() revel.Result {
    scanInput := &dynamodb.ScanInput{
        TableName: aws.String(TABLE_NAME),
        Limit:     aws.Int64(100),
    }
    res, err := c.ddb().Scan(scanInput)
    if err != nil {
        return c.RenderError(err)
    }

    messages := make([]string, 0)
    for _, v := range res.Items {
        messages = append(messages, *(v["Message"].S))
    }
    return c.RenderJSON(messages)
}

./conf/routes:
POST /sign Book.Sign
GET /list Book.List

Creating the resources and testing

For the purposes of this blog post, the application will be run and tested locally. We will store and retrieve messages from an Amazon DynamoDB table. Use the following AWS CLI command to create the guestbook table:

aws dynamodb create-table --region us-west-2 --table-name "guestbook" --attribute-definitions AttributeName=ID,AttributeType=S AttributeName=Epoch,AttributeType=N --key-schema AttributeName=ID,KeyType=HASH AttributeName=Epoch,KeyType=RANGE --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=5

Now, let’s test our sign and list routes. If everything is working correctly, the following result appears:

$ curl -d '{"message":"Hello from cURL!"}' -H "Content-Type: application/json" http://localhost:9000/book/sign
Success.
$ curl http://localhost:9000/book/list
[
  "Hello from cURL!"
]%

Integrating with AWS X-Ray

Download and run the AWS X-Ray daemon

The AWS SDKs emit trace segments over UDP on port 2000. (This port can be configured.) In order for the trace segments to make it to the X-Ray service, the daemon must listen on this port and batch the segments in calls to the PutTraceSegments API.
For information about downloading and running the X-Ray daemon, see the AWS X-Ray Developer Guide.

Installing the AWS X-Ray SDK for Go

To download the SDK from GitHub, run go get -u github.com/aws/aws-xray-sdk-go/... The SDK will appear in the $GOPATH.

Enabling the incoming request filter

The first step to instrumenting an application with AWS X-Ray is to enable the generation of trace segments on incoming requests. The SDK conveniently provides an implementation of http.Handler which does exactly that. To ensure incoming web requests travel through this handler, we can modify app/init.go, adding a custom function to be run on application start.

import (
    "github.com/aws/aws-xray-sdk-go/xray"
    "github.com/revel/revel"
)

...

func init() {
  ...
    revel.OnAppStart(installXRayHandler)
}

func installXRayHandler() {
    revel.Server.Handler = xray.Handler(xray.NewFixedSegmentNamer("GuestbookApp"), revel.Server.Handler)
}

The application will now emit a segment for each incoming web request. The service graph appears:

You can customize the name of the segment to make it more descriptive by providing an alternate implementation of SegmentNamer to xray.Handler. For example, you can use xray.NewDynamicSegmentNamer(fallback, pattern) in place of the fixed namer. This namer will use the host name from the incoming web request (if it matches pattern) as the segment name. This is often useful when you are trying to separate different instances of the same application.

In addition, HTTP-centric information such as method and URL is collected in the segment’s http subsection:

"http": {
    "request": {
        "url": "/book/list",
        "method": "GET",
        "user_agent": "curl/7.54.0",
        "client_ip": "::1"
    },
    "response": {
        "status": 200
    }
},

Instrumenting outbound calls

To provide detailed performance metrics for distributed applications, the AWS X-Ray SDK needs to measure the time it takes to make outbound requests. Trace context is passed to downstream services using the X-Amzn-Trace-Id header. To draw a detailed and accurate representation of a distributed application, outbound call instrumentation is required.

AWS SDK calls

The AWS X-Ray SDK for Go provides a one-line AWS client wrapper that enables the collection of detailed per-call metrics for any AWS client. We can modify the DynamoDB client instantiation to include this line:

// ddb returns the controller's DynamoDB client, instatiating a new client if necessary.
func (c Book) ddb() *dynamodb.DynamoDB {
    if c.ddbClient == nil {
        sess := session.Must(session.NewSession(&aws.Config{
            Region: aws.String(endpoints.UsWest2RegionID),
        }))
        c.ddbClient = dynamodb.New(sess)
        xray.AWS(c.ddbClient.Client) // add subsegment-generating X-Ray handlers to this client
    }
    return c.ddbClient
}

We also need to ensure that the segment generated by our xray.Handler is passed to these AWS calls so that the X-Ray SDK knows to which segment these generated subsegments belong. In Go, the context.Context object is passed throughout the call path to achieve this goal. (In most other languages, some variant of ThreadLocal is used.) AWS clients provide a *WithContext method variant for each AWS operation, which we need to switch to:

_, err = c.ddb().PutItemWithContext(c.Request.Context(), putItemInput)
    res, err := c.ddb().ScanWithContext(c.Request.Context(), scanInput)

We now see much more detail in the Timeline view of the trace for the sign and list operations:

We can use this detail to help diagnose throttling on our DynamoDB table. In the following screenshot, the purple in the DynamoDB service graph node indicates that our table is underprovisioned. The red in the GuestbookApp node indicates that the application is throwing faults due to this throttling.

HTTP calls

Although the guestbook application does not make any non-AWS outbound HTTP calls in its current state, there is a similar one-liner to wrap HTTP clients that make outbound requests. xray.Client(c *http.Client) wraps an existing http.Client (or nil if you want to use a default HTTP client). For example:

resp, err := ctxhttp.Get(ctx, xray.Client(nil), "https://aws.amazon.com/")

Instrumenting local operations

X-Ray can also assist in measuring the performance of local compute operations. To see this in action, let’s create a custom subsegment inside the randString method:


// randString returns a random string of len n, used for DynamoDB Hash key.
func randString(ctx context.Context, n int) string {
    xray.Capture(ctx, "randString", func(innerCtx context.Context) {
        b := make([]rune, n)
        for i := range b {
            b[i] = letters[rand.Intn(len(letters))]
        }
        s := string(b)
    })
    return s
}

// we'll also need to change the callsite

s.ID = randString(c.Request.Context(), 20)

Summary

By now, you are an expert on how to instrument X-Ray for your Go applications. Instrumenting X-Ray with your applications is an easy way to analyze and debug performance issues and understand customer impact. Please feel free to give any feedback or comments below.

For more information about advanced configuration of the AWS X-Ray SDK for Go, see the AWS X-Ray SDK for Go in the AWS X-Ray Developer Guide and the aws/aws-xray-sdk-go GitHub repository.

For more information about some of the advanced X-Ray features such as histograms, annotations, and filter expressions, see the Analyzing Performance for Amazon Rekognition Apps Written on AWS Lambda Using AWS X-Ray blog post.

How to Encrypt Amazon S3 Objects with the AWS SDK for Ruby

Post Syndicated from Doug Schwartz original https://aws.amazon.com/blogs/security/how-to-encrypt-amazon-s3-objects-with-the-aws-sdk-for-ruby/

AWS KMS image

Recently, Amazon announced some new Amazon S3 encryption and security features. The AWS Blog post showed how to use the Amazon S3 console to take advantage of these new features. However, if you have a large number of Amazon S3 buckets, using the console to implement these features could take hours, if not days. As an alternative, I created documentation topics in the AWS SDK for Ruby Developer Guide that include code examples showing you how to use the new Amazon S3 encryption features using the AWS SDK for Ruby.

What are my encryption options?

You can encrypt Amazon S3 bucket objects on a server or on a client:

  • When you encrypt objects on a server, you request that Amazon S3 encrypt the objects before saving them to disk in data centers and decrypt the objects when you download them. The main advantage of this approach is that Amazon S3 manages the entire encryption process.
  • When you encrypt objects on a client, you encrypt the objects before you upload them to Amazon S3. In this case, you manage the encryption process, the encryption keys, and related tools. Use this option when:
    • Company policy and standards require it.
    • You already have a development process in place that meets your needs.

    Encrypting on the client has always been available, but you should know the following points:

    • You must be diligent about protecting your encryption keys, which is analogous to having a burglar-proof lock on your front door. If you leave a key under the mat, your security is compromised.
    • If you lose your encryption keys, you won’t be able to decrypt your data.

    If you encrypt objects on the client, we strongly recommend that you use an AWS Key Management Service (AWS KMS) managed customer master key (CMK)

How to use encryption on a server

You can specify that Amazon S3 automatically encrypts objects as you upload them to a bucket or require that objects uploaded to an Amazon S3 bucket include encryption on a server before they are uploaded to an Amazon S3 bucket.

The advantage of these settings is that when you specify them, you ensure that objects uploaded to Amazon S3 are encrypted. Alternatively, you can have Amazon S3 encrypt individual objects on the server as you upload them to a bucket or encrypt them on the server with your own key as you upload them to a bucket.

The AWS SDK for Ruby Developer Guide now contains the following topics that explain your encryption options on a server:

How to use encryption on a client

You can encrypt objects on a client before you upload them to a bucket and decrypt them after you download them from a bucket by using the Amazon S3 encryption client.

The AWS SDK for Ruby Developer Guide now contains the following topics that explain your encryption options on the client:

Note: The Amazon S3 encryption client in the AWS SDK for Ruby is compatible with other Amazon S3 encryption clients, but it is not compatible with other AWS client-side encryption libraries, including the AWS Encryption SDK and the Amazon DynamoDB encryption client for Java. Each library returns a different ciphertext (“encrypted message”) format, so you can’t use one library to encrypt objects and a different library to decrypt them. For more information, see Protecting Data Using Client-Side Encryption.

If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about encrypting objects on servers and clients, start a new thread on the Amazon S3 forum or contact AWS Support.

– Doug

Serverless @ re:Invent 2017

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/serverless-reinvent-2017/

At re:Invent 2014, we announced AWS Lambda, what is now the center of the serverless platform at AWS, and helped ignite the trend of companies building serverless applications.

This year, at re:Invent 2017, the topic of serverless was everywhere. We were incredibly excited to see the energy from everyone attending 7 workshops, 15 chalk talks, 20 skills sessions and 27 breakout sessions. Many of these sessions were repeated due to high demand, so we are happy to summarize and provide links to the recordings and slides of these sessions.

Over the course of the week leading up to and then the week of re:Invent, we also had over 15 new features and capabilities across a number of serverless services, including AWS Lambda, Amazon API Gateway, AWS [email protected], AWS SAM, and the newly announced AWS Serverless Application Repository!

AWS Lambda

Amazon API Gateway

  • Amazon API Gateway Supports Endpoint Integrations with Private VPCs – You can now provide access to HTTP(S) resources within your VPC without exposing them directly to the public internet. This includes resources available over a VPN or Direct Connect connection!
  • Amazon API Gateway Supports Canary Release Deployments – You can now use canary release deployments to gradually roll out new APIs. This helps you more safely roll out API changes and limit the blast radius of new deployments.
  • Amazon API Gateway Supports Access Logging – The access logging feature lets you generate access logs in different formats such as CLF (Common Log Format), JSON, XML, and CSV. The access logs can be fed into your existing analytics or log processing tools so you can perform more in-depth analysis or take action in response to the log data.
  • Amazon API Gateway Customize Integration Timeouts – You can now set a custom timeout for your API calls as low as 50ms and as high as 29 seconds (the default is 30 seconds).
  • Amazon API Gateway Supports Generating SDK in Ruby – This is in addition to support for SDKs in Java, JavaScript, Android and iOS (Swift and Objective-C). The SDKs that Amazon API Gateway generates save you development time and come with a number of prebuilt capabilities, such as working with API keys, exponential back, and exception handling.

AWS Serverless Application Repository

Serverless Application Repository is a new service (currently in preview) that aids in the publication, discovery, and deployment of serverless applications. With it you’ll be able to find shared serverless applications that you can launch in your account, while also sharing ones that you’ve created for others to do the same.

AWS [email protected]

[email protected] now supports content-based dynamic origin selection, network calls from viewer events, and advanced response generation. This combination of capabilities greatly increases the use cases for [email protected], such as allowing you to send requests to different origins based on request information, showing selective content based on authentication, and dynamically watermarking images for each viewer.

AWS SAM

Twitch Launchpad live announcements

Other service announcements

Here are some of the other highlights that you might have missed. We think these could help you make great applications:

AWS re:Invent 2017 sessions

Coming up with the right mix of talks for an event like this can be quite a challenge. The Product, Marketing, and Developer Advocacy teams for Serverless at AWS spent weeks reading through dozens of talk ideas to boil it down to the final list.

From feedback at other AWS events and webinars, we knew that customers were looking for talks that focused on concrete examples of solving problems with serverless, how to perform common tasks such as deployment, CI/CD, monitoring, and troubleshooting, and to see customer and partner examples solving real world problems. To that extent we tried to settle on a good mix based on attendee experience and provide a track full of rich content.

Below are the recordings and slides of breakout sessions from re:Invent 2017. We’ve organized them for those getting started, those who are already beginning to build serverless applications, and the experts out there already running them at scale. Some of the videos and slides haven’t been posted yet, and so we will update this list as they become available.

Find the entire Serverless Track playlist on YouTube.

Talks for people new to Serverless

Advanced topics

Expert mode

Talks for specific use cases

Talks from AWS customers & partners

Looking to get hands-on with Serverless?

At re:Invent, we delivered instructor-led skills sessions to help attendees new to serverless applications get started quickly. The content from these sessions is already online and you can do the hands-on labs yourself!
Build a Serverless web application

Still looking for more?

We also recently completely overhauled the main Serverless landing page for AWS. This includes a new Resources page containing case studies, webinars, whitepapers, customer stories, reference architectures, and even more Getting Started tutorials. Check it out!

Now Open AWS EU (Paris) Region

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-open-aws-eu-paris-region/

Today we are launching our 18th AWS Region, our fourth in Europe. Located in the Paris area, AWS customers can use this Region to better serve customers in and around France.

The Details
The new EU (Paris) Region provides a broad suite of AWS services including Amazon API Gateway, Amazon Aurora, Amazon CloudFront, Amazon CloudWatch, CloudWatch Events, Amazon CloudWatch Logs, Amazon DynamoDB, Amazon Elastic Compute Cloud (EC2), EC2 Container Registry, Amazon ECS, Amazon Elastic Block Store (EBS), Amazon EMR, Amazon ElastiCache, Amazon Elasticsearch Service, Amazon Glacier, Amazon Kinesis Streams, Polly, Amazon Redshift, Amazon Relational Database Service (RDS), Amazon Route 53, Amazon Simple Notification Service (SNS), Amazon Simple Queue Service (SQS), Amazon Simple Storage Service (S3), Amazon Simple Workflow Service (SWF), Amazon Virtual Private Cloud, Auto Scaling, AWS Certificate Manager (ACM), AWS CloudFormation, AWS CloudTrail, AWS CodeDeploy, AWS Config, AWS Database Migration Service, AWS Direct Connect, AWS Elastic Beanstalk, AWS Identity and Access Management (IAM), AWS Key Management Service (KMS), AWS Lambda, AWS Marketplace, AWS OpsWorks Stacks, AWS Personal Health Dashboard, AWS Server Migration Service, AWS Service Catalog, AWS Shield Standard, AWS Snowball, AWS Snowball Edge, AWS Snowmobile, AWS Storage Gateway, AWS Support (including AWS Trusted Advisor), Elastic Load Balancing, and VM Import.

The Paris Region supports all sizes of C5, M5, R4, T2, D2, I3, and X1 instances.

There are also four edge locations for Amazon Route 53 and Amazon CloudFront: three in Paris and one in Marseille, all with AWS WAF and AWS Shield. Check out the AWS Global Infrastructure page to learn more about current and future AWS Regions.

The Paris Region will benefit from three AWS Direct Connect locations. Telehouse Voltaire is available today. AWS Direct Connect will also become available at Equinix Paris in early 2018, followed by Interxion Paris.

All AWS infrastructure regions around the world are designed, built, and regularly audited to meet the most rigorous compliance standards and to provide high levels of security for all AWS customers. These include ISO 27001, ISO 27017, ISO 27018, SOC 1 (Formerly SAS 70), SOC 2 and SOC 3 Security & Availability, PCI DSS Level 1, and many more. This means customers benefit from all the best practices of AWS policies, architecture, and operational processes built to satisfy the needs of even the most security sensitive customers.

AWS is certified under the EU-US Privacy Shield, and the AWS Data Processing Addendum (DPA) is GDPR-ready and available now to all AWS customers to help them prepare for May 25, 2018 when the GDPR becomes enforceable. The current AWS DPA, as well as the AWS GDPR DPA, allows customers to transfer personal data to countries outside the European Economic Area (EEA) in compliance with European Union (EU) data protection laws. AWS also adheres to the Cloud Infrastructure Service Providers in Europe (CISPE) Code of Conduct. The CISPE Code of Conduct helps customers ensure that AWS is using appropriate data protection standards to protect their data, consistent with the GDPR. In addition, AWS offers a wide range of services and features to help customers meet the requirements of the GDPR, including services for access controls, monitoring, logging, and encryption.

From Our Customers
Many AWS customers are preparing to use this new Region. Here’s a small sample:

Societe Generale, one of the largest banks in France and the world, has accelerated their digital transformation while working with AWS. They developed SG Research, an application that makes reports from Societe Generale’s analysts available to corporate customers in order to improve the decision-making process for investments. The new AWS Region will reduce latency between applications running in the cloud and in their French data centers.

SNCF is the national railway company of France. Their mobile app, powered by AWS, delivers real-time traffic information to 14 million riders. Extreme weather, traffic events, holidays, and engineering works can cause usage to peak at hundreds of thousands of users per second. They are planning to use machine learning and big data to add predictive features to the app.

Radio France, the French public radio broadcaster, offers seven national networks, and uses AWS to accelerate its innovation and stay competitive.

Les Restos du Coeur, a French charity that provides assistance to the needy, delivering food packages and participating in their social and economic integration back into French society. Les Restos du Coeur is using AWS for its CRM system to track the assistance given to each of their beneficiaries and the impact this is having on their lives.

AlloResto by JustEat (a leader in the French FoodTech industry), is using AWS to to scale during traffic peaks and to accelerate their innovation process.

AWS Consulting and Technology Partners
We are already working with a wide variety of consulting, technology, managed service, and Direct Connect partners in France. Here’s a partial list:

AWS Premier Consulting PartnersAccenture, Capgemini, Claranet, CloudReach, DXC, and Edifixio.

AWS Consulting PartnersABC Systemes, Atos International SAS, CoreExpert, Cycloid, Devoteam, LINKBYNET, Oxalide, Ozones, Scaleo Information Systems, and Sopra Steria.

AWS Technology PartnersAxway, Commerce Guys, MicroStrategy, Sage, Software AG, Splunk, Tibco, and Zerolight.

AWS in France
We have been investing in Europe, with a focus on France, for the last 11 years. We have also been developing documentation and training programs to help our customers to improve their skills and to accelerate their journey to the AWS Cloud.

As part of our commitment to AWS customers in France, we plan to train more than 25,000 people in the coming years, helping them develop highly sought after cloud skills. They will have access to AWS training resources in France via AWS Academy, AWSome days, AWS Educate, and webinars, all delivered in French by AWS Technical Trainers and AWS Certified Trainers.

Use it Today
The EU (Paris) Region is open for business now and you can start using it today!

Jeff;

 

How to Enhance the Security of Sensitive Customer Data by Using Amazon CloudFront Field-Level Encryption

Post Syndicated from Alex Tomic original https://aws.amazon.com/blogs/security/how-to-enhance-the-security-of-sensitive-customer-data-by-using-amazon-cloudfront-field-level-encryption/

Amazon CloudFront is a web service that speeds up distribution of your static and dynamic web content to end users through a worldwide network of edge locations. CloudFront provides a number of benefits and capabilities that can help you secure your applications and content while meeting compliance requirements. For example, you can configure CloudFront to help enforce secure, end-to-end connections using HTTPS SSL/TLS encryption. You also can take advantage of CloudFront integration with AWS Shield for DDoS protection and with AWS WAF (a web application firewall) for protection against application-layer attacks, such as SQL injection and cross-site scripting.

Now, CloudFront field-level encryption helps secure sensitive data such as a customer phone numbers by adding another security layer to CloudFront HTTPS. Using this functionality, you can help ensure that sensitive information in a POST request is encrypted at CloudFront edge locations. This information remains encrypted as it flows to and beyond your origin servers that terminate HTTPS connections with CloudFront and throughout the application environment. In this blog post, we demonstrate how you can enhance the security of sensitive data by using CloudFront field-level encryption.

Note: This post assumes that you understand concepts and services such as content delivery networks, HTTP forms, public-key cryptography, CloudFrontAWS Lambda, and the AWS CLI. If necessary, you should familiarize yourself with these concepts and review the solution overview in the next section before proceeding with the deployment of this post’s solution.

How field-level encryption works

Many web applications collect and store data from users as those users interact with the applications. For example, a travel-booking website may ask for your passport number and less sensitive data such as your food preferences. This data is transmitted to web servers and also might travel among a number of services to perform tasks. However, this also means that your sensitive information may need to be accessed by only a small subset of these services (most other services do not need to access your data).

User data is often stored in a database for retrieval at a later time. One approach to protecting stored sensitive data is to configure and code each service to protect that sensitive data. For example, you can develop safeguards in logging functionality to ensure sensitive data is masked or removed. However, this can add complexity to your code base and limit performance.

Field-level encryption addresses this problem by ensuring sensitive data is encrypted at CloudFront edge locations. Sensitive data fields in HTTPS form POSTs are automatically encrypted with a user-provided public RSA key. After the data is encrypted, other systems in your architecture see only ciphertext. If this ciphertext unintentionally becomes externally available, the data is cryptographically protected and only designated systems with access to the private RSA key can decrypt the sensitive data.

It is critical to secure private RSA key material to prevent unauthorized access to the protected data. Management of cryptographic key material is a larger topic that is out of scope for this blog post, but should be carefully considered when implementing encryption in your applications. For example, in this blog post we store private key material as a secure string in the Amazon EC2 Systems Manager Parameter Store. The Parameter Store provides a centralized location for managing your configuration data such as plaintext data (such as database strings) or secrets (such as passwords) that are encrypted using AWS Key Management Service (AWS KMS). You may have an existing key management system in place that you can use, or you can use AWS CloudHSM. CloudHSM is a cloud-based hardware security module (HSM) that enables you to easily generate and use your own encryption keys in the AWS Cloud.

To illustrate field-level encryption, let’s look at a simple form submission where Name and Phone values are sent to a web server using an HTTP POST. A typical form POST would contain data such as the following.

POST / HTTP/1.1
Host: example.com
Content-Type: application/x-www-form-urlencoded
Content-Length:60

Name=Jane+Doe&Phone=404-555-0150

Instead of taking this typical approach, field-level encryption converts this data similar to the following.

POST / HTTP/1.1
Host: example.com
Content-Type: application/x-www-form-urlencoded
Content-Length: 1713

Name=Jane+Doe&Phone=AYABeHxZ0ZqWyysqxrB5pEBSYw4AAA...

To further demonstrate field-level encryption in action, this blog post includes a sample serverless application that you can deploy by using a CloudFormation template, which creates an application environment using CloudFront, Amazon API Gateway, and Lambda. The sample application is only intended to demonstrate field-level encryption functionality and is not intended for production use. The following diagram depicts the architecture and data flow of this sample application.

Sample application architecture and data flow

Diagram of the solution's architecture and data flow

Here is how the sample solution works:

  1. An application user submits an HTML form page with sensitive data, generating an HTTPS POST to CloudFront.
  2. Field-level encryption intercepts the form POST and encrypts sensitive data with the public RSA key and replaces fields in the form post with encrypted ciphertext. The form POST ciphertext is then sent to origin servers.
  3. The serverless application accepts the form post data containing ciphertext where sensitive data would normally be. If a malicious user were able to compromise your application and gain access to your data, such as the contents of a form, that user would see encrypted data.
  4. Lambda stores data in a DynamoDB table, leaving sensitive data to remain safely encrypted at rest.
  5. An administrator uses the AWS Management Console and a Lambda function to view the sensitive data.
  6. During the session, the administrator retrieves ciphertext from the DynamoDB table.
  7. The administrator decrypts sensitive data by using private key material stored in the EC2 Systems Manager Parameter Store.
  8. Decrypted sensitive data is transmitted over SSL/TLS via the AWS Management Console to the administrator for review.

Deployment walkthrough

The high-level steps to deploy this solution are as follows:

  1. Stage the required artifacts
    When deployment packages are used with Lambda, the zipped artifacts have to be placed in an S3 bucket in the target AWS Region for deployment. This step is not required if you are deploying in the US East (N. Virginia) Region because the package has already been staged there.
  2. Generate an RSA key pair
    Create a public/private key pair that will be used to perform the encrypt/decrypt functionality.
  3. Upload the public key to CloudFront and associate it with the field-level encryption configuration
    After you create the key pair, the public key is uploaded to CloudFront so that it can be used by field-level encryption.
  4. Launch the CloudFormation stack
    Deploy the sample application for demonstrating field-level encryption by using AWS CloudFormation.
  5. Add the field-level encryption configuration to the CloudFront distribution
    After you have provisioned the application, this step associates the field-level encryption configuration with the CloudFront distribution.
  6. Store the RSA private key in the Parameter Store
    Store the private key in the Parameter Store as a SecureString data type, which uses AWS KMS to encrypt the parameter value.

Deploy the solution

1. Stage the required artifacts

(If you are deploying in the US East [N. Virginia] Region, skip to Step 2, “Generate an RSA key pair.”)

Stage the Lambda function deployment package in an Amazon S3 bucket located in the AWS Region you are using for this solution. To do this, download the zipped deployment package and upload it to your in-region bucket. For additional information about uploading objects to S3, see Uploading Object into Amazon S3.

2. Generate an RSA key pair

In this section, you will generate an RSA key pair by using OpenSSL:

  1. Confirm access to OpenSSL.
    $ openssl version

    You should see version information similar to the following.

    OpenSSL <version> <date>

  1. Create a private key using the following command.
    $ openssl genrsa -out private_key.pem 2048

    The command results should look similar to the following.

    Generating RSA private key, 2048 bit long modulus
    ................................................................................+++
    ..........................+++
    e is 65537 (0x10001)
  1. Extract the public key from the private key by running the following command.
    $ openssl rsa -pubout -in private_key.pem -out public_key.pem

    You should see output similar to the following.

    writing RSA key
  1. Restrict access to the private key.$ chmod 600 private_key.pem Note: You will use the public and private key material in Steps 3 and 6 to configure the sample application.

3. Upload the public key to CloudFront and associate it with the field-level encryption configuration

Now that you have created the RSA key pair, you will use the AWS Management Console to upload the public key to CloudFront for use by field-level encryption. Complete the following steps to upload and configure the public key.

Note: Do not include spaces or special characters when providing the configuration values in this section.

  1. From the AWS Management Console, choose Services > CloudFront.
  2. In the navigation pane, choose Public Key and choose Add Public Key.
    Screenshot of adding a public key

Complete the Add Public Key configuration boxes:

  • Key Name: Type a name such as DemoPublicKey.
  • Encoded Key: Paste the contents of the public_key.pem file you created in Step 2c. Copy and paste the encoded key value for your public key, including the -----BEGIN PUBLIC KEY----- and -----END PUBLIC KEY----- lines.
  • Comment: Optionally add a comment.
  1. Choose Create.
  2. After adding at least one public key to CloudFront, the next step is to create a profile to tell CloudFront which fields of input you want to be encrypted. While still on the CloudFront console, choose Field-level encryption in the navigation pane.
  3. Under Profiles, choose Create profile.
    Screenshot of creating a profile

Complete the Create profile configuration boxes:

  • Name: Type a name such as FLEDemo.
  • Comment: Optionally add a comment.
  • Public key: Select the public key you configured in Step 4.b.
  • Provider name: Type a provider name such as FLEDemo.
    This information will be used when the form data is encrypted, and must be provided to applications that need to decrypt the data, along with the appropriate private key.
  • Pattern to match: Type phone. This configures field-level encryption to match based on the phone.
  1. Choose Save profile.
  2. Configurations include options for whether to block or forward a query to your origin in scenarios where CloudFront can’t encrypt the data. Under Encryption Configurations, choose Create configuration.
    Screenshot of creating a configuration

Complete the Create configuration boxes:

  • Comment: Optionally add a comment.
  • Content type: Enter application/x-www-form-urlencoded. This is a common media type for encoding form data.
  • Default profile ID: Select the profile you added in Step 3e.
  1. Choose Save configuration

4. Launch the CloudFormation stack

Launch the sample application by using a CloudFormation template that automates the provisioning process.

Input parameter Input parameter description
ProviderID Enter the Provider name you assigned in Step 3e. The ProviderID is used in field-level encryption configuration in CloudFront (letters and numbers only, no special characters)
PublicKeyName Enter the Key Name you assigned in Step 3b. This name is assigned to the public key in field-level encryption configuration in CloudFront (letters and numbers only, no special characters).
PrivateKeySSMPath Leave as the default: /cloudfront/field-encryption-sample/private-key
ArtifactsBucket The S3 bucket with artifact files (staged zip file with app code). Leave as default if deploying in us-east-1.
ArtifactsPrefix The path in the S3 bucket containing artifact files. Leave as default if deploying in us-east-1.

To finish creating the CloudFormation stack:

  1. Choose Next on the Select Template page, enter the input parameters and choose Next.
    Note: The Artifacts configuration needs to be updated only if you are deploying outside of us-east-1 (US East [N. Virginia]). See Step 1 for artifact staging instructions.
  2. On the Options page, accept the defaults and choose Next.
  3. On the Review page, confirm the details, choose the I acknowledge that AWS CloudFormation might create IAM resources check box, and then choose Create. (The stack will be created in approximately 15 minutes.)

5. Add the field-level encryption configuration to the CloudFront distribution

While still on the CloudFront console, choose Distributions in the navigation pane, and then:

    1. In the Outputs section of the FLE-Sample-App stack, look for CloudFrontDistribution and click the URL to open the CloudFront console.
    2. Choose Behaviors, choose the Default (*) behavior, and then choose Edit.
    3. For Field-level Encryption Config, choose the configuration you created in Step 3g.
      Screenshot of editing the default cache behavior
    4. Choose Yes, Edit.
    5. While still in the CloudFront distribution configuration, choose the General Choose Edit, scroll down to Distribution State, and change it to Enabled.
    6. Choose Yes, Edit.

6. Store the RSA private key in the Parameter Store

In this step, you store the private key in the EC2 Systems Manager Parameter Store as a SecureString data type, which uses AWS KMS to encrypt the parameter value. For more information about AWS KMS, see the AWS Key Management Service Developer Guide. You will need a working installation of the AWS CLI to complete this step.

  1. Store the private key in the Parameter Store with the AWS CLI by running the following command. You will find the <KMSKeyID> in the KMSKeyID in the CloudFormation stack Outputs. Substitute it for the placeholder in the following command.
    $ aws ssm put-parameter --type "SecureString" --name /cloudfront/field-encryption-sample/private-key --value file://private_key.pem --key-id "<KMSKeyID>"
    
    ------------------
    |  PutParameter  |
    +----------+-----+
    |  Version |  1  |
    +----------+-----+

  1. Verify the parameter. Your private key material should be accessible through the ssm get-parameter in the following command in the Value The key material has been truncated in the following output.
    $ aws ssm get-parameter --name /cloudfront/field-encryption-sample/private-key --with-decryption
    
    -----…
    
    ||  Value  |  -----BEGIN RSA PRIVATE KEY-----
    MIIEowIBAAKCAQEAwGRBGuhacmw+C73kM6Z…….

    Notice we use the —with decryption argument in this command. This returns the private key as cleartext.

    This completes the sample application deployment. Next, we show you how to see field-level encryption in action.

  1. Delete the private key from local storage. On Linux for example, using the shred command, securely delete the private key material from your workstation as shown below. You may also wish to store the private key material within an AWS CloudHSM or other protected location suitable for your security requirements. For production implementations, you also should implement key rotation policies.
    $ shred -zvu -n  100 private*.pem
    
    shred: private_encrypted_key.pem: pass 1/101 (random)...
    shred: private_encrypted_key.pem: pass 2/101 (dddddd)...
    shred: private_encrypted_key.pem: pass 3/101 (555555)...
    ….

Test the sample application

Use the following steps to test the sample application with field-level encryption:

  1. Open sample application in your web browser by clicking the ApplicationURL link in the CloudFormation stack Outputs. (for example, https:d199xe5izz82ea.cloudfront.net/prod/). Note that it may take several minutes for the CloudFront distribution to reach the Deployed Status from the previous step, during which time you may not be able to access the sample application.
  2. Fill out and submit the HTML form on the page:
    1. Complete the three form fields: Full Name, Email Address, and Phone Number.
    2. Choose Submit.
      Screenshot of completing the sample application form
      Notice that the application response includes the form values. The phone number returns the following ciphertext encryption using your public key. This ciphertext has been stored in DynamoDB.
      Screenshot of the phone number as ciphertext
  3. Execute the Lambda decryption function to download ciphertext from DynamoDB and decrypt the phone number using the private key:
    1. In the CloudFormation stack Outputs, locate DecryptFunction and click the URL to open the Lambda console.
    2. Configure a test event using the “Hello World” template.
    3. Choose the Test button.
  4. View the encrypted and decrypted phone number data.
    Screenshot of the encrypted and decrypted phone number data

Summary

In this blog post, we showed you how to use CloudFront field-level encryption to encrypt sensitive data at edge locations and help prevent access from unauthorized systems. The source code for this solution is available on GitHub. For additional information about field-level encryption, see the documentation.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, please start a new thread on the CloudFront forum.

– Alex and Cameron

Simplify Querying Nested JSON with the AWS Glue Relationalize Transform

Post Syndicated from Trevor Roberts original https://aws.amazon.com/blogs/big-data/simplify-querying-nested-json-with-the-aws-glue-relationalize-transform/

AWS Glue has a transform called Relationalize that simplifies the extract, transform, load (ETL) process by converting nested JSON into columns that you can easily import into relational databases. Relationalize transforms the nested JSON into key-value pairs at the outermost level of the JSON document. The transformed data maintains a list of the original keys from the nested JSON separated by periods.

Let’s look at how Relationalize can help you with a sample use case.

An example of Relationalize in action

Suppose that the developers of a video game want to use a data warehouse like Amazon Redshift to run reports on player behavior based on data that is stored in JSON. Sample 1 shows example user data from the game. The player named “user1” has characteristics such as race, class, and location in nested JSON data. Further down, the player’s arsenal information includes additional nested JSON data. If the developers want to ETL this data into their data warehouse, they might have to resort to nested loops or recursive functions in their code.

Sample 1: Nested JSON

{
	"player": {
		"username": "user1",
		"characteristics": {
			"race": "Human",
			"class": "Warlock",
			"subclass": "Dawnblade",
			"power": 300,
			"playercountry": "USA"
		},
		"arsenal": {
			"kinetic": {
				"name": "Sweet Business",
				"type": "Auto Rifle",
				"power": 300,
				"element": "Kinetic"
			},
			"energy": {
				"name": "MIDA Mini-Tool",
				"type": "Submachine Gun",
				"power": 300,
				"element": "Solar"
			},
			"power": {
				"name": "Play of the Game",
				"type": "Grenade Launcher",
				"power": 300,
				"element": "Arc"
			}
		},
		"armor": {
			"head": "Eye of Another World",
			"arms": "Philomath Gloves",
			"chest": "Philomath Robes",
			"leg": "Philomath Boots",
			"classitem": "Philomath Bond"
		},
		"location": {
			"map": "Titan",
			"waypoint": "The Rig"
		}
	}
}

Instead, the developers can use the Relationalize transform. Sample 2 shows what the transformed data looks like.

Sample 2: Flattened JSON

{
    "player.username": "user1",
    "player.characteristics.race": "Human",
    "player.characteristics.class": "Warlock",
    "player.characteristics.subclass": "Dawnblade",
    "player.characteristics.power": 300,
    "player.characteristics.playercountry": "USA",
    "player.arsenal.kinetic.name": "Sweet Business",
    "player.arsenal.kinetic.type": "Auto Rifle",
    "player.arsenal.kinetic.power": 300,
    "player.arsenal.kinetic.element": "Kinetic",
    "player.arsenal.energy.name": "MIDA Mini-Tool",
    "player.arsenal.energy.type": "Submachine Gun",
    "player.arsenal.energy.power": 300,
    "player.arsenal.energy.element": "Solar",
    "player.arsenal.power.name": "Play of the Game",
    "player.arsenal.power.type": "Grenade Launcher",
    "player.arsenal.power.power": 300,
    "player.arsenal.power.element": "Arc",
    "player.armor.head": "Eye of Another World",
    "player.armor.arms": "Philomath Gloves",
    "player.armor.chest": "Philomath Robes",
    "player.armor.leg": "Philomath Boots",
    "player.armor.classitem": "Philomath Bond",
    "player.location.map": "Titan",
    "player.location.waypoint": "The Rig"
}

You can then write the data to a database or to a data warehouse. You can also write it to delimited text files, such as in comma-separated value (CSV) format, or columnar file formats such as Optimized Row Columnar (ORC) format. You can use either of these format types for long-term storage in Amazon S3. Storing the transformed files in S3 provides the additional benefit of being able to query this data using Amazon Athena or Amazon Redshift Spectrum. You can further extend the usefulness of the data by performing joins between data stored in S3 and the data stored in an Amazon Redshift data warehouse.

Before we get started…

In my example, I took two preparatory steps that save some time in your ETL code development:

  1. I stored my data in an Amazon S3 bucket and used an AWS Glue crawler to make my data available in the AWS Glue data catalog. You can find instructions on how to do that in Cataloging Tables with a Crawler in the AWS Glue documentation. The AWS Glue database name I used was “blog,” and the table name was “players.” You can see these values in use in the sample code that follows.
  2. I deployed a Zeppelin notebook using the automated deployment available within AWS Glue. If you already used an AWS Glue development endpoint to deploy a Zeppelin notebook, you can skip the deployment instructions. Otherwise, let’s quickly review how to deploy Zeppelin.

Deploying a Zeppelin notebook with AWS Glue

The following steps are outlined in the AWS Glue documentation, and I include a few screenshots here for clarity.

First, create two IAM roles:

Next, in the AWS Glue Management Console, choose Dev endpoints, and then choose Add endpoint.

Specify a name for the endpoint and the AWS Glue IAM role that you created.

On the networking screen, choose Skip Networking because our code only communicates with S3.

Complete the development endpoint process by providing a Secure Shell (SSH) public key and confirming your settings.

When your new development endpoint’s Provisioning status changes from PROVISIONING to READY, choose your endpoint, and then for Actions choose Create notebook server.

Enter the notebook server details, including the role you previously created and a security group with inbound access allowed on TCP port 443.

Doing this automatically launches an AWS CloudFormation template. The output specifies the URL that you can use to access your Zeppelin notebook with the username and password you specified in the wizard.

How do we flatten nested JSON?

With my data loaded and my notebook server ready, I accessed Zeppelin, created a new note, and set my interpreter to spark. I used some Python code that AWS Glue previously generated for another job that outputs to ORC. Then I added the Relationalize transform. You can see the resulting Python code in Sample 3.­

Sample 3: Python code to transform the nested JSON and output it to ORC

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
#from awsglue.transforms import Relationalize

# Begin variables to customize with your information
glue_source_database = "blog"
glue_source_table = "players"
glue_temp_storage = "s3://blog-example-edz/temp"
glue_relationalize_output_s3_path = "s3://blog-example-edz/output-flat"
dfc_root_table_name = "root" #default value is "roottable"
# End variables to customize with your information

glueContext = GlueContext(spark.sparkContext)
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = glue_source_database, table_name = glue_source_table, transformation_ctx = "datasource0")
dfc = Relationalize.apply(frame = datasource0, staging_path = glue_temp_storage, name = dfc_root_table_name, transformation_ctx = "dfc")
blogdata = dfc.select(dfc_root_table_name)
blogdataoutput = glueContext.write_dynamic_frame.from_options(frame = blogdata, connection_type = "s3", connection_options = {"path": glue_relationalize_output_s3_path}, format = "orc", transformation_ctx = "blogdataoutput")

What exactly is going on in this script?

After the import statements, we instantiate a GlueContext object, which allows us to work with the data in AWS Glue. Next, we create a DynamicFrame (datasource0) from the “players” table in the AWS Glue “blog” database. We use this DynamicFrame to perform any necessary operations on the data structure before it’s written to our desired output format. The source files remain unchanged.

We then run the Relationalize transform (Relationalize.apply()) with our datasource0 as one of the parameters. Another important parameter is the name parameter, which is a key that identifies our data after the transformation completes.

The Relationalize.apply() method returns a DynamicFrameCollection, and this is stored in the dfc variable. Before we can write our data to S3, we need to select the DynamicFrame from the DynamicFrameCollection object. We do this with the dfc.select() method. The correct DynamicFrame is stored in the blogdata variable.

You might be curious why a DynamicFrameCollection was returned when we started with a single DynamicFrame. This return value comes from the way Relationalize treats arrays in the JSON document: A DynamicFrame is created for each array. Together with the root data structure, each generated DynamicFrame is added to a DynamicFrameCollection when Relationalize completes its work. Although we didn’t have any arrays in our data, it’s good to keep this in mind. Finally, we output (blogdataoutput) the root DynamicFrame to ORC files in S3.

Using the transformed data

One of the use cases we discussed earlier was using Amazon Athena or Amazon Redshift Spectrum to query the ORC files.

I used the following SQL DDL statements to create external tables in both services to enable queries of my data stored in Amazon S3.

Sample 4: Amazon Athena DDL

CREATE EXTERNAL TABLE IF NOT EXISTS blog.blog_data_athena_test (
  `characteristics_race` string,
  `characteristics_class` string,
  `characteristics_subclass` string,
  `characteristics_power` int,
  `characteristics_playercountry` string,
  `kinetic_name` string,
  `kinetic_type` string,
  `kinetic_power` int,
  `kinetic_element` string,
  `energy_name` string,
  `energy_type` string,
  `energy_power` int,
  `energy_element` string,
  `power_name` string,
  `power_type` string,
  `power_power` int,
  `power_element` string,
  `armor_head` string,
  `armor_arms` string,
  `armor_chest` string,
  `armor_leg` string,
  `armor_classitem` string,
  `map` string,
  `waypoint` string 
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.orc.OrcSerde'
WITH SERDEPROPERTIES (
  'serialization.format' = '1'
) LOCATION 's3://blog-example-edz/output-flat/'
TBLPROPERTIES ('has_encrypted_data'='false');

 

Sample 5: Amazon Redshift Spectrum DDL

-- Create a Schema
-- A single schema can be used with multiple external tables.
-- This step is only required once for the external tables you create.
create external schema spectrum 
from data catalog 
database 'blog' 
iam_role 'arn:aws:iam::0123456789:role/redshift-role'
create external database if not exists;

-- Create an external table in the schema
create external table spectrum.blog(
  username VARCHAR,
  characteristics_race VARCHAR,
  characteristics_class VARCHAR,
  characteristics_subclass VARCHAR,
  characteristics_power INTEGER,
  characteristics_playercountry VARCHAR,
  kinetic_name VARCHAR,
  kinetic_type VARCHAR,
  kinetic_power INTEGER,
  kinetic_element VARCHAR,
  energy_name VARCHAR,
  energy_type VARCHAR,
  energy_power INTEGER,
  energy_element VARCHAR,
  power_name VARCHAR,
  power_type VARCHAR,
  power_power INTEGER,
  power_element VARCHAR,
  armor_head VARCHAR,
  armor_arms VARCHAR,
  armor_chest VARCHAR,
  armor_leg VARCHAR,
  armor_classItem VARCHAR,
  map VARCHAR,
  waypoint VARCHAR)
stored as orc
location 's3://blog-example-edz/output-flat';

I even ran a query, shown in Sample 6, that joined my Redshift Spectrum table (spectrum.playerdata) with data in an Amazon Redshift table (public.raids) to generate advanced reports. In the where clause, I join the two tables based on the username values that are common to both data sources.

Sample 6: Select statement with a join of Redshift Spectrum data with Amazon Redshift data

-- Get Total Raid Completions for the Hunter Class.
select spectrum.playerdata.characteristics_class as class, sum(public.raids."completions.val.raids.leviathan") as "Total Hunter Leviathan Raid Completions" from spectrum.playerdata, public.raids
where spectrum.playerdata.username = public.raids."completions.val.username"
and spectrum.playerdata.characteristics_class = 'Hunter'
group by spectrum.playerdata.characteristics_class;

Summary

This post demonstrated how simple it can be to flatten nested JSON data with AWS Glue, using the Relationalize transform to automate the conversion of nested JSON. AWS Glue also automates the deployment of Zeppelin notebooks that you can use to develop your Python automation script. Finally, AWS Glue can output the transformed data directly to a relational database, or to files in Amazon S3 for further analysis with tools such as Amazon Athena and Amazon Redshift Spectrum.

As great as Relationalize is, it’s not the only transform available with AWS Glue. You can see a complete list of available transforms in Built-In Transforms in the AWS Glue documentation. Try them out today!


Additional Reading

If you found this post useful, be sure to check out Using Amazon Redshift Spectrum, Amazon Athena and AWS Glue with Node.js in Production and Build a Data Lake Foundation with AWS Glue and Amazon S3.


About the Author

Trevor Roberts Jr is a Solutions Architect with AWS. He provides architectural guidance to help customers achieve success in the cloud. In his spare time, Trevor enjoys traveling to new places and spending time with family.

What is HAMR and How Does It Enable the High-Capacity Needs of the Future?

Post Syndicated from Andy Klein original https://www.backblaze.com/blog/hamr-hard-drives/

HAMR drive illustration

During Q4, Backblaze deployed 100 petabytes worth of Seagate hard drives to our data centers. The newly deployed Seagate 10 and 12 TB drives are doing well and will help us meet our near term storage needs, but we know we’re going to need more drives — with higher capacities. That’s why the success of new hard drive technologies like Heat-Assisted Magnetic Recording (HAMR) from Seagate are very relevant to us here at Backblaze and to the storage industry in general. In today’s guest post we are pleased to have Mark Re, CTO at Seagate, give us an insider’s look behind the hard drive curtain to tell us how Seagate engineers are developing the HAMR technology and making it market ready starting in late 2018.

What is HAMR and How Does It Enable the High-Capacity Needs of the Future?

Guest Blog Post by Mark Re, Seagate Senior Vice President and Chief Technology Officer

Earlier this year Seagate announced plans to make the first hard drives using Heat-Assisted Magnetic Recording, or HAMR, available by the end of 2018 in pilot volumes. Even as today’s market has embraced 10TB+ drives, the need for 20TB+ drives remains imperative in the relative near term. HAMR is the Seagate research team’s next major advance in hard drive technology.

HAMR is a technology that over time will enable a big increase in the amount of data that can be stored on a disk. A small laser is attached to a recording head, designed to heat a tiny spot on the disk where the data will be written. This allows a smaller bit cell to be written as either a 0 or a 1. The smaller bit cell size enables more bits to be crammed into a given surface area — increasing the areal density of data, and increasing drive capacity.

It sounds almost simple, but the science and engineering expertise required, the research, experimentation, lab development and product development to perfect this technology has been enormous. Below is an overview of the HAMR technology and you can dig into the details in our technical brief that provides a point-by-point rundown describing several key advances enabling the HAMR design.

As much time and resources as have been committed to developing HAMR, the need for its increased data density is indisputable. Demand for data storage keeps increasing. Businesses’ ability to manage and leverage more capacity is a competitive necessity, and IT spending on capacity continues to increase.

History of Increasing Storage Capacity

For the last 50 years areal density in the hard disk drive has been growing faster than Moore’s law, which is a very good thing. After all, customers from data centers and cloud service providers to creative professionals and game enthusiasts rarely go shopping looking for a hard drive just like the one they bought two years ago. The demands of increasing data on storage capacities inevitably increase, thus the technology constantly evolves.

According to the Advanced Storage Technology Consortium, HAMR will be the next significant storage technology innovation to increase the amount of storage in the area available to store data, also called the disk’s “areal density.” We believe this boost in areal density will help fuel hard drive product development and growth through the next decade.

Why do we Need to Develop Higher-Capacity Hard Drives? Can’t Current Technologies do the Job?

Why is HAMR’s increased data density so important?

Data has become critical to all aspects of human life, changing how we’re educated and entertained. It affects and informs the ways we experience each other and interact with businesses and the wider world. IDC research shows the datasphere — all the data generated by the world’s businesses and billions of consumer endpoints — will continue to double in size every two years. IDC forecasts that by 2025 the global datasphere will grow to 163 zettabytes (that is a trillion gigabytes). That’s ten times the 16.1 ZB of data generated in 2016. IDC cites five key trends intensifying the role of data in changing our world: embedded systems and the Internet of Things (IoT), instantly available mobile and real-time data, cognitive artificial intelligence (AI) systems, increased security data requirements, and critically, the evolution of data from playing a business background to playing a life-critical role.

Consumers use the cloud to manage everything from family photos and videos to data about their health and exercise routines. Real-time data created by connected devices — everything from Fitbit, Alexa and smart phones to home security systems, solar systems and autonomous cars — are fueling the emerging Data Age. On top of the obvious business and consumer data growth, our critical infrastructure like power grids, water systems, hospitals, road infrastructure and public transportation all demand and add to the growth of real-time data. Data is now a vital element in the smooth operation of all aspects of daily life.

All of this entails a significant infrastructure cost behind the scenes with the insatiable, global appetite for data storage. While a variety of storage technologies will continue to advance in data density (Seagate announced the first 60TB 3.5-inch SSD unit for example), high-capacity hard drives serve as the primary foundational core of our interconnected, cloud and IoT-based dependence on data.

HAMR Hard Drive Technology

Seagate has been working on heat assisted magnetic recording (HAMR) in one form or another since the late 1990s. During this time we’ve made many breakthroughs in making reliable near field transducers, special high capacity HAMR media, and figuring out a way to put a laser on each and every head that is no larger than a grain of salt.

The development of HAMR has required Seagate to consider and overcome a myriad of scientific and technical challenges including new kinds of magnetic media, nano-plasmonic device design and fabrication, laser integration, high-temperature head-disk interactions, and thermal regulation.

A typical hard drive inside any computer or server contains one or more rigid disks coated with a magnetically sensitive film consisting of tiny magnetic grains. Data is recorded when a magnetic write-head flies just above the spinning disk; the write head rapidly flips the magnetization of one magnetic region of grains so that its magnetic pole points up or down, to encode a 1 or a 0 in binary code.

Increasing the amount of data you can store on a disk requires cramming magnetic regions closer together, which means the grains need to be smaller so they won’t interfere with each other.

Heat Assisted Magnetic Recording (HAMR) is the next step to enable us to increase the density of grains — or bit density. Current projections are that HAMR can achieve 5 Tbpsi (Terabits per square inch) on conventional HAMR media, and in the future will be able to achieve 10 Tbpsi or higher with bit patterned media (in which discrete dots are predefined on the media in regular, efficient, very dense patterns). These technologies will enable hard drives with capacities higher than 100 TB before 2030.

The major problem with packing bits so closely together is that if you do that on conventional magnetic media, the bits (and the data they represent) become thermally unstable, and may flip. So, to make the grains maintain their stability — their ability to store bits over a long period of time — we need to develop a recording media that has higher coercivity. That means it’s magnetically more stable during storage, but it is more difficult to change the magnetic characteristics of the media when writing (harder to flip a grain from a 0 to a 1 or vice versa).

That’s why HAMR’s first key hardware advance required developing a new recording media that keeps bits stable — using high anisotropy (or “hard”) magnetic materials such as iron-platinum alloy (FePt), which resist magnetic change at normal temperatures. Over years of HAMR development, Seagate researchers have tested and proven out a variety of FePt granular media films, with varying alloy composition and chemical ordering.

In fact the new media is so “hard” that conventional recording heads won’t be able to flip the bits, or write new data, under normal temperatures. If you add heat to the tiny spot on which you want to write data, you can make the media’s coercive field lower than the magnetic field provided by the recording head — in other words, enable the write head to flip that bit.

So, a challenge with HAMR has been to replace conventional perpendicular magnetic recording (PMR), in which the write head operates at room temperature, with a write technology that heats the thin film recording medium on the disk platter to temperatures above 400 °C. The basic principle is to heat a tiny region of several magnetic grains for a very short time (~1 nanoseconds) to a temperature high enough to make the media’s coercive field lower than the write head’s magnetic field. Immediately after the heat pulse, the region quickly cools down and the bit’s magnetic orientation is frozen in place.

Applying this dynamic nano-heating is where HAMR’s famous “laser” comes in. A plasmonic near-field transducer (NFT) has been integrated into the recording head, to heat the media and enable magnetic change at a specific point. Plasmonic NFTs are used to focus and confine light energy to regions smaller than the wavelength of light. This enables us to heat an extremely small region, measured in nanometers, on the disk media to reduce its magnetic coercivity,

Moving HAMR Forward

HAMR write head

As always in advanced engineering, the devil — or many devils — is in the details. As noted earlier, our technical brief provides a point-by-point short illustrated summary of HAMR’s key changes.

Although hard work remains, we believe this technology is nearly ready for commercialization. Seagate has the best engineers in the world working towards a goal of a 20 Terabyte drive by 2019. We hope we’ve given you a glimpse into the amount of engineering that goes into a hard drive. Keeping up with the world’s insatiable appetite to create, capture, store, secure, manage, analyze, rapidly access and share data is a challenge we work on every day.

With thousands of HAMR drives already being made in our manufacturing facilities, our internal and external supply chain is solidly in place, and volume manufacturing tools are online. This year we began shipping initial units for customer tests, and production units will ship to key customers by the end of 2018. Prepare for breakthrough capacities.

The post What is HAMR and How Does It Enable the High-Capacity Needs of the Future? appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Now Open – AWS China (Ningxia) Region

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-open-aws-china-ningxia-region/

Today we launched our 17th Region globally, and the second in China. The AWS China (Ningxia) Region, operated by Ningxia Western Cloud Data Technology Co. Ltd. (NWCD), is generally available now and provides customers another option to run applications and store data on AWS in China.

The Details
At launch, the new China (Ningxia) Region, operated by NWCD, supports Auto Scaling, AWS Config, AWS CloudFormation, AWS CloudTrail, Amazon CloudWatch, CloudWatch Events, Amazon CloudWatch Logs, AWS CodeDeploy, AWS Direct Connect, Amazon DynamoDB, Amazon Elastic Compute Cloud (EC2), Amazon Elastic Block Store (EBS), Amazon EC2 Systems Manager, AWS Elastic Beanstalk, Amazon ElastiCache, Amazon Elasticsearch Service, Elastic Load Balancing, Amazon EMR, Amazon Glacier, AWS Identity and Access Management (IAM), Amazon Kinesis Streams, Amazon Redshift, Amazon Relational Database Service (RDS), Amazon Simple Storage Service (S3), Amazon Simple Notification Service (SNS), Amazon Simple Queue Service (SQS), AWS Support API, AWS Trusted Advisor, Amazon Simple Workflow Service (SWF), Amazon Virtual Private Cloud, and VM Import. Visit the AWS China Products page for additional information on these services.

The Region supports all sizes of C4, D2, M4, T2, R4, I3, and X1 instances.

Check out the AWS Global Infrastructure page to learn more about current and future AWS Regions.

Operating Partner
To comply with China’s legal and regulatory requirements, AWS has formed a strategic technology collaboration with NWCD to operate and provide services from the AWS China (Ningxia) Region. Founded in 2015, NWCD is a licensed datacenter and cloud services provider, based in Ningxia, China. NWCD joins Sinnet, the operator of the AWS China China (Beijing) Region, as an AWS operating partner in China. Through these relationships, AWS provides its industry-leading technology, guidance, and expertise to NWCD and Sinnet, while NWCD and Sinnet operate and provide AWS cloud services to local customers. While the cloud services offered in both AWS China Regions are the same as those available in other AWS Regions, the AWS China Regions are different in that they are isolated from all other AWS Regions and operated by AWS’s Chinese partners separately from all other AWS Regions. Customers using the AWS China Regions enter into customer agreements with Sinnet and NWCD, rather than with AWS.

Use it Today
The AWS China (Ningxia) Region, operated by NWCD, is open for business, and you can start using it now! Starting today, Chinese developers, startups, and enterprises, as well as government, education, and non-profit organizations, can leverage AWS to run their applications and store their data in the new AWS China (Ningxia) Region, operated by NWCD. Customers already using the AWS China (Beijing) Region, operated by Sinnet, can select the AWS China (Ningxia) Region directly from the AWS Management Console, while new customers can request an account at www.amazonaws.cn to begin using both AWS China Regions.

Jeff;