AWS Blog

Forward-looking ISVs (Indepdendent Software Vendors) are making great use of AWS Marketplace.  Users can find, buy, and start using products in minutes, without having to procure hardware or install any software. This streamlined delivery method can help ISVs to discover new customers while also decreasing the length of the sales cycle. The user pays for the products via their existing AWS account, per the regular AWS billing cycle.

As part of the on-boarding process for AWS Marketplace, each ISV has the freedom to determine the price of the software. The ISV can elect to offer prices for monthly and/or annual usage, generally with a discount. For software that is traditionally licensed on something other than time, ISVs make multiple entries in AWS Marketplace, representing licensing options on their chosen dimension.

This model has worked out well for many types of applications. However, as usual, there’s room to do even better!

More Pricing Options
ISVs have told us that they would like to have some more flexibility when it comes to packaging and pricing their software and we are happy to oblige. Some of them would like to extend the per-seat model without having to create multiple entries. Others would like to charge on other dimensions. A vendor of security products might want to charge by the number of hosts that were scanned. Or, a vendor of analytic products might want to charge based on the amount of data processed.

In order to accommodate all of these options, ISVs can now track and report on usage based on a pricing dimension that makes sense for their product (number of hosts scanned, amount of data processed, and so forth). They can also establish a per-unit price for this usage ($0.50 per host, $0.25 per GB of data, and so forth). Charges for this usage will appear on the user’s AWS bill.

I believe that this change will open the door to an even wider variety of products in the AWS Marketplace.

Implementing New Pricing Options
If you are an ISV and would like to use this new model price to your AWS Marketplace products, you need to add a little bit of code to your app. You simply measure usage along the appropriate dimension(s) and then call a new AWS API function to report on the usage. You must send this data (also known as a metering record) once per hour, even if there’s no usage for the hour. AWS Marketplace expects each running copy of the application to generate a metering record each hour in order to confirm that the application is still functioning properly. If the application stops sending records, AWS will email the customer and ask them to adjust their network configuration.

Here’s a sample call to the new MeterUsage function:

AWSMarketplaceMetering::MeterUsage("4w1vgsrkqdkypbz43g7qkk4uz","2015-05-19T07:31:23Z", "HostsScanned", 2);

The parameters are as follows:

1. AWS Marketplace product code.
2. Timestamp (UTC), in ISO-8601 format.
3. Usage dimension.
4. Usage quantity.

The usage data will be made available to you as part of the daily and monthly seller reports.

Some Examples
Here are a couple of examples of products that are already making use of this new pricing option. As you can see in the Infrastructure Fees, these vendors have chosen to price their products along a variety of interesting (and relevant) dimensions:

SoftNAS Cloud NAS:

Aspera faspex On-Demand:

Chef Server:

Trend Micro Deep Security:

Available Now
This new pricing option is available now and you can start using it today!

You can launch an EC2 Spot fleet with a couple of clicks. Once launched, the fleet allows you to draw resources from multiple pools of capacity, giving you access to cost-effective compute power regardless of the fleet size (from one instance to many thousands). For more information about this important EC2 feature, read my posts: Amazon EC2 Spot Fleet API – Manage Thousands of Spot Instances with One Request and Spot Fleet Update – Console Support, Fleet Scaling, CloudFormation.

I like to think of each Spot fleet as a single, collective entity. After a fleet has been launched, it is an autonomous group of EC2 instances. The instances may come and go from time to time as Spot prices change (and your mix of instances is altered in order to deliver results as cost-effectively as possible) or if the fleet’s capacity is updated, but the fleet itself retains its identity and its properties.

New Spot Fleet Metrics
In order to make it even easier for you to manage, monitor, and scale your Spot fleets as collective entities, we are introducing a new set of Spot fleet CloudWatch metrics.

The metrics are reported across multiple dimensions: for each Spot fleet, for each Availability Zone utilized by each Spot fleet, for each EC2 instance type within the fleet, and for each Availability Zone / instance type combination.

The following metrics are reported for each Spot fleet (you will need to enable EC2 Detailed Monitoring in order to ensure that they are all published):

• AvailableInstancePoolsCount
• BidsSubmittedForCapacity
• CPUUtilization
• DiskReadBytes
• DiskReadOps
• DiskWriteBytes
• DiskWriteOps
• EligibleInstancePoolCount
• FulfilledCapacity
• MaxPercentCapacityAllocation
• NetworkIn
• NetworkOut
• PendingCapacity
• StatusCheckFailed
• StatusCheckFailed_Instance
• StatusCheckFailed_System
• TargetCapacity
• TerminatingCapacity

Some of the metrics will give you some insights into the operation of the Spot fleet bidding process. For example:

• AvailableInstancePoolsCount – Indicates the number of instance pools included in the Spot fleet request.
• BidsSubmittedForCapacity – Indicates the number of bids that have been made for Spot fleet capacity.
• EligibleInstancePoolsCount – Indicates the number of instance pools that are eligible for Spot instance requests. A pool is ineligible when either (1) The Spot price is higher than the On-Demand price or (2) the bid price is lower than the Spot price.
• FulfilledCapacity – Indicates the amount of capacity that has been fulfilled for the fleet.
• PercentCapacityAllocation – Indicates the percent of capacity allocated for the given dimension. You can use this in conjunction with the instance type dimension to determine the percent of capacity allocated to a given instance type.
• PendingCapacity – The difference between TargetCapacity and FulfilledCapacity.
• TargetCapacity – The currently requested target capacity for the Spot fleet.
• TerminatingCapacity – The fleet capacity for instances that have received Spot instance termination notices.

These metrics will allow you to determine the overall status and performance of each of your Spot fleets. As you can see from the names of the metrics, you can easily observe the disk, CPU, and network resources consumed by the fleet. You can also get a sense for the work that is happening behind the scenes as bids are placed on your behalf for Spot capacity.

You can further inspect the following metrics across the Availability Zone and/or instance type dimensions:

• CPUUtilization
• DiskReadBytes
• DiskReadOps
• DiskWriteBytes
• FulfilledCapacity
• NetworkIn
• NetworkOut
• StatusCheckFailed
• StatusCheckFailed_Instance
• StatusCheckFailed_System

These metrics will allow you to see if you have an acceptable distribution of load across Availability Zones and/or instance types.

You can aggregate these metrics using Max, Min, or Avg in order to observe the overall utilization of your fleet. However, be aware that using Avg does not always make sense when used across a fleet comprised of two or more types of instances!

Available Now
The new metrics are available now.

Let’s take a quick look at what happened in AWS-land last week:

New & Notable Open Source

• Tumbless is a blogging platform based only on S3 and your browser.
• aws-amicleaner cleans up old, unused AMIs and related snapshots.
• alexa-aws-administration helps you to do various administration tasks in your AWS account using an Amazon Echo.
• aws-s3-zipper takes an S3 bucket folder and zips it for streaming.
• aws-lambda-helper is a collection of helper methods for Lambda.
• CloudSeed lets you describe a list of AWS stack components, then configure and build a custom stack.
• aws-ses-sns-dashboard is a Go-based dashboard with SES and SNS notifications.
• snowplow-scala-analytics-sdk is a Scala SDK for working with Snowplow-enriched events in Spark using Lambda.
• StackFormation is a lightweight CloudFormation stack manager.
• aws-keychain-util is a command-line utility to manage AWS credentials in the OS X keychain.

New SlideShare Presentations

• Account Separation and Mandatory Access Control on AWS.
• Crypto Options in AWS.
• Security Day IAM Recommended Practices.
• What’s Nearly New.

New Customer Success Stories

• AdiMap measures online advertising spend, app financials, and salary data. Using AWS, AdiMap builds predictive financial models without spending millions on compute resources and hardware, providing scalable financial intelligence and reducing time to market for new products.
• Change.org is the world’s largest and fastest growing social change platform, with more than 125 million users in 196 countries starting campaigns and mobilizing support for local causes and global issues. The organization runs its website and business intelligence cluster on AWS, and runs its continuous integration and testing on Solano CI from APN member Solano Labs.
• Flatiron Health has been able to reach 230 cancer clinics and 2,200 clinicians across the United States with a solution that captures and organizes oncology data, helping to support cancer treatments. Flatiron moved its solution to AWS to improve speed to market and to minimize the time and expense that the startup company needs to devote to its IT infrastructure.
• Global Red specializes in lifecycle marketing, including strategy, data, analytics, and execution across all digital channels. By re-architecting and migrating its data platform and related applications to AWS, Global Red reduced the time to onboard new customers for its advertising trading desk and marketing automation platforms by 50 percent.
• GMobi primarily sells its products and services to Original Design Manufacturers and Original Equipment Manufacturers in emerging markets. By running its “over the air” firmware updates, mobile billing, and advertising software development kits in an AWS infrastructure, GMobi has grown to support 120 million users while maintaining more than 99.9 percent availability
• Time Inc.’s new chief technology officer joined the renowned media organization in early 2014, and promised big changes. With AWS, Time Inc. can leverage security features and functionality that mirror the benefits of cloud computing, including rich tools, best-in-class industry standards and protocols and lower costs.
• Seaco Global is one of the world’s largest shipping companies. By using AWS to run SAP applications, it also reduced the time needed to complete monthly business processes to just one day, down from four days in the past.

New YouTube Videos

• AWS Database Migration Service.
• Introduction to Amazon WorkSpaces.
• AWS Pop-up Loft.
• Save the Date – AWS re:Invent 2016.

Upcoming Events

• March 22nd – Live Event (Seattle, Washington) – AWS Big Data Meetup – Intro to SparkR.
• March 22nd – Live Broadcast – VoiceOps: Commanding and Controlling Your AWS environments using Amazon Echo and Lambda.
• March 23rd – Live Event (Atlanta, Georgia) – AWS Key Management Service & AWS Storage Services for a Hybrid Cloud (Atlanta AWS Community).
• April 6th – Live Event (Boston, Massachusetts) AWS at Bio-IT World.
• April 18th & 19th – Live Event (Chicago, Illinois) – AWS Summit – Chicago.
• April 20th – Live Event (Melbourne, Australia) – Inaugural Melbourne Serverless Meetup.
• April 26th – Live Event (Sydney, Australia) – AWS Partner Summit.
• April 26th – Live Event (Sydney, Australia) – Inaugural Sydney Serverless Meetup.
• ParkMyCloud 2016 AWS Cost-Reduction Roadshow.
• AWS Loft – San Francisco.
• AWS Loft – New York.
• AWS Loft – Tel Aviv.
• AWS Zombie Microservices Roadshow.
• AWS Public Sector Events.
• AWS Global Summit Series.

Help Wanted

• AWS Careers.

Stay tuned for next week! In the meantime, follow me on Twitter and subscribe to the RSS feed.

— Jeff;

It is still a bit of a shock to me to realize that Amazon S3 is now ten years old! The intervening decade has simply flown by.

For several years, you have been able to use S3’s Lifecycle Management feature to control the storage class and the lifetime of your objects. As you may know, you can set up rules on a per-bucket or per-prefix basis. Each rule specifies an action to be taken when objects reach a certain age.

Today we are adding two rules that will give you additional control over two special types of objects: incomplete multipart uploads and expired object delete markers. Before we go any further, I should define these objects!

Incomplete Multipart Uploads – S3’s multipart upload feature accelerates the uploading of large objects by allowing you to split them up into logical parts that can be uploaded in parallel.  If you initiate a multipart upload but never finish it, the in-progress upload occupies some storage space and will incur storage charges. However, these uploads are not visible when you list the contents of a bucket and (until today’s release) had to be explicitly removed.

Expired Object Delete Markers – S3’s versioning feature allows you to preserve, retrieve, and restore every version of every object stored in a versioned bucket. When you delete a versioned object, a delete marker is created. If all previous versions of the object subsequently expire, an expired object delete marker is left. These markers do not incur storage charges. However, removing unneeded delete markers can improve the performance of S3’s LIST operation.

New Rules
You can now exercise additional control over these objects using some new lifecycle rules, lowering your costs and improving performance in the process. As usual, you can set these up using the AWS Management Console, the S3 APIs, the AWS Command Line Interface (CLI), or the AWS Tools for Windows PowerShell.

Here’s how you set up a rule for incomplete multipart uploads using the Console. Start by opening the console and navigating to the desired bucket (mine is called jbarr):

Then click on Properties, open up the Lifecycle section, and click on Add rule:

Decide on the target (the whole bucket or the prefixed subset of your choice) and then click on Configure Rule:

Then enable the new rule and select the desired expiration period:

As a best practice, we recommend that you enable this setting even if you are not sure that you are actually making use of multipart uploads. Some applications will default to the use of multipart uploads when uploading files above a particular, application-dependent, size.

Here’s how you set up a rule to remove delete markers for expired objects that have no previous versions:

S3 Best Practices
While you are here, here are some best practices that you should consider using for your own S3-based applications:

Versioning – You can enable Versioning for your S3 buckets in order to be able to recover from accidental overwrites and deletes. With versioning turned on, you can preserve, retrieve, and restore earlier versions of your data.

Replication – Take advantage of S3’s Cross-Region Replication in order to meet your organization’s compliance policies by creating a replica of your data in a second AWS Region.

Performance -If you anticipate a consistently high number of PUT, LIST, DELETE, or GET requests against your buckets, you can optimize your application’s performance by implementing the tips outlined in the performance section of the Amazon S3 documentation.

Cost Management – You can reduce your costs by setting up S3 lifecycle policies that will transition your data to other S3 storage tiers or expire data that is no longer needed.

Amazon Aurora is a fully-managed, MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source database (read my post, Amazon Aurora – New Cost-Effective MySQL-Compatible Database Engine for Amazon RDS, to learn more).

Aurora allows you create up to 15 read replicas to increase read throughput and for use as failover targets. The replicas share storage with the primary instance and provide lightweight, fine-grained replication that is almost synchronous, with a replication delay on the order of 10 to 20 milliseconds.

Additional Failover Control
Today we are making Aurora even more flexible by giving you control over the failover priority of each read replica. Each read replica is now associated with a priority tier (0-15).  In the event of a failover, Amazon RDS will promote the read replica that has the highest priority (the lowest numbered tier). If two or more replicas have the same priority, RDS will promote the one that is the same size as the previous primary instance.

You can set the priority when you create the Aurora DB instance:

This feature is available now and you can start using it today. To learn more, read about Fault Tolerance for an Aurora DB Cluster.

Do you currently store relational data in an on-premises Oracle, SQL Server, MySQL, MariaDB, or PostgreSQL database? Would you like to move it to the AWS cloud with virtually no downtime so that you can take advantage of the scale, operational efficiency, and the multitude of data storage options that are available to you?

If so, the new AWS Database Migration Service (DMS) is for you! First announced last fall at AWS re:Invent, our customers have already used it to migrate over 1,000 on-premises databases to AWS. You can move live, terabyte-scale databases to the cloud, with options to stick with your existing database platform or to upgrade to a new one that better matches your requirements.  If you are migrating to a new database platform as part of your move to the cloud, the AWS Schema Conversion Tool will convert your schemas and stored procedures for use on the new platform.

The AWS Database Migration Service works by setting up and then managing a replication instance on AWS. This instance unloads data from the source database and loads it into the destination database, and can be used for a one-time migration followed by on-going replication to support a migration that entails minimal downtime.  Along the way DMS handles many of the complex details associated with migration, including data type transformation and conversion from one database platform to another (Oracle to Aurora, for example). The service also monitors the replication and the health of the instance, notifies you if something goes wrong, and automatically provisions a replacement instance if necessary.

The service supports many different migration scenarios and networking options  One of the endpoints must always be in AWS; the other can be on-premises, running on an EC2 instance, or running on an RDS database instance. The source and destination can reside within the same Virtual Private Cloud (VPC) or in two separate VPCs (if you are migrating from one cloud database to another). You can connect to an on-premises database via the public Internet or via AWS Direct Connect.

Migrating a Database
You can set up your first migration with a couple of clicks! You simply create the target database, migrate the database schema, set up the data replication process, and initiate the migration. After the target database has caught up with the source, you simply switch to using it in your production environment.

I start by opening up the AWS Database Migration Service Console (in the Database section of the AWS Management Console as DMS) and clicking on Create migration.

The Console provides me with an overview of the migration process:

I click on Next and provide the parameters that are needed to create my replication instance:

For this blog post, I selected one of my existing VPCs and unchecked Publicly accessible. My colleagues had already set me up with an EC2 instance to represent my “on-premises” database.

After the replication instance has been created, I specify my source and target database endpoints and then click on Run test to make sure that the endpoints are accessible (truth be told, I spent some time adjusting my security groups in order to make the tests pass):

Now I create the actual migration task. I can (per the Migration type) migrate existing data, migrate and then replicate, or replicate going forward:

I could have clicked on Task Settings to set some other options (LOBs are Large Objects):

The migration task is ready, and will begin as soon as I select it and click on Start/Resume:

I can watch for progress, and then inspect the Table statistics to see what happened (these were test tables and the results are not very exciting):

At this point I would do some sanity checks and then point my application to the new endpoint. I could also have chosen to perform an ongoing replication.

The AWS Database Migration Service offers many options and I have barely scratched the surface. You can, for example, choose to migrate only certain tables. You can also create several different types of replication tasks and activate them at different times.  I highly recommend you read the DMS documentation as it does a great job of guiding you through your first migration.

If you need to migrate a collection of databases, you can automate your work using the AWS Command Line Interface (CLI) or the Database Migration Service API.

Price and Availability
The AWS Database Migration Service is available in the US East (Northern Virginia), US West (Oregon), US West (Northern California), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Tokyo), Asia Pacific (Singapore),  and Asia Pacific (Sydney) Regions and you can start using it today (we plan to add support for other Regions in the coming months).

Pricing is based on the compute resources used during the migration process, with a charge for longer-term storage of logs. See the Database Migration Service Pricing page for more information.

The AWS Partner Network (APN) helps our partners to build successful businesses around AWS. Members of APN provide consulting services (APN Consulting Partners) or software solutions (APN Technology Partners) that are integrated with the AWS platform.

I am happy to be able announce that AWS Advanced Technology Partner Splunk (read their APN entry) has named Amazon Web Services to be their Worldwide Alliance Partner of the Year (read the press release to learn more). We are thrilled to be able to work with them to make their solution available to AWS customers worldwide.

The Splunk App for AWS is one of the most popular apps on Splunkbase.  The app provides you with insight into the operational and security issues associated with your AWS account. It works in conjunction with AWS Config, AWS CloudTrail, VPC Flow Logs, AWS Billing, and S3 to provide you a a logical, toplogically-oriented dashboard designed to help you to optimize resources and detect problems.

Rob Leidle, Development Manager for Amazon EMR, wrote the guest post below to introduce you to the latest and greatest version!

Today we are announcing Amazon EMR release 4.4.0, which adds support for Apache Sqoop (1.4.6) and Apache HCatalog 1.0.0, an upgraded release of Apache Mahout (0.11.1), and upgraded sandbox releases for Presto (0.136) and Apache Zeppelin (0.5.6). We have also enhanced our default Apache Spark settings and added support for Java 8.

New Applications in Release 4.4.0
Amazon EMR provides an easy way to install and configure distributed big data applications in the Hadoop and Spark ecosystems on managed clusters of Amazon EC2 instances. You can create Amazon EMR clusters from the Amazon EMR Create Cluster Page in the AWS Management Console, AWS Command Line Interface (CLI), or using a SDK with an EMR API. In the latest release, we added support for several new versions of the following applications:

• Zeppelin 0.5.6 – Zeppelin is an open-source interactive and collaborative notebook for data exploration using Spark. Zeppelin 0.5.6 adds the ability to import or export a notebook, notebook storage in GitHub, auto-save on navigation, and better Pyspark support. View the Zeppelin release notes or learn more about Zeppelin on Amazon EMR.
• Presto 0.136 – Presto is an open-source, distributed SQL query engine designed for low-latency queries on large datasets in Amazon S3 and HDFS. This is a minor version release, with support for larger arrays, SQL binary literals, the ability to call connector-defined procedures, and improvements to the web interface. View the Presto release notes or learn more about Presto on Amazon EMR.
• Sqoop 1.4.6 – Sqoop is a tool for transferring bulk data between HDFS, S3 (using EMRFS), and structured datastores such as relational databases.  You can use Sqoop to transfer structured data from RDS and Aurora to EMR for processing, and write out results back to S3, HDFS, or another database. Learn more about Sqoop on Amazon EMR.
• Mahout 0.11.1 – Mahout is a collection of tools and libraries for building distributed machine learning applications. This release includes support for Spark as well as a new math environment based on Spark named Samsara. Learn more about Mahout on Amazon EMR.
• HCatalog 1.0.0 – HCatalog is a sub-project within the Apache Hive project. It is a table and storage management layer for Hadoop which utilizes the Hive Metastore. It enables tools to execute SQL on Hadoop through an easy to use REST interface.

Enhancements to the default settings for Spark
We have improved our default configuration for Spark executors from the Apache defaults to better utilize resources on your cluster. Starting with release 4.4.0, EMR has enabled dynamic allocation of executors by default, which lets YARN determine how many executors to utilize when running a Spark application. Additionally, the amount of memory used for each executor is now automatically determined by the instance family used for your cluster’s core instance group.

Enabling dynamic allocation and customizing the executor memory allows Spark to utilize all resources on your cluster, place additional executors on nodes added to your cluster, and better allow for multitenancy for Spark applications. The previous maximizeResourceAllocation parameter is still available. However, this doesn’t use dynamic allocation, and specifies a static number of executors for your Spark application. You can also still override the new defaults by using the configuration API or passing additional parameters when submitting your Spark application using spark-submit. Learn more about Spark configuration on Amazon EMR.

Using Java 8 with your applications on Amazon EMR
By default, applications on your Amazon EMR cluster use the Java Development Kit 7 (JDK 7) for their runtime environment. However, on release 4.4.0, you can use JDK 8 by setting JAVA_HOME to point to JDK 8 for the relevant environment variables using a configuration object (though please note that JDK 8 is not compatible with Apache Hive). Learn more about using Java 8 on Amazon EMR.

Launch an Amazon EMR Cluster with Release 4.4.0 Today
To create an Amazon EMR cluster with 4.4.0, select release 4.4.0 on the Create Cluster page in the AWS Management Console, or use the release label emr-4.4.0 when creating your cluster from the AWS CLI or using a SDK with the EMR API.

— Rob Leidle – Development Manager, Amazon EMR

Let’s take a quick look at what happened in AWS-land last week:

New & Notable Open Source

• sqs-to-lambda-via-lambda implements Amazon SQS to Lambda using Lambda.
• akiro magically compiles NPM packages with native extensions for Lambda.
• cloudwatch-to-sumo sends metrics from CloudWatch to Sumo Logic.
• awsam is an AWS Account Manager modeled after rvm.
• aws-jwt-auth is an API Gateway custom authorizer to validate JWTs created by WSO2.
• aws_mbedtls_mqtt is the source code to use the mbedTLS library to connect to AWS IoT.
• jaxrs-lib contains Jersey and Hibernate Components for building REST APIs hosted on Elastic Beanstalk.
• autosignr is a Puppet Certificate Auto-signer for AWS.
• llama-cli is Chaos Llama, a tool for testing resiliency and recoverability of AWS-based architectures.
• cfn-amibaker bakes EC2 AMIs using CloudFormation and Lambda.

New SlideShare Presentations

• Intro to AWS IoT.

Upcoming Events

• March 14th – Live Event (Seattle, Washington) – Seattle AWS Architects & Engineers – Lambda + Alexa AWS Teams.
• March 15th – Live Event (San Francisco, California) – Amazon Lumberyard team at GDC 2016.
• March 17th – Webinar – Security Best Practices for Retailers on AWS.
• March 17th – Live Event (Netherlands) – Security in the Cloud.
• March 22nd – Live Broadcast – VoiceOps: Commanding and Controlling Your AWS environments using Amazon Echo and Lambda.
• March 23rd – Live Event (Atlanta, Georgia) – AWS Key Management Service & AWS Storage Services for a Hybrid Cloud (Atlanta AWS Community).
• April 6th – Live Event (Boston, Massachusetts) AWS at Bio-IT World.
• April 18th & 19th – Live Event (Chicago, Illinois) – AWS Summit – Chicago.
• April 20th – Live Event (Melbourne, Australia) – Inaugural Melbourne Serverless Meetup.
• April 26th – Live Event (Sydney, Australia) – Inaugural Sydney Serverless Meetup.
• AWS Loft – San Francisco.
• AWS Loft – New York.
• AWS Loft – Tel Aviv.
• AWS Public Sector Events.
• AWS Global Summit Series.

Help Wanted

• AWS Careers.

Stay tuned for next week! In the meantime, follow me on Twitter and subscribe to the RSS feed.

— Jeff;

My colleague Jonathan Henson has great news for C++ developers who would like to use AWS.

I am happy to announce that the AWS SDK for C++ is now available as a developer preview. Last fall, we released the SDK in an experimental state to gather feedback and improve the APIs. Since then, we have received more than 100 issues and pull requests on GitHub. Many excited developers in the open source community gave valuable feedback that helped to improve the stability and expand the features of this SDK.

Changes and Additions
Here are some additions we’ve made since our experimental release:

• Full service coverage parity with the rest of the SDKs.
• Visual Studio 2015 support.
• OS X El Capitan support.
• Presigned URL support.
• Expansion of and improvements to the Amazon S3 TransferClient.
• Inline documentation improvements.
• More integration for custom memory management.
• Forward-compatible enumeration support.
• Improvements to our CMake exports to simplify consumer builds.
• Unicode support.
• Several service client fixes and improvements.
• Ability to build only the clients you need.
• Custom signed regions and endpoints.
• Common Crypto support for Apple platforms (OpenSSL is no longer required on iOS and OS X).
• Several stability updates related to multi-threading in our Curl interface on Unix and Linux.
• The Service Client Generator is now open sourced and integrated into the build process.

Also, NSURL support for Apple platforms will be committed within a week or so. After that, Curl will no longer be required on iOS or OS X.

The team would like to to thank those who have been involved in improving this SDK over the past six months. Please continue contributing and leaving feedback on our GitHub Issues page.

Before we move to General Availability, we would like to receive another round of feedback to help us pin down the API with a stable 1.0 release. If you are a C++ developer, please feel free to give this new SDK a try and let us know what you think.

In Other News
Here are a few other things that you may find interesting:

• We have moved our GitHub repository from the awslabs organization to aws/aws-sdk-cpp.
• We are now providing new releases for new services and features with the rest of the AWS SDKs.
• We now have a C++ developer blog. We’ll post tutorials and samples there throughout the year. We’ll also announce improvements and features there, so stay tuned!
• We will distribute pre-built binaries for our most popular platforms in the near future. We’ll let you know when they go live.

Sample Code
Here is some sample code that writes some data to a Kinesis stream and then consumes the data:

#include <aws/kinesis/model/PutRecordsRequest.h>
#include <aws/kinesis/KinesisClient.h>
#include <aws/core/utils/Outcome.h>

using namespace Aws::Utils;
using namespace Aws::Kinesis;
using namespace Aws::Kinesis::Model;

class KinesisProducer
{
public:
    KinesisProducer(const Aws::String& streamName, const Aws::String& partition) : m_partition(partition), m_streamName(streamName)
    {}

    void StreamData(const Aws::Vector& data)
    {
        PutRecordsRequest putRecordsRequest;
        putRecordsRequest.SetStreamName(m_streamName);

        for(auto& datum : data)
        {
            PutRecordsRequestEntry putRecordsRequestEntry;
            putRecordsRequestEntry.WithData(datum)
                    .WithPartitionKey(m_partition);

            putRecordsRequest.AddRecords(putRecordsRequestEntry);
        }

        m_client.PutRecordsAsync(putRecordsRequest,
               std::bind(&KinesisProducer::OnPutRecordsAsyncOutcomeReceived, this, std::placeholders::_1, std::placeholders::_2, std::placeholders::_3, std::placeholders::_4));
    }

private:
    void OnPutRecordsAsyncOutcomeReceived(const KinesisClient*, const Model::PutRecordsRequest&,
                                          const Model::PutRecordsOutcome& outcome, const std::shared_ptr&)
    {
        if(outcome.IsSuccess())
        {
            std::cout << "Records Put Successfully " << std::endl;
        }
        else
        {
            std::cout << "Put Records Failed with error " << outcome.GetError().GetMessage() << std::endl;
        }
    }

    KinesisClient m_client;
    Aws::String m_partition;
    Aws::String m_streamName;
};

int main()
{
    KinesisProducer producer("kinesis-sample", "announcements");

    while(true)
    {
        Aws::String annoucement1("AWS SDK for C++");
        Aws::String annoucement2("Is Now in Developer Preview");

        producer.StreamData( {
                                     ByteBuffer((unsigned char*)annoucement1.c_str(), annoucement1.length()),
                                     ByteBuffer((unsigned char*)annoucement2.c_str(), annoucement2.length())
                             });

        std::this_thread::sleep_for(std::chrono::milliseconds(5));
    }

    return 0;
}

— Jonathan Henson, Software Development Engineer (SDE)