AWS Developer Blog

Screencast using .NET Core with AWS Serverless from NDC Oslo

by Norm Johanson | on | in .NET | Permalink | Comments |  Share

Last month I had the pleasure of speaking at the NDC conference in Oslo talking about .NET Core and AWS Serverless technologies.

The talk focused around a new reference application I have been working on called Pollster. Two years ago at the 2015 AWS re:Invent conference we demoed a version of Pollster using .NET Core, which was back then called ASP.NET 5, and Docker. It was great revisiting this app and think of how to solve the technology challenges of the app using Serverless technology.

Thanks to the NDC team a screencast of my talk has been uploaded. Check out the screencast and see how I used AWS Serverless services like AWS Lambda, Amazon API Gateway and AWS Step Functions. The application isn’t feature complete yet but you can find the source on GitHub.

Developer Experience of the AWS SDK for C++ Now Simplified by CMake

by Andrew Tang | on | in C++ | Permalink | Comments |  Share

Building a cross-platform C or C++ project is tedious and time consuming. You often have to manage build files for each platform’s build system. On Unix-like systems, you might use Make, while on Windows you would have to use MSBuild. To make matters worse, in each of these build systems you have to manually maintain and configure compiler flags and linker flags.

We’re very pleased to announce that starting with version 1.0.109 of the AWS SDK for C++, you can more easily use CMake to build your project against the SDK. In addition, it’s easier to uninstall the SDK.

Here’s a simple example script that uses CMake to build a project against the SDK.

cmake_minimum_required(VERSION 2.8)
find_package(AWSSDK REQUIRED)
set(SERVICE s3-encryption)
add_executable(s3Encryption s3Encryption.cpp)
target_link_libraries(s3Encryption ${OUTPUT})
target_include_directories(s3Encryption PRIVATE ${AWSSDK_INCLUDE_DIR})

To uninstall the SDK, just run make uninstall inside your build directory.

In earlier versions, each SDK had its own CMake scripts. However, the functionality only told you that the SDK existed. Now when you run sudo make install, this latest version installs a new directory named AWSSDK.

On a Unix-like system, this is the default installation path:

On Windows, this is the default installation path:
“C:/Program Files/aws-cpp-sdk-all/lib/cmake/AWSSDK”

Several CMake scripts are created in this directory. The most important one is AWSSDKConfig.cmake. CMake can use it to find the AWSSDK module and load the script. For information about naming of this specific file name, see CMake Find Package Config Mode.

Calling find_package(AWSSDK) makes several useful variables and macros available to you, as follows.





You can use the AWSSDK_CPY_DYN_LIBS macro to copy all the SDKs that are specified in the SERVICE_LIST. In addition, it copies all their dependent libraries (including recursive dependencies) and the core library to DESTDIR. You use CONFIG to specify the compile time binary configuration of the SDKs. You don’t have to set it, or you can set it to Debug, Release, and others.

For example, S3 encryption depends on Core, Amazon S3, and AWS KMS. Both S3 and KMS depend on Core. So, the following script copies,, and to the current directory.

Set(SERVICE_LIST s3 s3-encryption)

You could use the AWSSDK_LIB_DEPS macro to output dependent libraries of SERVICE to DEPS. However, remember that SERVICE is just a single SDK’s name instead of a list of all the SDK names, and DEPS is a list of names of simplified libraries such as “core; s3; kms; s3-encryption”.

The AWSSDK_DETERMINE_LIBS_TO_LINK macro is similar to AWSSDK_CPY_DYN_LIBS. However, it doesn’t copy but does output the library names to OUTPUT. Notice that OUTPUT is a complete list of library names, which you could use as arguments of find_library(). For example, “aws-cpp-sdk-core; aws-cpp-sdk-s3;”.

The PkgConfig metadata file of each SDK is installed on all platforms under the same directory as CMake scripts. On Unix-like systems, we can use the PkgConfig module in CMake to simplify this, as we did in the previous example script. But if you want to try a command line build or a simple Makefile build, you can use a command like the following to generate all the flags, libs, and paths you want.

pkg_config –libs –cflags aws-cpp-sdk-s3-encryption

Try this sample project on your own platform. Before you begin, be sure to do the following:

  • Install the latest version of the AWS SDK for C++.
  • Create and set up AWS credentials on your test machine.
  • Create an Amazon S3 bucket under your account. The region must be the same as the region used in your AWS client configuration.
  • Create an AWS KMS master key.
  • Apply changes to main.cpp in this project, such as master key ID, bucket name, key you wanted to use, and so on.

Please reach out to us with questions and improvements. As always, pull requests are welcome!

Chalice Version 1.0.0b1 Is Now Available

by James Saryerwinnie | on | in Python | Permalink | Comments |  Share

We’ve just released Chalice version 1.0.0b1, the first preview release of Chalice 1.0.0. Since our last post that showcased the 0.9.0 release we’ve added numerous features we’re excited to share with you.

  • Support for built in authorizers. In earlier versions of Chalice, you could integrate a custom authorizer with your Chalice application. However, you had to manage the AWS Lambda function separately from your Chalice app. You can now use Chalice to manage the Lambda function used for your custom authorizer. When you define a built-in authorizer in your Chalice application, the chalice deploy command will manage both your Lambda function used for your API handler and the Lambda function used for your authorizer. You register an authorizer function with Chalice by using the @app.authorizer() decorator. Our user guide walks through an example of using built-in authorizers in Chalice.
  • Support for binary Python packges. When it’s possible, Chalice now automatically tries to download binary packages. This allows you to use Python packages that require C extensions, provided they have a manylinux1 wheel available. As a result, Python packages such as numpy, psycopg2, and Pillow will automatically work with Chalice. See 3rd Party Packages in our user guide for more information.
  • Support for scheduled events. Scheduled events has been one of the most requested features of Chalice. In version 1.0.0b1 of Chalice, you can now register a function to be called on a regular schedule. This is powered by Amazon CloudWatch Events. To create a scheduled event, you use the @app.schedule() decorator on any function in your application. Chalice takes care of creating the additional Lambda function, creating the necessary CloudWatch Events rules and targets, and adding the appropriate permissions to the Lambda function policy. See Event Sources in our user guide for more information on using scheduled events in Chalice.
  • Support for pure AWS Lambda functions. The @app.route(), @app.authorizer(), and @app.schedule() decorators not only create Lambda functions for you, they also offer a higher level of abstraction over a standard Lambda function. However, there are times when you just need a pure Lambda function with no additional levels of abstraction. Chalice now supports this with the @app.lambda_function() decorator. By using this decorator, you can still leverage all of Chalice’s deployment capabilities including automatic policy generation, deployment packaging for your requirements.txt file, stage support, etc. See pure Lambda functions in our user guide for more details.

If you’d like to try out this preview version of Chalice 1.0.0, you have two options when using pip:

  • You can specify the --pre flag: pip install --upgrade --pre chalice.
  • You can specify a version range that references this preview release: pip install chalice>=1.0.0b1,<2.0.0. This also installs any future 1.0.0 preview releases of Chalice.

We’d love to hear any feedback you have about Chalice. Try out these new features today and let us know what you think. You can chat with us on our Gitter channel and file feature requests and issues on our GitHub repo. We look forward to hearing from you.

Improvements for AWS CloudFormation and Amazon CloudWatch in the AWS Tools for PowerShell Modules

Trevor Sullivan, a Systems Development Engineer here at Amazon, recently contributed some new AWS CloudFormation helper cmdlets and improved formatting for types he works with on a daily basis. These updates were released in version of the AWS Tools for PowerShell modules (AWSPowerShell and AWSPowerShell.NetCore), in addition to new support in Amazon CloudWatch metrics for customizable dashboards. In this guest post, Trevor takes us through the updates.

Pause a script until a CloudFormation stack status is reached

If you want to pause your PowerShell script until a CloudFormation stack reaches a certain status, you can use the Wait-CFNStack cmdlet. You use Wait-CFNStack to specify a CloudFormation stack name and the status code that you want to wait for. All of the supported CloudFormation statuses are provided with IntelliSense/tab-completion for the -Status parameter, so you don’t need to look them up! Let’s take a look at how you use this cmdlet.

$Common = @{
    ProfileName = 'default'
    Region = 'us-east-2'
$CloudFormation = @{
    StackName = 'AWSCloudFormation'
    TemplateBody = @'
    AWSTemplateFormatVersion: '2010-09-09'
                Type: AWS::S3::Bucket
            Value: !Ref myBucket
New-CFNStack @CloudFormation @Common
Wait-CFNStack -StackName $CloudFormation.StackName @Common

Test the existence of the CloudFormation stack

Have you ever wanted to simply test whether a CloudFormation stack exists in a certain AWS Region? If so, we now have a cmdlet for that. The Test-CFNStack cmdlet simply returns a Boolean $true if the specified stack exists, or $false if it doesn’t. If your stack doesn’t exist, you no longer have to worry about catching exceptions thrown by the Get-CFNStack cmdlet!

$Common = @{
    ProfileName = 'default'
    Region = 'us-east-2'

if (Test-CFNStack -StackName $CloudFormation.StackName @Common) {
    Remove-CFNStack -StackName $CloudFormation.StackName –Force @Common

Format types

Another customer-obsessed enhancement in the latest version of the modules deals with the default display of certain objects. In earlier versions complex objects such as CloudFormation stacks were typically displayed in the vertical “list” format (see the Format-List PowerShell cmdlet). The “list” output format doesn’t use horizontal screen space very effectively. As a result, you have to scroll a lot to find what you want and the output isn’t easy to consume.

Instead, we opted to improve the default output to use the PowerShell table format. This makes data easier to consume, so you don’t have to scroll as much. It also limits focus to the object properties that you care about the most.

If you prefer the “list” format, you can still use it by piping your objects into the Format-List PowerShell cmdlet. The default output has simply been changed to use a tabular format to make data easier to interact with and consume.

The new format types work with cmdlets that emit complex objects, such as:

  • Get-CFNStackEvent
  • Get-CFNStack
  • Get-IAMRoleList
  • Get-CWERule
  • Get-LMFunctionList
  • Get-ASAutoScalingGroup
  • Get-WKSWorkspace
  • Get-CWAlarm

The changelog for version of the module on the PowerShell Gallery lists all the types that new formats have been specified for. You can view the changelog for the release on the PowerShell Gallery.

Manage CloudWatch dashboards

AWS customers who use CloudWatch to store and view metrics will appreciate the new CloudWatch dashboard APIs. You can now use PowerShell cmdlets to create, list, and delete CloudWatch dashboards!

I’ve already created a CloudWatch dashboard in my account, so let’s check out how we can export it, modify it, and then update it. Let’s start by discovering which AWS cmdlets relate to CloudWatch dashboards by using Get-AWSCmdletName.

PS /Users/tsulli> Get-AWSCmdletName –MatchWithRegex dashboard

CmdletName           ServiceOperation         ServiceName       CmdletNounPrefix
----------           ----------------         -----------       ----------------
Get-CWDashboard      GetDashboard             Amazon CloudWatch CW
Get-CWDashboardList  ListDashboards           Amazon CloudWatch CW
Remove-CWDashboard   DeleteDashboards         Amazon CloudWatch CW
Write-CWDashboard    PutDashboard             Amazon CloudWatch CW

Now, let’s discover which CloudWatch dashboards already exist in the us-west-2 AWS Region by using Get-CWDashboardList.

PS /Users/tsulli> Get-CWDashboardList -Region us-west-2

DashboardArn   DashboardName   LastModified        Size
------------   -------------   ------------        ----
               MacBook-Pro     7/6/17 7:50:16 PM   1510

As you can see, I’ve got a single CloudWatch dashboard in my test account, with some interesting metrics about my MacBook Pro. Coincidentally, these hardware metrics are also being written to CloudWatch metrics using the AWSPowerShell.NETCore module.

Now let’s grab some detailed information about this specific CloudWatch dashboard. We do this using the Get-CWDashboard cmdlet, and simply passing in the region and name of the dashboard. Be sure to remember that the dashboard name is a case-sensitive input parameter.

PS /Users/tsulli> $Dashboard = Get-CWDashboard -DashboardName MacBook-Pro -Region us-west-2 | Format-List

LoggedAt : 7/7/17 1:44:44 PM
DashboardArn : arn:aws:cloudwatch::123456789012:dashboard/MacBook-Pro
DashboardBody : {"widgets......
DashboardName :
ResponseMetadata : Amazon.Runtime.ResponseMetadata
ContentLength : 3221
HttpStatusCode : OK

For readability in this article, I’ve trimmed the DashboardBody property. However, it contains a lengthy string with the JSON that represents my CloudWatch dashboard. I can use the ConvertFrom-Json cmdlet to convert the string to a usable object in PowerShell.

PS /Users/tsulli> $DashboardObject = $Dashboard.DashboardBody | ConvertFrom-Json

Now let’s update the title field of all the widgets on the CloudWatch dashboard. Let’s change the beginning of each widget’s title from “Trevor” to “David”. Right now, the title reads “Trevor’s MacBook Pro”. After updating it, the widget titles will read “David’s MacBook Pro”. We’ll use the ForEach method syntax in PowerShell to do this. Each widget has a property named //properties//, which has a //title// string property. We’ll do a simple string replacement operation on this property’s value.

PS /Users/tsulli> $DashboardObject.widgets.ForEach({ $ = $'Trevor', 'David') })

Now that we’ve modified the widget titles, let’s convert the dashboard back to JSON and overwrite our dashboard! We’ll use ConvertTo-Json to convert the dashboard object back into its JSON representation. Then we’ll call Write-CWDashboard to commit the updated dashboard back to the CloudWatch service.

PS /Users/tsulli> $DashboardJson = $DashboardObject | ConvertTo-Json -Depth 8
PS /Users/tsulli> Write-CWDashboard -DashboardBody $DashboardJson -DashboardName MacBook-Pro -Region us-west-2

Great! Now if you go back to the AWS Management Console and visit your CloudWatch dashboard, you’ll see that your widgets have updated titles!


We hope you enjoy the continued improvements to the AWS Tools for PowerShell customer experience! If you have feedback on these improvements, please let us know. You can:

* Leave comments and feedback in our AWS SDK forums.
* Tweet to us at @awscloud and @awsfornet.
* Comment on this article!

AWS SDK for Go – Batch Operations with Amazon S3

by Ben Powell | on | in Go | Permalink | Comments |  Share

The v1.9.44 release of the AWS SDK for Go adds support for batched operations in the s3manager package. This enables you to easily upload, download, and delete Amazon S3 objects. The feature uses the iterator, also known as scanner pattern, to enable users to extend the functionality of batching. This blog post shows how to use and extend the new batched operations to fit a given use case.

Deleting objects using ListObjectsIterator

  sess := session.Must(session.NewSession(&aws.Config{}))
  svc := s3.New(sess)

  input := &s3.ListObjectsInput{
    Bucket:  aws.String("bucket"),
    MaxKeys: aws.Int64(100),
  // Create a delete list objects iterator
  iter := s3manager.NewDeleteListIterator(svc, input)
  // Create the BatchDelete client
  batcher := s3manager.NewBatchDeleteWithClient(svc)

  if err := batcher.Delete(aws.BackgroundContext(), iter); err != nil {

This example lists all objects, one hundred at a time, under the bucket passed in the command line arguments. The example above creates a new delete list iterator and dictates how the BatchDelete client behaves. This means that when we call Delete on the client it will require a BatchDeleteIterator.

Creating a custom iterator

The SDK enables you to pass custom iterators to the new batched operations. For example, if we want to upload a directory, none of the default iterators do this easily. The following example shows how to implement a custom iterator that uploads a directory to S3.

 // DirectoryIterator iterates through files and directories to be uploaded                                          
// to S3.                                                                                                               
type DirectoryIterator struct {                                                                                         
  filePaths []string                                                                                                    
  bucket    string                                                                                                      
  next      struct {                                                                                                    
    path string                                                                                                         
    f    *os.File                                                                                                       
  err error                                                                                                             
// NewDirectoryIterator creates and returns a new BatchUploadIterator                                                
func NewDirectoryIterator(bucket, dir string) s3manager.BatchUploadIterator {                                           
  paths := []string{}                                                                                                   
  filepath.Walk(dir, func(path string, info os.FileInfo, err error) error {                                             
    // We care only about files, not directories                                                                     
    if !info.IsDir() {                                                                                                  
      paths = append(paths, path)                                                                                       
    return nil                                                                                                          
  return &DirectoryIterator{                                                                                            
    filePaths: paths,                                                                                                   
    bucket:    bucket,                                                                                                  
// Next opens the next file and stops iteration if it fails to open                                             
// a file.                                                                                                              
func (iter *DirectoryIterator) Next() bool {                                                                            
  if len(iter.filePaths) == 0 {                                                                                     = nil                                                                                                   
    return false                                                                                                        
  f, err := os.Open(iter.filePaths[0])                                                                                  
  iter.err = err                                                                                                        
                                                                                                                  = f                                                                                                 = iter.filePaths[0]                                                                                    
  iter.filePaths = iter.filePaths[1:]                                                                                   
  return true && iter.Err() == nil                                                                                      
// Err returns an error that was set during opening the file
func (iter *DirectoryIterator) Err() error {                                                                            
  return iter.err                                                                                                       
// UploadObject returns a BatchUploadObject and sets the After field to                                              
// close the file.                                                                                                      
func (iter *DirectoryIterator) UploadObject() s3manager.BatchUploadObject {                                             
  f :=                                                                                                      
  return s3manager.BatchUploadObject{                                                                                   
    Object: &s3manager.UploadInput{                                                                                     
      Bucket: &iter.bucket,                                                                                             
      Key:    &,                                                                                          
      Body:   f,                                                                                                        
	// After was introduced in version 1.10.7
    After: func() error {                                                                                               
      return f.Close()                                                                                                  

We have defined a new iterator named DirectoryIterator. This satisfies the BatchUploadIterator by defining the three necessary methods of Next, Err, and UploadObject. The Next method on the iterator will let the batch operation know to continue the iteration or not. Err returns an error if there was one. In this case, the only time we will return an error is when we fail to open a file. If this occurs, the Next method will return false. Finally, the UploadObject returns the BatchUploadObject that is used to upload contents to the service. In this example, we see that we create an input object and a closure. This closure ensures that we’re not leaking files. Now let’s define our main function using what we defined above.

func main() {
  region := os.Args[1]
  bucket := os.Args[2]
  path := os.Args[3]
  iter := NewDirectoryIterator(bucket, path)                                                                  
  uploader := s3manager.NewUploader(session.New(&aws.Config{                                                            
    Region: &region,                                                                                    
  if err := uploader.UploadWithIterator(aws.BackgroundContext(), iter); err != nil {                                    
  fmt.Printf("Successfully uploaded %q to %q", path, bucket)                                                                                                

You can verify that the directory has been uploaded by looking in S3.

Please chat with us on gitter and file feature requests or issues in github. We look forward to your feedback and recommendations!

AWS SDK for Java 2.0 – Developer Preview

by Andrew Shore | on | in Java | Permalink | Comments |  Share

We’re pleased to announce the Developer Preview of the AWS SDK for Java 2.0. The 2.0 version of the SDK is a major rewrite of the 1.11.x code base. It’s built on top of Java 8 and adds several, frequently requested features, like support for non-blocking I/O and the ability to use a different HTTP implementation at runtime. In addition to these new features, many aspects of the SDK have been refactored and cleaned up with a strong focus on consistency, immutability, and ease of use. The Developer Preview is your chance to influence the direction of the AWS SDK for Java 2.0. Tell us what you like, tell us what you don’t like. Your feedback matters to us. Find details on various ways to give feedback at the bottom of this post.

Although we’re excited about the AWS SDK for Java 2.0 Developer Preview, we also want to reassure customers that we’re not dropping support for the 1.x line of the SDK any time soon. We know there are lots of customers who depend on 1.x versions of the SDK, and we will continue to support them. Version 2.0 is also able to run alongside version 1.x in the same JVM to allow partial migration to the new product. As we get closer to general availability for version 2.0, we’ll share a more detailed plan on how we’ll support the 1.x line.

Getting started

Let’s walk through setting up a project that depends on the SDK and makes a simple service call. The following steps use Maven as an example but you can use any build system that supports MavenCentral as an artifact source (Gradle, sbt, etc). These steps assume you have Maven and a Java 8 JDK already installed. See the developer guide for a more detailed tutorial on getting started.

    1. Create a new Java8 Maven project.
    2. Open the pom.xml file, and add a dependency on the Amazon DynamoDB module (see services/pom.xml for a full list of supported services).
    3. Create a new class with a main method, and create a DynamoDB service client using the client builder.
      package com.example;
      public class Main {
          public static void main(String[] args) {
              // The region and credentials provider are for demonstration purposes. Feel free to use whatever region and credentials
              // are appropriate for you, or load them from the environment (See
              DynamoDBClient client = DynamoDBClient.builder()
    4. Make a service request and do something with the response.
      ListTablesResponse response = client.listTables(ListTablesRequest.builder()

New features

Non-blocking I/O

The SDK now supports truly non-blocking I/O. The 1.11.x version of the SDK already has async variants of service clients. However, they are just a wrapper around a thread pool and the blocking sync client, so they don’t provide the benefits of non-blocking I/O (high concurrency with very few threads). Due to the limitations and poor resource use of the thread-per-connection model, many customers requested support for non-blocking I/O, so we are pleased to announce first class support for non-blocking I/O in our async clients. Under the hood, we use an HTTP client built on top of Netty to make the non-blocking HTTP call.

For non-streaming operations, the interfaces are nearly identical to the sync client. The only difference is that a CompletableFuture containing the response is returned immediately instead of blocking the thread until the response is available. Exceptions are delivered by completing the future exceptionally and can be accessed using the appropriate callbacks on the future (see Here’s an example of a simple service call using the async/non-blocking client.

// Creates a default async client with credentials and regions loaded from the environment
DynamoDBAsyncClient client = DynamoDBAsyncClient.create();
CompletableFuture<ListTablesResponse> response = client.listTables(ListTablesRequest.builder()
// Map the response to another CompletableFuture containing just the table names
CompletableFuture<List<String>> tableNames = response.thenApply(ListTablesResponse::tableNames);
// When future is complete (either successfully or in error) handle the response
tableNames.whenComplete((tables, err) -> {
    if (tables != null) {
    } else {
        // Handle error

Streaming operations are a bit different to allow for full non-blocking I/O. For streaming inputs (like the Amazon S3 PutObject operation), you must supply an AsyncRequestProvider that can produce content incrementally. To support asynchronous backpressure (to prevent out of memory errors if the SDK can’t send data as fast as it’s being produced) the SDK uses the reactive pull model. This is based on the well-known reactive streams interfaces. In fact, the request provider is simply a Publisher of ByteBuffer chunks. The SDK will call subscribe on that Publisher and request chunks of data as its buffer allows.

Here we upload a file asynchronously using the PutObject operation in Amazon S3. We’re using an implementation of AsyncRequestProvider that produces data from a file. It handles backpressure and retries automatically, reducing the burden on the developer. We want to support common implementations and sources of data out of the box, so if you have any suggestions or requests, please let us know.

public static void main(String[] args) {
    S3AsyncClient client = S3AsyncClient.create();
    CompletableFuture<PutObjectResponse> future = client.putObject(
    future.whenComplete((resp, err) -> {
        try {
            if (resp != null) {
            } else {
                // Handle error
        } finally {
            // Lets the application shut down. Only close the client when you are completely done with it.

For operations that have a streaming response (such as Amazon S3 GetObject), you must provide an AsyncResponseHandler that processes and transforms the response. This response handler has callback methods for various events in the response lifecycle. It follows the same reactive streams model for handling the data. (In this case, however, it’s the reverse. The SDK is the data publisher and the response handler implementation must subscribe to the publisher and request data from it.) Consult the Java documentation for a more detailed explanation of how to implement AsyncResponseHandler. In the following example we will use a pre-canned implementation that just emits the data to a file.

public static void main(String[] args) {
    S3AsyncClient client = S3AsyncClient.create();
    final CompletableFuture<Void> future = client.getObject(
    future.whenComplete((resp, err) -> {
        try {
            if (resp != null) {
            } else {
                // Handle error
        } finally {
            // Lets the application shut down. Only close the client when you are completely done with it

Pluggable HTTP layer

All earlier 1.x.x versions of the SDK have had a hard dependency on the Apache HTTP client to make HTTP calls. Although this is fine for most customers, some advanced customers wanted to swap out the default HTTP implementation to be able to use a more optimized one that’s better suited for their runtime environment. The AWS SDK for Java 2.0 now fully supports a pluggable HTTP layer. The SDK continues to ship Apache as the default, but you can remove it and replace it with another implementation that conforms to the appropriate SPI.

The SDK attempts to load an HTTP implementation from the classpath using the ServiceLoader utility. This enables end users to create their own distributions of the SDK with a different default HTTP implementation (by removing the dependency on Apache’s implementation and replacing it with their own). Customers who want to avoid potentially expensive classpath scanning can set the system property to explicitly identify the implementation to use. Finally, for customers wanting precise control over how the HTTP client is created and configured, the SDK accepts either an SdkHttpClient instance or SdkHttpClientFactory instance in each service client builder. Passing in an SdkHttpClient enables customers to share a connection pool across multiple service clients for better resource utilization.

Configuring HTTP settings

Due to the pluggable nature of the HTTP layer, customers who want to configure HTTP specific settings such as socket timeout, proxy settings, etc., must declare a dependency on the underlying implementation and configure the client through implementation provided interfaces. In the following examples we show how to configure the default Apache implementation.

  1. Declare a dependency on the Apache implementation in your project.
  2. Create and configure the Apache client factory.
    ApacheSdkHttpClientFactory apacheClientFactory = 
  3. Use the Apache client factory to create a SDK service client.
    DynamoDBClient client =

Sharing HTTP clients

The SDK now supports sharing HTTP client instances across multiple service clients. This allows you to reuse the same connection pool for better resource utilization. To share a client across multiple SDK service clients, you must depend on a specific implementation and create an HTTP client factory for that implementation, as shown above.

  1. Create an SdkHttpClient instance using the HTTP client factory we created earlier (only follows steps 1 and 2 from above).
    SdkHttpClient sharedClient = apacheClientFactory.createHttpClient();
  2. Register that HTTP client instance with multiple SDK service clients. (You can even share clients across multiple services.)
    DynamoDBClient clientOne =
    DynamoDBClient clientTwo =
  3. Because the client is shared, the SDK will not close it when the service client is closed. Be sure to explicitly close it when it’s no longer needed.

Pluggable Async HTTP

The non-blocking async HTTP client is also pluggable, and you can configure or share it in exactly the same way as sync. The interfaces for the factory and client are SdkAsyncHttpClient and SdkAsyncHttpClientFactory, respectively. An implementation built on top of Netty is the default. Add the following to your pom.xml file to configure the default Netty implementation.


API changes

We’ve made several public API changes to improve consistency, make the SDK easier to use, strongly enforce immutability for safer concurrent programming, and remove deprecated or confusing APIs. The following are some of the bigger changes included in the AWS SDK for Java 2.0 Developer Preview.

Client Builders

In 1.11.x versions, we recently deprecated all client constructors and all mutable methods on the client in favor of the client builders. In version 2.0, the client builders are now the only way to create a service client. In addition, clients are 100 percent immutable after creation. For a cleaner programming experience, all interaction with the clients is done through interfaces.

To obtain an instance of the builder, you can use a static factory method on the client interface like this.

DynamoDBClient client = DynamoDBClient.builder().build();

If you want just a quick default client that loads region and credentials from the environment you can use the following. This will fail if region or credentials are not properly setup.

DynamoDBClient client = DynamoDBClient.create();

All builders and POJOs in version 2.0 now follow a new naming convention for setter methods. There is no set/with prefix. The setter method is simply the property name. The setter methods return the builder for method chaining.

DynamoDBClient client = DynamoDBClient.builder()

Most advanced configuration in 1.11.x versions was HTTP related. Due to the pluggable nature of the HTTP layer, you must now configure this via the HTTP implementation directly (see “New features”, earlier in this post). You can change the non-HTTP related advanced configuration via the overrideConfiguration method.

DynamoDBClient client =

Immutable POJOs

Previously, all request/response POJOs were mutable, which violated the thread safety guarantees of the client. In version 2.0, all POJOs are immutable and must be created through a builder.

ListTablesRequest request = ListTablesRequest.builder()

You can modify POJOs only by converting the object into a builder, making the modifications, and rebuilding the object. In the example below, originalRequest is unchanged and a new instance of ListTablesRequest is created and returned.

public static ListTablesRequest updatePaginationToken(ListTablesRequest originalRequest, ListTablesResponse response) {
    return originalRequest.toBuilder()

Due to the immutability of POJOs and the fluent setters, serialization requires some special care. Here’s an example of serializing a request object to JSON using the Jackson library, and deserializing it back into a request object.

ObjectMapper mapper = new ObjectMapper();
ListTablesRequest request = ListTablesRequest.builder()
String serialized = mapper.writeValueAsString(request.toBuilder());

ListTablesRequest deserialized = mapper.readValue(serialized, ListTablesRequest.serializableBuilderClass())


In 1.11.x versions of the SDK, there were many different classes used for configuring regions or accessing region metadata (Region, Regions, s3.Region, RegionUtils, etc). In version 2.0, these are all collapsed into a single Region class for simplicity and ease of use.

The new Region class looks similar to an enum and has constants for each region.

DynamoDBClient client = DynamoDBClient.builder()

Creating a new region is safe to do using the static factory method of. This is useful when the region is coming from an external source such as a configuration file, or for using a region that the SDK doesn’t know about yet.

Region newRegion = Region.of("us-east-42");

You can access metadata about the region (name, partition, or domain) via the RegionMetadata interface.

String domain = RegionMetadata.of(Region.US_EAST_1).getDomain();

You can access region metadata for a service (such as which regions that service is supported in) via the ServiceMetadata interface.



There are substantial changes in the APIs for streaming operations (such as the Amazon S3 GetObject and PutObject) due to the newly added support for non-blocking I/O. Because the programming models for blocking I/O and non-blocking I/O are so radically different, we’ve removed the InputStream from the request/response POJO. Now, the sync and async clients have additional parameters for streaming operations to accept streamed content (PutObject) and to process a streamed response (GetObject). We explained the async streaming APIs earlier in this post, so let’s take a look at the sync versions.

In the following example, we’re uploading a file to S3 via the PutObject operation. Notice that we don’t set the content in the PutObjectRequest, but instead provide it as a second parameter to the putObject method. This content is provided using a new class, RequestBody, which has overloads for many common sources of data (File, String, byte array, ByteBuffer, InputStream).

S3Client client = S3Client.create();

Next, we download the same object to a file using the GetObject operation. Again, instead of accessing the InputStream from the GetObjectResponse object, you can now provide a StreamingResponseHandler implementation to process the response contents. This is a functional interface that provides the unmarshalled GetObjectResponse and the input stream as parameters and returns some transformed value (or Void). This transformed value becomes the return value of the getObject method. There are a couple of convenience static factory methods on the interface to create handlers for common situations like dumping the data into a file or writing it to an OutputStream. We use the file one below.

S3Client client = S3Client.create();

S3 client changes

In 1.11.x the S3 service client is not generated like the rest of the SDK. Because of this, it’s somewhat inconsistent with other service clients in the AWS SDK for Java. It also doesn’t exactly match the service’s API, so it can be confusing using another SDK’s S3 client after getting used to the Java client. In version 2.0 we are now generating the S3 client like every other service. Play around with it and let us know what you think.

Giving feedback and contributing

You can provide feedback to us in several ways. Both positive and negative feedback is appreciated.

Public feedback

GitHub issues. Customers who are comfortable giving public feedback can open a Github issue in the V2 repo. This is the preferred mechanism to give feedback so that other customers can engage in the conversation, +1 issues, etc. Issues you open will be evaluated, and included in our roadmap for the GA launch.

Gitter Channel. For informal discussion or general feedback, you may join the Gitter chat for the V2 repo. The Gitter channel is also a great place to get help with the Developer Preview, although feel free to open an issue as well.

Private feedback

Those who prefer not to give public feedback can instead email the mailing list. This list is monitored by the AWS SDK for Java team and will not be shared with anyone outside of AWS. An SDK team member may respond back to ask for clarification or acknowledge that the feedback was received and is being evaluated.


You can open pull requests for fixes or additions to the AWS SDK for Java 2.0 Developer Preview. All pull requests must be submitted under the Apache 2.0 license and will be reviewed by an SDK team member prior to merging. Accompanying unit tests are appreciated.

Writing Custom Metrics to Amazon CloudWatch Using the AWS SDK for Java

by Sascha Moellering | on | in Java | Permalink | Comments |  Share

Metrics measure the performance of your system. Several AWS services provide free metrics, such as the CPU usage of an Amazon EC2 instance. You can create Amazon CloudWatch alarms based on metrics and send Amazon SNS messages when the alarm state changes. You can use this mechanism to implement elastic scaling if the message is sent to an Auto Scaling group to change the desired capacity of the group. For many workloads, metrics like CPU usage are sufficient. However, from time to time, workloads have specific requirements and need a more complex metric to scale efficiently. It’s possible to publish your own metrics to CloudWatch, known as custom metrics, by using the AWS CLI, an API, or the CloudWatch collectd plugin. In this blog post, we’ll show you a more complex example of using the capabilities of the AWS SDK for Java to implement a framework integration to publish framework-related custom metrics to CloudWatch.

Integrating Vert.x and Amazon CloudWatch

Vert.x is an event-driven, reactive, nonblocking, and 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, 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. Those verticles share certain similarities with actors in the Actor Model, and to use them, you have to implement the `Verticle` interface.
The following example shows a basic verticle implementation.

public class SimpleVerticle extends AbstractVerticle {
      // Method is called when the verticle is deployed
      public void start() {

      // Optional method, called when verticle is undeployed
      public void stop() {

Verticles communicate with each other using 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. In our example, we use the default event bus address cloudwatch.metrics. Then we register this address to consume all messages and push this data into CloudWatch.

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. Basically, this means that worker verticles don’t block the event loop.

If you start writing low-latency applications, you can reach a certain point where internal metrics of frameworks are required for further optimization. By default, Vert.x doesn’t record any metrics, but offers a Service Provider Interface (SPI) that you can implement to get more information about the behavior of Vert.x internals. The interface that you have to implement is described in the API documentation.
Vert.x provides an in-depth look into the framework by offering metrics for the following:

  • Datagram/UDP
  • Vert.x event bus
  • HTTP client
  • HTTP server
  • TCP client
  • TCP server
  • Pools used by Vert.x, such as execute blocking or worker verticle

To receive metrics from Vert.x, for example, HTTP server metrics, you have to implement the `HttpServerMetrics` interface and the following method from the `VertxMetrics` interface :

HttpServerMetrics<?, ?, ?> createMetrics(HttpServer httpServer, SocketAddress address, HttpServerOptions serverOptions);

The following code snippet shows a typical implementation of `HttpServerMetrics`.

private final LongAdder processingTime = new LongAdder();
    private final LongAdder requestCount = new LongAdder();
    private final LongAdder requests = new LongAdder();
    private final SocketAddress localAddress;
    private final HttpServerMetricsSupplier httpServerMetricsSupplier;

    public  HttpServerMetricsImpl(SocketAddress localAddress, HttpServerMetricsSupplier httpServerMetricsSupplier) {
        this.localAddress = localAddress;
        this.httpServerMetricsSupplier = httpServerMetricsSupplier;

    public void responseEnd(Long nanoStart, HttpServerResponse response) {
        long requestProcessingTime = System.nanoTime() - nanoStart;

In this example, the `responseEnd` method is called if an HTTP server response has ended. The processing time of the request is calculated, the number of requests is incremented, and the number of current requests is decremented. Now we have to send the data we collected to CloudWatch.
To collect metrics data and send it to CloudWatch, we need to implement the `MetricSupplier` interface and override the `collect()` method. Each metric value is represented by an object of type `CloudWatchDataPoint`. This data point class is a simple POJO containing the name of the metric, the value, the timestamp of collection, and a CloudWatch StandardUnit. The `StandardUnit` enumeration represents the unit of the data point in CloudWatch (e.g., Bytes). After collecting a list of data points, the `Sender` class pushes the data to CloudWatch. To connect to CloudWatch, the Sender class uses the AWS SDK for Java and the `DefaultAWSCredentialsProviderChain`. This enables you to use Vert.x-CloudWatch SPI on an EC2 instance, as well as on your local development workstation.

    public Sender(Vertx vertx, VertxCloudwatchOptions options, Context context) {
        this.vertx = vertx;

        // Configuring the CloudWatch client
        // AWS credentials provider chain that looks for credentials in this order:
        //      - Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY (RECOMMENDED since they are recognized by all the AWS SDKs and CLI except for .NET), or AWS_ACCESS_KEY and AWS_SECRET_KEY (only recognized by the SDK for Java)
        //      - Java System Properties - aws.accessKeyId and aws.secretKey
        //      - Credential profiles file at the default location (~/.aws/credentials) shared by all AWS SDKs and the AWS CLI
        //      - Instance profile credentials delivered through the Amazon EC2 metadata service
        this.cloudWatchClient = initCloudWatchClient(options.getCloudwatchRegion());
        this.namespace = options.getNamespace();
        this.instanceId = options.getInstanceId();

        batchSize = options.getBatchSize();
        batchDelay = NANOSECONDS.convert(options.getBatchDelay(), SECONDS);
        queue = new ArrayList<>(batchSize);
        sendTime = System.nanoTime();

        context.runOnContext(aVoid -> timerId = vertx.setPeriodic(MILLISECONDS.convert(batchDelay, NANOSECONDS), this::flushIfIdle));


    private void send(List<CloudWatchDataPoint> dataPoints) {
        List<MetricDatum> cwData = toCloudwatchData(dataPoints);
        PutMetricDataRequest metricDataRequest = new PutMetricDataRequest();
        Future future = cloudWatchClient.putMetricDataAsync(metricDataRequest);
        sendTime = System.nanoTime();

        try {
        } catch (Exception exc) {

    private List<MetricDatum> toCloudwatchData(List<CloudWatchDataPoint> dataPoints) {
        List<MetricDatum> metrics = new ArrayList<>();

        dataPoints.forEach(metric -> {

            MetricDatum point = new MetricDatum();

            point.setTimestamp(new Date(metric.getTimestamp()));
            point.setValue((double) metric.getValue());
            List<Dimension> dimensionList = new ArrayList<>();
            dimensionList.add(new Dimension().withName("InstanceId").withValue(this.instanceId));


        return metrics;

To use the CloudWatch Vert.x SPI implementation, we have to set the necessary metrics options. In our case, we want to use the CloudWatch namespace `Vertx/CloudWatch`. Let’s assume that the application runs on an EC2 instance. In this case, the CloudWatch SPI automatically detects the region that the EC2 instance is running in and the instance ID. This information is determined by using the EC2MetadataUtils-class.
After setting the metrics options, we initiate a Vert.x instance and create a simple HTTP server on port 8080 that returns “Hello Vert.x!” in plain text. The SPI automatically detects that an HTTP server is created and collects HTTP server-related metrics such as the number of HTTP connections, the number of bytes sent, and a set of other metrics.
In addition to that, we want to send the consumed memory of the JVM to CloudWatch. This custom metric isn’t collected by the SPI, so we have to calculate the consumed memory by using the Runtime-class. A timer sends this data as a JSON message every five seconds over the event bus to the CloudWatch SPI. The SPI collects the data and sends it to CloudWatch.

    VertxOptions options = new VertxOptions().setMetricsOptions(
                new VertxCloudwatchOptions()
    vertx = Vertx.vertx(options);

    // Creating HTTP server for metrics
    HttpServer server = vertx.createHttpServer();

    server.requestHandler(request -> {

        // This handler is called for each request that arrives on the server
        HttpServerResponse response = request.response();
        response.putHeader("content-type", "text/plain");

        // Write to the response and end it
        response.end("Hello Vert.x!");

    vertx.setPeriodic(5000, id -> {
        long usedMem = this.getUsedMemory();
        JsonObject message = new JsonObject()
                .put("metricName", "JVMMemory")
                .put("unit", StandardUnit.Megabytes.toString())
                .put("value", usedMem);

        vertx.eventBus().publish("cloudwatch.metrics", message);


The following figure shows metrics such as the number of HTTP connections, the number of requests, the amount of bytes sent, and the consumed memory displayed as a graph in CloudWatch.

Vert.x metrics

Note that a custom metric is defined as the unique combination of metric name and dimensions associated with the metric. Custom metrics are priced based on monthly usage per metric. See CloudWatch pricing for details.


In this blog post we created a Vert.x SPI implementation to write framework metrics to CloudWatch. We used the capabilities of the AWS SDK for Java not only for the communication with CloudWatch, but also to get insights about the instance and the region using EC2 metadata. We hope we’ve given you ideas for creating your own applications and framework integrations by using the AWS SDK for Java. Feel free to share your ideas and thoughts in the comments below!


Using Go 1.8’s Plugin for Credentials with the AWS SDK for Go

by Jason Del Ponte | on | in Go | Permalink | Comments |  Share

The v1.10.0 release of the AWS SDK for Go adds a new way to configure the SDK to retrieve AWS credentials. With this release, you can configure the SDK to retrieve AWS credentials from a Go plugin that is dynamically loaded during your application’s runtime. In this post, we explain how you can build a plugin and configure the SDK to use it. The SDK also includes a runnable example for you to try out the new plugin credential provider feature.

The SDK does takes advantage of the Go 1.8 plugin package, and associated build mode for Linux operating systems. The plugin package and associated build mode enable you to write components that can be loaded dynamically while your application runs. Plugins help you add functionality to your application while it’s running instead of only when the application is compiled.

The SDK’s plugincreds package enables you to use the plugins to retrieve AWS credentials. This package includes utilities to create a credentials Provider and Credentials loader.

Building a credential provider plugin

To use a plugin with the SDK, the SDK requires the plugin to export a function that returns two function pointers. The SDK uses these two returned function pointers to retrieve credentials and to determine if the credentials are expired. By default, the SDK expects the plugin to export the symbol named GetAWSSDKCredentialProvider for the getter function that returns the retrieve and isExpired function pointers.

The SDK requires the plugin’s getter function signature to match the following signature. If the getter function doesn’t match the signature, the SDK returns an error with the code ErrCodeInvalidSymbolError.

func() (RetrieveFn func() (key, secret, token string, err error), IsExpiredFn func() bool)

The SDK includes the NewCredentials helper function that looks up and validates the symbol, creating the SDK’s Credentials value automatically. You can use the returned Credentials value to configure a session or service client.

To use a custom symbol name, use the GetPluginProviderFnsByName function to look up the getter function from the plugin by name. This verifies that the symbol matches the expected signature. It also gets the credential provider’s retrieve and isExpired function pointers by calling the getter function. The retrieve and isExpired function pointers are returned. The SDK requires both function pointers to be valid and not nil.

Here is an example of a plugin that provides credential retrieve and expired functions to the application that loaded the plugin.

package main

func main() {}

// Build: go build -o -buildmode=plugin plugin.go
func init() {
	// Initialize a mock credential provider with mock values. In a real-world usage
	// the provider's Retrieve method could reach out to the source of credentials
	// and return the credentials there, instead of this mock credential provider that statically
	// sets the credential values.
	myCredProvider = provider{"key","secret","token"}

// GetAWSSDKCredentialProvider is the symbol the SDK will look up and use to
// get the credential provider's retrieve and isExpired functions.
func GetAWSSDKCredentialProvider() (func() (key, secret, token string, err error), func() bool) {
	return myCredProvider.Retrieve,	myCredProvider.IsExpired

// Mock implementation of a type that retrieves credentials and
// returns if they are expired.
type provider struct {
	key, secret, token string

// Return the credentials that were previously set into the provider value.
func (p provider) Retrieve() (key, secret, token string, err error) {
	return p.key, p.secret, p.token, nil

func (p *provider) IsExpired() bool {
	return false;

Once you’ve written the code for your plugin, you can build it as a plugin file that can be loaded dynamically into your application with the -buildmode=plugin build flag.

go build -o -buildmode=plugin plugin.go

You can find an example you can start from in the SDK’s plugincreds example.

Using a credential provider plugin

Once you’ve built your plugin, you can configure the SDK to retrieve credentials using it. The SDK makes this easy with the plugincreds package’s NewCredential function. This function takes a Plugin pointer value and looks up the expected credentials provider getter functions. See the plugincreds package for errors that can be returned.

The following example shows you how an application can open a Go plugin dynamically at runtime, and configure the SDK to use the plugin to retrieve AWS credentials.

// In your application code, open the plugin using its file name. This loads
// the plugin into memory, executing the plugin's main package init function.
p, err := plugin.Open(pluginFilename)
if err != nil {
	return nil, errors.Wrapf(err, "failed to open plugin, %s", pluginFilename)

// NewCredentials looks up the symbol from the plugin and configures the Credentials
// value that can be used to configure a session or service client.
// You can share the Credentials value and credentials, across many session and service clients 
// concurrently and safely.
creds, err := plugincreds.NewCredentials(p)
if err != nil {
	return nil, errors.Wrapf(err, "failed to load plugin credentials provider, %s", pluginFilename)

// Configure a session to use the credentials sourced from the plugin that is loaded.
sess := session.Must(session.NewSession(&aws.Config{
	Credentials: creds,

// Return the configured session so it can be used to create service clients.
return sess, nil

You can find a usable example of this in the SDK’s plugincreds example.

Putting it all together

With this configuration, you can deploy your plugin and application independently to the platforms that your application will run on. Loading plugins dynamically allows you to separate your application from where your AWS credentials are retrieved. This practice allows your application to be more flexible when working with multiple environments. This technique is particularly useful for CLI applications where users of the CLI need to provide custom ways of retrieving credentials.

Let us know how you use the credentials plugin in your applications.

AWS Toolkit for Eclipse: Improved Support for Serverless Applications (Part 3 of 3)

In the first part of the blog series, we created a new application named rekognition-service from the rekognition blueprint. In the second part, we deployed this serverless application to AWS CloudFormation. In this last part of the blog series, we describe how to test and check the result of the newly deployed rekognition-service application.

Test the rekognition-service application by using the Amazon S3 bucket editor

You can drag and drop a group of files, including folders, to the Amazon S3 bucket editor so you can upload them to an Amazon S3 bucket. The .jpg files trigger the underlying Lambda function to be tagged with the name and confidence value returned by Amazon Rekognition. You can also manually update and delete these tags by using the tag dialog box.

Check the Lambda logs by using the AWS Lambda function editor

You can also check the Lambda function logs by using the Lambda function editor. All the Amazon CloudWatch streams for the Lambda function are listed on the Logs tab in the editor. You can double-click one item to open the underlying one stream in Eclipse. You can also select multiple items, right-click, and then select Show Log Events to open the underlying streams in one batch.

This concludes our three-part series. What do you think of the rekognition serverless blueprint and the working flow in the AWS Toolkit for Eclipse? If you have any requests for new blueprints and features in the AWS Toolkit for Eclipse, please let us know. We appreciate your feedback.

AWS Toolkit for Eclipse: Improved Support for Serverless Applications (Part 2 of 3)

In the first part of this blog post, we talked about how to create a new AWS SAM application from the rekognition serverless blueprint. In this second part, we describe how to deploy the application to AWS CloudFormation.

Deploy the rekognition-service application to a new CloudFormation stack

This .gif animation shows the steps to deploy an AWS SAM application to AWS CloudFormation.

What does the AWS Toolkit for Eclipse do for you during deployment

  • Creates a .zip file that contains the project and all its dependencies. Then uploads the file to the specified Amazon S3 bucket.
  • Updates the serverless.template (as shown in the following snippet) to fill in the complete properties for the AWS::Serverless::Function resource type:
    • Replaces the Handler with the FQCN of the AWS Lambda function handler.
    • Generates the actual code URI for CodeUri so that AWS CloudFormation can reference the Lambda function artifact in the S3 bucket.
    • Adds the missing configurations (Runtime, Description, MemorySize, Timeout, Role) and use the default values.
  • Creates a new AWS CloudFormation stack using the updated serverless.template file.

Here is the updated snippet for TagImage in the CloudFormation template.

"TagImage" : {
      "Type" : "AWS::Serverless::Function",
      "Properties" : {
        "Handler" : "com.serverless.demo.function.TagImage",
        "Runtime" : "java8", "CodeUri" : "s3://zhaoxiz-us-west-1/", "Description" : null, "MemorySize" : 512, "Timeout" : 300, "Role" : null,
        "Policies" : [ "AmazonS3FullAccess", "AmazonRekognitionFullAccess" ],
        "Events" : {
          "ProcessNewImage" : {
            "Type" : "S3",
            "Properties" : {
              "Bucket" : {"Ref" : "ImageBucket"},
              "Events" : "s3:ObjectCreated:*",
              "Filter" : {
                "S3Key" : {
                  "Rules" : [{"Name": "suffix", "Value": ".jpg"}]

Deploy the rekognition-service application to an existing CloudFormation stack

We want to update the recognition confidence value to 80 in the Lambda function code and redeploy it to the CloudFormation stack. The following .gif animation shows how you can achieve that. When doing a second deployment for the same project, the AWS toolkit for Eclipse remembers the parameters used in the last deployment, so if you want to keep them the same, you don’t have to retype them.

Notice that we need to change the parameter value of ImageBucketExists to true in the parameter page (Fill in stack template parameters) because the bucket was already created during the first deployment. The underlying CloudFormation stack is updated with the new version of the Lambda function whether or not you update the parameters.

Update the Lambda event source by using the parameters page

Now, we want to configure the trigger event for the Lambda function to another new S3 bucket. This removes the bucket we created in the first deployment and creates a new bucket for this deployment. We only need to redeploy the application and update the ImageBucketExists parameter to false, and the ImageBucketName parameter to the new bucket name. After deployment, you see that the name of the ImageStack in the stack outputs is updated to the new name.

In the third part of this blog post, we’ll talk about how to use the AWS Toolkit for Eclipse to check the result of the rekognition-service application.