Tag Archives: couchbase

Bye Bye Couchbase, Hello Amazon Web Services!

After spending a little over 18 months at Couchbase, the future is cloudy, very cloudy!

Friday, April 7th, 2017, was my last day at Couchbase. This Monday, April 10th, 2017, is my first day at Amazon.

aws

What will I be doing?

I’ll be part of the newly formed Open Source team at Amazon Web Services. I’m super excited to be working with Adrian Cockroft (@adrianco) and Zaheda Bhorat (@zahedab).

As a Principal Open Source Technologist, my initial focus will be to make sure AWS continues to be the best platform for running your containerized solutions. Yes, we’d like you to use EC2 Container Service. But if you want to use Docker, Kubernetes, DC/OS or any other open source orchestration framework, so be it! We will continue to work with our partners and the open source community, including contributing to these projects, to make sure AWS remains the best place to run your containerized workloads.

In addition, there are numerous other opportunities around open source and AWS like mxnet, Blox and likely many more to be created.

Why change?

I had a lot of fun working at a Silicon Valley startup. The amount of learning in terms of implementing the pipeline from adoption -> engagement -> monetization was immense. Working with different teams very closely, learning their machinery and helping them understand the relevance of community was quite a thrilling experience. Having significant part of the company colocated in a single location allowed a different level of interaction altogether. Working with Developer Advocacy team to meet, and quite often exceed, the metrics every month was a lot of fun.

However, for those who’ve followed me speaking at conferences and read my content over the past couple of years know that I’m passionate about containers. As Oprah Winfrey said:

Passion is energy!

Feel the power that comes from focusing on what excites you

This opportunity at AWS allows me to follow my heart and passion.

Some other quotes that truly symbolize my state of mind at this time …

passion1 passion3

This personal change is by no means any indication on the quality of Couchbase products. Both Couchbase Server and Couchbase Mobile are very well positioned for enterprise adoption. N1QL allows database developers to leverage their SQL skills and apply them to a NoSQL document database. Couchbase Mobile is a unique offering that provides offline capability for mobile applications and synchronization with a backend database when online. It will continue to blaze new trails and bring new types of customers. I wish all of them good luck!

A popular saying is “change is the only constant”. And so here I go again making another change in my career. Looking forward to see you at conferences and meetups around the world.

This also means, the blog title will change to Miles to go 4.0 now (2.0, 3.0)!

Where will you see me?

Some upcoming speaking engagements are DockerCon (Austin), GIDS (Bangalore), OSCON (Austin). Amazon Web Services is a gold sponsor at DockerCon and OSCON and so you can find me at the booth as well.

You’ll also see me at some AWS Summits and re:Invent.

And of course, you can follow me on twitter @arungupta to find out what’s keeping me busy!

In the meanwhile, here are some links for you to learn more about AWS:

  • AWS on Twitch
  • This is My Architecture that shows innovative architectural solutions on the AWS Cloud
  • AWS Blog
  • Follow @awscloud

Looking forward to AWSome and exciting weeks/months/years ahead!

 

Service Discovery with Java and Database application in Kubernetes

This blog will show how a simple Java application can talk to a database using service discovery in Kubernetes.

 Kubernetes Logo WildFly Logo

Service Discovery with Java and Database application in DC/OS explains why service discovery is an important aspect for a multi-container application. That blog also explained how this can be done for DC/OS.

Let’s see how this can be accomplished in Kubernetes with a single instance of application server and database server. This blog will use WildFly for application server and Couchbase for database.

This blog will use the following main steps:

  • Start Kubernetes one-node cluster
  • Kubernetes application definition
  • Deploy the application
  • Access the application

Start Kubernetes Cluster

Minikube is the easiest way to start a one-node Kubernetes cluster in a VM on your laptop. The binary needs to be downloaded first and then installed.

Complete installation instructions are available at github.com/kubernetes/minikube.

The latest release can be installed on OSX as as:

It also requires kubectl to be installed. Installing and Setting up kubectl provide detailed instructions on how to setup kubectl. On OSX, it can be installed as:

Now, start the cluster as:

The kubectl version command shows more details about the kubectl client and minikube server version:

More details about the cluster can be obtained using the kubectl cluster-info command:

Kubernetes Application Definition

Application definition is defined at github.com/arun-gupta/kubernetes-java-sample/blob/master/service-discovery.yml. It consists of:

  • A Couchbase service
  • Couchbase replica set with a single pod
  • A WildFly replica set with a single pod
The key part is where the value of the COUCHBASE_URI environment variable is name of the Couchbase service. This allows the application deployed in WildFly to dynamically discovery the service and communicate with the database.

arungupta/couchbase:travel Docker image is created using github.com/arun-gupta/couchbase-javaee/blob/master/couchbase/Dockerfile.

arungupta/wildfly-couchbase-javaee:travel Docker image is created using github.com/arun-gupta/couchbase-javaee/blob/master/Dockerfile.

Java EE application waits for database initialization to be complete before it starts querying the database. This can be seen at github.com/arun-gupta/couchbase-javaee/blob/master/src/main/java/org/couchbase/sample/javaee/Database.java#L25.

Deploy Application

This application can be deployed as:

The list of service and replica set can be shown using the command kubectl get svc,rs:

Logs for the single replica of Couchbase can be obtained using the command kubectl logs rs/couchbase-rs:

Logs for the WildFly replica set can be seen using the command kubectl logs rs/wildfly-rs:

Access Application

The kubectl proxy command starts a proxy to the Kubernetes API server. Let’s start a Kubernetes proxy to access our application:

Expose the WildFly replica set as a service using:

The list of services can be seen again using kubectl get svc command:

Now, the application is accessible at:

A formatted output looks like:

Now, new pods may be added as part of Couchbase service by scaling the replica set. Existing pods may be terminated or get rescheduled. But the Java EE application will continue to access the database service using the logical name.

This blog showed how a simple Java application can talk to a database using service discovery in Kubernetes.

For further information check out:

  • Kubernetes Docs
  • Couchbase on Containers
  • Couchbase Developer Portal
  • Ask questions on Couchbase Forums or Stack Overflow
  • Download Couchbase

Microservice using Docker stack deploy – WildFly, Java EE and Couchbase

There is plenty of material on microservices, just google it! I gave a presentation on refactoring monolith to microservices at Devoxx Belgium a couple of years back and it has good reviews:

This blog will show how Docker simplifies creation and shutting down of a microservice.

All code used in this blog is at github.com/arun-gupta/couchbase-javaee.

Microservice Definition using Compose

Docker 1.13 introduced a v3 of Docker Compose. The changes in the syntax are minimal but the key difference is addition of deploy attribute. This attribute allows to specify replicas, rolling update and restart policy for the container.

Our microservice will start a WldFly application server with a Java EE application pre-deployed. This application will talk to a Couchbase database to CRUD application data.

Here is the Compose definition:

In this Compose file:

  1. Two services in this Compose are defined by the name db and web attributes
  2. Image name for each service defined using image attribute
  3. The arungupta/couchbase:travel image starts Couchbase server, configures it using Couchbase REST API, and loads travel-sample bucket with ~32k JSON documents.
  4. The arungupta/couchbase-javaee:travel image starts WildFly and deploys application WAR file built from https://github.com/arun-gupta/couchbase-javaee. Clone that project if you want to build your own image.
  5. envrionment attribute defines environment variables accessible by the application deployed in WildFly. COUCHBASE_URI refers to the database service. This is used in the application code as shown at https://github.com/arun-gupta/couchbase-javaee/blob/master/src/main/java/org/couchbase/sample/javaee/Database.java.
  6. Port forwarding is achieved using ports attribute
  7. depends_on attribute in Compose definition file ensures the container start up order. But application-level start up needs to be ensured by the applications running inside container. In our case, WildFly starts up rather quickly but takes a few seconds for the database to start up. This means the Java EE application deployed in WildFly is not able to communicate with the database. This outlines a best practice when building micro services applications: you must code defensively and ensure in your application initialization that the micro services you depend on have started, without assuming startup order. This is shown in the database initialization code at https://github.com/arun-gupta/couchbase-javaee/blob/master/src/main/java/org/couchbase/sample/javaee/Database.java. It performs the following checks:

    1. Bucket exists
    2. Query service of Couchbase is up and running
    3. Sample bucket is fully loaded

This application can be started using docker-compose up -d command on a single host. Or a cluster of Docker engines in swarm-mode using docker stack deploy command.

Setup Docker Swarm-mode

Initialize Swarm mode using the following command:

This starts a Swarm Manager. By default, manager node are also worker but can be configured to be manager-only.

Find some information about this one-node cluster using the command docker info command:

This cluster has 1 node, and that is manager.

Alternatively, a multi-host cluster can be easily setup using Docker for AWS.

Deploy Microservice

The microservice can be started as:

This shows the output:

WildFly and Couchbase services are started on this node. Each service has a single container. If the Swarm mode is enabled on multiple nodes then the containers will be distributed across multiple nodes.

A new overlay network is created. This allows multiple containers on different hosts to communicate with each other.

Verify that the WildFly and Couchbase services are running using docker service ls:

Logs for the service can be seen using docker service logs -f webapp_web:

Make sure to wait for the last log statement to show.

Access Microservice

Get 10 airlines from the microservice:

This shows the results as:

Docker for Java Developers workshop is a self-paced hands-on lab and allows you to get started with Docker easily.

Get a single resource:

Create a new resource:

Update a resource:

Delete a resource:

Detailed output from each of these commands is at github.com/arun-gupta/couchbase-javaee.

Delete Microservice

The microservice can be removed using  the command docker stack rm webapp:

Want to get started with Couchbase? Look at Couchbase Starter Kits.

Want to learn more about running Couchbase in containers?

  • Couchbase on Containers
  • Couchbase Forums
  • Couchbase Developer Portal
  • @couchhasedev and @couchbase

Source: https://blog.couchbase.com/2017/february/microservice-using-docker-stack-deploy-wildfly-javaee-couchbase

Deploy Docker Compose Services to Swarm

Docker 1.13 introduced a new version of Docker Compose. The main feature of this release is that it allow services defined using Docker Compose files to be directly deployed to Docker Engine enabled with Swarm mode. This enables simplified deployment of multi-container application on multi-host.

Docker 1.13

This blog will show use a simple Docker Compose file to show how services are created and deployed in Docker 1.13.

Here is a Docker Compose v2 definition for starting a Couchbase database node:

This definition can be started on a Docker Engine without Swarm mode as:

This will start a single replica of the service define in the Compose file. This service can be scaled as:

If the ports are not exposed then this would work fine on a single host. If swarm mode is enabled on on Docker Engine, then it shows the message:

Docker Compose gives us multi-container applications but the applications are still restricted to a single host. And that is a single point of failure.

Swarm mode allows to create a cluster of Docker Engines. With 1.13, docker stack deploy command can be used to deploy a Compose file to Swarm mode.

Here is a Docker Compose v3 definition:

As you can see, the only change is the value of version attribute. There are other changes in Docker Compose v3. Also, read about different Docker Compose versions and how to upgrade from v2 to v3.

Enable swarm mode:

Other nodes can join this swarm cluster and this would easily allow to deploy the multi-container application to a multi-host as well.

Deploy the services defined in Compose file as:

A default value of Compose file here would make the command a bit shorter. #30352 should take care of that.

List of services running can be verified using docker service ls command:

The list of containers running within the service can be seen using docker service ps command:

In this case, a single container is running as part of the service. The node is listed as moby which is the default name of Docker Engine running using Docker for Mac.

The service can now be scaled as:

The list of container can then be seen again as:

Note that the containers are given the name using the format <service-name>_n. Both the containers are running on the same host.

Also note, the two containers are independent Couchbase nodes and are not configured in a cluster yet. This has already been explained at Couchbase Cluster using Docker and a refresh of the steps is coming soon.

A service will typically have multiple containers running spread across multiple hosts. Docker 1.13 introduces a new command docker service logs <service-name> to stream the log of service across all the containers on all hosts to your console. In our case, this can be seen using the command docker service logs couchbase_db and looks like:

The preamble of the log statement uses the format <container-name>.<container-id>@<host>. And then actual log message from your container shows up.

At first instance, attaching container id may seem redundant. But Docker services are self-healing. This means that if a container dies then the Docker Engine will start another container to ensure the specified number of replicas at a given time. This new container will have a new id. And thus it allows  to attach the log message from the right container.

So a quick comparison of commands:

 Docker Compose v2  Docker compose v3
 Start services docker-compose up -d docker stack deploy --compose-file=docker-compose.yml <stack-name> 
 Scale service docker-compose scale <service>=<replicas> docker service scale <service>=<replicas>
 Shutdown docker-compose down docker stack rm <stack-name>
 Multi-host No Yes

Want to get started with Couchbase? Look at Couchbase Starter Kits.

Want to learn more about running Couchbase in containers?

  • Couchbase on Containers
  • Couchbase Forums
  • Couchbase Developer Portal
  • @couchhasedev and @couchbase

Source: https://blog.couchbase.com/2017/deploy-docker-compose-services-swarm

Analyze Donald Trump Tweets with Couchbase and N1QL

AWS Serverless Lambda Scheduled Events to Store Tweets in Couchbase explained how to store tweets in Couchbase using AWS Serverless Lambda. Now, this Lambda Function has been running for a few days and has collected 269 tweets from @realDonaldTrump. This blog , inspired by SQL on Twitter: Analysis Made Easy Using N1QL, will show how these tweets can be analyzed using N1QL.

trump-tweets
N1QL is a SQL-like query language from Couchbase that operates on JSON documents. N1QL and SQL Differences provide differences between N1QL and SQL. Let’s use N1QL to reveal some interesting information from @realDonaldTrump‘s tweets.

Many thanks to Sitaram from N1QL team to help hack the queries.

How Many Tweets

First query is to find out how many tweets are available in the database. The query is pretty simple:

Query:

As you notice, the syntax is very similar to SQL. SELECT, COUNT and FROM clauses are what you are already familiar with from SQL syntax. tweet_count is an alias defined for the returned result. twitter is the bucket where all the JSON documents are stored.

Results:

The result is a JSON document as well.

Tweet Sample JSON Document

In order to write queries on a JSON document, you need to know the structure of the document. The next query will give you that.

Query:

The new clause introduced here is LIMIT. This allows to restrict the number of objects that are returned in a result set of SELECT.

Results:

Top 5 Tweeting Days

After the basic queries are out of the way, let’s look at some interesting data now.

What are the top 5 days on which @realDonaldTrump tweeted and the tweet count?

Query:

Usual GROUP BY and ORDER BY SQL clauses perform the same function.

N1QL Functions apply a function to values. The createdAt field is returned a number as a String. TO_NUM function converts the String to a number. MILLIS_TO_STR function converts the String to a date. Finally, SUBSTR function extracts the relevant part of the date.

Results:

Jan 17th, 2017 is the most tweeted day. Now, this result is of course restricted to the data from the JSON documents stored in the database.

Does anybody have a more comprehensive database of @realDonaldTrump tweets?

Tweet Frequency

OK, our database shows that that maximum number of tweets in a day were 13. How do I find out how many days @realDonaldTrump tweeted a certain number of times?

Query:

This is easily achieved using N1QL nested queries.

Results:

In 47 days, there is only one day with a single tweet. A sum total of tweet_count shows that there is no single day without a tweet :)

Most Common Hour In a Day To Tweet

@realDonaldTrump is known to tweet at 3am. Let’s take a look what are the most common hours for him to tweet.

Query:

Results:

Now seems like the controversial tweets come at 3am. But 39 tweets are coming at 1pm ET, likely right after lunch and while having a dessert 😉

Common Day of The Week to Tweet

Let’s find out what are the most common day of the week to tweet.

Query:

DATE_PART_STR is a new function returns date part of the date. Further day_of_week attribute is used to get day of the week.

Results:

Seems like Tuesday is the most common day to tweet. Then comes Sunday and Wednesday at the same level. The performance tends to fizzle out closer to the weekend.

Here is a nice chart that shows the same trend:

realdonaldtrump-tweets-per-day

#22417 should allow to report the weekday part in English.

Top 5 Mentions in Tweets

Query:

userMentionEntities is a nested array in the JSON document. UNNEST conceptually performs a join of the nested array with its parent object. Each resulting joined object becomes an input to the query.

Results:

Needless to say, he mentions his own name the most in tweets! And his two favorite TV stations Fox News and CNN.

Top 5 Tweets with RTs

Lambda Function wakes up every 3 hours and fetches the latest tweets. So the database is a snapshot of tweets and associated information such as RTs and Favorites. So depending upon when the tweet was archived, the RTs and Favorites may not be an accurate representation. But given this information, let’s take a look at the tweets with most RTs.

Query:

Pretty straight forward query.

Results:

Original vs RTs

How many of tweets were written vs retweeted?

Query:

Results:

Most of the tweets are original with only a few RTs.

Most Common Words in Tweet

Query:

This query uses SPLIT function that

Results:

Frequency of words “media”, “fake” and “America” in tweets

Query:

LOWER function is used to compare words independent of the case.

Result:

Lambda function will continue to store tweets in the database.

Try these queries yourself?

N1QL References

  • N1QL Interactive Tutorial
  • N1QL Cheatsheet
  • N1QL Language Reference
  • Run Your First N1QL Query

Source: https://blog.couchbase.com/2017/january/analyze-donald-trump-tweets-couchbase-n1ql

AWS Serverless Lambda Scheduled Events to Store Tweets in Couchbase

This blog has explained a few Serverless concepts with code samples:

  • Serverless FaaS with AWS Lambda and Java
  • AWS IoT Button, Lambda and Couchbase
  • Microservice using AWS API Gateway, AWS Lambda and Couchbase
  • Microservice using AWS Serverless Application Model and Couchbase

This particular blog entry will show how to use AWS Lambda to store tweets of a tweeter in Couchbase. Here are the high level components:

 

lambda-twitter-couchbase

The key concepts are:

  • Lambda Function deployed using Serverless Application Model
  • Triggered every 3 hours using Scheduled Events
  • Uses Twitter4J API to query new tweets since the last fetch
  • Use Couchbase Java SDK API to store JSON documents in the Couchbase Server

Complete sample code for this blog is available at github.com/arun-gupta/twitter-n1ql.

Serverless Application Model

Serverless Application Model, or SAM, defines simplified syntax for expressing serverless resources. SAM extends AWS CloudFormation to add support for API Gateway, AWS Lambda and Amazon DynamoDB. Read more details in Microservice using AWS Serverless Application Model and Couchbase.

For our application, SAM template is available at github.com/arun-gupta/twitter-n1ql/blob/master/template-example.yml and shown below:

What do we see here?

  • Function is packaged and available in a S3 bucket
  • Handler class is org.sample.twittter.TwitterRequestHandler and is at github.com/arun-gupta/twitter-n1ql/blob/master/twitter-feed/src/main/java/org/sample/twitter/TwitterRequestHandler.java. It looks like:
    By default, this class reads the twitter handle of Donald Trump. More fun on that coming in a subsequent blog.
  • COUCHBASE_HOST and COUCHBASE_BUCKET_PASSWORD are environment variables that provide EC2 host where Couchbase database is running and the password of the bucket.
  • Function can be triggered by different events. In our case, this is triggered every three hours. More details about the expression used here are at Schedule Expressions Using Rate or Cron.

Fetching Tweets using Twitter4J

Tweets are read using Twitter4J API. It is an unofficial Twitter API that provides a Java abstraction over Twitter REST API. Here is a simple example:

 

Twitter4J Docs and Javadocs are pretty comprehensive.

Twitter API allows to read only last 200 tweets. Lambda function is invoked every 3 hours. The tweet frequency of @realDonaldTrump is not 200 every 3 hours, at least yet. If it does reach that dangerous level then we can adjust the rate to trigger Lambda function more frequently.

JSON representation of each tweet is stored in Couchbase server using Couchbase Java SDK. AWS Lambda supports Node, Python and C#. And so you can use Couchbase Node SDK, Couchbase Python SDK or Couchbase .NET SDK to write these functions as well.

Twitter4J API allows to fetch tweets since the id of a particular tweet. This allows to ensure that duplicate tweets are not fetched. This requires us to sort all tweets in a particular order and then pick the id of the most recent tweet. This was solved using the simple N1QL query:

The syntax is very SQL-like. More on this in a subsequent blog.

Store Tweets in Couchbase

The final item is to store the retrieved tweets in Couchbase.

Value of COUCHABSE_HOST environment variable is used to connect to the Couchbase instance. The value of COUCHBASE_BUCKET_PASSWORD environment variable is to connect to the secure bucket where all JSON documents are stored. Its very critical that the bucket be password protected and not directly specified in the source code. More on this in a subsequent blog.

The JSON document is upserted (insert or update) in Couchbase using the Couchbase Java API:

 

This Lambda Function has been running for a few days now and has captured 258 tweets from @realDonaldTrump.

serverless-lambda-couchbase-twitter-bucket

An interesting analysis of his tweets is coming shortly!

Talk to us:

  • Couchbase Forums
  • Couchbase Database Developer Portal
  • @couchbasedev and @couchbase

Complete sample code for this blog is available at github.com/arun-gupta/twitter-n1ql.

Source: https://blog.couchbase.com/2017/january/aws-serverless-lambda-scheduled-events-tweets-couchbase

Microservice using AWS Serverless Application Model and Couchbase

Amazon Web Services introduced Serverless Application Model, or SAM, a couple of months ago. It defines simplified syntax for expressing serverless resources. SAM extends AWS CloudFormation to add support for API Gateway, AWS Lambda and Amazon DynamoDB. This blog will show how to create a simple microservice using SAM. Of course, we’ll use Couchbase instead of DynamoDB!

This blog will also use the basic concepts explained in Microservice using AWS API Gateway, AWS Lambda and Couchbase. SAM will show the ease with which the entire stack for microservice can be deployed and managed.

As a refresher, here are key components in the architecture:

serverless-microservice

  • Client could be curl, AWS CLI/Console, Postman client or any other tool/API that can invoke a REST endpoint.
  • AWS API Gateway is used to provision APIs. The top level resource is available at path /books. HTTP GET and POST methods are published for the resource.
  • Each API triggers a Lambda function. Two Lambda functions are created, book-list function for listing all the books available and book-create function to create a new book.
  • Couchbase is used as a persistence store in EC2. All the JSON documents are stored and retrieved from this database.

Other blogs on serverless:

  • Microservice using AWS API Gateway, AWS Lambda and Couchbase
  • AWS IoT Button, Lambda and Couchbase
  • Serverless FaaS with Lambda and Java

Let’s get started!

Serverless Application Model (SAM) Template

An AWS CloudFormation template with serverless resources conforming to the AWS SAM model is referred to as a SAM file or template. It is deployed as a CloudFormation stack.

Let’s take a look at our SAM template:

This template is available at github.com/arun-gupta/serverless/blob/master/aws/microservice/template.yml.

SAM template Specification provide complete details about contents in the template. The key parts of the template are:

  • Defines two resources, both of Lambda Function type identified by AWS::Serverless::Function attribute. Name of the Lambda function is defined by Resources.<resource>.
  • Class for each handler is defined by the value of Resources.<resource>.Properties.Handler attribute
  • Java 8 runtime is used to run the Function defined by Resources.<resource>.Properties.Runtime attribute
  • Code for the class is uploaded to an S3 bucket, in our case to s3://serverless-microservice/microservice-http-endpoint-1.0-SNAPSHOT.jar
  • Resources.<resource>.Properties.Environment.Variables.COUCHBASE_HOST attribute value defines the host where Couchbase is running. This can be easily deployed on EC2 as explained at Setup Couchbase.
  • Each Lambda function is triggered by an API. It is deployed using AWS API Gateway. The path is defined by Events.GetResource.Properties.Path. HTTP method is defined using Events.GetResource.Properties.Method attribute.

Java Application

The Java application that contains the Lambda functions is at github.com/arun-gupta/serverless/tree/master/aws/microservice/microservice-http-endpoint.

Lambda function that is triggered by HTTP GET method is shown:

A little bit of explanation:

  • Each Lambda function needs to implement the interface com.amazonaws.services.lambda.runtime.RequestHandler.
  • API Gateway and Lambda integration require a specific input format and output format. These formats are defined as GatewayRequest and GatewayResponse classes.
  • Function logic uses Couchbase Java SDK to query the Couchbase database. N1QL query is used to query the database. The results and exception are then wrapped in GatewayRequest and GatewayResponse.

Lambda function triggered by HTTP POST method is pretty straightforward as well:

A bit of explanation:

  • Incoming request payload is retrieved from GatewayRequest
  • Document inserted in Couchbase is returned as response.
  • Like the previous method, Function logic uses Couchbase Java SDK to query the Couchbase database. The results and exception are then wrapped in GatewayRequest and GatewayResponse.

Build the Java application as:

Upload Lambda Function to S3

SAM template reads the code from an S3 bucket. Let’s create a S3 bucket:

us-west-2 region is one of the supported regions for API Gateway. S3 bucket names are globally unique but their location is region specific.

Upload the code to S3 bucket:

The code is now uploaded to S3 bucket. SAM template is ready to be deployed!

Deploy SAM Template

Deploy the SAM template:

It shows the output:

This one command deploys Lambda functions and REST Resource/APIs that trigger these Lambda functions.

Invoke the Microservice

API Gateway publishes a REST API that can be invoked by curl, wget, AWS CLI/Console, Postman or any other app that can call a REST API. This blog will use AWS Console to show the interaction.

API Gateway home at us-west-2.console.aws.amazon.com/apigateway/home?region=us-west-2#/apis shows:

AWS SAM Microservice API

Click on the API to see all the APIs in this resource:

AWS SAM Microservice API Resources

Click on POST to see the default page for POST method execution:

AWS SAM Microservice API POST

Click on Test to test the API:

AWS SAM Microservice API POST Input

Add the payload in Request Body and click on Test to invoke the API. The results are shown as below:

AWS SAM Microservice API POST Output

Now click on GET to see the default execution page:

AWS SAM Microservice API GET

Click on Test to test the API:

AWS SAM Microservice API GET Input

No request body is needed, just click on Test the invoke the API. The results are as shown:

AWS SAM Microservice API GET Output

Output from the Couchbase database is shown in the Response Body.

References

  • Deploying Lambda-based Applications
  • Serverless Architectures
  • AWS API Gateway
  • Creating a simple Microservice using Lambda and API Gateway
  • Couchbase Server Docs
  • Couchbase Forums
  • Follow us at @couchbasedev

Source: blog.couchbase.com/2017/january/microservice-aws-serverless-application-model-couchbase

Microservice using AWS API Gateway, AWS Lambda and Couchbase

This blog has explained the following concepts for serverless applications so far:

  • Serverless FaaS with AWS Lambda and Java
  • AWS IoT Button, Lambda and Couchbase

The third blog in serverless series will explain how to create a simple microservice using Amazon API Gateway, AWS Lambda and Couchbase.

Read previous blogs for more context on AWS Lambda.

Amazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. Amazon API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management.

Here are the key components in this architecture:

serverless-microservice

  • Client could be curl, AWS CLI, Postman client or any other tool/API that can invoke a REST endpoint.
  • API Gateway is used to provision APIs. The top level resource is available at path /books. HTTP GET and POST methods are published for the resource.
  • Each API triggers a Lambda function. Two Lambda functions are created, book-list function for listing all the books available and book-create function to create a new book.
  • Couchbase is used as a persistence store in EC2. All the JSON documents are stored and retrieved from this database.

Let’s get started!

Create IAM Role

IAM roles will have policies and trust relationships that will allow this role to be used in API Gateway and execute Lambda function.

Let’s create a new IAM role:

--assume-role-policy-document defines the trust relationship policy document that grants an entity permission to assume the role. trust.json is at github.com/arun-gupta/serverless/blob/master/aws/microservice/trust.json and looks like:

This trust relationship allows Lambda functions and API Gateway to assume this role during execution.

Associate policies with this role as:

policy.json is at github.com/arun-gupta/serverless/blob/master/aws/microservice/policy.json and looks like:

This generous policy allows any permissions over logs generated in CloudWatch for all resources. In addition it allows all Lambda and API Gateway permissions to all resources. In general, only required policy would be given to specific resources.

Create Lambda Functions

Detailed steps to create Lambda functions are explained in Serverless FaaS with AWS Lambda and Java. Let’s create the two Lambda functions as required in our case:

Couple of key items to note in this function are:

  • IAM role microserviceRole created in previous step is explicitly specified here
  • Handler is org.sample.serverless.aws.couchbase.BucketGetAll class. This class queries the Couchbase database defined using the COUCHBASE_HOST environment variable.

Create the second Lambda function:

The handler for this function is org.sample.serverless.aws.couchbase.BucketPost class. This class creates a new JSON document in the Couchbase database identified by COUCHBASE_HOST environment variable.

The complete source code for these classes is at github.com/arun-gupta/serverless/tree/master/aws/microservice/microservice-http-endpoint.

API Gateway Resource

Create an API using Amazon API Gateway and Test It and Build an API to Expose a Lambda Function provide detailed steps and explanation on how to use API Gateway and Lambda Functions to build powerful backend systems. This blog will do a quick run down of the steps in case you want to cut the chase.

Let’s create API Gateway resources.

  1. The first step is to create an API:
    This shows the output as:
    The value of id attribute is API ID. In our case, this is lb2qgujjif.
  2. Find ROOT ID of the created API as this is required for the next AWS CLI invocation:
    This shows the output:
    Value of id attribute is ROOT ID. This is also the PARENT ID for the top level resource.
  3. Create a resource
    This shows the output:
    Value of id attribute is RESOURCE ID.

API ID and RESOURCE ID are used for subsequent AWS CLI invocations.

API Gateway POST Method

Now that the resource is created, let’s create HTTP POST method on this resource.

  1. Create a POST method
    to see the response:
  2. Set Lambda function as destination of the POST method:
    Make sure to replace <act-id> with your AWS account id. API ID and RESOURCE ID from previous section are used here as well. --uri is used to specify the URI of integration input. The format of the URI is fixed. This CLI will show the result as: