Tag Archives: node.js

Voice-controlled magnification glasses

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/voice-controlled-magnification-glasses/

Go hands-free in the laboratory or makerspace with Mauro Pichiliani’s voice-controlled magnification glasses.

Voice Controlled Glasses With Magnifying Lens

This video presents the project MoveLens: a voice controlled glasses with magnifying lens. It was the my entry for the Voice Activated context on unstructables. Check the step by step guide at Voice Controlled Glasses With Magnifying Lens. Source code: https://github.com/pichiliani/MoveLens Step by Step guide: https://www.instructables.com/id/Voice-Controlled-Glasses-With-Magnifying-Lens/

It’s a kind of magnification

We’ve all been there – that moment when you need another pair of hands to complete a task. And while these glasses may not hold all the answers, they’re a perfect addition to any hobbyist’s arsenal.

Introducing Mauro Pichilliani’s voice-activated glasses: a pair of frames with magnification lenses that can flip up and down in response to a voice command, depending on the task at hand. No more needing to put down your tools in order to put magnifying glasses on. No more trying to re-position a magnifying glass with the back of your left wrist, or getting grease all over your lenses.

As Mauro explains in his tutorial for the glasses:

Many professionals work for many hours looking at very small areas, such as surgeons, watchmakers, jewellery designers and so on. Most of the time these professionals use some kind of magnification glasses that helps them to see better the area they are working with and other tiny items used on the job. The devices that had magnifications lens on a form factor of a glass usually allow the professional to move the lens out of their eye sight, i.e. put aside the lens. However, in some scenarios touching the lens or the glass rim to move away the lens can contaminate the fingers. Also, it is cumbersome and can break the concentration of the professional.

Voice-controlled magnification glasses

Using a Raspberry Pi Zero W, a servo motor, a microphone, and the IBM Watson speech-to-text service, Mauro built a pair of glasses that lets users control the position of the magnification lenses with voice commands.

Magnification glasses, before modification and addition of Raspberry Pi

The glasses Mauro modified, before he started work on them; you have to move the lenses with your hands, like it’s October 2015

Mauro started by dismantling a pair of standard magnification glasses in order to modify the lens supports to allow them to move freely. He drilled a hole in one of the lens supports to provide a place to attach the servo, and used lollipop sticks and hot glue to fix the lenses relative to one another, so they would both move together under the control of the servo. Then, he set up a Raspberry Pi Zero, installing Raspbian and software to use a USB microphone; after connecting the servo to the Pi Zero’s GPIO pins, he set up the Watson speech-to-text service.

Finally, he wrote the code to bring the project together. Two Python scripts direct the servo to raise and lower the lenses, and a Node.js script captures audio from the microphone, passes it on to Watson, checks for an “up” or “down” command, and calls the appropriate Python script as required.

Your turn

You can follow the tutorial on the Instructables website, where Mauro entered the glasses into the Instructables Voice Activated Challenge. And if you’d like to take your first steps into digital making using the Raspberry Pi, take a look at our free online projects.

The post Voice-controlled magnification glasses appeared first on Raspberry Pi.

Amazon Redshift – 2017 Recap

Post Syndicated from Larry Heathcote original https://aws.amazon.com/blogs/big-data/amazon-redshift-2017-recap/

We have been busy adding new features and capabilities to Amazon Redshift, and we wanted to give you a glimpse of what we’ve been doing over the past year. In this article, we recap a few of our enhancements and provide a set of resources that you can use to learn more and get the most out of your Amazon Redshift implementation.

In 2017, we made more than 30 announcements about Amazon Redshift. We listened to you, our customers, and delivered Redshift Spectrum, a feature of Amazon Redshift, that gives you the ability to extend analytics to your data lake—without moving data. We launched new DC2 nodes, doubling performance at the same price. We also announced many new features that provide greater scalability, better performance, more automation, and easier ways to manage your analytics workloads.

To see a full list of our launches, visit our what’s new page—and be sure to subscribe to our RSS feed.

Major launches in 2017

Amazon Redshift Spectrumextend analytics to your data lake, without moving data

We launched Amazon Redshift Spectrum to give you the freedom to store data in Amazon S3, in open file formats, and have it available for analytics without the need to load it into your Amazon Redshift cluster. It enables you to easily join datasets across Redshift clusters and S3 to provide unique insights that you would not be able to obtain by querying independent data silos.

With Redshift Spectrum, you can run SQL queries against data in an Amazon S3 data lake as easily as you analyze data stored in Amazon Redshift. And you can do it without loading data or resizing the Amazon Redshift cluster based on growing data volumes. Redshift Spectrum separates compute and storage to meet workload demands for data size, concurrency, and performance. Redshift Spectrum scales processing across thousands of nodes, so results are fast, even with massive datasets and complex queries. You can query open file formats that you already use—such as Apache Avro, CSV, Grok, ORC, Apache Parquet, RCFile, RegexSerDe, SequenceFile, TextFile, and TSV—directly in Amazon S3, without any data movement.

For complex queries, Redshift Spectrum provided a 67 percent performance gain,” said Rafi Ton, CEO, NUVIAD. “Using the Parquet data format, Redshift Spectrum delivered an 80 percent performance improvement. For us, this was substantial.

To learn more about Redshift Spectrum, watch our AWS Summit session Intro to Amazon Redshift Spectrum: Now Query Exabytes of Data in S3, and read our announcement blog post Amazon Redshift Spectrum – Exabyte-Scale In-Place Queries of S3 Data.

DC2 nodes—twice the performance of DC1 at the same price

We launched second-generation Dense Compute (DC2) nodes to provide low latency and high throughput for demanding data warehousing workloads. DC2 nodes feature powerful Intel E5-2686 v4 (Broadwell) CPUs, fast DDR4 memory, and NVMe-based solid state disks (SSDs). We’ve tuned Amazon Redshift to take advantage of the better CPU, network, and disk on DC2 nodes, providing up to twice the performance of DC1 at the same price. Our DC2.8xlarge instances now provide twice the memory per slice of data and an optimized storage layout with 30 percent better storage utilization.

Redshift allows us to quickly spin up clusters and provide our data scientists with a fast and easy method to access data and generate insights,” said Bradley Todd, technology architect at Liberty Mutual. “We saw a 9x reduction in month-end reporting time with Redshift DC2 nodes as compared to DC1.”

Read our customer testimonials to see the performance gains our customers are experiencing with DC2 nodes. To learn more, read our blog post Amazon Redshift Dense Compute (DC2) Nodes Deliver Twice the Performance as DC1 at the Same Price.

Performance enhancements— 3x-5x faster queries

On average, our customers are seeing 3x to 5x performance gains for most of their critical workloads.

We introduced short query acceleration to speed up execution of queries such as reports, dashboards, and interactive analysis. Short query acceleration uses machine learning to predict the execution time of a query, and to move short running queries to an express short query queue for faster processing.

We launched results caching to deliver sub-second response times for queries that are repeated, such as dashboards, visualizations, and those from BI tools. Results caching has an added benefit of freeing up resources to improve the performance of all other queries.

We also introduced late materialization to reduce the amount of data scanned for queries with predicate filters by batching and factoring in the filtering of predicates before fetching data blocks in the next column. For example, if only 10 percent of the table rows satisfy the predicate filters, Amazon Redshift can potentially save 90 percent of the I/O for the remaining columns to improve query performance.

We launched query monitoring rules and pre-defined rule templates. These features make it easier for you to set metrics-based performance boundaries for workload management (WLM) queries, and specify what action to take when a query goes beyond those boundaries. For example, for a queue that’s dedicated to short-running queries, you might create a rule that aborts queries that run for more than 60 seconds. To track poorly designed queries, you might have another rule that logs queries that contain nested loops.

Customer insights

Amazon Redshift and Redshift Spectrum serve customers across a variety of industries and sizes, from startups to large enterprises. Visit our customer page to see the success that customers are having with our recent enhancements. Learn how companies like Liberty Mutual Insurance saw a 9x reduction in month-end reporting time using DC2 nodes. On this page, you can find case studies, videos, and other content that show how our customers are using Amazon Redshift to drive innovation and business results.

In addition, check out these resources to learn about the success our customers are having building out a data warehouse and data lake integration solution with Amazon Redshift:

Partner solutions

You can enhance your Amazon Redshift data warehouse by working with industry-leading experts. Our AWS Partner Network (APN) Partners have certified their solutions to work with Amazon Redshift. They offer software, tools, integration, and consulting services to help you at every step. Visit our Amazon Redshift Partner page and choose an APN Partner. Or, use AWS Marketplace to find and immediately start using third-party software.

To see what our Partners are saying about Amazon Redshift Spectrum and our DC2 nodes mentioned earlier, read these blog posts:


Blog posts

Visit the AWS Big Data Blog for a list of all Amazon Redshift articles.

YouTube videos


Our community of experts contribute on GitHub to provide tips and hints that can help you get the most out of your deployment. Visit GitHub frequently to get the latest technical guidance, code samples, administrative task automation utilities, the analyze & vacuum schema utility, and more.

Customer support

If you are evaluating or considering a proof of concept with Amazon Redshift, or you need assistance migrating your on-premises or other cloud-based data warehouse to Amazon Redshift, our team of product experts and solutions architects can help you with architecting, sizing, and optimizing your data warehouse. Contact us using this support request form, and let us know how we can assist you.

If you are an Amazon Redshift customer, we offer a no-cost health check program. Our team of database engineers and solutions architects give you recommendations for optimizing Amazon Redshift and Amazon Redshift Spectrum for your specific workloads. To learn more, email us at [email protected].

If you have any questions, email us at [email protected].


Additional Reading

If you found this post useful, be sure to check out Amazon Redshift Spectrum – Exabyte-Scale In-Place Queries of S3 Data, Using Amazon Redshift for Fast Analytical Reports and How to Migrate Your Oracle Data Warehouse to Amazon Redshift Using AWS SCT and AWS DMS.

About the Author

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




Reactive Microservices Architecture on AWS

Post Syndicated from Sascha Moellering original https://aws.amazon.com/blogs/architecture/reactive-microservices-architecture-on-aws/

Microservice-application requirements have changed dramatically in recent years. These days, applications operate with petabytes of data, need almost 100% uptime, and end users expect sub-second response times. Typical N-tier applications can’t deliver on these requirements.

Reactive Manifesto, published in 2014, describes the essential characteristics of reactive systems including: responsiveness, resiliency, elasticity, and being message driven.

Being message driven is perhaps the most important characteristic of reactive systems. Asynchronous messaging helps in the design of loosely coupled systems, which is a key factor for scalability. In order to build a highly decoupled system, it is important to isolate services from each other. As already described, isolation is an important aspect of the microservices pattern. Indeed, reactive systems and microservices are a natural fit.

Implemented Use Case
This reference architecture illustrates a typical ad-tracking implementation.

Many ad-tracking companies collect massive amounts of data in near-real-time. In many cases, these workloads are very spiky and heavily depend on the success of the ad-tech companies’ customers. Typically, an ad-tracking-data use case can be separated into a real-time part and a non-real-time part. In the real-time part, it is important to collect data as fast as possible and ask several questions including:,  “Is this a valid combination of parameters?,””Does this program exist?,” “Is this program still valid?”

Because response time has a huge impact on conversion rate in advertising, it is important for advertisers to respond as fast as possible. This information should be kept in memory to reduce communication overhead with the caching infrastructure. The tracking application itself should be as lightweight and scalable as possible. For example, the application shouldn’t have any shared mutable state and it should use reactive paradigms. In our implementation, one main application is responsible for this real-time part. It collects and validates data, responds to the client as fast as possible, and asynchronously sends events to backend systems.

The non-real-time part of the application consumes the generated events and persists them in a NoSQL database. In a typical tracking implementation, clicks, cookie information, and transactions are matched asynchronously and persisted in a data store. The matching part is not implemented in this reference architecture. Many ad-tech architectures use frameworks like Hadoop for the matching implementation.

The system can be logically divided into the data collection partand the core data updatepart. The data collection part is responsible for collecting, validating, and persisting the data. In the core data update part, the data that is used for validation gets updated and all subscribers are notified of new data.

Components and Services

Main Application
The main application is implemented using Java 8 and uses Vert.x as the main framework. Vert.x is an event-driven, reactive, non-blocking, polyglot framework to implement microservices. It runs on the Java virtual machine (JVM) by using the low-level IO library Netty. You can write applications in Java, JavaScript, Groovy, Ruby, Kotlin, Scala, and Ceylon. The framework offers a simple and scalable actor-like concurrency model. Vert.x calls handlers by using a thread known as an event loop. To use this model, you have to write code known as “verticles.” Verticles share certain similarities with actors in the actor model. To use them, you have to implement the verticle interface. Verticles communicate with each other by generating messages in  a single event bus. Those messages are sent on the event bus to a specific address, and verticles can register to this address by using handlers.

With only a few exceptions, none of the APIs in Vert.x block the calling thread. Similar to Node.js, Vert.x uses the reactor pattern. However, in contrast to Node.js, Vert.x uses several event loops. Unfortunately, not all APIs in the Java ecosystem are written asynchronously, for example, the JDBC API. Vert.x offers a possibility to run this, blocking APIs without blocking the event loop. These special verticles are called worker verticles. You don’t execute worker verticles by using the standard Vert.x event loops, but by using a dedicated thread from a worker pool. This way, the worker verticles don’t block the event loop.

Our application consists of five different verticles covering different aspects of the business logic. The main entry point for our application is the HttpVerticle, which exposes an HTTP-endpoint to consume HTTP-requests and for proper health checking. Data from HTTP requests such as parameters and user-agent information are collected and transformed into a JSON message. In order to validate the input data (to ensure that the program exists and is still valid), the message is sent to the CacheVerticle.

This verticle implements an LRU-cache with a TTL of 10 minutes and a capacity of 100,000 entries. Instead of adding additional functionality to a standard JDK map implementation, we use Google Guava, which has all the features we need. If the data is not in the L1 cache, the message is sent to the RedisVerticle. This verticle is responsible for data residing in Amazon ElastiCache and uses the Vert.x-redis-client to read data from Redis. In our example, Redis is the central data store. However, in a typical production implementation, Redis would just be the L2 cache with a central data store like Amazon DynamoDB. One of the most important paradigms of a reactive system is to switch from a pull- to a push-based model. To achieve this and reduce network overhead, we’ll use Redis pub/sub to push core data changes to our main application.

Vert.x also supports direct Redis pub/sub-integration, the following code shows our subscriber-implementation:

vertx.eventBus().<JsonObject>consumer(REDIS_PUBSUB_CHANNEL_VERTX, received -> {

JsonObject value = received.body().getJsonObject("value");

String message = value.getString("message");

JsonObject jsonObject = new JsonObject(message);



redis.subscribe(Constants.REDIS_PUBSUB_CHANNEL, res -> {

if (res.succeeded()) {

LOGGER.info("Subscribed to " + Constants.REDIS_PUBSUB_CHANNEL);

} else {




The verticle subscribes to the appropriate Redis pub/sub-channel. If a message is sent over this channel, the payload is extracted and forwarded to the cache-verticle that stores the data in the L1-cache. After storing and enriching data, a response is sent back to the HttpVerticle, which responds to the HTTP request that initially hit this verticle. In addition, the message is converted to ByteBuffer, wrapped in protocol buffers, and send to an Amazon Kinesis Data Stream.

The following example shows a stripped-down version of the KinesisVerticle:

public class KinesisVerticle extends AbstractVerticle {

private static final Logger LOGGER = LoggerFactory.getLogger(KinesisVerticle.class);

private AmazonKinesisAsync kinesisAsyncClient;

private String eventStream = "EventStream";


public void start() throws Exception {

EventBus eb = vertx.eventBus();

kinesisAsyncClient = createClient();

eventStream = System.getenv(STREAM_NAME) == null ? "EventStream" : System.getenv(STREAM_NAME);

eb.consumer(Constants.KINESIS_EVENTBUS_ADDRESS, message -> {

try {

TrackingMessage trackingMessage = Json.decodeValue((String)message.body(), TrackingMessage.class);

String partitionKey = trackingMessage.getMessageId();

byte [] byteMessage = createMessage(trackingMessage);

ByteBuffer buf = ByteBuffer.wrap(byteMessage);

sendMessageToKinesis(buf, partitionKey);



catch (KinesisException exc) {





Kinesis Consumer
This AWS Lambda function consumes data from an Amazon Kinesis Data Stream and persists the data in an Amazon DynamoDB table. In order to improve testability, the invocation code is separated from the business logic. The invocation code is implemented in the class KinesisConsumerHandler and iterates over the Kinesis events pulled from the Kinesis stream by AWS Lambda. Each Kinesis event is unwrapped and transformed from ByteBuffer to protocol buffers and converted into a Java object. Those Java objects are passed to the business logic, which persists the data in a DynamoDB table. In order to improve duration of successive Lambda calls, the DynamoDB-client is instantiated lazily and reused if possible.

Redis Updater
From time to time, it is necessary to update core data in Redis. A very efficient implementation for this requirement is using AWS Lambda and Amazon Kinesis. New core data is sent over the AWS Kinesis stream using JSON as data format and consumed by a Lambda function. This function iterates over the Kinesis events pulled from the Kinesis stream by AWS Lambda. Each Kinesis event is unwrapped and transformed from ByteBuffer to String and converted into a Java object. The Java object is passed to the business logic and stored in Redis. In addition, the new core data is also sent to the main application using Redis pub/sub in order to reduce network overhead and converting from a pull- to a push-based model.

The following example shows the source code to store data in Redis and notify all subscribers:

public void updateRedisData(final TrackingMessage trackingMessage, final Jedis jedis, final LambdaLogger logger) {

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

Map<String, String> map = marshal(jsonString);

String statusCode = jedis.hmset(trackingMessage.getProgramId(), map);


catch (Exception exc) {

if (null == logger)






public void notifySubscribers(final TrackingMessage trackingMessage, final Jedis jedis, final LambdaLogger logger) {

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

jedis.publish(Constants.REDIS_PUBSUB_CHANNEL, jsonString);


catch (final IOException e) {

log(e.getMessage(), logger);



Similarly to our Kinesis Consumer, the Redis-client is instantiated somewhat lazily.

Infrastructure as Code
As already outlined, latency and response time are a very critical part of any ad-tracking solution because response time has a huge impact on conversion rate. In order to reduce latency for customers world-wide, it is common practice to roll out the infrastructure in different AWS Regions in the world to be as close to the end customer as possible. AWS CloudFormation can help you model and set up your AWS resources so that you can spend less time managing those resources and more time focusing on your applications that run in AWS.

You create a template that describes all the AWS resources that you want (for example, Amazon EC2 instances or Amazon RDS DB instances), and AWS CloudFormation takes care of provisioning and configuring those resources for you. Our reference architecture can be rolled out in different Regions using an AWS CloudFormation template, which sets up the complete infrastructure (for example, Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Container Service (Amazon ECS) cluster, Lambda functions, DynamoDB table, Amazon ElastiCache cluster, etc.).

In this blog post we described reactive principles and an example architecture with a common use case. We leveraged the capabilities of different frameworks in combination with several AWS services in order to implement reactive principles—not only at the application-level but also at the system-level. I hope I’ve given you ideas for creating your own reactive applications and systems on AWS.

About the Author

Sascha Moellering is a Senior Solution Architect. Sascha is primarily interested in automation, infrastructure as code, distributed computing, containers and JVM. He can be reached at [email protected]



Invoking AWS Lambda from Amazon MQ

Post Syndicated from Tara Van Unen original https://aws.amazon.com/blogs/compute/invoking-aws-lambda-from-amazon-mq/

Contributed by Josh Kahn, AWS Solutions Architect

Message brokers can be used to solve a number of needs in enterprise architectures, including managing workload queues and broadcasting messages to a number of subscribers. Amazon MQ is a managed message broker service for Apache ActiveMQ that makes it easy to set up and operate message brokers in the cloud.

In this post, I discuss one approach to invoking AWS Lambda from queues and topics managed by Amazon MQ brokers. This and other similar patterns can be useful in integrating legacy systems with serverless architectures. You could also integrate systems already migrated to the cloud that use common APIs such as JMS.

For example, imagine that you work for a company that produces training videos and which recently migrated its video management system to AWS. The on-premises system used to publish a message to an ActiveMQ broker when a video was ready for processing by an on-premises transcoder. However, on AWS, your company uses Amazon Elastic Transcoder. Instead of modifying the management system, Lambda polls the broker for new messages and starts a new Elastic Transcoder job. This approach avoids changes to the existing application while refactoring the workload to leverage cloud-native components.

This solution uses Amazon CloudWatch Events to trigger a Lambda function that polls the Amazon MQ broker for messages. Instead of starting an Elastic Transcoder job, the sample writes the received message to an Amazon DynamoDB table with a time stamp indicating the time received.

Getting started

To start, navigate to the Amazon MQ console. Next, launch a new Amazon MQ instance, selecting Single-instance Broker and supplying a broker name, user name, and password. Be sure to document the user name and password for later.

For the purposes of this sample, choose the default options in the Advanced settings section. Your new broker is deployed to the default VPC in the selected AWS Region with the default security group. For this post, you update the security group to allow access for your sample Lambda function. In a production scenario, I recommend deploying both the Lambda function and your Amazon MQ broker in your own VPC.

After several minutes, your instance changes status from “Creation Pending” to “Available.” You can then visit the Details page of your broker to retrieve connection information, including a link to the ActiveMQ web console where you can monitor the status of your broker, publish test messages, and so on. In this example, use the Stomp protocol to connect to your broker. Be sure to capture the broker host name, for example:


You should also modify the Security Group for the broker by clicking on its Security Group ID. Click the Edit button and then click Add Rule to allow inbound traffic on port 8162 for your IP address.

Deploying and scheduling the Lambda function

To simplify the deployment of this example, I’ve provided an AWS Serverless Application Model (SAM) template that deploys the sample function and DynamoDB table, and schedules the function to be invoked every five minutes. Detailed instructions can be found with sample code on GitHub in the amazonmq-invoke-aws-lambda repository, with sample code. I discuss a few key aspects in this post.

First, SAM makes it easy to deploy and schedule invocation of our function:

	Type: AWS::Serverless::Function
		CodeUri: subscriber/
		Handler: index.handler
		Runtime: nodejs6.10
		Role: !GetAtt SubscriberFunctionRole.Arn
		Timeout: 15
				HOST: !Ref AmazonMQHost
				LOGIN: !Ref AmazonMQLogin
				PASSWORD: !Ref AmazonMQPassword
				QUEUE_NAME: !Ref AmazonMQQueueName
				WORKER_FUNCTIOn: !Ref WorkerFunction
				Type: Schedule
					Schedule: rate(5 minutes)

Type: AWS::Serverless::Function
		CodeUri: worker/
		Handler: index.handler
		Runtime: nodejs6.10
Role: !GetAtt WorkerFunctionRole.Arn
				TABLE_NAME: !Ref MessagesTable

In the code, you include the URI, user name, and password for your newly created Amazon MQ broker. These allow the function to poll the broker for new messages on the sample queue.

The sample Lambda function is written in Node.js, but clients exist for a number of programming languages.

stomp.connect(options, (error, client) => {
	if (error) { /* do something */ }

	let headers = {
		destination: ‘/queue/SAMPLE_QUEUE’,
		ack: ‘auto’

	client.subscribe(headers, (error, message) => {
		if (error) { /* do something */ }

		message.readString(‘utf-8’, (error, body) => {
			if (error) { /* do something */ }

			let params = {
				FunctionName: MyWorkerFunction,
				Payload: JSON.stringify({
					message: body,
					timestamp: Date.now()

			let lambda = new AWS.Lambda()
			lambda.invoke(params, (error, data) => {
				if (error) { /* do something */ }

Sending a sample message

For the purpose of this example, use the Amazon MQ console to send a test message. Navigate to the details page for your broker.

About midway down the page, choose ActiveMQ Web Console. Next, choose Manage ActiveMQ Broker to launch the admin console. When you are prompted for a user name and password, use the credentials created earlier.

At the top of the page, choose Send. From here, you can send a sample message from the broker to subscribers. For this example, this is how you generate traffic to test the end-to-end system. Be sure to set the Destination value to “SAMPLE_QUEUE.” The message body can contain any text. Choose Send.

You now have a Lambda function polling for messages on the broker. To verify that your function is working, you can confirm in the DynamoDB console that the message was successfully received and processed by the sample Lambda function.

First, choose Tables on the left and select the table name “amazonmq-messages” in the middle section. With the table detail in view, choose Items. If the function was successful, you’ll find a new entry similar to the following:

If there is no message in DynamoDB, check again in a few minutes or review the CloudWatch Logs group for Lambda functions that contain debug messages.

Alternative approaches

Beyond the approach described here, you may consider other approaches as well. For example, you could use an intermediary system such as Apache Flume to pass messages from the broker to Lambda or deploy Apache Camel to trigger Lambda via a POST to API Gateway. There are trade-offs to each of these approaches. My goal in using CloudWatch Events was to introduce an easily repeatable pattern familiar to many Lambda developers.


I hope that you have found this example of how to integrate AWS Lambda with Amazon MQ useful. If you have expertise or legacy systems that leverage APIs such as JMS, you may find this useful as you incorporate serverless concepts in your enterprise architectures.

To learn more, see the Amazon MQ website and Developer Guide. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.

Turn your smartphone into a universal remote

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/zero-universal-remote/

Honolulu-based software developer bbtinkerer was tired of never being able to find the TV remote. So he made his own using a Raspberry Pi Zero, and connected it to a web app accessible on his smartphone.

bbtinkerer universal remote Raspberry Pi zero

Finding a remote alternative

“I needed one because the remote in my house tends to go missing a lot,” explains Bernard aka bbtinkerer on the Instructables page for his Raspberry Pi Zero Universal Remote.”If I want the controller, I have to hunt down three people and hope one of them remembers that they took it.”

bbtinkerer universal remote Raspberry Pi zero

For the build, Bernard used a Raspberry Pi Zero, an IR LED and corresponding receiver, Raspbian Lite, and a neat little 3D-printed housing.

bbtinkerer universal remote Raspberry Pi zero
bbtinkerer universal remote Raspberry Pi zero
bbtinkerer universal remote Raspberry Pi zero

First, he soldered a circuit for the LED and resistors on a small piece of perf board. Then he assembled the hardware components. Finally, all he needed to do was to write the code to control his devices (including a tower fan), and to set up the app.

bbtinkerer universal remote Raspberry Pi zero

Bernard employed the Linux Infrared Remote Control (LIRC) package to control the television with the Raspberry Pi Zero, accessing the Zero via SSH. He gives a complete rundown of the installation process on Instructables.

bbtinkerer universal remote Raspberry Pi zero

Setting up a remote’s buttons with LIRC is a simple case of pressing them and naming their functions one by one. You’ll need the remote to set up the system, but after that, feel free to lock it in a drawer and use your smartphone instead.

Finally, Bernard created the web interface using Node.js, and again, because he’s lovely, he published the code for anyone wanting to build their own. Thanks, Bernard!

Life hacks

If you’ve used a Raspberry Pi to build a time-saving life hack like Bernard’s, be sure to share it with us. Other favourites of ours include fridge cameras, phone app doorbell notifications, and Alan’s ocarina home automation system. I’m not sure if this last one can truly be considered a time-saving life hack. It’s still cool though!

The post Turn your smartphone into a universal remote appeared first on Raspberry Pi.

AWS Architecture Monthly for Kindle

Post Syndicated from Jamey Tisdale original https://aws.amazon.com/blogs/architecture/aws-architecture-monthly-for-kindle/

We recently launched AWS Architecture Monthly, a new subscription service on Kindle that will push a selection of the best content around cloud architecture from AWS, with a few pointers to other content you might also enjoy.

From building a simple website to crafting an AI-based chat bot, the choices of technologies and the best practices in how to apply them are constantly evolving. Our goal is to supply you each month with a broad selection of the best new tech content from AWS — from deep-dive tutorials to industry-trend articles.

With your free subscription, you can look forward to fresh content delivered directly to your Kindledevice or Kindle app including:
– Technical whitepapers
– Reference architectures
– New solutions and implementation guides
– Training and certification opportunities
– Industry trends

The January issue is now live. This month includes:
– AWS Architecture Blog: Glenn Gore’s Take on re:Invent 2017 (Chief Architect for AWS)
– AWS Reference Architectures: Java Microservices Deployed on EC2 Container Service; Node.js Microservices Deployed on EC2 Container Service
– AWS Training & Certification: AWS Certified Solutions Architect – Associate
– Sample Code: aws-serverless-express
– Technical Whitepaper: Serverless Architectures with AWS Lambda – Overview and Best Practices

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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'
  '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;


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.

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

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

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

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

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

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

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

Amazon Redshift as our foundation

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

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

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

Why we extended Amazon Redshift to Redshift Spectrum

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

Seamless scalability, high performance, and unlimited concurrency

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

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

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

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

Keeping it SQL

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

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

Leveraging Parquet for higher performance

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

Lower cost

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

What we learned about Amazon Redshift vs. Redshift Spectrum performance

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

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

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

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

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

Simple query

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

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

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

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

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

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

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

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

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

Complex query

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

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

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

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

Optimizing the data structure for different workloads

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

Data permutations

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

  ts BETWEEN ‘2017-08-01 14:00:00’ AND ‘2017-08-01 14:00:59’ 

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

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

Creating Parquet data efficiently

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

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

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

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

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

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

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

Data validation

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

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

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

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

  tablename = 'blog_clicks';

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

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

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

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

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

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

Near real-time data loading with Kinesis Firehose

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

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

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

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

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

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

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

Automating data distribution using AWS Lambda

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


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

	object key structure in the event object:

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

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

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

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

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

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

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

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

Migrating CSV to Parquet using AWS Glue and Amazon EMR

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

Creating AWS Glue jobs

What this simple AWS Glue script does:

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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

Creating a Lambda function to trigger conversion

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

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

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

Redshift Spectrum and Node.js

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

Node.js and Parquet

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

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

Timestamp data type

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

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

Lessons learned

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

Lesson #1: Data validation is critical

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

Lesson #2: Structure and partition data effectively

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

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

Storing data in the right format

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

Creating small tables for frequent tasks

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

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

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

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

Lesson #4: Sort your Parquet data within the partition

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



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

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

About the Author

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



Access Resources in a VPC from AWS CodeBuild Builds

Post Syndicated from John Pignata original https://aws.amazon.com/blogs/devops/access-resources-in-a-vpc-from-aws-codebuild-builds/

John Pignata, Startup Solutions Architect, Amazon Web Services

In this blog post we’re going to discuss a new AWS CodeBuild feature that is available starting today. CodeBuild builds can now access resources in a VPC directly without these resources being exposed to the public internet. These resources include Amazon Relational Database Service (Amazon RDS) databases, Amazon ElastiCache clusters, internal services running on Amazon Elastic Compute Cloud (Amazon EC2), and Amazon EC2 Container Service (Amazon ECS), or any service endpoints that are only reachable from within a specific VPC.

CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. As part of the build process, developers often require access to resources that should be isolated from the public Internet. Now CodeBuild builds can be optionally configured to have VPC connectivity and access these resources directly.

Accessing Resources in a VPC

You can configure builds to have access to a VPC when you create a CodeBuild project or you can update an existing CodeBuild project with VPC configuration attributes. Here’s how it looks in the console:


To configure VPC connectivity: select a VPC, one or more subnets within that VPC, and one or more VPC security groups that CodeBuild should apply when attaching to your VPC. Once configured, commands running as part of your build will be able to access resources in your VPC without transiting across the public Internet.

Use Cases

The availability of VPC connectivity from CodeBuild builds unlocks many potential uses. For example, you can:

  • Run integration tests from your build against data in an Amazon RDS instance that’s isolated on a private subnet.
  • Query data in an ElastiCache cluster directly from tests.
  • Interact with internal web services hosted on Amazon EC2, Amazon ECS, or services that use internal Elastic Load Balancing.
  • Retrieve dependencies from self-hosted, internal artifact repositories such as PyPI for Python, Maven for Java, npm for Node.js, and so on.
  • Access objects in an Amazon S3 bucket configured to allow access only through a VPC endpoint.
  • Query external web services that require fixed IP addresses through the Elastic IP address of the NAT gateway associated with your subnet(s).

… and more! Your builds can now access any resource that’s hosted in your VPC without any compromise on network isolation.

Internet Connectivity

CodeBuild requires access to resources on the public Internet to successfully execute builds. At a minimum, it must be able to reach your source repository system (such as AWS CodeCommit, GitHub, Bitbucket), Amazon Simple Storage Service (Amazon S3) to deliver build artifacts, and Amazon CloudWatch Logs to stream logs from the build process. The interface attached to your VPC will not be assigned a public IP address so to enable Internet access from your builds, you will need to set up a managed NAT Gateway or NAT instance for the subnets you configure. You must also ensure your security groups allow outbound access to these services.

IP Address Space

Each running build will be assigned an IP address from one of the subnets in your VPC that you designate for CodeBuild to use. As CodeBuild scales to meet your build volume, ensure that you select subnets with enough address space to accommodate your expected number of concurrent builds.

Service Role Permissions

CodeBuild requires new permissions in order to manage network interfaces on your VPCs. If you create a service role for your new projects, these permissions will be included in that role’s policy automatically. For existing service roles, you can edit the policy document to include the additional actions. For the full policy document to apply to your service role, see Advanced Setup in the CodeBuild documentation.

For more information, see VPC Support in the CodeBuild documentation. We hope you find the ability to access internal resources on a VPC useful in your build processes! If you have any questions or feedback, feel free to reach out to us through the AWS CodeBuild forum or leave a comment!

Capturing Custom, High-Resolution Metrics from Containers Using AWS Step Functions and AWS Lambda

Post Syndicated from Nathan Taber original https://aws.amazon.com/blogs/compute/capturing-custom-high-resolution-metrics-from-containers-using-aws-step-functions-and-aws-lambda/

Contributed by Trevor Sullivan, AWS Solutions Architect

When you deploy containers with Amazon ECS, are you gathering all of the key metrics so that you can correctly monitor the overall health of your ECS cluster?

By default, ECS writes metrics to Amazon CloudWatch in 5-minute increments. For complex or large services, this may not be sufficient to make scaling decisions quickly. You may want to respond immediately to changes in workload or to identify application performance problems. Last July, CloudWatch announced support for high-resolution metrics, up to a per-second basis.

These high-resolution metrics can be used to give you a clearer picture of the load and performance for your applications, containers, clusters, and hosts. In this post, I discuss how you can use AWS Step Functions, along with AWS Lambda, to cost effectively record high-resolution metrics into CloudWatch. You implement this solution using a serverless architecture, which keeps your costs low and makes it easier to troubleshoot the solution.

To show how this works, you retrieve some useful metric data from an ECS cluster running in the same AWS account and region (Oregon, us-west-2) as the Step Functions state machine and Lambda function. However, you can use this architecture to retrieve any custom application metrics from any resource in any AWS account and region.

Why Step Functions?

Step Functions enables you to orchestrate multi-step tasks in the AWS Cloud that run for any period of time, up to a year. Effectively, you’re building a blueprint for an end-to-end process. After it’s built, you can execute the process as many times as you want.

For this architecture, you gather metrics from an ECS cluster, every five seconds, and then write the metric data to CloudWatch. After your ECS cluster metrics are stored in CloudWatch, you can create CloudWatch alarms to notify you. An alarm can also trigger an automated remediation activity such as scaling ECS services, when a metric exceeds a threshold defined by you.

When you build a Step Functions state machine, you define the different states inside it as JSON objects. The bulk of the work in Step Functions is handled by the common task state, which invokes Lambda functions or Step Functions activities. There is also a built-in library of other useful states that allow you to control the execution flow of your program.

One of the most useful state types in Step Functions is the parallel state. Each parallel state in your state machine can have one or more branches, each of which is executed in parallel. Another useful state type is the wait state, which waits for a period of time before moving to the next state.

In this walkthrough, you combine these three states (parallel, wait, and task) to create a state machine that triggers a Lambda function, which then gathers metrics from your ECS cluster.

Step Functions pricing

This state machine is executed every minute, resulting in 60 executions per hour, and 1,440 executions per day. Step Functions is billed per state transition, including the Start and End state transitions, and giving you approximately 37,440 state transitions per day. To reach this number, I’m using this estimated math:

26 state transitions per-execution x 60 minutes x 24 hours

Based on current pricing, at $0.000025 per state transition, the daily cost of this metric gathering state machine would be $0.936.

Step Functions offers an indefinite 4,000 free state transitions every month. This benefit is available to all customers, not just customers who are still under the 12-month AWS Free Tier. For more information and cost example scenarios, see Step Functions pricing.

Why Lambda?

The goal is to capture metrics from an ECS cluster, and write the metric data to CloudWatch. This is a straightforward, short-running process that makes Lambda the perfect place to run your code. Lambda is one of the key services that makes up “Serverless” application architectures. It enables you to consume compute capacity only when your code is actually executing.

The process of gathering metric data from ECS and writing it to CloudWatch takes a short period of time. In fact, my average Lambda function execution time, while developing this post, is only about 250 milliseconds on average. For every five-second interval that occurs, I’m only using 1/20th of the compute time that I’d otherwise be paying for.

Lambda pricing

For billing purposes, Lambda execution time is rounded up to the nearest 100-ms interval. In general, based on the metrics that I observed during development, a 250-ms runtime would be billed at 300 ms. Here, I calculate the cost of this Lambda function executing on a daily basis.

Assuming 31 days in each month, there would be 535,680 five-second intervals (31 days x 24 hours x 60 minutes x 12 five-second intervals = 535,680). The Lambda function is invoked every five-second interval, by the Step Functions state machine, and runs for a 300-ms period. At current Lambda pricing, for a 128-MB function, you would be paying approximately the following:

Total compute

Total executions = 535,680
Total compute = total executions x (3 x $0.000000208 per 100 ms) = $0.334 per day

Total requests

Total requests = (535,680 / 1000000) * $0.20 per million requests = $0.11 per day

Total Lambda Cost

$0.11 requests + $0.334 compute time = $0.444 per day

Similar to Step Functions, Lambda offers an indefinite free tier. For more information, see Lambda Pricing.


In the following sections, I step through the process of configuring the solution just discussed. If you follow along, at a high level, you will:

  • Configure an IAM role and policy
  • Create a Step Functions state machine to control metric gathering execution
  • Create a metric-gathering Lambda function
  • Configure a CloudWatch Events rule to trigger the state machine
  • Validate the solution


You should already have an AWS account with a running ECS cluster. If you don’t have one running, you can easily deploy a Docker container on an ECS cluster using the AWS Management Console. In the example produced for this post, I use an ECS cluster running Windows Server (currently in beta), but either a Linux or Windows Server cluster works.

Create an IAM role and policy

First, create an IAM role and policy that enables Step Functions, Lambda, and CloudWatch to communicate with each other.

  • The CloudWatch Events rule needs permissions to trigger the Step Functions state machine.
  • The Step Functions state machine needs permissions to trigger the Lambda function.
  • The Lambda function needs permissions to query ECS and then write to CloudWatch Logs and metrics.

When you create the state machine, Lambda function, and CloudWatch Events rule, you assign this role to each of those resources. Upon execution, each of these resources assumes the specified role and executes using the role’s permissions.

  1. Open the IAM console.
  2. Choose Roles, create New Role.
  3. For Role Name, enter WriteMetricFromStepFunction.
  4. Choose Save.

Create the IAM role trust relationship
The trust relationship (also known as the assume role policy document) for your IAM role looks like the following JSON document. As you can see from the document, your IAM role needs to trust the Lambda, CloudWatch Events, and Step Functions services. By configuring your role to trust these services, they can assume this role and inherit the role permissions.

  1. Open the IAM console.
  2. Choose Roles and select the IAM role previously created.
  3. Choose Trust RelationshipsEdit Trust Relationships.
  4. Enter the following trust policy text and choose Save.
  "Version": "2012-10-17",
  "Statement": [
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      "Action": "sts:AssumeRole"
      "Effect": "Allow",
      "Principal": {
        "Service": "events.amazonaws.com"
      "Action": "sts:AssumeRole"
      "Effect": "Allow",
      "Principal": {
        "Service": "states.us-west-2.amazonaws.com"
      "Action": "sts:AssumeRole"

Create an IAM policy

After you’ve finished configuring your role’s trust relationship, grant the role access to the other AWS resources that make up the solution.

The IAM policy is what gives your IAM role permissions to access various resources. You must whitelist explicitly the specific resources to which your role has access, because the default IAM behavior is to deny access to any AWS resources.

I’ve tried to keep this policy document as generic as possible, without allowing permissions to be too open. If the name of your ECS cluster is different than the one in the example policy below, make sure that you update the policy document before attaching it to your IAM role. You can attach this policy as an inline policy, instead of creating the policy separately first. However, either approach is valid.

  1. Open the IAM console.
  2. Select the IAM role, and choose Permissions.
  3. Choose Add in-line policy.
  4. Choose Custom Policy and then enter the following policy. The inline policy name does not matter.
    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Action": [ "logs:*" ],
            "Resource": "*"
            "Effect": "Allow",
            "Action": [ "cloudwatch:PutMetricData" ],
            "Resource": "*"
            "Effect": "Allow",
            "Action": [ "states:StartExecution" ],
            "Resource": [
            "Effect": "Allow",
            "Action": [ "lambda:InvokeFunction" ],
            "Resource": "arn:aws:lambda:*:*:function:WriteMetricFromStepFunction"
            "Effect": "Allow",
            "Action": [ "ecs:Describe*" ],
            "Resource": "arn:aws:ecs:*:*:cluster/ECSEsgaroth"

Create a Step Functions state machine

In this section, you create a Step Functions state machine that invokes the metric-gathering Lambda function every five (5) seconds, for a one-minute period. If you divide a minute (60) seconds into equal parts of five-second intervals, you get 12. Based on this math, you create 12 branches, in a single parallel state, in the state machine. Each branch triggers the metric-gathering Lambda function at a different five-second marker, throughout the one-minute period. After all of the parallel branches finish executing, the Step Functions execution completes and another begins.

Follow these steps to create your Step Functions state machine:

  1. Open the Step Functions console.
  2. Choose DashboardCreate State Machine.
  3. For State Machine Name, enter WriteMetricFromStepFunction.
  4. Enter the state machine code below into the editor. Make sure that you insert your own AWS account ID for every instance of “676655494xxx”
  5. Choose Create State Machine.
  6. Select the WriteMetricFromStepFunction IAM role that you previously created.
    "Comment": "Writes ECS metrics to CloudWatch every five seconds, for a one-minute period.",
    "StartAt": "ParallelMetric",
    "States": {
      "ParallelMetric": {
        "Type": "Parallel",
        "Branches": [
            "StartAt": "WriteMetricLambda",
            "States": {
             	"WriteMetricLambda": {
                  "Type": "Task",
				  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "WaitFive",
            "States": {
            	"WaitFive": {
            		"Type": "Wait",
            		"Seconds": 5,
            		"Next": "WriteMetricLambdaFive"
             	"WriteMetricLambdaFive": {
                  "Type": "Task",
				  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "WaitTen",
            "States": {
            	"WaitTen": {
            		"Type": "Wait",
            		"Seconds": 10,
            		"Next": "WriteMetricLambda10"
             	"WriteMetricLambda10": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "WaitFifteen",
            "States": {
            	"WaitFifteen": {
            		"Type": "Wait",
            		"Seconds": 15,
            		"Next": "WriteMetricLambda15"
             	"WriteMetricLambda15": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "Wait20",
            "States": {
            	"Wait20": {
            		"Type": "Wait",
            		"Seconds": 20,
            		"Next": "WriteMetricLambda20"
             	"WriteMetricLambda20": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "Wait25",
            "States": {
            	"Wait25": {
            		"Type": "Wait",
            		"Seconds": 25,
            		"Next": "WriteMetricLambda25"
             	"WriteMetricLambda25": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "Wait30",
            "States": {
            	"Wait30": {
            		"Type": "Wait",
            		"Seconds": 30,
            		"Next": "WriteMetricLambda30"
             	"WriteMetricLambda30": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "Wait35",
            "States": {
            	"Wait35": {
            		"Type": "Wait",
            		"Seconds": 35,
            		"Next": "WriteMetricLambda35"
             	"WriteMetricLambda35": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "Wait40",
            "States": {
            	"Wait40": {
            		"Type": "Wait",
            		"Seconds": 40,
            		"Next": "WriteMetricLambda40"
             	"WriteMetricLambda40": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "Wait45",
            "States": {
            	"Wait45": {
            		"Type": "Wait",
            		"Seconds": 45,
            		"Next": "WriteMetricLambda45"
             	"WriteMetricLambda45": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "Wait50",
            "States": {
            	"Wait50": {
            		"Type": "Wait",
            		"Seconds": 50,
            		"Next": "WriteMetricLambda50"
             	"WriteMetricLambda50": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
            "StartAt": "Wait55",
            "States": {
            	"Wait55": {
            		"Type": "Wait",
            		"Seconds": 55,
            		"Next": "WriteMetricLambda55"
             	"WriteMetricLambda55": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
        "End": true

Now you’ve got a shiny new Step Functions state machine! However, you might ask yourself, “After the state machine has been created, how does it get executed?” Before I answer that question, create the Lambda function that writes the custom metric, and then you get the end-to-end process moving.

Create a Lambda function

The meaty part of the solution is a Lambda function, written to consume the Python 3.6 runtime, that retrieves metric values from ECS, and then writes them to CloudWatch. This Lambda function is what the Step Functions state machine is triggering every five seconds, via the Task states. Key points to remember:

The Lambda function needs permission to:

  • Write CloudWatch metrics (PutMetricData API).
  • Retrieve metrics from ECS clusters (DescribeCluster API).
  • Write StdOut to CloudWatch Logs.

Boto3, the AWS SDK for Python, is included in the Lambda execution environment for Python 2.x and 3.x.

Because Lambda includes the AWS SDK, you don’t have to worry about packaging it up and uploading it to Lambda. You can focus on writing code and automatically take a dependency on boto3.

As for permissions, you’ve already created the IAM role and attached a policy to it that enables your Lambda function to access the necessary API actions. When you create your Lambda function, make sure that you select the correct IAM role, to ensure it is invoked with the correct permissions.

The following Lambda function code is generic. So how does the Lambda function know which ECS cluster to gather metrics for? Your Step Functions state machine automatically passes in its state to the Lambda function. When you create your CloudWatch Events rule, you specify a simple JSON object that passes the desired ECS cluster name into your Step Functions state machine, which then passes it to the Lambda function.

Use the following property values as you create your Lambda function:

Function Name: WriteMetricFromStepFunction
Description: This Lambda function retrieves metric values from an ECS cluster and writes them to Amazon CloudWatch.
Runtime: Python3.6
Memory: 128 MB
IAM Role: WriteMetricFromStepFunction

import boto3

def handler(event, context):
    cw = boto3.client('cloudwatch')
    ecs = boto3.client('ecs')
    print('Got boto3 client objects')
    Dimension = {
        'Name': 'ClusterName',
        'Value': event['ECSClusterName']

    cluster = get_ecs_cluster(ecs, Dimension['Value'])
    cw_args = {
       'Namespace': 'ECS',
       'MetricData': [
               'MetricName': 'RunningTask',
               'Dimensions': [ Dimension ],
               'Value': cluster['runningTasksCount'],
               'Unit': 'Count',
               'StorageResolution': 1
               'MetricName': 'PendingTask',
               'Dimensions': [ Dimension ],
               'Value': cluster['pendingTasksCount'],
               'Unit': 'Count',
               'StorageResolution': 1
               'MetricName': 'ActiveServices',
               'Dimensions': [ Dimension ],
               'Value': cluster['activeServicesCount'],
               'Unit': 'Count',
               'StorageResolution': 1
               'MetricName': 'RegisteredContainerInstances',
               'Dimensions': [ Dimension ],
               'Value': cluster['registeredContainerInstancesCount'],
               'Unit': 'Count',
               'StorageResolution': 1
    print('Finished writing metric data')
def get_ecs_cluster(client, cluster_name):
    cluster = client.describe_clusters(clusters = [ cluster_name ])
    print('Retrieved cluster details from ECS')
    return cluster['clusters'][0]

Create the CloudWatch Events rule

Now you’ve created an IAM role and policy, Step Functions state machine, and Lambda function. How do these components actually start communicating with each other? The final step in this process is to set up a CloudWatch Events rule that triggers your metric-gathering Step Functions state machine every minute. You have two choices for your CloudWatch Events rule expression: rate or cron. In this example, use the cron expression.

A couple key learning points from creating the CloudWatch Events rule:

  • You can specify one or more targets, of different types (for example, Lambda function, Step Functions state machine, SNS topic, and so on).
  • You’re required to specify an IAM role with permissions to trigger your target.
    NOTE: This applies only to certain types of targets, including Step Functions state machines.
  • Each target that supports IAM roles can be triggered using a different IAM role, in the same CloudWatch Events rule.
  • Optional: You can provide custom JSON that is passed to your target Step Functions state machine as input.

Follow these steps to create the CloudWatch Events rule:

  1. Open the CloudWatch console.
  2. Choose Events, RulesCreate Rule.
  3. Select Schedule, Cron Expression, and then enter the following rule:
    0/1 * * * ? *
  4. Choose Add Target, Step Functions State MachineWriteMetricFromStepFunction.
  5. For Configure Input, select Constant (JSON Text).
  6. Enter the following JSON input, which is passed to Step Functions, while changing the cluster name accordingly:
    { "ECSClusterName": "ECSEsgaroth" }
  7. Choose Use Existing Role, WriteMetricFromStepFunction (the IAM role that you previously created).

After you’ve completed with these steps, your screen should look similar to this:

Validate the solution

Now that you have finished implementing the solution to gather high-resolution metrics from ECS, validate that it’s working properly.

  1. Open the CloudWatch console.
  2. Choose Metrics.
  3. Choose custom and select the ECS namespace.
  4. Choose the ClusterName metric dimension.

You should see your metrics listed below.

Troubleshoot configuration issues

If you aren’t receiving the expected ECS cluster metrics in CloudWatch, check for the following common configuration issues. Review the earlier procedures to make sure that the resources were properly configured.

  • The IAM role’s trust relationship is incorrectly configured.
    Make sure that the IAM role trusts Lambda, CloudWatch Events, and Step Functions in the correct region.
  • The IAM role does not have the correct policies attached to it.
    Make sure that you have copied the IAM policy correctly as an inline policy on the IAM role.
  • The CloudWatch Events rule is not triggering new Step Functions executions.
    Make sure that the target configuration on the rule has the correct Step Functions state machine and IAM role selected.
  • The Step Functions state machine is being executed, but failing part way through.
    Examine the detailed error message on the failed state within the failed Step Functions execution. It’s possible that the
  • IAM role does not have permissions to trigger the target Lambda function, that the target Lambda function may not exist, or that the Lambda function failed to complete successfully due to invalid permissions.
    Although the above list covers several different potential configuration issues, it is not comprehensive. Make sure that you understand how each service is connected to each other, how permissions are granted through IAM policies, and how IAM trust relationships work.


In this post, you implemented a Serverless solution to gather and record high-resolution application metrics from containers running on Amazon ECS into CloudWatch. The solution consists of a Step Functions state machine, Lambda function, CloudWatch Events rule, and an IAM role and policy. The data that you gather from this solution helps you rapidly identify issues with an ECS cluster.

To gather high-resolution metrics from any service, modify your Lambda function to gather the correct metrics from your target. If you prefer not to use Python, you can implement a Lambda function using one of the other supported runtimes, including Node.js, Java, or .NET Core. However, this post should give you the fundamental basics about capturing high-resolution metrics in CloudWatch.

If you found this post useful, or have questions, please comment below.

Hacker House’s gesture-controlled holographic visualiser

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/hacker-house-holographic-visualiser/

YouTube makers Hacker House are back with a beautiful Flick-controlled holographic music visualiser that we’d really like to have at Pi Towers, please and thank you.

Make a Holographic Audio Visualizer with Gesture Control

Find all the code and materials on: https://www.hackster.io/hackerhouse/holographic-audio-visualizer-with-motion-control-e72fee A 3D holographic audio visualizer with gesture control can definitely spice up your party and impress your friends. This display projects an image from a monitor down onto an acrylic pyramid, or “frustum”, which then creates a 3D effect.

Homemade holographic visualiser

You may have seen a similar trick for creating holograms in this tutorial by American Hacker:

How To Make 3D Hologram Projector – No Glasses

Who will know that from plastic cd case we can make mini 3d hologram generator and you can watch 3d videos without glasses.

The illusion works due to the way in which images reflect off a flat-topped pyramid or frustum, to use its proper name. In the wonderful way they always do, the residents of Hacker House have now taken this trick one step further.

The Hacker House upgrade

Using an LCD monitor, 3D-printed parts, a Raspberry Pi, and a Flick board, the Hacker House team has produced a music visualiser truly worthy of being on display.

Hacker House Raspberry Pi holographic visualiser

The Pi Supply Flick is a 3D-tracking and gesture board for your Raspberry Pi, enabling you to channel your inner Jedi and control devices with a mere swish of your hand. As the Hacker House makers explain, in this music player project, there are various ways in which you could control the playlist, visualisation, and volume. However, using the Flick adds a wow-factor that we highly approve of.

The music and visualisations are supplied by a Mac running node.js. As the Raspberry Pi is running on the same network as the Mac, it can communicate with the it via HTTP requests.

Sketch of network for Hacker House Raspberry Pi holographic visualiser

The Pi processes incoming commands from the Flick board, and in response send requests to the Mac. Swipe upward above the Flick board, for example, and the Raspberry Pi will request a change of visualisation. Swipe right, and the song will change.

Hacker House Raspberry Pi holographic visualiser

As for the hologram itself, it is formed on an acrylic pyramid sitting below an LCD screen. Images on the screen reflect off the three sides of the pyramid, creating the illusion of a three-dimensional image within. Standard hocus pocus trickery.

Full details on the holographic visualiser, including the scripts, can be found on the hackster.io project page. And if you make your own, we’d love to see it.

Your turn

Using ideas from this Hacker House build and the American Hacker tutorial, our maker community is bound to create amazing things with the Raspberry Pi, holograms, and tricks of the eye. We’re intrigued to see what you come up with!

For inspiration, another example of a Raspberry Pi optical illusion project is Brian Corteil’s Digital Zoetrope:

Brian Corteil's Digital Zoetrope - Hacker House Raspberry Pi holographic visualiser

Are you up for the challenge of incorporating optical illusions into your Raspberry Pi builds? Share your project ideas and creations in the comments below!

The post Hacker House’s gesture-controlled holographic visualiser appeared first on Raspberry Pi.

AWS HIPAA Eligibility Update (October 2017) – Sixteen Additional Services

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-hipaa-eligibility-post-update-october-2017-sixteen-additional-services/

Our Health Customer Stories page lists just a few of the many customers that are building and running healthcare and life sciences applications that run on AWS. Customers like Verge Health, Care Cloud, and Orion Health trust AWS with Protected Health Information (PHI) and Personally Identifying Information (PII) as part of their efforts to comply with HIPAA and HITECH.

Sixteen More Services
In my last HIPAA Eligibility Update I shared the news that we added eight additional services to our list of HIPAA eligible services. Today I am happy to let you know that we have added another sixteen services to the list, bringing the total up to 46. Here are the newest additions, along with some short descriptions and links to some of my blog posts to jog your memory:

Amazon Aurora with PostgreSQL Compatibility – This brand-new addition to Amazon Aurora allows you to encrypt your relational databases using keys that you create and manage through AWS Key Management Service (KMS). When you enable encryption for an Amazon Aurora database, the underlying storage is encrypted, as are automated backups, read replicas, and snapshots. Read New – Encryption at Rest for Amazon Aurora to learn more.

Amazon CloudWatch Logs – You can use the logs to monitor and troubleshoot your systems and applications. You can monitor your existing system, application, and custom log files in near real-time, watching for specific phrases, values, or patterns. Log data can be stored durably and at low cost, for as long as needed. To learn more, read Store and Monitor OS & Application Log Files with Amazon CloudWatch and Improvements to CloudWatch Logs and Dashboards.

Amazon Connect – This self-service, cloud-based contact center makes it easy for you to deliver better customer service at a lower cost. You can use the visual designer to set up your contact flows, manage agents, and track performance, all without specialized skills. Read Amazon Connect – Customer Contact Center in the Cloud and New – Amazon Connect and Amazon Lex Integration to learn more.

Amazon ElastiCache for Redis – This service lets you deploy, operate, and scale an in-memory data store or cache that you can use to improve the performance of your applications. Each ElastiCache for Redis cluster publishes key performance metrics to Amazon CloudWatch. To learn more, read Caching in the Cloud with Amazon ElastiCache and Amazon ElastiCache – Now With a Dash of Redis.

Amazon Kinesis Streams – This service allows you to build applications that process or analyze streaming data such as website clickstreams, financial transactions, social media feeds, and location-tracking events. To learn more, read Amazon Kinesis – Real-Time Processing of Streaming Big Data and New: Server-Side Encryption for Amazon Kinesis Streams.

Amazon RDS for MariaDB – This service lets you set up scalable, managed MariaDB instances in minutes, and offers high performance, high availability, and a simplified security model that makes it easy for you to encrypt data at rest and in transit. Read Amazon RDS Update – MariaDB is Now Available to learn more.

Amazon RDS SQL Server – This service lets you set up scalable, managed Microsoft SQL Server instances in minutes, and also offers high performance, high availability, and a simplified security model. To learn more, read Amazon RDS for SQL Server and .NET support for AWS Elastic Beanstalk and Amazon RDS for Microsoft SQL Server – Transparent Data Encryption (TDE) to learn more.

Amazon Route 53 – This is a highly available Domain Name Server. It translates names like www.example.com into IP addresses. To learn more, read Moving Ahead with Amazon Route 53.

AWS Batch – This service lets you run large-scale batch computing jobs on AWS. You don’t need to install or maintain specialized batch software or build your own server clusters. Read AWS Batch – Run Batch Computing Jobs on AWS to learn more.

AWS CloudHSM – A cloud-based Hardware Security Module (HSM) for key storage and management at cloud scale. Designed for sensitive workloads, CloudHSM lets you manage your own keys using FIPS 140-2 Level 3 validated HSMs. To learn more, read AWS CloudHSM – Secure Key Storage and Cryptographic Operations and AWS CloudHSM Update – Cost Effective Hardware Key Management at Cloud Scale for Sensitive & Regulated Workloads.

AWS Key Management Service – This service makes it easy for you to create and control the encryption keys used to encrypt your data. It uses HSMs to protect your keys, and is integrated with AWS CloudTrail in order to provide you with a log of all key usage. Read New AWS Key Management Service (KMS) to learn more.

AWS Lambda – This service lets you run event-driven application or backend code without thinking about or managing servers. To learn more, read AWS Lambda – Run Code in the Cloud, AWS Lambda – A Look Back at 2016, and AWS Lambda – In Full Production with New Features for Mobile Devs.

[email protected] – You can use this new feature of AWS Lambda to run Node.js functions across the global network of AWS locations without having to provision or manager servers, in order to deliver rich, personalized content to your users with low latency. Read [email protected] – Intelligent Processing of HTTP Requests at the Edge to learn more.

AWS Snowball Edge – This is a data transfer device with 100 terabytes of on-board storage as well as compute capabilities. You can use it to move large amounts of data into or out of AWS, as a temporary storage tier, or to support workloads in remote or offline locations. To learn more, read AWS Snowball Edge – More Storage, Local Endpoints, Lambda Functions.

AWS Snowmobile – This is an exabyte-scale data transfer service. Pulled by a semi-trailer truck, each Snowmobile packs 100 petabytes of storage into a ruggedized 45-foot long shipping container. Read AWS Snowmobile – Move Exabytes of Data to the Cloud in Weeks to learn more (and to see some of my finest LEGO work).

AWS Storage Gateway – This hybrid storage service lets your on-premises applications use AWS cloud storage (Amazon Simple Storage Service (S3), Amazon Glacier, and Amazon Elastic File System) in a simple and seamless way, with storage for volumes, files, and virtual tapes. To learn more, read The AWS Storage Gateway – Integrate Your Existing On-Premises Applications with AWS Cloud Storage and File Interface to AWS Storage Gateway.

And there you go! Check out my earlier post for a list of resources that will help you to build applications that comply with HIPAA and HITECH.



Vinyl Shelf Finder

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/vinyl-shelf-finder/

It is a truth universally acknowledged that a person in possession of a large record collection must be in want of a good shelving system. Valentin Galea has solved this problem by developing the Vinyl Shelf Finder. In this build, a web-based app directs a pan-and-tilt laser to point out your record of choice among your collection.

Vinyl Shelf Finder demo by Valentin Galea


Collector’s issues

People love to collect stuff. Stamps; soap bars; Troll dolls; belly button fluff (no, really); if you can think of a tangible item, someone out there in the world is collecting it. Of course, every collector needs to solve two issues — which system to use for cataloguing and sorting their collection, and how to best retrieve items from it. This is where Valentin’s Vinyl Shelf Finder comes in. He says:

My vinyl collection is pretty modest — about 500 records in one vertical shelf and a couple of boxes. This is enough to get cumbersome when I’m searching for specific stuff, so I came up with the idea of a automated laser pointer finder.

The Vinyl Shelf Finder

Valentin keeps an online record of his vinyl collection using Discogs. He entered each LP’s shelf position into the record, and wrote a Node.js app to access the Discogs database. The mobile app has a GUI from which he chooses records based on their name and cover image. To build the hardware, he mounted a Pimoroni Pan-Tilt HAT on a Raspberry Pi, and affixed a laser pointer to the HAT. When he selects a record in the app, the pan-and-tilt laser moves to point out the LP’s location.

Valentin Galea on Twitter

my latest hobby prj: #vinyl finder – with lazers and #raspberrypi #iot and #nodejs – https://t.co/IGGzQDgUFI https://t.co/7YBE3svGyE

Not only does the app help Valentin find records – he has also set it up to collect listening statistics using the Last.fm API. He plans to add more sophisticated statistics, and is looking into how to automate the entry of the shelf positions into his database.

If you’re interested in the Vinyl Shelf Finder, head over to Valentin’s GitHub to learn more, and to find out about updates he is making to this work in progress.

GUI of Valentin Galea's Vinyl Shelf Finder app


Vinyl + Pi

We’ve previously blogged about Mike Smith’s kaleidoscopic Recordshelf build — maybe he and Valentin could team up to create the ultimate, beautiful, practical vinyl-shelving system!

If you listen to lots of LP records and would like to learn about digitising them, check out this Pi-powered project from Mozilla HQ. If, on the other hand, you have a vinyl player you never use, why not make amazing art with it by hacking it into a CNC Wood Burner?

Are you a collector of things common or unusual? Could Raspberry Pi technology help make your collection better? Share your ideas with us in the comments!

The post Vinyl Shelf Finder appeared first on Raspberry Pi.

timeShift(GrafanaBuzz, 1w) Issue 14

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/09/22/timeshiftgrafanabuzz-1w-issue-14/

Summer is officially in the rear-view mirror, but we at Grafana Labs are excited. Next week, the team will gather in Stockholm, Sweden where we’ll be discussing Grafana 5.0, GrafanaCon EU and setting other goals. If you’re attending Percona Live Europe 2017 in Dublin, be sure and catch Grafana developer, Daniel Lee on Tuesday, September 26. He’ll be showing off the new MySQL data source and a sneak peek of Grafana 5.0.

And with that – we hope you enjoy this issue of TimeShift!

Latest Release

Grafana 4.5.2 is now available! Various fixes to the Graphite data source, HTTP API, and templating.

To see details on what’s been fixed in the newest version, please see the release notes.

Download Grafana 4.5.2 Now

From the Blogosphere

A Monitoring Solution for Docker Hosts, Containers and Containerized Services: Stefan was searching for an open source, self-hosted monitoring solution. With an ever-growing number of open source TSDBs, Stefan outlines why he chose Prometheus and provides a rundown of how he’s monitoring his Docker hosts, containers and services.

Real-time API Performance Monitoring with ES, Beats, Logstash and Grafana: As APIs become a centerpiece for businesses, monitoring API performance is extremely important. Hiren recently configured real time API response time monitoring for a project and shares his implementation plan and configurations.

Monitoring SSL Certificate Expiry in GCP and Kubernetes: This article discusses how to use Prometheus and Grafana to automatically monitor SSL certificates in use by load balancers across GCP projects.

Node.js Performance Monitoring with Prometheus: This is a good primer for monitoring in general. It discusses what monitoring is, important signals to know, instrumentation, and things to consider when selecting a monitoring tool.

DIY Dashboard with Grafana and MariaDB: Mark was interested in testing out the new beta MySQL support in Grafana, so he wrote a short article on how he is using Grafana with MariaDB.

Collecting Temperature Data with Raspberry Pi Computers: Many of us use monitoring for tracking mission-critical systems, but setting up environment monitoring can be a fun way to improve your programming skills as well.

GrafanaCon EU CFP is Open

Have a big idea to share? A shorter talk or a demo you’d like to show off? We’re looking for technical and non-technical talks of all sizes. The proposals are rolling in, but we are happy to save a speaking slot for you!

I’d Like to Speak at GrafanaCon

Grafana Plugins

There were a lot of plugin updates to highlight this week, many of which were due to changes in Grafana 4.5. It’s important to keep your plugins up to date, since bug fixes and new features are added frequently. We’ve made the process of installing and updating plugins simple. On an on-prem instance, use the Grafana-cli, or on Hosted Grafana, install and update with 1-click.


Linksmart HDS Data Source – The LinkSmart Historical Data Store is a new Grafana data source plugin. LinkSmart is an open source IoT platform for developing IoT applications. IoT applications need to deal with large amounts of data produced by a growing number of sensors and other devices. The Historical Datastore is for storing, querying, and aggregating (time-series) sensor data.

Install Now


Simple JSON Data Source – This plugin received a bug fix for the query editor.

Update Now


Stagemonitor Elasticsearch App – Numerous small updates and the version updated to match the StageMonitor version number.

Update Now


Discrete Panel – Update to fix breaking change in Grafana 4.5.

Update Now


Status Dot Panel – Minor HTML Update in this version.

Update Now


Alarm Box Panel – This panel was updated to fix breaking changes in Grafana 4.5.

Update Now

This week’s MVC (Most Valuable Contributor)

Each week we highlight a contributor to Grafana or the surrounding ecosystem as a thank you for their participation in making open source software great.

Sven Klemm opened a PR for adding a new Postgres data source and has been very quick at implementing proposed changes. The Postgres data source is on our roadmap for Grafana 5.0 so this PR really helps. Thanks Sven!

Tweet of the Week

We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove

Glad you’re finding Grafana useful! Curious about that annotation just before midnight 🙂

We Need Your Help

Last week we announced an experiment we were conducting, and need your help! Do you have a graph that you love because the data is beautiful or because the graph provides interesting information? Please get in touch. Tweet or send us an email with a screenshot, and we’ll tell you about this fun experiment.

I Want to Help

Grafana Labs is Hiring!

We are passionate about open source software and thrive on tackling complex challenges to build the future. We ship code from every corner of the globe and love working with the community. If this sounds exciting, you’re in luck – WE’RE HIRING!

Check out our Open Positions

What do you think?

What would you like to see here? Submit a comment on this article below, or post something at our community forum. Help us make these weekly roundups better!

Follow us on Twitter, like us on Facebook, and join the Grafana Labs community.