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Improve the Operational Efficiency of Amazon Elasticsearch Service Domains with Automated Alarms Using Amazon CloudWatch

Post Syndicated from Veronika Megler original https://aws.amazon.com/blogs/big-data/improve-the-operational-efficiency-of-amazon-elasticsearch-service-domains-with-automated-alarms-using-amazon-cloudwatch/

A customer has been successfully creating and running multiple Amazon Elasticsearch Service (Amazon ES) domains to support their business users’ search needs across products, orders, support documentation, and a growing suite of similar needs. The service has become heavily used across the organization.  This led to some domains running at 100% capacity during peak times, while others began to run low on storage space. Because of this increased usage, the technical teams were in danger of missing their service level agreements.  They contacted me for help.

This post shows how you can set up automated alarms to warn when domains need attention.

Solution overview

Amazon ES is a fully managed service that delivers Elasticsearch’s easy-to-use APIs and real-time analytics capabilities along with the availability, scalability, and security that production workloads require.  The service offers built-in integrations with a number of other components and AWS services, enabling customers to go from raw data to actionable insights quickly and securely.

One of these other integrated services is Amazon CloudWatch. CloudWatch is a monitoring service for AWS Cloud resources and the applications that you run on AWS. You can use CloudWatch to collect and track metrics, collect and monitor log files, set alarms, and automatically react to changes in your AWS resources.

CloudWatch collects metrics for Amazon ES. You can use these metrics to monitor the state of your Amazon ES domains, and set alarms to notify you about high utilization of system resources.  For more information, see Amazon Elasticsearch Service Metrics and Dimensions.

While the metrics are automatically collected, the missing piece is how to set alarms on these metrics at appropriate levels for each of your domains. This post includes sample Python code to evaluate the current state of your Amazon ES environment, and to set up alarms according to AWS recommendations and best practices.

There are two components to the sample solution:

  • es-check-cwalarms.py: This Python script checks the CloudWatch alarms that have been set, for all Amazon ES domains in a given account and region.
  • es-create-cwalarms.py: This Python script sets up a set of CloudWatch alarms for a single given domain.

The sample code can also be found in the amazon-es-check-cw-alarms GitHub repo. The scripts are easy to extend or combine, as described in the section “Extensions and Adaptations”.

Assessing the current state

The first script, es-check-cwalarms.py, is used to give an overview of the configurations and alarm settings for all the Amazon ES domains in the given region. The script takes the following parameters:

python es-checkcwalarms.py -h
usage: es-checkcwalarms.py [-h] [-e ESPREFIX] [-n NOTIFY] [-f FREE][-p PROFILE] [-r REGION]
Checks a set of recommended CloudWatch alarms for Amazon Elasticsearch Service domains (optionally, those beginning with a given prefix).
optional arguments:
  -h, --help   		show this help message and exit
  -e ESPREFIX, --esprefix ESPREFIX	Only check Amazon Elasticsearch Service domains that begin with this prefix.
  -n NOTIFY, --notify NOTIFY    List of CloudWatch alarm actions; e.g. ['arn:aws:sns:xxxx']
  -f FREE, --free FREE  Minimum free storage (MB) on which to alarm
  -p PROFILE, --profile PROFILE     IAM profile name to use
  -r REGION, --region REGION       AWS region for the domain. Default: us-east-1

The script first identifies all the domains in the given region (or, optionally, limits them to the subset that begins with a given prefix). It then starts running a set of checks against each one.

The script can be run from the command line or set up as a scheduled Lambda function. For example, for one customer, it was deemed appropriate to regularly run the script to check that alarms were correctly set for all domains. In addition, because configuration changes—cluster size increases to accommodate larger workloads being a common change—might require updates to alarms, this approach allowed the automatic identification of alarms no longer appropriately set as the domain configurations changed.

The output shown below is the output for one domain in my account.

Starting checks for Elasticsearch domain iotfleet , version is 53
Iotfleet Automated snapshot hour (UTC): 0
Iotfleet Instance configuration: 1 instances; type:m3.medium.elasticsearch
Iotfleet Instance storage definition is: 4 GB; free storage calced to: 819.2 MB
iotfleet Desired free storage set to (in MB): 819.2
iotfleet WARNING: Not using VPC Endpoint
iotfleet WARNING: Does not have Zone Awareness enabled
iotfleet WARNING: Instance count is ODD. Best practice is for an even number of data nodes and zone awareness.
iotfleet WARNING: Does not have Dedicated Masters.
iotfleet WARNING: Neither index nor search slow logs are enabled.
iotfleet WARNING: EBS not in use. Using instance storage only.
iotfleet Alarm ok; definition matches. Test-Elasticsearch-iotfleet-ClusterStatus.yellow-Alarm ClusterStatus.yellow
iotfleet Alarm ok; definition matches. Test-Elasticsearch-iotfleet-ClusterStatus.red-Alarm ClusterStatus.red
iotfleet Alarm ok; definition matches. Test-Elasticsearch-iotfleet-CPUUtilization-Alarm CPUUtilization
iotfleet Alarm ok; definition matches. Test-Elasticsearch-iotfleet-JVMMemoryPressure-Alarm JVMMemoryPressure
iotfleet WARNING: Missing alarm!! ('ClusterIndexWritesBlocked', 'Maximum', 60, 5, 'GreaterThanOrEqualToThreshold', 1.0)
iotfleet Alarm ok; definition matches. Test-Elasticsearch-iotfleet-AutomatedSnapshotFailure-Alarm AutomatedSnapshotFailure
iotfleet Alarm: Threshold does not match: Test-Elasticsearch-iotfleet-FreeStorageSpace-Alarm Should be:  819.2 ; is 3000.0

The output messages fall into the following categories:

  • System overview, Informational: The Amazon ES version and configuration, including instance type and number, storage, automated snapshot hour, etc.
  • Free storage: A calculation for the appropriate amount of free storage, based on the recommended 20% of total storage.
  • Warnings: best practices that are not being followed for this domain. (For more about this, read on.)
  • Alarms: An assessment of the CloudWatch alarms currently set for this domain, against a recommended set.

The script contains an array of recommended CloudWatch alarms, based on best practices for these metrics and statistics. Using the array allows alarm parameters (such as free space) to be updated within the code based on current domain statistics and configurations.

For a given domain, the script checks if each alarm has been set. If the alarm is set, it checks whether the values match those in the array esAlarms. In the output above, you can see three different situations being reported:

  • Alarm ok; definition matches. The alarm set for the domain matches the settings in the array.
  • Alarm: Threshold does not match. An alarm exists, but the threshold value at which the alarm is triggered does not match.
  • WARNING: Missing alarm!! The recommended alarm is missing.

All in all, the list above shows that this domain does not have a configuration that adheres to best practices, nor does it have all the recommended alarms.

Setting up alarms

Now that you know that the domains in their current state are missing critical alarms, you can correct the situation.

To demonstrate the script, set up a new domain named “ver”, in us-west-2. Specify 1 node, and a 10-GB EBS disk. Also, create an SNS topic in us-west-2 with a name of “sendnotification”, which sends you an email.

Run the second script, es-create-cwalarms.py, from the command line. This script creates (or updates) the desired CloudWatch alarms for the specified Amazon ES domain, “ver”.

python es-create-cwalarms.py -r us-west-2 -e test -c ver -n "['arn:aws:sns:us-west-2:xxxxxxxxxx:sendnotification']"
EBS enabled: True type: gp2 size (GB): 10 No Iops 10240  total storage (MB)
Desired free storage set to (in MB): 2048.0
Creating  Test-Elasticsearch-ver-ClusterStatus.yellow-Alarm
Creating  Test-Elasticsearch-ver-ClusterStatus.red-Alarm
Creating  Test-Elasticsearch-ver-CPUUtilization-Alarm
Creating  Test-Elasticsearch-ver-JVMMemoryPressure-Alarm
Creating  Test-Elasticsearch-ver-FreeStorageSpace-Alarm
Creating  Test-Elasticsearch-ver-ClusterIndexWritesBlocked-Alarm
Creating  Test-Elasticsearch-ver-AutomatedSnapshotFailure-Alarm
Successfully finished creating alarms!

As with the first script, this script contains an array of recommended CloudWatch alarms, based on best practices for these metrics and statistics. This approach allows you to add or modify alarms based on your use case (more on that below).

After running the script, navigate to Alarms on the CloudWatch console. You can see the set of alarms set up on your domain.

Because the “ver” domain has only a single node, cluster status is yellow, and that alarm is in an “ALARM” state. It’s already sent a notification that the alarm has been triggered.

What to do when an alarm triggers

After alarms are set up, you need to identify the correct action to take for each alarm, which depends on the alarm triggered. For ideas, guidance, and additional pointers to supporting documentation, see Get Started with Amazon Elasticsearch Service: Set CloudWatch Alarms on Key Metrics. For information about common errors and recovery actions to take, see Handling AWS Service Errors.

In most cases, the alarm triggers due to an increased workload. The likely action is to reconfigure the system to handle the increased workload, rather than reducing the incoming workload. Reconfiguring any backend store—a category of systems that includes Elasticsearch—is best performed when the system is quiescent or lightly loaded. Reconfigurations such as setting zone awareness or modifying the disk type cause Amazon ES to enter a “processing” state, potentially disrupting client access.

Other changes, such as increasing the number of data nodes, may cause Elasticsearch to begin moving shards, potentially impacting search performance on these shards while this is happening. These actions should be considered in the context of your production usage. For the same reason I also do not recommend running a script that resets all domains to match best practices.

Avoid the need to reconfigure during heavy workload by setting alarms at a level that allows a considered approach to making the needed changes. For example, if you identify that each weekly peak is increasing, you can reconfigure during a weekly quiet period.

While Elasticsearch can be reconfigured without being quiesced, it is not a best practice to automatically scale it up and down based on usage patterns. Unlike some other AWS services, I recommend against setting a CloudWatch action that automatically reconfigures the system when alarms are triggered.

There are other situations where the planned reconfiguration approach may not work, such as low or zero free disk space causing the domain to reject writes. If the business is dependent on the domain continuing to accept incoming writes and deleting data is not an option, the team may choose to reconfigure immediately.

Extensions and adaptations

You may wish to modify the best practices encoded in the scripts for your own environment or workloads. It’s always better to avoid situations where alerts are generated but routinely ignored. All alerts should trigger a review and one or more actions, either immediately or at a planned date. The following is a list of common situations where you may wish to set different alarms for different domains:

  • Dev/test vs. production
    You may have a different set of configuration rules and alarms for your dev environment configurations than for test. For example, you may require zone awareness and dedicated masters for your production environment, but not for your development domains. Or, you may not have any alarms set in dev. For test environments that mirror your potential peak load, test to ensure that the alarms are appropriately triggered.
  • Differing workloads or SLAs for different domains
    You may have one domain with a requirement for superfast search performance, and another domain with a heavy ingest load that tolerates slower search response. Your reaction to slow response for these two workloads is likely to be different, so perhaps the thresholds for these two domains should be set at a different level. In this case, you might add a “max CPU utilization” alarm at 100% for 1 minute for the fast search domain, while the other domain only triggers an alarm when the average has been higher than 60% for 5 minutes. You might also add a “free space” rule with a higher threshold to reflect the need for more space for the heavy ingest load if there is danger that it could fill the available disk quickly.
  • “Normal” alarms versus “emergency” alarms
    If, for example, free disk space drops to 25% of total capacity, an alarm is triggered that indicates action should be taken as soon as possible, such as cleaning up old indexes or reconfiguring at the next quiet period for this domain. However, if free space drops below a critical level (20% free space), action must be taken immediately in order to prevent Amazon ES from setting the domain to read-only. Similarly, if the “ClusterIndexWritesBlocked” alarm triggers, the domain has already stopped accepting writes, so immediate action is needed. In this case, you may wish to set “laddered” alarms, where one threshold causes an alarm to be triggered to review the current workload for a planned reconfiguration, but a different threshold raises a “DefCon 3” alarm that immediate action is required.

The sample scripts provided here are a starting point, intended for you to adapt to your own environment and needs.

Running the scripts one time can identify how far your current state is from your desired state, and create an initial set of alarms. Regularly re-running these scripts can capture changes in your environment over time and adjusting your alarms for changes in your environment and configurations. One customer has set them up to run nightly, and to automatically create and update alarms to match their preferred settings.

Removing unwanted alarms

Each CloudWatch alarm costs approximately $0.10 per month. You can remove unwanted alarms in the CloudWatch console, under Alarms. If you set up a “ver” domain above, remember to remove it to avoid continuing charges.


Setting CloudWatch alarms appropriately for your Amazon ES domains can help you avoid suboptimal performance and allow you to respond to workload growth or configuration issues well before they become urgent. This post gives you a starting point for doing so. The additional sleep you’ll get knowing you don’t need to be concerned about Elasticsearch domain performance will allow you to focus on building creative solutions for your business and solving problems for your customers.


Additional Reading

If you found this post useful, be sure to check out Analyzing Amazon Elasticsearch Service Slow Logs Using Amazon CloudWatch Logs Streaming and Kibana and Get Started with Amazon Elasticsearch Service: How Many Shards Do I Need?


About the Author

Dr. Veronika Megler is a senior consultant at Amazon Web Services. She works with our customers to implement innovative big data, AI and ML projects, helping them accelerate their time-to-value when using AWS.




Using JWT For Sessions

Post Syndicated from Bozho original https://techblog.bozho.net/using-jwt-sessions/

The topic has been discussed many times, on hacker news, reddit, blogs. And the consensus is – DON’T USE JWT (for user sessions).

And I largely agree with the criticism of typical arguments for the JWT, the typical “but I can make it work…” explanations and the flaws of the JWT standard..

I won’t repeat everything here, so please go and read those articles. You can really shoot yourself in the foot with JWT, it’s complex to get to know it well and it has little benefits for most of the usecases. I guess for API calls it makes sense, especially if you reuse the same API in a single-page application and for your RESTful clients, but I’ll focus on the user session usecase.

Having all this criticism, I’ve gone against what the articles above recommend, and use JWT, navigating through their arguments and claiming I’m in a sweet spot. I can very well be wrong.

I store the user ID in a JWT token stored as a cookie. Not local storage, as that’s problematic. Not the whole state, as I don’t need that may lead to problems (pointed out in the linked articles). In fact, I don’t have any session state apart from the user data, which I think is a good practice.

What I want to avoid in my setup is sharing sessions across nodes. And this is a very compelling reason to not use the session mechanism of your web server/framework. No, you don’t need to have millions of users in order to need your application to run on more than one node. In fact, it should almost always run on (at least) two nodes, because nodes die and you don’t want downtime. Sticky sessions at the load balancer are a solution to that problem but you are just outsourcing the centralized session storage to the load balancer (and some load balancers might not support it). Shared session cache (e.g. memcached, elasticache, hazelcast) is also an option, and many web servers (at least in Java) support pluggable session replication mechanisms, but that introduces another component to the archtecture, another part of the stack to be supported and that can possibly break. It is not necessarily bad, but if there’s a simple way to avoid it, I’d go for it.

In order to avoid shared session storage, you need either the whole session state to be passed in the request/response cycle (as cookie, request parameter, header), or to receive a userId and load the user from the database or a cache. As we’ve learned, the former might be a bad choice. Despite that fact that frameworks like ASP.NET and JSF dump the whole state in the HTML of the page, it doesn’t intuitively sound good.

As for the latter – you may say “ok, if you are going to load the user from the database on every request this is going to be slow and if you use a cache, then why not use the cache for the sessions themselves?”. Well, the cache can be local. Remember we have just a few application nodes. Each node can have a local, in-memory cache for the currently active users. The fact that all nodes will have the same user loaded (after a few requests are routed to them by the load balancer in a round-robin fashion) is not important, as that cache is small. But you won’t have to take any care for replicating it across nodes, taking care of new nodes coming and going from the cluster, dealing with network issues between the nodes, etc. Each application node will be an island not caring about any other application node.

So here goes my first objection to the linked articles – just storing the user identifier in a JWT token is not pointless, as it saves you from session replication.

What about the criticism for the JWT standard and the security implications of its cryptography? Entirely correct, it’s easy to shoot yourself in the foot. That’s why I’m using JWT only with MAC, and only with a particular algorithm that I verify upon receiving the token, thus (allegedly) avoiding all the pitfalls. In all fairness, I’m willing to use the alternative proposed in one of the articles – PASETO – but it doesn’t have a Java library and it will take some time implementing one (might do in the future). To summarize – if there was another easy to use way for authenticated encryption of cookies, I’d use it.

So I’m basically using JWT in “PASETO-mode”, with only one operation and only one algorithm. And that should be fine as a general approach – the article doesn’t criticize the idea of having a user identifier in a token (and a stateless application node), it criticizes the complexity and vulnerabilities of the standard. This is sort of my second objection – “Don’t use JWT” is widely understood to mean “Don’t use tokens”, where that is not the case.

Have I introduced some vulnerability in my strive for architectural simplicity and lack of shared state? I hope not.

The post Using JWT For Sessions appeared first on Bozho's tech blog.

Best Practices for Running Apache Kafka on AWS

Post Syndicated from Prasad Alle original https://aws.amazon.com/blogs/big-data/best-practices-for-running-apache-kafka-on-aws/

This post was written in partnership with Intuit to share learnings, best practices, and recommendations for running an Apache Kafka cluster on AWS. Thanks to Vaishak Suresh and his colleagues at Intuit for their contribution and support.

Intuit, in their own words: Intuit, a leading enterprise customer for AWS, is a creator of business and financial management solutions. For more information on how Intuit partners with AWS, see our previous blog post, Real-time Stream Processing Using Apache Spark Streaming and Apache Kafka on AWS. Apache Kafka is an open-source, distributed streaming platform that enables you to build real-time streaming applications.

The best practices described in this post are based on our experience in running and operating large-scale Kafka clusters on AWS for more than two years. Our intent for this post is to help AWS customers who are currently running Kafka on AWS, and also customers who are considering migrating on-premises Kafka deployments to AWS.

AWS offers Amazon Kinesis Data Streams, a Kafka alternative that is fully managed.

Running your Kafka deployment on Amazon EC2 provides a high performance, scalable solution for ingesting streaming data. AWS offers many different instance types and storage option combinations for Kafka deployments. However, given the number of possible deployment topologies, it’s not always trivial to select the most appropriate strategy suitable for your use case.

In this blog post, we cover the following aspects of running Kafka clusters on AWS:

  • Deployment considerations and patterns
  • Storage options
  • Instance types
  • Networking
  • Upgrades
  • Performance tuning
  • Monitoring
  • Security
  • Backup and restore

Note: While implementing Kafka clusters in a production environment, make sure also to consider factors like your number of messages, message size, monitoring, failure handling, and any operational issues.

Deployment considerations and patterns

In this section, we discuss various deployment options available for Kafka on AWS, along with pros and cons of each option. A successful deployment starts with thoughtful consideration of these options. Considering availability, consistency, and operational overhead of the deployment helps when choosing the right option.

Single AWS Region, Three Availability Zones, All Active

One typical deployment pattern (all active) is in a single AWS Region with three Availability Zones (AZs). One Kafka cluster is deployed in each AZ along with Apache ZooKeeper and Kafka producer and consumer instances as shown in the illustration following.

In this pattern, this is the Kafka cluster deployment:

  • Kafka producers and Kafka cluster are deployed on each AZ.
  • Data is distributed evenly across three Kafka clusters by using Elastic Load Balancer.
  • Kafka consumers aggregate data from all three Kafka clusters.

Kafka cluster failover occurs this way:

  • Mark down all Kafka producers
  • Stop consumers
  • Debug and restack Kafka
  • Restart consumers
  • Restart Kafka producers

Following are the pros and cons of this pattern.

Pros Cons
  • Highly available
  • Can sustain the failure of two AZs
  • No message loss during failover
  • Simple deployment


  • Very high operational overhead:
    • All changes need to be deployed three times, one for each Kafka cluster
    • Maintaining and monitoring three Kafka clusters
    • Maintaining and monitoring three consumer clusters

A restart is required for patching and upgrading brokers in a Kafka cluster. In this approach, a rolling upgrade is done separately for each cluster.

Single Region, Three Availability Zones, Active-Standby

Another typical deployment pattern (active-standby) is in a single AWS Region with a single Kafka cluster and Kafka brokers and Zookeepers distributed across three AZs. Another similar Kafka cluster acts as a standby as shown in the illustration following. You can use Kafka mirroring with MirrorMaker to replicate messages between any two clusters.

In this pattern, this is the Kafka cluster deployment:

  • Kafka producers are deployed on all three AZs.
  • Only one Kafka cluster is deployed across three AZs (active).
  • ZooKeeper instances are deployed on each AZ.
  • Brokers are spread evenly across all three AZs.
  • Kafka consumers can be deployed across all three AZs.
  • Standby Kafka producers and a Multi-AZ Kafka cluster are part of the deployment.

Kafka cluster failover occurs this way:

  • Switch traffic to standby Kafka producers cluster and Kafka cluster.
  • Restart consumers to consume from standby Kafka cluster.

Following are the pros and cons of this pattern.

Pros Cons
  • Less operational overhead when compared to the first option
  • Only one Kafka cluster to manage and consume data from
  • Can handle single AZ failures without activating a standby Kafka cluster
  • Added latency due to cross-AZ data transfer among Kafka brokers
  • For Kafka versions before 0.10, replicas for topic partitions have to be assigned so they’re distributed to the brokers on different AZs (rack-awareness)
  • The cluster can become unavailable in case of a network glitch, where ZooKeeper does not see Kafka brokers
  • Possibility of in-transit message loss during failover

Intuit recommends using a single Kafka cluster in one AWS Region, with brokers distributing across three AZs (single region, three AZs). This approach offers stronger fault tolerance than otherwise, because a failed AZ won’t cause Kafka downtime.

Storage options

There are two storage options for file storage in Amazon EC2:

Ephemeral storage is local to the Amazon EC2 instance. It can provide high IOPS based on the instance type. On the other hand, Amazon EBS volumes offer higher resiliency and you can configure IOPS based on your storage needs. EBS volumes also offer some distinct advantages in terms of recovery time. Your choice of storage is closely related to the type of workload supported by your Kafka cluster.

Kafka provides built-in fault tolerance by replicating data partitions across a configurable number of instances. If a broker fails, you can recover it by fetching all the data from other brokers in the cluster that host the other replicas. Depending on the size of the data transfer, it can affect recovery process and network traffic. These in turn eventually affect the cluster’s performance.

The following table contrasts the benefits of using an instance store versus using EBS for storage.

Instance store EBS
  • Instance storage is recommended for large- and medium-sized Kafka clusters. For a large cluster, read/write traffic is distributed across a high number of brokers, so the loss of a broker has less of an impact. However, for smaller clusters, a quick recovery for the failed node is important, but a failed broker takes longer and requires more network traffic for a smaller Kafka cluster.
  • Storage-optimized instances like h1, i3, and d2 are an ideal choice for distributed applications like Kafka.


  • The primary advantage of using EBS in a Kafka deployment is that it significantly reduces data-transfer traffic when a broker fails or must be replaced. The replacement broker joins the cluster much faster.
  • Data stored on EBS is persisted in case of an instance failure or termination. The broker’s data stored on an EBS volume remains intact, and you can mount the EBS volume to a new EC2 instance. Most of the replicated data for the replacement broker is already available in the EBS volume and need not be copied over the network from another broker. Only the changes made after the original broker failure need to be transferred across the network. That makes this process much faster.



Intuit chose EBS because of their frequent instance restacking requirements and also other benefits provided by EBS.

Generally, Kafka deployments use a replication factor of three. EBS offers replication within their service, so Intuit chose a replication factor of two instead of three.

Instance types

The choice of instance types is generally driven by the type of storage required for your streaming applications on a Kafka cluster. If your application requires ephemeral storage, h1, i3, and d2 instances are your best option.

Intuit used r3.xlarge instances for their brokers and r3.large for ZooKeeper, with ST1 (throughput optimized HDD) EBS for their Kafka cluster.

Here are sample benchmark numbers from Intuit tests.

Configuration Broker bytes (MB/s)
  • r3.xlarge
  • ST1 EBS
  • 12 brokers
  • 12 partitions


Aggregate 346.9

If you need EBS storage, then AWS has a newer-generation r4 instance. The r4 instance is superior to R3 in many ways:

  • It has a faster processor (Broadwell).
  • EBS is optimized by default.
  • It features networking based on Elastic Network Adapter (ENA), with up to 10 Gbps on smaller sizes.
  • It costs 20 percent less than R3.

Note: It’s always best practice to check for the latest changes in instance types.


The network plays a very important role in a distributed system like Kafka. A fast and reliable network ensures that nodes can communicate with each other easily. The available network throughput controls the maximum amount of traffic that Kafka can handle. Network throughput, combined with disk storage, is often the governing factor for cluster sizing.

If you expect your cluster to receive high read/write traffic, select an instance type that offers 10-Gb/s performance.

In addition, choose an option that keeps interbroker network traffic on the private subnet, because this approach allows clients to connect to the brokers. Communication between brokers and clients uses the same network interface and port. For more details, see the documentation about IP addressing for EC2 instances.

If you are deploying in more than one AWS Region, you can connect the two VPCs in the two AWS Regions using cross-region VPC peering. However, be aware of the networking costs associated with cross-AZ deployments.


Kafka has a history of not being backward compatible, but its support of backward compatibility is getting better. During a Kafka upgrade, you should keep your producer and consumer clients on a version equal to or lower than the version you are upgrading from. After the upgrade is finished, you can start using a new protocol version and any new features it supports. There are three upgrade approaches available, discussed following.

Rolling or in-place upgrade

In a rolling or in-place upgrade scenario, upgrade one Kafka broker at a time. Take into consideration the recommendations for doing rolling restarts to avoid downtime for end users.

Downtime upgrade

If you can afford the downtime, you can take your entire cluster down, upgrade each Kafka broker, and then restart the cluster.

Blue/green upgrade

Intuit followed the blue/green deployment model for their workloads, as described following.

If you can afford to create a separate Kafka cluster and upgrade it, we highly recommend the blue/green upgrade scenario. In this scenario, we recommend that you keep your clusters up-to-date with the latest Kafka version. For additional details on Kafka version upgrades or more details, see the Kafka upgrade documentation.

The following illustration shows a blue/green upgrade.

In this scenario, the upgrade plan works like this:

  • Create a new Kafka cluster on AWS.
  • Create a new Kafka producers stack to point to the new Kafka cluster.
  • Create topics on the new Kafka cluster.
  • Test the green deployment end to end (sanity check).
  • Using Amazon Route 53, change the new Kafka producers stack on AWS to point to the new green Kafka environment that you have created.

The roll-back plan works like this:

  • Switch Amazon Route 53 to the old Kafka producers stack on AWS to point to the old Kafka environment.

For additional details on blue/green deployment architecture using Kafka, see the re:Invent presentation Leveraging the Cloud with a Blue-Green Deployment Architecture.

Performance tuning

You can tune Kafka performance in multiple dimensions. Following are some best practices for performance tuning.

 These are some general performance tuning techniques:

  • If throughput is less than network capacity, try the following:
    • Add more threads
    • Increase batch size
    • Add more producer instances
    • Add more partitions
  • To improve latency when acks =-1, increase your num.replica.fetches value.
  • For cross-AZ data transfer, tune your buffer settings for sockets and for OS TCP.
  • Make sure that num.io.threads is greater than the number of disks dedicated for Kafka.
  • Adjust num.network.threads based on the number of producers plus the number of consumers plus the replication factor.
  • Your message size affects your network bandwidth. To get higher performance from a Kafka cluster, select an instance type that offers 10 Gb/s performance.

For Java and JVM tuning, try the following:

  • Minimize GC pauses by using the Oracle JDK, which uses the new G1 garbage-first collector.
  • Try to keep the Kafka heap size below 4 GB.


Knowing whether a Kafka cluster is working correctly in a production environment is critical. Sometimes, just knowing that the cluster is up is enough, but Kafka applications have many moving parts to monitor. In fact, it can easily become confusing to understand what’s important to watch and what you can set aside. Items to monitor range from simple metrics about the overall rate of traffic, to producers, consumers, brokers, controller, ZooKeeper, topics, partitions, messages, and so on.

For monitoring, Intuit used several tools, including Newrelec, Wavefront, Amazon CloudWatch, and AWS CloudTrail. Our recommended monitoring approach follows.

For system metrics, we recommend that you monitor:

  • CPU load
  • Network metrics
  • File handle usage
  • Disk space
  • Disk I/O performance
  • Garbage collection
  • ZooKeeper

For producers, we recommend that you monitor:

  • Batch-size-avg
  • Compression-rate-avg
  • Waiting-threads
  • Buffer-available-bytes
  • Record-queue-time-max
  • Record-send-rate
  • Records-per-request-avg

For consumers, we recommend that you monitor:

  • Batch-size-avg
  • Compression-rate-avg
  • Waiting-threads
  • Buffer-available-bytes
  • Record-queue-time-max
  • Record-send-rate
  • Records-per-request-avg


Like most distributed systems, Kafka provides the mechanisms to transfer data with relatively high security across the components involved. Depending on your setup, security might involve different services such as encryption, Kerberos, Transport Layer Security (TLS) certificates, and advanced access control list (ACL) setup in brokers and ZooKeeper. The following tells you more about the Intuit approach. For details on Kafka security not covered in this section, see the Kafka documentation.

Encryption at rest

For EBS-backed EC2 instances, you can enable encryption at rest by using Amazon EBS volumes with encryption enabled. Amazon EBS uses AWS Key Management Service (AWS KMS) for encryption. For more details, see Amazon EBS Encryption in the EBS documentation. For instance store–backed EC2 instances, you can enable encryption at rest by using Amazon EC2 instance store encryption.

Encryption in transit

Kafka uses TLS for client and internode communications.


Authentication of connections to brokers from clients (producers and consumers) to other brokers and tools uses either Secure Sockets Layer (SSL) or Simple Authentication and Security Layer (SASL).

Kafka supports Kerberos authentication. If you already have a Kerberos server, you can add Kafka to your current configuration.


In Kafka, authorization is pluggable and integration with external authorization services is supported.

Backup and restore

The type of storage used in your deployment dictates your backup and restore strategy.

The best way to back up a Kafka cluster based on instance storage is to set up a second cluster and replicate messages using MirrorMaker. Kafka’s mirroring feature makes it possible to maintain a replica of an existing Kafka cluster. Depending on your setup and requirements, your backup cluster might be in the same AWS Region as your main cluster or in a different one.

For EBS-based deployments, you can enable automatic snapshots of EBS volumes to back up volumes. You can easily create new EBS volumes from these snapshots to restore. We recommend storing backup files in Amazon S3.

For more information on how to back up in Kafka, see the Kafka documentation.


In this post, we discussed several patterns for running Kafka in the AWS Cloud. AWS also provides an alternative managed solution with Amazon Kinesis Data Streams, there are no servers to manage or scaling cliffs to worry about, you can scale the size of your streaming pipeline in seconds without downtime, data replication across availability zones is automatic, you benefit from security out of the box, Kinesis Data Streams is tightly integrated with a wide variety of AWS services like Lambda, Redshift, Elasticsearch and it supports open source frameworks like Storm, Spark, Flink, and more. You may refer to kafka-kinesis connector.

If you have questions or suggestions, please comment below.

Additional Reading

If you found this post useful, be sure to check out Implement Serverless Log Analytics Using Amazon Kinesis Analytics and Real-time Clickstream Anomaly Detection with Amazon Kinesis Analytics.

About the Author

Prasad Alle is a Senior Big Data Consultant with AWS Professional Services. He spends his time leading and building scalable, reliable Big data, Machine learning, Artificial Intelligence and IoT solutions for AWS Enterprise and Strategic customers. His interests extend to various technologies such as Advanced Edge Computing, Machine learning at Edge. In his spare time, he enjoys spending time with his family.



Best Practices for Running Apache Cassandra on Amazon EC2

Post Syndicated from Prasad Alle original https://aws.amazon.com/blogs/big-data/best-practices-for-running-apache-cassandra-on-amazon-ec2/

Apache Cassandra is a commonly used, high performance NoSQL database. AWS customers that currently maintain Cassandra on-premises may want to take advantage of the scalability, reliability, security, and economic benefits of running Cassandra on Amazon EC2.

Amazon EC2 and Amazon Elastic Block Store (Amazon EBS) provide secure, resizable compute capacity and storage in the AWS Cloud. When combined, you can deploy Cassandra, allowing you to scale capacity according to your requirements. Given the number of possible deployment topologies, it’s not always trivial to select the most appropriate strategy suitable for your use case.

In this post, we outline three Cassandra deployment options, as well as provide guidance about determining the best practices for your use case in the following areas:

  • Cassandra resource overview
  • Deployment considerations
  • Storage options
  • Networking
  • High availability and resiliency
  • Maintenance
  • Security

Before we jump into best practices for running Cassandra on AWS, we should mention that we have many customers who decided to use DynamoDB instead of managing their own Cassandra cluster. DynamoDB is fully managed, serverless, and provides multi-master cross-region replication, encryption at rest, and managed backup and restore. Integration with AWS Identity and Access Management (IAM) enables DynamoDB customers to implement fine-grained access control for their data security needs.

Several customers who have been using large Cassandra clusters for many years have moved to DynamoDB to eliminate the complications of administering Cassandra clusters and maintaining high availability and durability themselves. Gumgum.com is one customer who migrated to DynamoDB and observed significant savings. For more information, see Moving to Amazon DynamoDB from Hosted Cassandra: A Leap Towards 60% Cost Saving per Year.

AWS provides options, so you’re covered whether you want to run your own NoSQL Cassandra database, or move to a fully managed, serverless DynamoDB database.

Cassandra resource overview

Here’s a short introduction to standard Cassandra resources and how they are implemented with AWS infrastructure. If you’re already familiar with Cassandra or AWS deployments, this can serve as a refresher.

Resource Cassandra AWS

A single Cassandra deployment.


This typically consists of multiple physical locations, keyspaces, and physical servers.

A logical deployment construct in AWS that maps to an AWS CloudFormation StackSet, which consists of one or many CloudFormation stacks to deploy Cassandra.
Datacenter A group of nodes configured as a single replication group.

A logical deployment construct in AWS.


A datacenter is deployed with a single CloudFormation stack consisting of Amazon EC2 instances, networking, storage, and security resources.


A collection of servers.


A datacenter consists of at least one rack. Cassandra tries to place the replicas on different racks.

A single Availability Zone.
Server/node A physical virtual machine running Cassandra software. An EC2 instance.
Token Conceptually, the data managed by a cluster is represented as a ring. The ring is then divided into ranges equal to the number of nodes. Each node being responsible for one or more ranges of the data. Each node gets assigned with a token, which is essentially a random number from the range. The token value determines the node’s position in the ring and its range of data. Managed within Cassandra.
Virtual node (vnode) Responsible for storing a range of data. Each vnode receives one token in the ring. A cluster (by default) consists of 256 tokens, which are uniformly distributed across all servers in the Cassandra datacenter. Managed within Cassandra.
Replication factor The total number of replicas across the cluster. Managed within Cassandra.

Deployment considerations

One of the many benefits of deploying Cassandra on Amazon EC2 is that you can automate many deployment tasks. In addition, AWS includes services, such as CloudFormation, that allow you to describe and provision all your infrastructure resources in your cloud environment.

We recommend orchestrating each Cassandra ring with one CloudFormation template. If you are deploying in multiple AWS Regions, you can use a CloudFormation StackSet to manage those stacks. All the maintenance actions (scaling, upgrading, and backing up) should be scripted with an AWS SDK. These may live as standalone AWS Lambda functions that can be invoked on demand during maintenance.

You can get started by following the Cassandra Quick Start deployment guide. Keep in mind that this guide does not address the requirements to operate a production deployment and should be used only for learning more about Cassandra.

Deployment patterns

In this section, we discuss various deployment options available for Cassandra in Amazon EC2. A successful deployment starts with thoughtful consideration of these options. Consider the amount of data, network environment, throughput, and availability.

  • Single AWS Region, 3 Availability Zones
  • Active-active, multi-Region
  • Active-standby, multi-Region

Single region, 3 Availability Zones

In this pattern, you deploy the Cassandra cluster in one AWS Region and three Availability Zones. There is only one ring in the cluster. By using EC2 instances in three zones, you ensure that the replicas are distributed uniformly in all zones.

To ensure the even distribution of data across all Availability Zones, we recommend that you distribute the EC2 instances evenly in all three Availability Zones. The number of EC2 instances in the cluster is a multiple of three (the replication factor).

This pattern is suitable in situations where the application is deployed in one Region or where deployments in different Regions should be constrained to the same Region because of data privacy or other legal requirements.

Pros Cons

●     Highly available, can sustain failure of one Availability Zone.

●     Simple deployment

●     Does not protect in a situation when many of the resources in a Region are experiencing intermittent failure.


Active-active, multi-Region

In this pattern, you deploy two rings in two different Regions and link them. The VPCs in the two Regions are peered so that data can be replicated between two rings.

We recommend that the two rings in the two Regions be identical in nature, having the same number of nodes, instance types, and storage configuration.

This pattern is most suitable when the applications using the Cassandra cluster are deployed in more than one Region.

Pros Cons

●     No data loss during failover.

●     Highly available, can sustain when many of the resources in a Region are experiencing intermittent failures.

●     Read/write traffic can be localized to the closest Region for the user for lower latency and higher performance.

●     High operational overhead

●     The second Region effectively doubles the cost


Active-standby, multi-region

In this pattern, you deploy two rings in two different Regions and link them. The VPCs in the two Regions are peered so that data can be replicated between two rings.

However, the second Region does not receive traffic from the applications. It only functions as a secondary location for disaster recovery reasons. If the primary Region is not available, the second Region receives traffic.

We recommend that the two rings in the two Regions be identical in nature, having the same number of nodes, instance types, and storage configuration.

This pattern is most suitable when the applications using the Cassandra cluster require low recovery point objective (RPO) and recovery time objective (RTO).

Pros Cons

●     No data loss during failover.

●     Highly available, can sustain failure or partitioning of one whole Region.

●     High operational overhead.

●     High latency for writes for eventual consistency.

●     The second Region effectively doubles the cost.

Storage options

In on-premises deployments, Cassandra deployments use local disks to store data. There are two storage options for EC2 instances:

Your choice of storage is closely related to the type of workload supported by the Cassandra cluster. Instance store works best for most general purpose Cassandra deployments. However, in certain read-heavy clusters, Amazon EBS is a better choice.

The choice of instance type is generally driven by the type of storage:

  • If ephemeral storage is required for your application, a storage-optimized (I3) instance is the best option.
  • If your workload requires Amazon EBS, it is best to go with compute-optimized (C5) instances.
  • Burstable instance types (T2) don’t offer good performance for Cassandra deployments.

Instance store

Ephemeral storage is local to the EC2 instance. It may provide high input/output operations per second (IOPs) based on the instance type. An SSD-based instance store can support up to 3.3M IOPS in I3 instances. This high performance makes it an ideal choice for transactional or write-intensive applications such as Cassandra.

In general, instance storage is recommended for transactional, large, and medium-size Cassandra clusters. For a large cluster, read/write traffic is distributed across a higher number of nodes, so the loss of one node has less of an impact. However, for smaller clusters, a quick recovery for the failed node is important.

As an example, for a cluster with 100 nodes, the loss of 1 node is 3.33% loss (with a replication factor of 3). Similarly, for a cluster with 10 nodes, the loss of 1 node is 33% less capacity (with a replication factor of 3).

  Ephemeral storage Amazon EBS Comments


(translates to higher query performance)

Up to 3.3M on I3




This results in a higher query performance on each host. However, Cassandra implicitly scales well in terms of horizontal scale. In general, we recommend scaling horizontally first. Then, scale vertically to mitigate specific issues.


Note: 3.3M IOPS is observed with 100% random read with a 4-KB block size on Amazon Linux.

AWS instance types I3 Compute optimized, C5 Being able to choose between different instance types is an advantage in terms of CPU, memory, etc., for horizontal and vertical scaling.
Backup/ recovery Custom Basic building blocks are available from AWS.

Amazon EBS offers distinct advantage here. It is small engineering effort to establish a backup/restore strategy.

a) In case of an instance failure, the EBS volumes from the failing instance are attached to a new instance.

b) In case of an EBS volume failure, the data is restored by creating a new EBS volume from last snapshot.

Amazon EBS

EBS volumes offer higher resiliency, and IOPs can be configured based on your storage needs. EBS volumes also offer some distinct advantages in terms of recovery time. EBS volumes can support up to 32K IOPS per volume and up to 80K IOPS per instance in RAID configuration. They have an annualized failure rate (AFR) of 0.1–0.2%, which makes EBS volumes 20 times more reliable than typical commodity disk drives.

The primary advantage of using Amazon EBS in a Cassandra deployment is that it reduces data-transfer traffic significantly when a node fails or must be replaced. The replacement node joins the cluster much faster. However, Amazon EBS could be more expensive, depending on your data storage needs.

Cassandra has built-in fault tolerance by replicating data to partitions across a configurable number of nodes. It can not only withstand node failures but if a node fails, it can also recover by copying data from other replicas into a new node. Depending on your application, this could mean copying tens of gigabytes of data. This adds additional delay to the recovery process, increases network traffic, and could possibly impact the performance of the Cassandra cluster during recovery.

Data stored on Amazon EBS is persisted in case of an instance failure or termination. The node’s data stored on an EBS volume remains intact and the EBS volume can be mounted to a new EC2 instance. Most of the replicated data for the replacement node is already available in the EBS volume and won’t need to be copied over the network from another node. Only the changes made after the original node failed need to be transferred across the network. That makes this process much faster.

EBS volumes are snapshotted periodically. So, if a volume fails, a new volume can be created from the last known good snapshot and be attached to a new instance. This is faster than creating a new volume and coping all the data to it.

Most Cassandra deployments use a replication factor of three. However, Amazon EBS does its own replication under the covers for fault tolerance. In practice, EBS volumes are about 20 times more reliable than typical disk drives. So, it is possible to go with a replication factor of two. This not only saves cost, but also enables deployments in a region that has two Availability Zones.

EBS volumes are recommended in case of read-heavy, small clusters (fewer nodes) that require storage of a large amount of data. Keep in mind that the Amazon EBS provisioned IOPS could get expensive. General purpose EBS volumes work best when sized for required performance.


If your cluster is expected to receive high read/write traffic, select an instance type that offers 10–Gb/s performance. As an example, i3.8xlarge and c5.9xlarge both offer 10–Gb/s networking performance. A smaller instance type in the same family leads to a relatively lower networking throughput.

Cassandra generates a universal unique identifier (UUID) for each node based on IP address for the instance. This UUID is used for distributing vnodes on the ring.

In the case of an AWS deployment, IP addresses are assigned automatically to the instance when an EC2 instance is created. With the new IP address, the data distribution changes and the whole ring has to be rebalanced. This is not desirable.

To preserve the assigned IP address, use a secondary elastic network interface with a fixed IP address. Before swapping an EC2 instance with a new one, detach the secondary network interface from the old instance and attach it to the new one. This way, the UUID remains same and there is no change in the way that data is distributed in the cluster.

If you are deploying in more than one region, you can connect the two VPCs in two regions using cross-region VPC peering.

High availability and resiliency

Cassandra is designed to be fault-tolerant and highly available during multiple node failures. In the patterns described earlier in this post, you deploy Cassandra to three Availability Zones with a replication factor of three. Even though it limits the AWS Region choices to the Regions with three or more Availability Zones, it offers protection for the cases of one-zone failure and network partitioning within a single Region. The multi-Region deployments described earlier in this post protect when many of the resources in a Region are experiencing intermittent failure.

Resiliency is ensured through infrastructure automation. The deployment patterns all require a quick replacement of the failing nodes. In the case of a regionwide failure, when you deploy with the multi-Region option, traffic can be directed to the other active Region while the infrastructure is recovering in the failing Region. In the case of unforeseen data corruption, the standby cluster can be restored with point-in-time backups stored in Amazon S3.


In this section, we look at ways to ensure that your Cassandra cluster is healthy:

  • Scaling
  • Upgrades
  • Backup and restore


Cassandra is horizontally scaled by adding more instances to the ring. We recommend doubling the number of nodes in a cluster to scale up in one scale operation. This leaves the data homogeneously distributed across Availability Zones. Similarly, when scaling down, it’s best to halve the number of instances to keep the data homogeneously distributed.

Cassandra is vertically scaled by increasing the compute power of each node. Larger instance types have proportionally bigger memory. Use deployment automation to swap instances for bigger instances without downtime or data loss.


All three types of upgrades (Cassandra, operating system patching, and instance type changes) follow the same rolling upgrade pattern.

In this process, you start with a new EC2 instance and install software and patches on it. Thereafter, remove one node from the ring. For more information, see Cassandra cluster Rolling upgrade. Then, you detach the secondary network interface from one of the EC2 instances in the ring and attach it to the new EC2 instance. Restart the Cassandra service and wait for it to sync. Repeat this process for all nodes in the cluster.

Backup and restore

Your backup and restore strategy is dependent on the type of storage used in the deployment. Cassandra supports snapshots and incremental backups. When using instance store, a file-based backup tool works best. Customers use rsync or other third-party products to copy data backups from the instance to long-term storage. For more information, see Backing up and restoring data in the DataStax documentation. This process has to be repeated for all instances in the cluster for a complete backup. These backup files are copied back to new instances to restore. We recommend using S3 to durably store backup files for long-term storage.

For Amazon EBS based deployments, you can enable automated snapshots of EBS volumes to back up volumes. New EBS volumes can be easily created from these snapshots for restoration.


We recommend that you think about security in all aspects of deployment. The first step is to ensure that the data is encrypted at rest and in transit. The second step is to restrict access to unauthorized users. For more information about security, see the Cassandra documentation.

Encryption at rest

Encryption at rest can be achieved by using EBS volumes with encryption enabled. Amazon EBS uses AWS KMS for encryption. For more information, see Amazon EBS Encryption.

Instance store–based deployments require using an encrypted file system or an AWS partner solution. If you are using DataStax Enterprise, it supports transparent data encryption.

Encryption in transit

Cassandra uses Transport Layer Security (TLS) for client and internode communications.


The security mechanism is pluggable, which means that you can easily swap out one authentication method for another. You can also provide your own method of authenticating to Cassandra, such as a Kerberos ticket, or if you want to store passwords in a different location, such as an LDAP directory.


The authorizer that’s plugged in by default is org.apache.cassandra.auth.Allow AllAuthorizer. Cassandra also provides a role-based access control (RBAC) capability, which allows you to create roles and assign permissions to these roles.


In this post, we discussed several patterns for running Cassandra in the AWS Cloud. This post describes how you can manage Cassandra databases running on Amazon EC2. AWS also provides managed offerings for a number of databases. To learn more, see Purpose-built databases for all your application needs.

If you have questions or suggestions, please comment below.

Additional Reading

If you found this post useful, be sure to check out Analyze Your Data on Amazon DynamoDB with Apache Spark and Analysis of Top-N DynamoDB Objects using Amazon Athena and Amazon QuickSight.

About the Authors

Prasad Alle is a Senior Big Data Consultant with AWS Professional Services. He spends his time leading and building scalable, reliable Big data, Machine learning, Artificial Intelligence and IoT solutions for AWS Enterprise and Strategic customers. His interests extend to various technologies such as Advanced Edge Computing, Machine learning at Edge. In his spare time, he enjoys spending time with his family.




Provanshu Dey is a Senior IoT Consultant with AWS Professional Services. He works on highly scalable and reliable IoT, data and machine learning solutions with our customers. In his spare time, he enjoys spending time with his family and tinkering with electronics & gadgets.




Amazon Redshift – 2017 Recap

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

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

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

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

Major launches in 2017

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

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

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

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

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

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

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

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

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

Performance enhancements— 3x-5x faster queries

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

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

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

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

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

Customer insights

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

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

Partner solutions

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

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


Blog posts

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

YouTube videos


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

Customer support

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

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

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


Additional Reading

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

About the Author

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




HiveMQ 3.3.3 released

Post Syndicated from The HiveMQ Team original https://www.hivemq.com/blog/hivemq-3-3-3-released/

The HiveMQ team is pleased to announce the availability of HiveMQ 3.3.3. This is a maintenance release for the 3.3 series and brings the following improvements:

  • Adds global option to rate-limit plugin service calls
  • Improved Logging for configured TLS Cipher Suites
  • Improved Retained Message Metrics
  • Improved support for Java 9
  • Fixed an issue where the metric half-full-queue.count could show an incorrect value
  • Fixed an issue that could cause cluster nodes to wait for operational nodes on startup indefinitely
  • Improved payload reference counting for single node deployments
  • Fixed an issue with rolling upgrades in an edge case where a node with a newer version is joining during network-split
  • Improved Shutdown behaviour for OnPublishReceivedCallbacks and plugin system services
  • Fixed an issue where assignments in the ClientGroupingService got cleaned up prematurely
  • Improved example configuration file for in-memory persistence

You can download the new HiveMQ version here.

We recommend to upgrade if you are an HiveMQ 3.3.x user.

Have a great day,
The HiveMQ Team

HiveMQ 3.2.9 released

Post Syndicated from The HiveMQ Team original https://www.hivemq.com/blog/hivemq-3-2-9-released/

The HiveMQ team is pleased to announce the availability of HiveMQ 3.2.9. This is a maintenance release for the 3.2 series and brings the following improvements:

  • Improved Logging for configured TLS Cipher Suites
  • Improved Retained Message Metrics
  • Improved support for Java 9
  • Fixed an issue where the metric half-full-queue.count could show an incorrect value
  • Fixed an issue that could cause cluster nodes to wait for operational nodes on startup indefinitely
  • Improved payload reference counting for single node deployments
  • Fixed an issue with rolling upgrades in an edge case where a node with a newer version is joining during network-split
  • Improved Shutdown behaviour for OnPublishReceivedCallbacks and plugin system services

You can download the new HiveMQ version here.

We recommend to upgrade if you are an HiveMQ 3.2.x user.

Have a great day,
The HiveMQ Team

How I built a data warehouse using Amazon Redshift and AWS services in record time

Post Syndicated from Stephen Borg original https://aws.amazon.com/blogs/big-data/how-i-built-a-data-warehouse-using-amazon-redshift-and-aws-services-in-record-time/

This is a customer post by Stephen Borg, the Head of Big Data and BI at Cerberus Technologies.

Cerberus Technologies, in their own words: Cerberus is a company founded in 2017 by a team of visionary iGaming veterans. Our mission is simple – to offer the best tech solutions through a data-driven and a customer-first approach, delivering innovative solutions that go against traditional forms of working and process. This mission is based on the solid foundations of reliability, flexibility and security, and we intend to fundamentally change the way iGaming and other industries interact with technology.

Over the years, I have developed and created a number of data warehouses from scratch. Recently, I built a data warehouse for the iGaming industry single-handedly. To do it, I used the power and flexibility of Amazon Redshift and the wider AWS data management ecosystem. In this post, I explain how I was able to build a robust and scalable data warehouse without the large team of experts typically needed.

In two of my recent projects, I ran into challenges when scaling our data warehouse using on-premises infrastructure. Data was growing at many tens of gigabytes per day, and query performance was suffering. Scaling required major capital investment for hardware and software licenses, and also significant operational costs for maintenance and technical staff to keep it running and performing well. Unfortunately, I couldn’t get the resources needed to scale the infrastructure with data growth, and these projects were abandoned. Thanks to cloud data warehousing, the bottleneck of infrastructure resources, capital expense, and operational costs have been significantly reduced or have totally gone away. There is no more excuse for allowing obstacles of the past to delay delivering timely insights to decision makers, no matter how much data you have.

With Amazon Redshift and AWS, I delivered a cloud data warehouse to the business very quickly, and with a small team: me. I didn’t have to order hardware or software, and I no longer needed to install, configure, tune, or keep up with patches and version updates. Instead, I easily set up a robust data processing pipeline and we were quickly ingesting and analyzing data. Now, my data warehouse team can be extremely lean, and focus more time on bringing in new data and delivering insights. In this post, I show you the AWS services and the architecture that I used.

Handling data feeds

I have several different data sources that provide everything needed to run the business. The data includes activity from our iGaming platform, social media posts, clickstream data, marketing and campaign performance, and customer support engagements.

To handle the diversity of data feeds, I developed abstract integration applications using Docker that run on Amazon EC2 Container Service (Amazon ECS) and feed data to Amazon Kinesis Data Streams. These data streams can be used for real time analytics. In my system, each record in Kinesis is preprocessed by an AWS Lambda function to cleanse and aggregate information. My system then routes it to be stored where I need on Amazon S3 by Amazon Kinesis Data Firehose. Suppose that you used an on-premises architecture to accomplish the same task. A team of data engineers would be required to maintain and monitor a Kafka cluster, develop applications to stream data, and maintain a Hadoop cluster and the infrastructure underneath it for data storage. With my stream processing architecture, there are no servers to manage, no disk drives to replace, and no service monitoring to write.

Setting up a Kinesis stream can be done with a few clicks, and the same for Kinesis Firehose. Firehose can be configured to automatically consume data from a Kinesis Data Stream, and then write compressed data every N minutes to Amazon S3. When I want to process a Kinesis data stream, it’s very easy to set up a Lambda function to be executed on each message received. I can just set a trigger from the AWS Lambda Management Console, as shown following.

I also monitor the duration of function execution using Amazon CloudWatch and AWS X-Ray.

Regardless of the format I receive the data from our partners, I can send it to Kinesis as JSON data using my own formatters. After Firehose writes this to Amazon S3, I have everything in nearly the same structure I received but compressed, encrypted, and optimized for reading.

This data is automatically crawled by AWS Glue and placed into the AWS Glue Data Catalog. This means that I can immediately query the data directly on S3 using Amazon Athena or through Amazon Redshift Spectrum. Previously, I used Amazon EMR and an Amazon RDS–based metastore in Apache Hive for catalog management. Now I can avoid the complexity of maintaining Hive Metastore catalogs. Glue takes care of high availability and the operations side so that I know that end users can always be productive.

Working with Amazon Athena and Amazon Redshift for analysis

I found Amazon Athena extremely useful out of the box for ad hoc analysis. Our engineers (me) use Athena to understand new datasets that we receive and to understand what transformations will be needed for long-term query efficiency.

For our data analysts and data scientists, we’ve selected Amazon Redshift. Amazon Redshift has proven to be the right tool for us over and over again. It easily processes 20+ million transactions per day, regardless of the footprint of the tables and the type of analytics required by the business. Latency is low and query performance expectations have been more than met. We use Redshift Spectrum for long-term data retention, which enables me to extend the analytic power of Amazon Redshift beyond local data to anything stored in S3, and without requiring me to load any data. Redshift Spectrum gives me the freedom to store data where I want, in the format I want, and have it available for processing when I need it.

To load data directly into Amazon Redshift, I use AWS Data Pipeline to orchestrate data workflows. I create Amazon EMR clusters on an intra-day basis, which I can easily adjust to run more or less frequently as needed throughout the day. EMR clusters are used together with Amazon RDS, Apache Spark 2.0, and S3 storage. The data pipeline application loads ETL configurations from Spring RESTful services hosted on AWS Elastic Beanstalk. The application then loads data from S3 into memory, aggregates and cleans the data, and then writes the final version of the data to Amazon Redshift. This data is then ready to use for analysis. Spark on EMR also helps with recommendations and personalization use cases for various business users, and I find this easy to set up and deliver what users want. Finally, business users use Amazon QuickSight for self-service BI to slice, dice, and visualize the data depending on their requirements.

Each AWS service in this architecture plays its part in saving precious time that’s crucial for delivery and getting different departments in the business on board. I found the services easy to set up and use, and all have proven to be highly reliable for our use as our production environments. When the architecture was in place, scaling out was either completely handled by the service, or a matter of a simple API call, and crucially doesn’t require me to change one line of code. Increasing shards for Kinesis can be done in a minute by editing a stream. Increasing capacity for Lambda functions can be accomplished by editing the megabytes allocated for processing, and concurrency is handled automatically. EMR cluster capacity can easily be increased by changing the master and slave node types in Data Pipeline, or by using Auto Scaling. Lastly, RDS and Amazon Redshift can be easily upgraded without any major tasks to be performed by our team (again, me).

In the end, using AWS services including Kinesis, Lambda, Data Pipeline, and Amazon Redshift allows me to keep my team lean and highly productive. I eliminated the cost and delays of capital infrastructure, as well as the late night and weekend calls for support. I can now give maximum value to the business while keeping operational costs down. My team pushed out an agile and highly responsive data warehouse solution in record time and we can handle changing business requirements rapidly, and quickly adapt to new data and new user requests.

Additional Reading

If you found this post useful, be sure to check out Deploy a Data Warehouse Quickly with Amazon Redshift, Amazon RDS for PostgreSQL and Tableau Server and Top 8 Best Practices for High-Performance ETL Processing Using Amazon Redshift.

About the Author

Stephen Borg is the Head of Big Data and BI at Cerberus Technologies. He has a background in platform software engineering, and first became involved in data warehousing using the typical RDBMS, SQL, ETL, and BI tools. He quickly became passionate about providing insight to help others optimize the business and add personalization to products. He is now the Head of Big Data and BI at Cerberus Technologies.




Migrating Your Amazon ECS Containers to AWS Fargate

Post Syndicated from Tiffany Jernigan original https://aws.amazon.com/blogs/compute/migrating-your-amazon-ecs-containers-to-aws-fargate/

AWS Fargate is a new technology that works with Amazon Elastic Container Service (ECS) to run containers without having to manage servers or clusters. What does this mean? With Fargate, you no longer need to provision or manage a single virtual machine; you can just create tasks and run them directly!

Fargate uses the same API actions as ECS, so you can use the ECS console, the AWS CLI, or the ECS CLI. I recommend running through the first-run experience for Fargate even if you’re familiar with ECS. It creates all of the one-time setup requirements, such as the necessary IAM roles. If you’re using a CLI, make sure to upgrade to the latest version

In this blog, you will see how to migrate ECS containers from running on Amazon EC2 to Fargate.

Getting started

Note: Anything with code blocks is a change in the task definition file. Screen captures are from the console. Additionally, Fargate is currently available in the us-east-1 (N. Virginia) region.

Launch type

When you create tasks (grouping of containers) and clusters (grouping of tasks), you now have two launch type options: EC2 and Fargate. The default launch type, EC2, is ECS as you knew it before the announcement of Fargate. You need to specify Fargate as the launch type when running a Fargate task.

Even though Fargate abstracts away virtual machines, tasks still must be launched into a cluster. With Fargate, clusters are a logical infrastructure and permissions boundary that allow you to isolate and manage groups of tasks. ECS also supports heterogeneous clusters that are made up of tasks running on both EC2 and Fargate launch types.

The optional, new requiresCompatibilities parameter with FARGATE in the field ensures that your task definition only passes validation if you include Fargate-compatible parameters. Tasks can be flagged as compatible with EC2, Fargate, or both.

"requiresCompatibilities": [


"networkMode": "awsvpc"

In November, we announced the addition of task networking with the network mode awsvpc. By default, ECS uses the bridge network mode. Fargate requires using the awsvpc network mode.

In bridge mode, all of your tasks running on the same instance share the instance’s elastic network interface, which is a virtual network interface, IP address, and security groups.

The awsvpc mode provides this networking support to your tasks natively. You now get the same VPC networking and security controls at the task level that were previously only available with EC2 instances. Each task gets its own elastic networking interface and IP address so that multiple applications or copies of a single application can run on the same port number without any conflicts.

The awsvpc mode also provides a separation of responsibility for tasks. You can get complete control of task placement within your own VPCs, subnets, and the security policies associated with them, even though the underlying infrastructure is managed by Fargate. Also, you can assign different security groups to each task, which gives you more fine-grained security. You can give an application only the permissions it needs.

"portMappings": [
        "containerPort": "3000"

What else has to change? First, you only specify a containerPort value, not a hostPort value, as there is no host to manage. Your container port is the port that you access on your elastic network interface IP address. Therefore, your container ports in a single task definition file need to be unique.

"environment": [
        "name": "WORDPRESS_DB_HOST",
        "value": ""

Additionally, links are not allowed as they are a property of the “bridge” network mode (and are now a legacy feature of Docker). Instead, containers share a network namespace and communicate with each other over the localhost interface. They can be referenced using the following:


CPU and memory

"memory": "1024",
 "cpu": "256"

"memory": "1gb",
 "cpu": ".25vcpu"

When launching a task with the EC2 launch type, task performance is influenced by the instance types that you select for your cluster combined with your task definition. If you pick larger instances, your applications make use of the extra resources if there is no contention.

In Fargate, you needed a way to get additional resource information so we created task-level resources. Task-level resources define the maximum amount of memory and cpu that your task can consume.

  • memory can be defined in MB with just the number, or in GB, for example, “1024” or “1gb”.
  • cpu can be defined as the number or in vCPUs, for example, “256” or “.25vcpu”.
    • vCPUs are virtual CPUs. You can look at the memory and vCPUs for instance types to get an idea of what you may have used before.

The memory and CPU options available with Fargate are:

CPU Memory
256 (.25 vCPU) 0.5GB, 1GB, 2GB
512 (.5 vCPU) 1GB, 2GB, 3GB, 4GB
1024 (1 vCPU) 2GB, 3GB, 4GB, 5GB, 6GB, 7GB, 8GB
2048 (2 vCPU) Between 4GB and 16GB in 1GB increments
4096 (4 vCPU) Between 8GB and 30GB in 1GB increments

IAM roles

Because Fargate uses awsvpc mode, you need an Amazon ECS service-linked IAM role named AWSServiceRoleForECS. It provides Fargate with the needed permissions, such as the permission to attach an elastic network interface to your task. After you create your service-linked IAM role, you can delete the remaining roles in your services.

"executionRoleArn": "arn:aws:iam::<your_account_id>:role/ecsTaskExecutionRole"

With the EC2 launch type, an instance role gives the agent the ability to pull, publish, talk to ECS, and so on. With Fargate, the task execution IAM role is only needed if you’re pulling from Amazon ECR or publishing data to Amazon CloudWatch Logs.

The Fargate first-run experience tutorial in the console automatically creates these roles for you.


Fargate currently supports non-persistent, empty data volumes for containers. When you define your container, you no longer use the host field and only specify a name.

Load balancers

For awsvpc mode, and therefore for Fargate, use the IP target type instead of the instance target type. You define this in the Amazon EC2 service when creating a load balancer.

If you’re using a Classic Load Balancer, change it to an Application Load Balancer or a Network Load Balancer.

Tip: If you are using an Application Load Balancer, make sure that your tasks are launched in the same VPC and Availability Zones as your load balancer.

Let’s migrate a task definition!

Here is an example NGINX task definition. This type of task definition is what you’re used to if you created one before Fargate was announced. It’s what you would run now with the EC2 launch type.

    "containerDefinitions": [
            "name": "nginx",
            "image": "nginx",
            "memory": "512",
            "cpu": "100",
            "essential": true,
            "portMappings": [
                    "hostPort": "80",
                    "containerPort": "80",
                    "protocol": "tcp"
            "logConfiguration": {
                "logDriver": "awslogs",
                "options": {
                    "awslogs-group": "/ecs/",
                    "awslogs-region": "us-east-1",
                    "awslogs-stream-prefix": "ecs"
    "family": "nginx-ec2"

OK, so now what do you need to do to change it to run with the Fargate launch type?

  • Add FARGATE for requiredCompatibilities (not required, but a good safety check for your task definition).
  • Use awsvpc as the network mode.
  • Just specify the containerPort (the hostPortvalue is the same).
  • Add a task executionRoleARN value to allow logging to CloudWatch.
  • Provide cpu and memory limits for the task.
    "requiresCompatibilities": [
    "containerDefinitions": [
            "name": "nginx",
            "image": "nginx",
            "memory": "512",
            "cpu": "100",
            "essential": true,
            "portMappings": [
                    "containerPort": "80",
                    "protocol": "tcp"
            "logConfiguration": {
                "logDriver": "awslogs",
                "options": {
                    "awslogs-group": "/ecs/",
                    "awslogs-region": "us-east-1",
                    "awslogs-stream-prefix": "ecs"
    "networkMode": "awsvpc",
    "executionRoleArn": "arn:aws:iam::<your_account_id>:role/ecsTaskExecutionRole",
    "family": "nginx-fargate",
    "memory": "512",
    "cpu": "256"

Are there more examples?

Yep! Head to the AWS Samples GitHub repo. We have several sample task definitions you can try for both the EC2 and Fargate launch types. Contributions are very welcome too :).


tiffany jernigan

Build a Multi-Tenant Amazon EMR Cluster with Kerberos, Microsoft Active Directory Integration and EMRFS Authorization

Post Syndicated from Songzhi Liu original https://aws.amazon.com/blogs/big-data/build-a-multi-tenant-amazon-emr-cluster-with-kerberos-microsoft-active-directory-integration-and-emrfs-authorization/

One of the challenges faced by our customers—especially those in highly regulated industries—is balancing the need for security with flexibility. In this post, we cover how to enable multi-tenancy and increase security by using EMRFS (EMR File System) authorization, the Amazon S3 storage-level authorization on Amazon EMR.

Amazon EMR is an easy, fast, and scalable analytics platform enabling large-scale data processing. EMRFS authorization provides Amazon S3 storage-level authorization by configuring EMRFS with multiple IAM roles. With this functionality enabled, different users and groups can share the same cluster and assume their own IAM roles respectively.

Simply put, on Amazon EMR, we can now have an Amazon EC2 role per user assumed at run time instead of one general EC2 role at the cluster level. When the user is trying to access Amazon S3 resources, Amazon EMR evaluates against a predefined mappings list in EMRFS configurations and picks up the right role for the user.

In this post, we will discuss what EMRFS authorization is (Amazon S3 storage-level access control) and show how to configure the role mappings with detailed examples. You will then have the desired permissions in a multi-tenant environment. We also demo Amazon S3 access from HDFS command line, Apache Hive on Hue, and Apache Spark.

EMRFS authorization for Amazon S3

There are two prerequisites for using this feature:

  1. Users must be authenticated, because EMRFS needs to map the current user/group/prefix to a predefined user/group/prefix. There are several authentication options. In this post, we launch a Kerberos-enabled cluster that manages the Key Distribution Center (KDC) on the master node, and enable a one-way trust from the KDC to a Microsoft Active Directory domain.
  2. The application must support accessing Amazon S3 via Applications that have their own S3FileSystem APIs (for example, Presto) are not supported at this time.

EMRFS supports three types of mapping entries: user, group, and Amazon S3 prefix. Let’s use an example to show how this works.

Assume that you have the following three identities in your organization, and they are defined in the Active Directory:

To enable all these groups and users to share the EMR cluster, you need to define the following IAM roles:

In this case, you create a separate Amazon EC2 role that doesn’t give any permission to Amazon S3. Let’s call the role the base role (the EC2 role attached to the EMR cluster), which in this example is named EMR_EC2_RestrictedRole. Then, you define all the Amazon S3 permissions for each specific user or group in their own roles. The restricted role serves as the fallback role when the user doesn’t belong to any user/group, nor does the user try to access any listed Amazon S3 prefixes defined on the list.

Important: For all other roles, like emrfs_auth_group_role_data_eng, you need to add the base role (EMR_EC2_RestrictedRole) as the trusted entity so that it can assume other roles. See the following example:

  "Version": "2012-10-17",
  "Statement": [
      "Effect": "Allow",
      "Principal": {
        "Service": "ec2.amazonaws.com"
      "Action": "sts:AssumeRole"
      "Effect": "Allow",
      "Principal": {
        "AWS": "arn:aws:iam::511586466501:role/EMR_EC2_RestrictedRole"
      "Action": "sts:AssumeRole"

The following is an example policy for the admin user role (emrfs_auth_user_role_admin_user):

    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Action": "s3:*",
            "Resource": "*"

We are assuming the admin user has access to all buckets in this example.

The following is an example policy for the data science group role (emrfs_auth_group_role_data_sci):

    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Resource": [
            "Action": [

This role grants all Amazon S3 permissions to the emrfs-auth-data-science-bucket-demo bucket and all the objects in it. Similarly, the policy for the role emrfs_auth_group_role_data_eng is shown below:

    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Resource": [
            "Action": [

Example role mappings configuration

To configure EMRFS authorization, you use EMR security configuration. Here is the configuration we use in this post

Consider the following scenario.

First, the admin user admin1 tries to log in and run a command to access Amazon S3 data through EMRFS. The first role emrfs_auth_user_role_admin_user on the mapping list, which is a user role, is mapped and picked up. Then admin1 has access to the Amazon S3 locations that are defined in this role.

Then a user from the data engineer group (grp_data_engineering) tries to access a data bucket to run some jobs. When EMRFS sees that the user is a member of the grp_data_engineering group, the group role emrfs_auth_group_role_data_eng is assumed, and the user has proper access to Amazon S3 that is defined in the emrfs_auth_group_role_data_eng role.

Next, the third user comes, who is not an admin and doesn’t belong to any of the groups. After failing evaluation of the top three entries, EMRFS evaluates whether the user is trying to access a certain Amazon S3 prefix defined in the last mapping entry. This type of mapping entry is called the prefix type. If the user is trying to access s3://emrfs-auth-default-bucket-demo/, then the prefix mapping is in effect, and the prefix role emrfs_auth_prefix_role_default_s3_prefix is assumed.

If the user is not trying to access any of the Amazon S3 paths that are defined on the list—which means it failed the evaluation of all the entries—it only has the permissions defined in the EMR_EC2RestrictedRole. This role is assumed by the EC2 instances in the cluster.

In this process, all the mappings defined are evaluated in the defined order, and the first role that is mapped is assumed, and the rest of the list is skipped.

Setting up an EMR cluster and mapping Active Directory users and groups

Now that we know how EMRFS authorization role mapping works, the next thing we need to think about is how we can use this feature in an easy and manageable way.

Active Directory setup

Many customers manage their users and groups using Microsoft Active Directory or other tools like OpenLDAP. In this post, we create the Active Directory on an Amazon EC2 instance running Windows Server and create the users and groups we will be using in the example below. After setting up Active Directory, we use the Amazon EMR Kerberos auto-join capability to establish a one-way trust from the KDC running on the EMR master node to the Active Directory domain on the EC2 instance. You can use your own directory services as long as it talks to the LDAP (Lightweight Directory Access Protocol).

To create and join Active Directory to Amazon EMR, follow the steps in the blog post Use Kerberos Authentication to Integrate Amazon EMR with Microsoft Active Directory.

After configuring Active Directory, you can create all the users and groups using the Active Directory tools and add users to appropriate groups. In this example, we created users like admin1, dataeng1, datascientist1, grp_data_engineering, and grp_data_science, and then add the users to the right groups.

Join the EMR cluster to an Active Directory domain

For clusters with Kerberos, Amazon EMR now supports automated Active Directory domain joins. You can use the security configuration to configure the one-way trust from the KDC to the Active Directory domain. You also configure the EMRFS role mappings in the same security configuration.

The following is an example of the EMR security configuration with a trusted Active Directory domain EMRKRB.TEST.COM and the EMRFS role mappings as we discussed earlier:

The EMRFS role mapping configuration is shown in this example:

We will also provide an example AWS CLI command that you can run.

Launching the EMR cluster and running the tests

Now you have configured Kerberos and EMRFS authorization for Amazon S3.

Additionally, you need to configure Hue with Active Directory using the Amazon EMR configuration API in order to log in using the AD users created before. The following is an example of Hue AD configuration.








Note: In the preceding configuration JSON file, change the values as required before pasting it into the software setting section in the Amazon EMR console.

Now let’s use this configuration and the security configuration you created before to launch the cluster.

In the Amazon EMR console, choose Create cluster. Then choose Go to advanced options. On the Step1: Software and Steps page, under Edit software settings (optional), paste the configuration in the box.

The rest of the setup is the same as an ordinary cluster setup, except in the Security Options section. In Step 4: Security, under Permissions, choose Custom, and then choose the RestrictedRole that you created before.

Choose the appropriate subnets (these should meet the base requirement in order for a successful Active Directory join—see the Amazon EMR Management Guide for more details), and choose the appropriate security groups to make sure it talks to the Active Directory. Choose a key so that you can log in and configure the cluster.

Most importantly, choose the security configuration that you created earlier to enable Kerberos and EMRFS authorization for Amazon S3.

You can use the following AWS CLI command to create a cluster.

aws emr create-cluster --name "TestEMRFSAuthorization" \ 
--release-label emr-5.10.0 \ --instance-type m3.xlarge \ 
--instance-count 3 \ 
--ec2-attributes InstanceProfile=EMR_EC2_DefaultRole,KeyName=MyEC2KeyPair \ --service-role EMR_DefaultRole \ 
--security-configuration MyKerberosConfig \ 
--configurations file://hue-config.json \
--applications Name=Hadoop Name=Hive Name=Hue Name=Spark \ 
--kerberos-attributes Realm=EC2.INTERNAL, \ KdcAdminPassword=<YourClusterKDCAdminPassword>, \ ADDomainJoinUser=<YourADUserLogonName>,ADDomainJoinPassword=<YourADUserPassword>, \ 

Note: If you create the cluster using CLI, you need to save the JSON configuration for Hue into a file named hue-config.json and place it on the server where you run the CLI command.

After the cluster gets into the Waiting state, try to connect by using SSH into the cluster using the Active Directory user name and password.

ssh -l [email protected] <EMR IP or DNS name>

Quickly run two commands to show that the Active Directory join is successful:

  1. id [user name] shows the mapped AD users and groups in Linux.
  2. hdfs groups [user name] shows the mapped group in Hadoop.

Both should return the current Active Directory user and group information if the setup is correct.

Now, you can test the user mapping first. Log in with the admin1 user, and run a Hadoop list directory command:

hadoop fs -ls s3://emrfs-auth-data-science-bucket-demo/

Now switch to a user from the data engineer group.

Retry the previous command to access the admin’s bucket. It should throw an Amazon S3 Access Denied exception.

When you try listing the Amazon S3 bucket that a data engineer group member has accessed, it triggers the group mapping.

hadoop fs -ls s3://emrfs-auth-data-engineering-bucket-demo/

It successfully returns the listing results. Next we will test Apache Hive and then Apache Spark.


To run jobs successfully, you need to create a home directory for every user in HDFS for staging data under /user/<username>. Users can configure a step to create a home directory at cluster launch time for every user who has access to the cluster. In this example, you use Hue since Hue will create the home directory in HDFS for the user at the first login. Here Hue also needs to be integrated with the same Active Directory as explained in the example configuration described earlier.

First, log in to Hue as a data engineer user, and open a Hive Notebook in Hue. Then run a query to create a new table pointing to the data engineer bucket, s3://emrfs-auth-data-engineering-bucket-demo/table1_data_eng/.

You can see that the table was created successfully. Now try to create another table pointing to the data science group’s bucket, where the data engineer group doesn’t have access.

It failed and threw an Amazon S3 Access Denied error.

Now insert one line of data into the successfully create table.

Next, log out, switch to a data science group user, and create another table, test2_datasci_tb.

The creation is successful.

The last task is to test Spark (it requires the user directory, but Hue created one in the previous step).

Now let’s come back to the command line and run some Spark commands.

Login to the master node using the datascientist1 user:

Start the SparkSQL interactive shell by typing spark-sql, and run the show tables command. It should list the tables that you created using Hive.

As a data science group user, try select on both tables. You will find that you can only select the table defined in the location that your group has access to.


EMRFS authorization for Amazon S3 enables you to have multiple roles on the same cluster, providing flexibility to configure a shared cluster for different teams to achieve better efficiency. The Active Directory integration and group mapping make it much easier for you to manage your users and groups, and provides better auditability in a multi-tenant environment.

Additional Reading

If you found this post useful, be sure to check out Use Kerberos Authentication to Integrate Amazon EMR with Microsoft Active Directory and Launching and Running an Amazon EMR Cluster inside a VPC.

About the Authors

Songzhi Liu is a Big Data Consultant with AWS Professional Services. He works closely with AWS customers to provide them Big Data & Machine Learning solutions and best practices on the Amazon cloud.





Reactive Microservices Architecture on AWS

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

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

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

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

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

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

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

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

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

Components and Services

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

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

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

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

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

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

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

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

JsonObject jsonObject = new JsonObject(message);



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

if (res.succeeded()) {

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

} else {




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

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

public class KinesisVerticle extends AbstractVerticle {

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

private AmazonKinesisAsync kinesisAsyncClient;

private String eventStream = "EventStream";


public void start() throws Exception {

EventBus eb = vertx.eventBus();

kinesisAsyncClient = createClient();

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

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

try {

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

String partitionKey = trackingMessage.getMessageId();

byte [] byteMessage = createMessage(trackingMessage);

ByteBuffer buf = ByteBuffer.wrap(byteMessage);

sendMessageToKinesis(buf, partitionKey);



catch (KinesisException exc) {





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

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

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

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

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

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

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


catch (Exception exc) {

if (null == logger)






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

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

jedis.publish(Constants.REDIS_PUBSUB_CHANNEL, jsonString);


catch (final IOException e) {

log(e.getMessage(), logger);



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

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

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

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

About the Author

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



Success at Apache: A Newbie’s Narrative

Post Syndicated from mikesefanov original https://yahooeng.tumblr.com/post/170536010891


Kuhu Shukla (bottom center) and team at the 2017 DataWorks Summit

By Kuhu Shukla

This post first appeared here on the Apache Software Foundation blog as part of ASF’s “Success at Apache” monthly blog series.

As I sit at my desk on a rather frosty morning with my coffee, looking up new JIRAs from the previous day in the Apache Tez project, I feel rather pleased. The latest community release vote is complete, the bug fixes that we so badly needed are in and the new release that we tested out internally on our many thousand strong cluster is looking good. Today I am looking at a new stack trace from a different Apache project process and it is hard to miss how much of the exceptional code I get to look at every day comes from people all around the globe. A contributor leaves a JIRA comment before he goes on to pick up his kid from soccer practice while someone else wakes up to find that her effort on a bug fix for the past two months has finally come to fruition through a binding +1.

Yahoo – which joined AOL, HuffPost, Tumblr, Engadget, and many more brands to form the Verizon subsidiary Oath last year – has been at the frontier of open source adoption and contribution since before I was in high school. So while I have no historical trajectories to share, I do have a story on how I found myself in an epic journey of migrating all of Yahoo jobs from Apache MapReduce to Apache Tez, a then-new DAG based execution engine.

Oath grid infrastructure is through and through driven by Apache technologies be it storage through HDFS, resource management through YARN, job execution frameworks with Tez and user interface engines such as Hive, Hue, Pig, Sqoop, Spark, Storm. Our grid solution is specifically tailored to Oath’s business-critical data pipeline needs using the polymorphic technologies hosted, developed and maintained by the Apache community.

On the third day of my job at Yahoo in 2015, I received a YouTube link on An Introduction to Apache Tez. I watched it carefully trying to keep up with all the questions I had and recognized a few names from my academic readings of Yarn ACM papers. I continued to ramp up on YARN and HDFS, the foundational Apache technologies Oath heavily contributes to even today. For the first few weeks I spent time picking out my favorite (necessary) mailing lists to subscribe to and getting started on setting up on a pseudo-distributed Hadoop cluster. I continued to find my footing with newbie contributions and being ever more careful with whitespaces in my patches. One thing was clear – Tez was the next big thing for us. By the time I could truly call myself a contributor in the Hadoop community nearly 80-90% of the Yahoo jobs were now running with Tez. But just like hiking up the Grand Canyon, the last 20% is where all the pain was. Being a part of the solution to this challenge was a happy prospect and thankfully contributing to Tez became a goal in my next quarter.

The next sprint planning meeting ended with me getting my first major Tez assignment – progress reporting. The progress reporting in Tez was non-existent – “Just needs an API fix,”  I thought. Like almost all bugs in this ecosystem, it was not easy. How do you define progress? How is it different for different kinds of outputs in a graph? The questions were many.

I, however, did not have to go far to get answers. The Tez community actively came to a newbie’s rescue, finding answers and posing important questions. I started attending the bi-weekly Tez community sync up calls and asking existing contributors and committers for course correction. Suddenly the team was much bigger, the goals much more chiseled. This was new to anyone like me who came from the networking industry, where the most open part of the code are the RFCs and the implementation details are often hidden. These meetings served as a clean room for our coding ideas and experiments. Ideas were shared, to the extent of which data structure we should pick and what a future user of Tez would take from it. In between the usual status updates and extensive knowledge transfers were made.

Oath uses Apache Pig and Apache Hive extensively and most of the urgent requirements and requests came from Pig and Hive developers and users. Each issue led to a community JIRA and as we started running Tez at Oath scale, new feature ideas and bugs around performance and resource utilization materialized. Every year most of the Hadoop team at Oath travels to the Hadoop Summit where we meet our cohorts from the Apache community and we stand for hours discussing the state of the art and what is next for the project. One such discussion set the course for the next year and a half for me.

We needed an innovative way to shuffle data. Frameworks like MapReduce and Tez have a shuffle phase in their processing lifecycle wherein the data from upstream producers is made available to downstream consumers. Even though Apache Tez was designed with a feature set corresponding to optimization requirements in Pig and Hive, the Shuffle Handler Service was retrofitted from MapReduce at the time of the project’s inception. With several thousands of jobs on our clusters leveraging these features in Tez, the Shuffle Handler Service became a clear performance bottleneck. So as we stood talking about our experience with Tez with our friends from the community, we decided to implement a new Shuffle Handler for Tez. All the conversation points were tracked now through an umbrella JIRA TEZ-3334 and the to-do list was long. I picked a few JIRAs and as I started reading through I realized, this is all new code I get to contribute to and review. There might be a better way to put this, but to be honest it was just a lot of fun! All the whiteboards were full, the team took walks post lunch and discussed how to go about defining the API. Countless hours were spent debugging hangs while fetching data and looking at stack traces and Wireshark captures from our test runs. Six months in and we had the feature on our sandbox clusters. There were moments ranging from sheer frustration to absolute exhilaration with high fives as we continued to address review comments and fixing big and small issues with this evolving feature.

As much as owning your code is valued everywhere in the software community, I would never go on to say “I did this!” In fact, “we did!” It is this strong sense of shared ownership and fluid team structure that makes the open source experience at Apache truly rewarding. This is just one example. A lot of the work that was done in Tez was leveraged by the Hive and Pig community and cross Apache product community interaction made the work ever more interesting and challenging. Triaging and fixing issues with the Tez rollout led us to hit a 100% migration score last year and we also rolled the Tez Shuffle Handler Service out to our research clusters. As of last year we have run around 100 million Tez DAGs with a total of 50 billion tasks over almost 38,000 nodes.

In 2018 as I move on to explore Hadoop 3.0 as our future release, I hope that if someone outside the Apache community is reading this, it will inspire and intrigue them to contribute to a project of their choice. As an astronomy aficionado, going from a newbie Apache contributor to a newbie Apache committer was very much like looking through my telescope - it has endless possibilities and challenges you to be your best.

About the Author:

Kuhu Shukla is a software engineer at Oath and did her Masters in Computer Science at North Carolina State University. She works on the Big Data Platforms team on Apache Tez, YARN and HDFS with a lot of talented Apache PMCs and Committers in Champaign, Illinois. A recent Apache Tez Committer herself she continues to contribute to YARN and HDFS and spoke at the 2017 Dataworks Hadoop Summit on “Tez Shuffle Handler: Shuffling At Scale With Apache Hadoop”. Prior to that she worked on Juniper Networks’ router and switch configuration APIs. She likes to participate in open source conferences and women in tech events. In her spare time she loves singing Indian classical and jazz, laughing, whale watching, hiking and peering through her Dobsonian telescope.

The Floodgates Are Open – Increased Network Bandwidth for EC2 Instances

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/the-floodgates-are-open-increased-network-bandwidth-for-ec2-instances/

I hope that you have configured your AMIs and your current-generation EC2 instances to use the Elastic Network Adapter (ENA) that I told you about back in mid-2016. The ENA gives you high throughput and low latency, while minimizing the load on the host processor. It is designed to work well in the presence of multiple vCPUs, with intelligent packet routing backed up by multiple transmit and receive queues.

Today we are opening up the floodgates and giving you access to more bandwidth in all AWS Regions. Here are the specifics (in each case, the actual bandwidth is dependent on the instance type and size):

EC2 to S3 – Traffic to and from Amazon Simple Storage Service (S3) can now take advantage of up to 25 Gbps of bandwidth. Previously, traffic of this type had access to 5 Gbps of bandwidth. This will be of benefit to applications that access large amounts of data in S3 or that make use of S3 for backup and restore.

EC2 to EC2 – Traffic to and from EC2 instances in the same or different Availability Zones within a region can now take advantage of up to 5 Gbps of bandwidth for single-flow traffic, or 25 Gbps of bandwidth for multi-flow traffic (a flow represents a single, point-to-point network connection) by using private IPv4 or IPv6 addresses, as described here.

EC2 to EC2 (Cluster Placement Group) – Traffic to and from EC2 instances within a cluster placement group can continue to take advantage of up to 10 Gbps of lower-latency bandwidth for single-flow traffic, or 25 Gbps of lower-latency bandwidth for multi-flow traffic.

To take advantage of this additional bandwidth, make sure that you are using the latest, ENA-enabled AMIs on current-generation EC2 instances. ENA-enabled AMIs are available for Amazon Linux, Ubuntu 14.04 & 16.04, RHEL 7.4, SLES 12, and Windows Server (2008 R2, 2012, 2012 R2, and 2016). The FreeBSD AMI in AWS Marketplace is also ENA-enabled, as is VMware Cloud on AWS.


Top 8 Best Practices for High-Performance ETL Processing Using Amazon Redshift

Post Syndicated from Thiyagarajan Arumugam original https://aws.amazon.com/blogs/big-data/top-8-best-practices-for-high-performance-etl-processing-using-amazon-redshift/

An ETL (Extract, Transform, Load) process enables you to load data from source systems into your data warehouse. This is typically executed as a batch or near-real-time ingest process to keep the data warehouse current and provide up-to-date analytical data to end users.

Amazon Redshift is a fast, petabyte-scale data warehouse that enables you easily to make data-driven decisions. With Amazon Redshift, you can get insights into your big data in a cost-effective fashion using standard SQL. You can set up any type of data model, from star and snowflake schemas, to simple de-normalized tables for running any analytical queries.

To operate a robust ETL platform and deliver data to Amazon Redshift in a timely manner, design your ETL processes to take account of Amazon Redshift’s architecture. When migrating from a legacy data warehouse to Amazon Redshift, it is tempting to adopt a lift-and-shift approach, but this can result in performance and scale issues long term. This post guides you through the following best practices for ensuring optimal, consistent runtimes for your ETL processes:

  • COPY data from multiple, evenly sized files.
  • Use workload management to improve ETL runtimes.
  • Perform table maintenance regularly.
  • Perform multiple steps in a single transaction.
  • Loading data in bulk.
  • Use UNLOAD to extract large result sets.
  • Use Amazon Redshift Spectrum for ad hoc ETL processing.
  • Monitor daily ETL health using diagnostic queries.

1. COPY data from multiple, evenly sized files

Amazon Redshift is an MPP (massively parallel processing) database, where all the compute nodes divide and parallelize the work of ingesting data. Each node is further subdivided into slices, with each slice having one or more dedicated cores, equally dividing the processing capacity. The number of slices per node depends on the node type of the cluster. For example, each DS2.XLARGE compute node has two slices, whereas each DS2.8XLARGE compute node has 16 slices.

When you load data into Amazon Redshift, you should aim to have each slice do an equal amount of work. When you load the data from a single large file or from files split into uneven sizes, some slices do more work than others. As a result, the process runs only as fast as the slowest, or most heavily loaded, slice. In the example shown below, a single large file is loaded into a two-node cluster, resulting in only one of the nodes, “Compute-0”, performing all the data ingestion:

When splitting your data files, ensure that they are of approximately equal size – between 1 MB and 1 GB after compression. The number of files should be a multiple of the number of slices in your cluster. Also, I strongly recommend that you individually compress the load files using gzip, lzop, or bzip2 to efficiently load large datasets.

When loading multiple files into a single table, use a single COPY command for the table, rather than multiple COPY commands. Amazon Redshift automatically parallelizes the data ingestion. Using a single COPY command to bulk load data into a table ensures optimal use of cluster resources, and quickest possible throughput.

2. Use workload management to improve ETL runtimes

Use Amazon Redshift’s workload management (WLM) to define multiple queues dedicated to different workloads (for example, ETL versus reporting) and to manage the runtimes of queries. As you migrate more workloads into Amazon Redshift, your ETL runtimes can become inconsistent if WLM is not appropriately set up.

I recommend limiting the overall concurrency of WLM across all queues to around 15 or less. This WLM guide helps you organize and monitor the different queues for your Amazon Redshift cluster.

When managing different workloads on your Amazon Redshift cluster, consider the following for the queue setup:

  • Create a queue dedicated to your ETL processes. Configure this queue with a small number of slots (5 or fewer). Amazon Redshift is designed for analytics queries, rather than transaction processing. The cost of COMMIT is relatively high, and excessive use of COMMIT can result in queries waiting for access to the commit queue. Because ETL is a commit-intensive process, having a separate queue with a small number of slots helps mitigate this issue.
  • Claim extra memory available in a queue. When executing an ETL query, you can take advantage of the wlm_query_slot_count to claim the extra memory available in a particular queue. For example, a typical ETL process might involve COPYing raw data into a staging table so that downstream ETL jobs can run transformations that calculate daily, weekly, and monthly aggregates. To speed up the COPY process (so that the downstream tasks can start in parallel sooner), the wlm_query_slot_count can be increased for this step.
  • Create a separate queue for reporting queries. Configure query monitoring rules on this queue to further manage long-running and expensive queries.
  • Take advantage of the dynamic memory parameters. They swap the memory from your ETL to your reporting queue after the ETL job has completed.

3. Perform table maintenance regularly

Amazon Redshift is a columnar database, which enables fast transformations for aggregating data. Performing regular table maintenance ensures that transformation ETLs are predictable and performant. To get the best performance from your Amazon Redshift database, you must ensure that database tables regularly are VACUUMed and ANALYZEd. The Analyze & Vacuum schema utility helps you automate the table maintenance task and have VACUUM & ANALYZE executed in a regular fashion.

  • Use VACUUM to sort tables and remove deleted blocks

During a typical ETL refresh process, tables receive new incoming records using COPY, and unneeded data (cold data) is removed using DELETE. New rows are added to the unsorted region in a table. Deleted rows are simply marked for deletion.

DELETE does not automatically reclaim the space occupied by the deleted rows. Adding and removing large numbers of rows can therefore cause the unsorted region and the number of deleted blocks to grow. This can degrade the performance of queries executed against these tables.

After an ETL process completes, perform VACUUM to ensure that user queries execute in a consistent manner. The complete list of tables that need VACUUMing can be found using the Amazon Redshift Util’s table_info script.

Use the following approaches to ensure that VACCUM is completed in a timely manner:

  • Use wlm_query_slot_count to claim all the memory allocated in the ETL WLM queue during the VACUUM process.
  • DROP or TRUNCATE intermediate or staging tables, thereby eliminating the need to VACUUM them.
  • If your table has a compound sort key with only one sort column, try to load your data in sort key order. This helps reduce or eliminate the need to VACUUM the table.
  • Consider using time series This helps reduce the amount of data you need to VACUUM.
  • Use ANALYZE to update database statistics

Amazon Redshift uses a cost-based query planner and optimizer using statistics about tables to make good decisions about the query plan for the SQL statements. Regular statistics collection after the ETL completion ensures that user queries run fast, and that daily ETL processes are performant. The Amazon Redshift utility table_info script provides insights into the freshness of the statistics. Keeping the statistics off (pct_stats_off) less than 20% ensures effective query plans for the SQL queries.

4. Perform multiple steps in a single transaction

ETL transformation logic often spans multiple steps. Because commits in Amazon Redshift are expensive, if each ETL step performs a commit, multiple concurrent ETL processes can take a long time to execute.

To minimize the number of commits in a process, the steps in an ETL script should be surrounded by a BEGIN…END statement so that a single commit is performed only after all the transformation logic has been executed. For example, here is an example multi-step ETL script that performs one commit at the end:

CREATE temporary staging_table;
INSERT INTO staging_table SELECT .. FROM source (transformation logic);
DELETE FROM daily_table WHERE dataset_date =?;
INSERT INTO daily_table SELECT .. FROM staging_table (daily aggregate);
DELETE FROM weekly_table WHERE weekending_date=?;
INSERT INTO weekly_table SELECT .. FROM staging_table(weekly aggregate);

5. Loading data in bulk

Amazon Redshift is designed to store and query petabyte-scale datasets. Using Amazon S3 you can stage and accumulate data from multiple source systems before executing a bulk COPY operation. The following methods allow efficient and fast transfer of these bulk datasets into Amazon Redshift:

  • Use a manifest file to ingest large datasets that span multiple files. The manifest file is a JSON file that lists all the files to be loaded into Amazon Redshift. Using a manifest file ensures that Amazon Redshift has a consistent view of the data to be loaded from S3, while also ensuring that duplicate files do not result in the same data being loaded more than one time.
  • Use temporary staging tables to hold the data for transformation. These tables are automatically dropped after the ETL session is complete. Temporary tables can be created using the CREATE TEMPORARY TABLE syntax, or by issuing a SELECT … INTO #TEMP_TABLE query. Explicitly specifying the CREATE TEMPORARY TABLE statement allows you to control the DISTRIBUTION KEY, SORT KEY, and compression settings to further improve performance.
  • User ALTER table APPEND to swap data from the staging tables to the target table. Data in the source table is moved to matching columns in the target table. Column order doesn’t matter. After data is successfully appended to the target table, the source table is empty. ALTER TABLE APPEND is much faster than a similar CREATE TABLE AS or INSERT INTO operation because it doesn’t involve copying or moving data.

6. Use UNLOAD to extract large result sets

Fetching a large number of rows using SELECT is expensive and takes a long time. When a large amount of data is fetched from the Amazon Redshift cluster, the leader node has to hold the data temporarily until the fetches are complete. Further, data is streamed out sequentially, which results in longer elapsed time. As a result, the leader node can become hot, which not only affects the SELECT that is being executed, but also throttles resources for creating execution plans and managing the overall cluster resources. Here is an example of a large SELECT statement. Notice that the leader node is doing most of the work to stream out the rows:

Use UNLOAD to extract large results sets directly to S3. After it’s in S3, the data can be shared with multiple downstream systems. By default, UNLOAD writes data in parallel to multiple files according to the number of slices in the cluster. All the compute nodes participate to quickly offload the data into S3.

If you are extracting data for use with Amazon Redshift Spectrum, you should make use of the MAXFILESIZE parameter to and keep files are 150 MB. Similar to item 1 above, having many evenly sized files ensures that Redshift Spectrum can do the maximum amount of work in parallel.

7. Use Redshift Spectrum for ad hoc ETL processing

Events such as data backfill, promotional activity, and special calendar days can trigger additional data volumes that affect the data refresh times in your Amazon Redshift cluster. To help address these spikes in data volumes and throughput, I recommend staging data in S3. After data is organized in S3, Redshift Spectrum enables you to query it directly using standard SQL. In this way, you gain the benefits of additional capacity without having to resize your cluster.

For tips on getting started with and optimizing the use of Redshift Spectrum, see the previous post, 10 Best Practices for Amazon Redshift Spectrum.

8. Monitor daily ETL health using diagnostic queries

Monitoring the health of your ETL processes on a regular basis helps identify the early onset of performance issues before they have a significant impact on your cluster. The following monitoring scripts can be used to provide insights into the health of your ETL processes:

Script Use when… Solution
commit_stats.sql – Commit queue statistics from past days, showing largest queue length and queue time first DML statements such as INSERT/UPDATE/COPY/DELETE operations take several times longer to execute when multiple of these operations are in progress Set up separate WLM queues for the ETL process and limit the concurrency to < 5.
copy_performance.sql –  Copy command statistics for the past days Daily COPY operations take longer to execute • Follow the best practices for the COPY command.
• Analyze data growth with the incoming datasets and consider cluster resize to meet the expected SLA.
table_info.sql – Table skew and unsorted statistics along with storage and key information Transformation steps take longer to execute • Set up regular VACCUM jobs to address unsorted rows and claim the deleted blocks so that transformation SQL execute optimally.
• Consider a table redesign to avoid data skewness.
v_check_transaction_locks.sql – Monitor transaction locks INSERT/UPDATE/COPY/DELETE operations on particular tables do not respond back in timely manner, compared to when run after the ETL Multiple DML statements are operating on the same target table at the same moment from different transactions. Set up ETL job dependency so that they execute serially for the same target table.
v_get_schema_priv_by_user.sql – Get the schema that the user has access to Reporting users can view intermediate tables Set up separate database groups for reporting and ETL users, and grants access to objects using GRANT.
v_generate_tbl_ddl.sql – Get the table DDL You need to create an empty table with same structure as target table for data backfill Generate DDL using this script for data backfill.
v_space_used_per_tbl.sql – monitor space used by individual tables Amazon Redshift data warehouse space growth is trending upwards more than normal

Analyze the individual tables that are growing at higher rate than normal. Consider data archival using UNLOAD to S3 and Redshift Spectrum for later analysis.

Use unscanned_table_summary.sql to find unused table and archive or drop them.

top_queries.sql – Return the top 50 time consuming statements aggregated by its text ETL transformations are taking longer to execute Analyze the top transformation SQL and use EXPLAIN to find opportunities for tuning the query plan.

There are several other useful scripts available in the amazon-redshift-utils repository. The AWS Lambda Utility Runner runs a subset of these scripts on a scheduled basis, allowing you to automate much of monitoring of your ETL processes.

Example ETL process

The following ETL process reinforces some of the best practices discussed in this post. Consider the following four-step daily ETL workflow where data from an RDBMS source system is staged in S3 and then loaded into Amazon Redshift. Amazon Redshift is used to calculate daily, weekly, and monthly aggregations, which are then unloaded to S3, where they can be further processed and made available for end-user reporting using a number of different tools, including Redshift Spectrum and Amazon Athena.

Step 1:  Extract from the RDBMS source to a S3 bucket

In this ETL process, the data extract job fetches change data every 1 hour and it is staged into multiple hourly files. For example, the staged S3 folder looks like the following:

 [[email protected] ~]$ aws s3 ls s3://<<S3 Bucket>>/batch/2017/07/02/
2017-07-02 01:59:58   81900220 20170702T01.export.gz
2017-07-02 02:59:56   84926844 20170702T02.export.gz
2017-07-02 03:59:54   78990356 20170702T03.export.gz
2017-07-02 22:00:03   75966745 20170702T21.export.gz
2017-07-02 23:00:02   89199874 20170702T22.export.gz
2017-07-02 00:59:59   71161715 20170702T23.export.gz

Organizing the data into multiple, evenly sized files enables the COPY command to ingest this data using all available resources in the Amazon Redshift cluster. Further, the files are compressed (gzipped) to further reduce COPY times.

Step 2: Stage data to the Amazon Redshift table for cleansing

Ingesting the data can be accomplished using a JSON-based manifest file. Using the manifest file ensures that S3 eventual consistency issues can be eliminated and also provides an opportunity to dedupe any files if needed. A sample manifest20170702.json file looks like the following:

  "entries": [
    {"url":" s3://<<S3 Bucket>>/batch/2017/07/02/20170702T01.export.gz", "mandatory":true},
    {"url":" s3://<<S3 Bucket>>/batch/2017/07/02/20170702T02.export.gz", "mandatory":true},
    {"url":" s3://<<S3 Bucket>>/batch/2017/07/02/20170702T23.export.gz", "mandatory":true}

The data can be ingested using the following command:

SET wlm_query_slot_count TO <<max available concurrency in the ETL queue>>;
COPY stage_tbl FROM 's3:// <<S3 Bucket>>/batch/manifest20170702.json' iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole' manifest;

Because the downstream ETL processes depend on this COPY command to complete, the wlm_query_slot_count is used to claim all the memory available to the queue. This helps the COPY command complete as quickly as possible.

Step 3: Transform data to create daily, weekly, and monthly datasets and load into target tables

Data is staged in the “stage_tbl” from where it can be transformed into the daily, weekly, and monthly aggregates and loaded into target tables. The following job illustrates a typical weekly process:

INSERT into ETL_LOG (..) values (..);
DELETE from weekly_tbl where dataset_week = <<current week>>;
INSERT into weekly_tbl (..)
  SELECT date_trunc('week', dataset_day) AS week_begin_dataset_date, SUM(C1) AS C1, SUM(C2) AS C2
	FROM   stage_tbl
GROUP BY date_trunc('week', dataset_day);
INSERT into AUDIT_LOG values (..);

As shown above, multiple steps are combined into one transaction to perform a single commit, reducing contention on the commit queue.

Step 4: Unload the daily dataset to populate the S3 data lake bucket

The transformed results are now unloaded into another S3 bucket, where they can be further processed and made available for end-user reporting using a number of different tools, including Redshift Spectrum and Amazon Athena.

unload ('SELECT * FROM weekly_tbl WHERE dataset_week = <<current week>>’) TO 's3:// <<S3 Bucket>>/datalake/weekly/20170526/' iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole';


Amazon Redshift lets you easily operate petabyte-scale data warehouses on the cloud. This post summarized the best practices for operating scalable ETL natively within Amazon Redshift. I demonstrated efficient ways to ingest and transform data, along with close monitoring. I also demonstrated the best practices being used in a typical sample ETL workload to transform the data into Amazon Redshift.

If you have questions or suggestions, please comment below.


About the Author

Thiyagarajan Arumugam is a Big Data Solutions Architect at Amazon Web Services and designs customer architectures to process data at scale. Prior to AWS, he built data warehouse solutions at Amazon.com. In his free time, he enjoys all outdoor sports and practices the Indian classical drum mridangam.


MagPi 66: Raspberry Pi media projects for your home

Post Syndicated from Rob Zwetsloot original https://www.raspberrypi.org/blog/magpi-66-media-pi/

Hey folks, Rob from The MagPi here! Issue 66 of The MagPi is out right now, with the ultimate guide to powering your home media with Raspberry Pi. We think the Pi is the perfect replacement or upgrade for many media devices, so in this issue we show you how to build a range of Raspberry Pi media projects.

MagPi 66

Yes, it does say Pac-Man robotics on the cover. They’re very cool.

The article covers file servers for sharing media across your network, music streaming boxes that connect to Spotify, a home theatre PC to make your TV-watching more relaxing, a futuristic Pi-powered moving photoframe, and even an Alexa voice assistant to control all these devices!

More to see

That’s not all though — The MagPi 66 also shows you how to build a Raspberry Pi cluster computer, how to control LEGO robots using the GPIO, and why your Raspberry Pi isn’t affected by Spectre and Meltdown.

In addition, you’ll also find our usual selection of product reviews and excellent project showcases.

Get The MagPi 66

Issue 66 is available today from WHSmith, Tesco, Sainsbury’s, and Asda. If you live in the US, head over to your local Barnes & Noble or Micro Center in the next few days. You can also get the new issue online from our store, or digitally via our Android and iOS apps. And don’t forget, there’s always the free PDF as well.

Subscribe for free goodies

Want to support the Raspberry Pi Foundation and the magazine, and get some cool free stuff? If you take out a twelve-month print subscription to The MagPi, you’ll get a Pi Zero W, Pi Zero case, and adapter cables absolutely free! This offer does not currently have an end date.

I hope you enjoy this issue! See you next month.

The post MagPi 66: Raspberry Pi media projects for your home appeared first on Raspberry Pi.