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HomeBig DataSafely take away Kafka brokers from Amazon MSK provisioned clusters

Safely take away Kafka brokers from Amazon MSK provisioned clusters

At the moment, we’re asserting dealer elimination functionality for Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned clusters, which helps you to take away a number of brokers out of your provisioned clusters. Now you can scale back your cluster’s storage and compute capability by eradicating units of brokers, with no availability influence, knowledge sturdiness danger, or disruption to your knowledge streaming purposes. Amazon MSK is a completely managed Apache Kafka service that makes it simple for builders to construct and run extremely accessible, safe, and scalable streaming purposes. Directors can optimize the prices of their Amazon MSK clusters by lowering dealer rely and adapting the cluster capability to the adjustments within the streaming knowledge demand, with out affecting their clusters’ efficiency, availability, or knowledge sturdiness.

You should utilize Amazon MSK as a core basis to construct quite a lot of real-time streaming purposes and high-performance event-driven architectures. As enterprise wants and site visitors patterns change, cluster capability is commonly adjusted to optimize prices. Amazon MSK offers flexibility and elasticity for directors to right-size MSK clusters. You possibly can enhance dealer rely or the dealer measurement to handle the surge in site visitors throughout peak occasions or lower the occasion measurement of brokers of the cluster to cut back capability. Nevertheless, to cut back the dealer rely, earlier you needed to undertake effort-intensive migration to a different cluster.

With the dealer elimination functionality, now you can take away a number of brokers out of your provisioned clusters to satisfy the various wants of your streaming workloads. Throughout and publish dealer elimination, the cluster continues to deal with learn and write requests from the shopper purposes. MSK performs the required validations to safeguard towards knowledge sturdiness dangers and gracefully removes the brokers from the cluster. By utilizing dealer elimination functionality, you’ll be able to exactly modify MSK cluster capability, eliminating the necessity to change the occasion measurement of each dealer within the cluster or having emigrate to a different cluster to cut back dealer rely.

How the dealer elimination function works

Earlier than you execute the dealer elimination operation, you have to make some brokers eligible for elimination by shifting all partitions off of them. You should utilize Kafka admin APIs or Cruise Management to maneuver partitions to different brokers that you just intend to retain within the cluster.

You select which brokers to take away and transfer the partitions from these brokers to different brokers utilizing Kafka instruments. Alternatively, you might have brokers that aren’t internet hosting any partitions. Then use Edit variety of brokers function utilizing the AWS Administration Console, or the Amazon MSK API UpdateBrokerCount. Listed here are particulars on how you need to use this new function:

  • You possibly can take away a most of 1 dealer per Availability Zone (AZ) in a single dealer elimination operation. To take away extra brokers, you’ll be able to name a number of dealer elimination operations consecutively after the prior operation has been accomplished. You need to retain not less than one dealer per AZ in your MSK cluster.
  • The goal variety of dealer nodes within the cluster have to be a a number of of the variety of availability zones (AZs) within the shopper subnets parameter. For instance, a cluster with subnets in two AZs should have a goal variety of nodes that may be a a number of of two.
  • If the brokers you eliminated have been current within the bootstrap dealer string, MSK will carry out the required routing in order that the shopper’s connectivity to the cluster shouldn’t be disrupted. You don’t have to make any shopper adjustments to vary your bootstrap strings.
  • You possibly can add brokers again to your cluster anytime utilizing AWS Console, or the UpdateBrokerCount API.
  • Dealer elimination is supported on Kafka variations 2.8.1 and above. When you have clusters in decrease variations, you have to first improve to model 2.8.1 or above after which take away brokers.
  • Dealer elimination doesn’t assist the t3.small occasion sort.
  • You’ll cease incurring prices for the eliminated brokers as soon as the dealer elimination operation is accomplished efficiently.
  • When brokers are faraway from a cluster, their related native storage is eliminated as nicely.

Issues earlier than eradicating brokers

Eradicating brokers from an present Apache Kafka cluster is a essential operation that wants cautious planning to keep away from service disruption. When deciding what number of brokers you must take away from the cluster, decide your cluster’s minimal dealer rely by contemplating your necessities round availability, sturdiness, native knowledge retention, and partition rely. Right here are some things you must contemplate:

  • Test Amazon CloudWatch BytesInPerSec and BytesOutPerSec metrics to your cluster. Search for the height load over a interval of 1 month. Use this knowledge with MSK sizing Excel file to determine what number of brokers you want to deal with your peak load. If the variety of brokers listed within the Excel file is increased than the variety of brokers that will stay after eradicating brokers, don’t proceed with this operation. This means that eradicating brokers would lead to too few brokers for the cluster, which might result in availability influence to your cluster or purposes.
  • Test UserPartitionExists metrics to confirm that you’ve not less than 1 empty dealer per AZ in your cluster. If not, be certain that to take away partitions from not less than one dealer per AZ earlier than invoking the operation.
  • When you have a couple of dealer per AZ with no consumer partitions on them, MSK will randomly choose a type of in the course of the elimination operation.
  • Test the PartitionCount metrics to know the variety of partitions that exist in your cluster. Test per dealer partition restrict. The dealer elimination function won’t enable the elimination of brokers if the service detects that any brokers within the cluster have breached the partition restrict. In that case, test if any unused matters may very well be eliminated as a substitute to unencumber dealer assets.
  • Test if the estimated storage within the Excel file exceeds the at the moment provisioned storage for the cluster. In that case, first provision extra storage on that cluster. In case you are hitting per-broker storage limits, contemplate approaches like utilizing MSK tiered storage or eradicating unused matters. In any other case, keep away from shifting partitions to just some brokers as that will result in a disk full problem.
  • If the brokers you might be planning to take away host partitions, be certain that these partitions are reassigned to different brokers within the cluster. Use the instrument or Cruise Management to provoke partition reassignment. Monitor the progress of reassignment to completion. Disregard the __amazon_msk_canary, __amazon_msk_canary_state inside matters, as a result of they’re managed by the service and will likely be mechanically eliminated by MSK whereas executing the operation.
  • Confirm the cluster standing is Energetic, earlier than beginning the elimination course of.
  • Test the efficiency of the workload in your manufacturing surroundings after you progress these partitions. We suggest monitoring this for per week earlier than you take away the brokers to guarantee that the opposite brokers in your cluster can safely deal with your site visitors patterns.
  • In case you expertise any influence in your purposes or cluster availability after eradicating brokers, you’ll be able to add the identical variety of brokers that you just eliminated earlier by utilizing the UpdateBrokerCount API, after which reassign partitions to the newly added brokers.
  • We suggest you take a look at all the course of in a non-production surroundings, to determine and resolve any points earlier than making adjustments within the manufacturing surroundings.


Amazon MSK’s new dealer elimination functionality offers a protected method to scale back the capability of your provisioned Apache Kafka clusters. By permitting you to take away brokers with out impacting availability, knowledge sturdiness, or disrupting your streaming purposes, this function lets you optimize prices and right-size your MSK clusters based mostly on altering enterprise wants and site visitors patterns. With cautious planning and by following the really useful finest practices, you’ll be able to confidently use this functionality to handle your MSK assets extra effectively.

Begin profiting from the dealer elimination function in Amazon MSK as we speak. Assessment the documentation and observe the step-by-step information to check the method in a non-production surroundings. As soon as you might be comfy with the workflow, plan and execute dealer elimination in your manufacturing MSK clusters to optimize prices and align your streaming infrastructure along with your evolving workload necessities.

In regards to the Authors

Vidhi Taneja is a Principal Product Supervisor for Amazon Managed Streaming for Apache Kafka (Amazon MSK) at AWS. She is enthusiastic about serving to clients construct streaming purposes at scale and derive worth from real-time knowledge. Earlier than becoming a member of AWS, Vidhi labored at Apple, Goldman Sachs and Nutanix in product administration and engineering roles. She holds an MS diploma from Carnegie Mellon College.

Anusha Dasarakothapalli is a Principal Software program Engineer for Amazon Managed Streaming for Apache Kafka (Amazon MSK) at AWS. She began her software program engineering profession with Amazon in 2015 and labored on merchandise akin to S3-Glacier and S3 Glacier Deep Archive, earlier than transitioning to MSK in 2022. Her main areas of focus lie in streaming expertise, distributed techniques, and storage.

Masudur Rahaman Sayem is a Streaming Information Architect at AWS. He works with AWS clients globally to design and construct knowledge streaming architectures to resolve real-world enterprise issues. He makes a speciality of optimizing options that use streaming knowledge companies and NoSQL. Sayem may be very enthusiastic about distributed computing.



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