Tag Archives: lambda

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

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:

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:

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:

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:

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

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:

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:
  3. Set content-type of POST method response:
    to see the response:
  4. Set content-type of POST method integration response:
    to see the response:
  5. Deploy the API
    to see the response
  6. Grant permission to allow API Gateway to invoke Lambda Function:
    Also, grant permission to the deployed API:
  7. Test the API method:
    to see the response:
    Value of status attribute is 200 and indicates this was a successful invocation. Value of log attribute shows the log statement from CloudWatch Logs. Detailed logs can also be obtained using aws logs filter-log-events --log-group /aws/lambda/MicroservicePost.
  8. This command stores a single JSON document in Couchbase. This can be easily verified using the Couchbase CLI Tool cbq.Connect to the Couchbase server as:
    Create a primary index on default bucket as this is required to query the bucket with no clauses:
  9. Write a N1QL query to access the data:
    The results show the JSON document that was stored by our Lambda function.

API Gateway GET Method

Let’s create HTTP GET method on the resource:

  1. Create a GET method:
  2. Set correct Lambda function as destination of GET:
  3. Set content-type of GET method response:
  4. Set content-type of GET method integration response:
  5. Grant permission to allow API Gateway to invoke Lambda Function
  6. Grant permission to the deployed API:
  7. Test the method:
    to see the output:
    Once again, 200 status code shows a successful invocation. Detailed logs can be obtained using aws logs filter-log-events --log-group /aws/lambda/MicroservicePost.

This blog only shows one simple POST and GET methods. Other HTTP methods can be very easily included in this microservice as well.

API Gateway and Lambda References

Source: blog.couchbase.com/2016/december/microservice-aws-api-gateway-lambda-couchbase

Serverless FaaS with AWS Lambda and Java

What is Serverless Architecture?

Serverless architecture runs custom code in ephemeral containers that are fully managed by a 3rd party. The custom code is typically a small part of a complete application. It is also called as function. This gives another name for serverless architecture as Function as a Service (FaaS). The container is ephemeral because it may only last for one invocation. The container may be reused but that’s not something you can rely upon. As a developer, you upload the code to FaaS platform, the service then handles all the capacity, scaling, patching and administration of the infrastructure to run your code.

An application built using Serverless Architecture follows the event-driven approach. For example, an activity happened in the application such as a click. This is

This is very different from a classical architecture where the application code is typically deployed in an application server such as Tomcat or WildFly. Scaling your application means starting additional instances of the application server or spinning up additional containers with the packaged application server. The Load Balancer need to be updated with the new IP addresses. Operating system need to be patched, upgraded and maintained.

Serverless Architectures explain the difference between the classical programming model and this new serverless architecture.

FaaS platform takes your application is divided into multiple functions. Each function is deployed in FaaS. The service spins up additional compute instances to meet the scalability demands of your application. FaaS platform provides the execution environment and takes care of starting and tearing down the containers to run your function.

Read Serverless Architectures for more details about these images.

One of the big advantages of FaaS is that you are only charged for the compute time, i.e. the time your code is running. There is no charge when your code is not running.

Another way to look at how Functions are different from VMs and Containers:

vm-containers-serverless

Note that Linux containers instead of Docker containers are used as an implementation for AWS Lambda.

How is FaaS different from PaaS?

As quoted at Serverless Architectures, a quick answer is provided by the following tweet:

In other words most PaaS applications are not geared towards bringing entire applications up and down for every request, whereas FaaS platforms do exactly this.

Abstracting the Back-end with FaaS explain the difference with different *aaS offerings. The image from the blog is captured below:

faas

Serverless Architectures also provide great details about what FaaS is and is not.

AWS Lambda, Google Cloud Functions and Azure Functions are some of the options for running serverless applications.

This blog will show how to write your first AWS Lambda function.

What is AWS Lambda?

AWS Lambda is FaaS service from Amazon Web Services. It runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring and logging.

AWS Lambda charges you for the duration your code runs in increments of 100ms. There is no cost associated with storing the Lambda function in AWS. First million requests per month are free and the pricing after that is nominal. Read more details on Lambda pricing. It also provides visibility into performance by providing real time metrics and logs to AWS CloudWatch. All you need to do is write the code!

Here is a quick introduction:

Also check out What’s New in AWS Lambda from AWS ReInvent 2016:

Also checkout Serverless Architectural Patterns and Best Practices from AWS ReInvent 2016:

The code you run on AWS Lambda is called a Lambda Function. You upload your code as a zip file or design it using the AWS Lambda Management Console. There is a built-in support for AWS SDK and this simplifies the ability to call other AWS services.

In short, Lambda is scalable, serverless, compute in the cloud.

AWS Lambda provides several execution environments:

  • Node.js – v0.10.36, v4.3.2 (recommended)
  • Java – Java 8
  • Python – Python 2.7
  • .NET Core – .NET Core 1.0.1 (C#)

This blog will show:

  • Build a Java application that stores a JSON document to Couchbase
  • Use Maven to create a deployment package for Java application
  • Create a Lambda Function
  • Update the Lambda Function

The complete code in this blog is available at github.com/arun-gupta/serverless/tree/master/aws/hellocouchbase.

Java Application for AWS Lambda

First, lets look at a Java application that will be used for this Lambda function. Programming Model for Lambda Functions in Java provide more details about how to write your Lambda function code in Java.

Our Lambda function will implemented the pre-defined interface com.amazonaws.services.lambda.runtime.RequestHandler. The code looks like:

handleRequest method is where the function code is implemented. Context provides useful information about Lambda execution environment. Some of the information from the context is stored a JSON document. Finally, Couchbase Java SDK API upsert is used to write a JSON document to the identified Couchbase instance. Couchbase on Amazon EC2 provide complete instructions to install Couchbase on AWS EC2.

Information about the Couchbase server is obtained as:

This is once again using Couchbase Java API CouchbaseCluster as a main entry point to the Couchbase cluster. The COUCHBASE_HOST environment variable is passed when the Lambda function is created. In our case, this would point to a single node Couchbase cluster running on AWS EC2. Environment variables were recently introduced in AWS Lambda.

Finally, you need to access bucket in the server:

The bucket name is serverless and all JSON documents are stored in this.

A simple Hello World application may be used for creating this function as well.

Create AWS Lambda Deployment Package

AWS Lambda function needs a deployment package. This package is either a .zip or .jar file that contains all the dependencies of the function. Our application is packaged using Maven, and so we’ll use a Maven plugin to create a deployment package.

The application has pom.xml with the following plugin fragment:

More details about Maven configuration are available in Creating a .jar Deployment Package Using Maven without any IDE. The maven-shade-plugin allows to create an uber-jar including all the dependencies. The shade goal is tied to the package phase. So the mvn package command will generate a single deployment jar.

Package the application using mvn package command. This will show the output:

The target/hello-couchbase-1.0-SNAPSHOT.jar is the shaded jar that will be deployed to AWS Lambda.

More details about creating a deployment package are at Creating a Deployment Package.

Create AWS Lambda Function

Create AWS Lambda Function using AWS CLI. The CLI command in this case looks like:

In this CLI:

  • create-function creates a Lambda function
  • --function-name provides the function name. The function name is case sensitive.
  • --role specifies Amazon Resource Name (ARN) of an IAM role that Lambda assume when it executes your function to access any other AWS resources. If you’ve executed a Lambda function using AWS Console then this role is created for you.
  • --zip-file points to the deployment package that was created in previous step. fileb is an AWS CLI specific protocol to indicate that the content uploaded is binary.
  • --handler is the Java class that is called to begin execution of the function
  • --publish request AWS Lambda to create the Lambda function and publish a version as an atomic operation. Otherwise multiple versions may be created and may be published at a later point.

Lambda Console shows:

servleress-couchbase-lambda-function

Test AWS Lambda Function

Test the AWS Lambda Function using AWS CLI.

It shows the output as:

The output from the command is stored in hellocouchbase.out and looks like:

Invoking this function stores a JSON document in Couchbase. Documents stored in Couchbase can be seen using Couchbase Web Console. The password is Administrator and password is the EC2 instance id.

All data buckets in this Couchbase instance are shown below:

serverless-couchbase-bucket-overview

Note that the serverless bucket is manually created.

Clicking on Documents shows details of different documents stored in the bucket:

serverless-couchbase-bucket-documents

Clicking on each document shows more details about the JSON document:

serverless-couchbase-bucket-document

Lambda function can also be tested using the Console:

serverless-couchbase-console-test

Update AWS Lambda Function

If the application logic changes then a new deployment package needs to be uploaded for the Lambda function. In this case, mvn package will create a deployment package and aws lambda CLI command is used to update the function code:

Shows the result:

The function can then be invoked again.

During writing of this blog, this was often used to debug the function as well. This is because Lambda functions do not have any state or box associated with them. And so you cannot log in to a box to check out if the function did not deploy correctly. You can certainly use CloudWatch log statements once the function is working.

AWS Lambda References

Source: https://blog.couchbase.com/2016/december/serverless-faas-aws-lambda-java