autoscaling kafka consumers
Grab is a leading superapp in Southeast Asia, providing everyday services that matter to consumers. These are essentially Kafka consumer pods that consume data, process it, and then materialise the results into various sinks (RDMS, other Kafka topics). Running On-Demand brings all the guarantees of instance availability but it is definitely very expensive. Applications which need to read data from Kafka use a KafkaConsumer to subscribe to Kafka topics and receive messages from these topics. 12 is a good number. In the next few months, we plan to work on custom resources for combining VPA and fixed deployment size. Obviously there is a need to scale consumer consumption from topics. In a typical deployment of Kafka with many topics and partitions, scaling the Kafka consumer efficiently is one of the important tasks in maintaining overall smooth Kafka operations. Using the ratio of current and target consumer lag is NOT the best way to calculate consumer replicas. Clipping is a handy way to collect important slides you want to go back to later. that you can do in advance).
We saw a ~45% reduction in our total resource usage vs resource requested after moving to VPA with a fixed number of pods from HPA. Honestly, although I personally would makemsg.hi and msg.lo be different topics entirely, that's not a requirement -- you can do custom parititoning to divide messages between partitions. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. It is a common operation for Kafka consumers to do high-latency operations such as writing to databases or a time-consuming computation. For example, Critical pipelines (latency sensitive) run on On-Demand worker node groups and Non-critical pipelines (latency tolerant) on Spot instance worker node groups. Evolution is an ongoing process. As Grabs traffic is uneven, wed always have to provision for peak traffic. With spot instances running we realised a need to make our cluster quickly respond to failures. Copyright 2012-2022 ProphetStor Data Services, Inc. All Rights Reserved. The zookeepers maintain the offset of the last message sent by a producer for a topic, and the offset of the last committed message notified by a consumer for a topic. Understanding Kafka Topics and Partitions, How kafka consumer works if consumers are more that partitions, Consuming from single kafka partition by multiple consumers, How to scale kafka consumers in Node.js on Kubernetes. These were the areas of application wastage we observed on our platform: We initially kept the number of pods for a pipeline equal to the number of partitions in the topic the pipeline consumes from. The HPA controller measures the relevant metrics to determine the number of pods required to meet the criteria as defined the HPAs configuration, which is implemented as API resource with information like the desiredMetricValue. If you know that you will need many consumers to parallelize the processing, you can plan accordingly with the number of partitions to parallel the consumer works. Building a Secure, Tamper-Proof & Scalable Blockchain on Top of Apache Kafka Elastically Scaling Kafka Using Confluent, user Behavior Analysis with Session Windows and Apache Kafka's Streams API, Blockchain and Kafka - A Modern Love Story | Suhavi Sandhu, Guidewire Software. All the names starting with A-I go to Partition 1, J-R in partition 2 and S-Z in partition 3. Our current architecture setup works fine for now, but we would like to create a more tuneable in-house CRD(Custom Resource Definition) for VPA that incorporates rightsizing our Kubernetes deployment horizontally. Users can utilize Federator.ais intelligent autoscaling to achieve more cost-effective application deployment without compromising performance requirement. If you have a topic with four partitions and only one consumer in a group, that consumer would consume records from all the partitions. Kafka Summit Americas 2021 There is no guessing and no experimenting on what metric threshold to set in Kubernetes native HPA, which resulting in better use of resource for desired performance. So, we need to choose the partitions accordingly. Mind the App: How to Monitor Your Kafka Streams Applications | Bruno Cadonna, Building Streaming Data Pipelines with Google Cloud Dataflow and Confluent Cl Evergreen: Building Airbnbs Merge Queue With Kafka Streams with Janusz Kudel Apache Kafka in the era of Java Microframeworks. Lastly, overprovisioning also helped improve the deployment time because there is no dependency on the time required for Auto Scaling Groups (ASG) to add a new node to the cluster every time we want to deploy a new application. Reading data from Kafka is a bit different than reading data from other messaging systems, and there are few unique concepts involved. Ming Sheu is EVP of Product at ProphetStor with more than 25 years of experiences in networking, WiFi systems, and native cloud applications.
In particular, we observed 20% less average number of replicas than Kubernetes native HPA while meeting the performance target. The number of consumer replicas matches the message production rates. Learn More | Confluent Terraform Provider, Independent Network Lifecycle Management and more within our Q322 launch! Free access to premium services like Tuneln, Mubi and more. When there is a burst of messages received by the brokers, the messages will be stored in the queues longer if a consumer cannot process the messages fast enough, affecting overall application performance. Your assumption about messages being consumed twice is correct (since each group consumes 100% of messages from a topic). A multi-threaded model may be processing each message in a separate thread taken from a thread pool, while using automatic offset commits. We call moving partition ownership from one consumer to another a rebalance. Now if you suddenly increase the number of partitions to 5 from 3, It will create a different A-Z range now. For that, we will have to have n*k partitions and scale the consumers by k each time, which is complex. What does "Rebalancing" mean in Apache Kafka context? Now customize the name of a clipboard to store your clips. Fan-out, fan-in & the multiplexer: Replication recipes for global platform di Death of the dumb pipes: Using Apache Kafka for Integration projects, Achieve Sub-Second Analytics on Apache Kafka with Confluent and Imply, Building Event-Driven Services with Apache Kafka. We observed steep increase/decrease of consumer replicas between minimum and maximum number of replicas and it does not match real workloads of message production rates. Why had climate change not been proven beyond doubt for so long?
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Consumer produces a message to Kafka, to a special __consumer_offsets topic, with the committed offset for each partition. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. This article shows that the Native Kubernetes HPA algorithms (K8sHPA mechanism), based on either, Another class of autocalers is called KEDA or.
To compare the performance of some of the HPA schemes, we set up an environment with the following configuration: We first utilize the native HPA scaling based on average CPU utilization of Kafka consumer replicas. Amazing list of Python bots / APIs you can use! If a creature with damage transfer is grappling a target, and the grappled target hits the creature, does the target still take half the damage? What's inside the SPIKE Essential small angular motor?
Its also clear that the target lag offsets cannot set to too high, which will render an unacceptable consumer lags. If all the consumer instances have the same consumer group id, then the records will effectively be load-balanced over the consumer instances and each consumer in the group will receive messages from a different subset of the partitions in the topic. The controller computes the, Message Production Rate: various rates from 20K msg/min ~ 120K msg/min, average 85K msg/min. Our AI-based algorithm fuses workload metrics, KPI, and predictive analytics without guessing on what the right target metric threshold should be as in the Kubernetes native HPA and KEDA. Intelligent Auto-scaling of Kafka Consumers with Workload Prediction | Ming Sheu, ProphetStor Data Services Inc. Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning from It, Autonomy: The Quest to Build the Driverless CarAnd How It Will Reshape Our World, The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives, Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think, SAM: One Robot, a Dozen Engineers, and the Race to Revolutionize the Way We Build, So You Want to Start a Podcast: Finding Your Voice, Telling Your Story, and Building a Community That Will Listen, Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy, Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are, Future Presence: How Virtual Reality Is Changing Human Connection, Intimacy, and the Limits of Ordinary Life, From Gutenberg to Google: The History of Our Future, Live Work Work Work Die: A Journey into the Savage Heart of Silicon Valley, Ninety Percent of Everything: Inside Shipping, the Invisible Industry That Puts Clothes on Your Back, Gas in Your Car, and Food on Your Plate, Carrying the Fire: 50th Anniversary Edition, How to Survive a Robot Uprising: Tips on Defending Yourself Against the Coming Rebellion, Einstein's Fridge: How the Difference Between Hot and Cold Explains the Universe, Dignity in a Digital Age: Making Tech Work for All of Us, Liftoff: Elon Musk and the Desperate Early Days That Launched SpaceX. The extra consumers will just sit idle, since all the partitions are taken. We call this action of updating the current position in the Kafka partition a commit. Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. At the same time, HPA also needs to maintain a low latency of processing messages, which is a KPI or Key Performance Index of Kafka Consumers. In this approach, scaling of consumers cant go beyond the number of partitions. A consumer is lagging when its unable to read from a partition as fast as messages are produced to it. The Kubernetes native HPA on the other hand does not have predictive analytics capability nor ability to fuse various metrics intelligently. In order to abstract this from the end user, we automated the application deployment process to directly call the Kafka API to fetch the number of partitions during runtime.
The Science of Time Travel: The Secrets Behind Time Machines, Time Loops, Alternate Realities, and More! In order to achieve reasonable results as in the case of Consumer 2, and to avoid the bad performance as observed in the cases of Consumer 1 and 3, we need to set proper, Intelligent Autoscaling of Kafka Consumers with Workload Prediction, In a Kafka-based application, messages for specific topics are generated from some producers, and sent to the Kafka brokers. The more appropriate workload metric for Kafka consumer is the number of messages in Kafka broker queue. A native HPA controller supports the following types of metrics to determine how to scale: Federator.ai uses message production rate as a workload indicator, and makes predictions for this workload. Instant access to millions of ebooks, audiobooks, magazines, podcasts and more.
Moreover, it may not be feasible to adjust the configurations to match the characteristics of a Kafka consumer at run time in a production environment. For example let's say I have a messaging system with hi/lo priorities, so I create a topic for messages and partitions for hi and lo priority messages. Enjoy access to millions of ebooks, audiobooks, magazines, and more from Scribd. We will cover why and how we focus on optimal scalability and availability of our infrastructure. The Stream Processing Framework(SPF) is essentially Kafka consumers consuming from Kafka topics, hence the number of pods scaling in and out resulted in unequal partition load per pod. Activate your 30 day free trialto unlock unlimited reading. It helps to calculate the right number of consumer replicas. Trending is based off of the highest score sort and falls back to it if no posts are trending. For example, in AWS Kinesis with Lambda architecture, you can change the batch size which controls the maximum number of records that can be sent to your Lambda function with each invoke. Announcing the Stacks Editor Beta release! Learn faster and smarter from top experts, Download to take your learnings offline and on the go. The Coban platform provides lightweight Golang plugin architecture-based data processing pipelines running in Kubernetes. The Kubernetes native HPA on the other hand does not have predictive analytics capability nor ability to. Earlier this year, we took you on a journey on how we built and deployed our event sourcing and stream processing framework at Grab. Were happy to share that were able to reliably maintain our uptime and continue to service close to 400 billion events a week. As a result, we could optimise scheduling and cost efficiency using priority classes and overprovisioning on heterogeneous node types on AWS. When the consumers are performing different operations on the same topics, we should use different consumer groups. AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017, Pew Research Center's Internet & American Life Project, Harry Surden - Artificial Intelligence and Law Overview, Pinot: Realtime Distributed OLAP datastore, How to Become a Thought Leader in Your Niche, UX, ethnography and possibilities: for Libraries, Museums and Archives, Winners and Losers - All the (Russian) President's Men, No public clipboards found for this slide. If a consumer crashes or a new consumer joins the consumer group, this will trigger a rebalance. The average number of replicas are around 22 for consumer lag target of 500 and 1,000. More specifically, the message production rate of a specific topic would be the right workload metric for a Kafka consumer. Learn on the go with our new app. Home blog Intelligent Autoscaling of Kafka Consumers with Workload Prediction. Kafka Summit NYC 2017 - Achieving Predictability and Compliance with BNY Mell Work is a Stream of Applications (Audun Strand, NAV) Kafka Summit London 2019. The traditional Kubernetes Horizontal Pod Scaling (HPA) that uses basic CPU and/or memory metrics is not suitable for scaling Kafka consumers. So is 60. We have seen how Kafka consumer groups work and how we could horizontally parallelize consumers by sharing the same group-id. How can I achieve the capability I had in the Rabbit design w/Kafka and still maintain the "queue-ness" of the behavior (I don't want to send a message twice)? rev2022.7.20.42634. In the native HPA scheme with external metrics, we used three different the target lag offsets (500, 1000, and 2000) to adjust the number of replicas accordingly. Asking for help, clarification, or responding to other answers. Topics and Partitions. Is there a PRNG that visits every number exactly once, in a non-trivial bitspace, without repetition, without large memory usage, before it cycles? We just deploy the application and let VPA handle the resources required for its operation. The HPA controller measures the relevant metrics to determine the number of pods required to meet the criteria as defined the HPAs configuration, which is implemented as API resource with information like the.
The time required to recover from lag depends on how quickly the consumer is able to consume messages per second: Consumer can be grouped together for a given topic for maximizing read throughput. While using message production rate is a better way to decide the number of consumer replicas, this is still a reaction based auto-scaling. More specifically, the message production rate of a specific topic would be the right workload metric for a Kafka consumer. The testing results were shown as below. 1. Looks like youve clipped this slide to already. Using machine-learning based forecasting, it is possible for predict the upcoming increase or decrease of message production rate. In order to know where to pick up the work, the consumer will read the latest committed offset of each partition and continue from there. So you need to be aware of this. You can now choose to sort by Trending, which boosts votes that have happened recently, helping to surface more up-to-date answers. After receiving messages from the brokers, a consumer will perform some tasks and let the brokers know the messages have been committed (or consumed). In particular, we are calibrating the number of replicas with the following trade-offs: A Kubernetes HPA controller is a controller that can determine the number of pods of a deployment, a replica set, or a stateful set. A Kafka cluster consists of one or more brokers. However, it remains difficult to determine whats the best values to configure them without a real production environment. The brokers perform required replications and distribute messages to the consumers of the respective topics. The SlideShare family just got bigger. While using message production rate is a better way to decide the number of consumer replicas, this is still a reaction based auto-scaling. Each message within a partition gets an incremental id, called offset.
More info is here - http://www.vinsguru.com/kafka-scaling-consumers-out-for-a-consumer-group/. Different priorities would result in different node affinity to different kinds of instance groups (On-Demand/Spot). Lag is expressed as the number of offsets that are behind the head of the partition. Copyright Confluent, Inc. 2014-2022. recover time in seconds = messages / (consume message per second - produce message per second), https://www.confluent.io/blog/kafka-consumer-multi-threaded-messaging, Offset might be committed before a record is processed by consumers. The message production rates the consumption rates by all consumer pods match closely. If this was RabbitMQ, I'd have an auto-scalable group of consumers assigned to each partition, like this: If I understand the Kafka model I can't have >1 consumer per partition in a consumer group, so that picture doesn't work for Kafka, right? With the uncertainty of losing a spot instance, we started assigning priority to our various applications. The brokers perform required replications and distribute messages to the consumers of the respective topics. At best, it can recommend maximum number of replicas among the recommended replicas determined by various configured metrics. Industry-Leading Chunghwa Telecom Adopts ProphetStor Federator.ai for Green IT and Intelligent Continuous Cloud Operations, ProphetStor Partners with Nextlink Technology to Exponentially Expand Managed Service Providers Businesses and Achieve Customer Obsession, Cloud Cost Management with ML-based Resource Predictions (Part II), Intelligent Autoscaling for Kafka Consumers, Per POD resource metrics the resource metrics, such as CPU usage, are represented as utilization or raw metrics depends on the choice of desiredMetricValue.
In the above picture, we have only one consumer. Serializing data in PHP II: A simple primer on database interactions, openBoM: from sequential process to simultaneous BOM changes and data sharing, Getting dump file from oracle database and import into your local machine. Just like multiple producers can write to the same topic, we need a similar mechanism to allow multiple consumers to read from the same topic, sharing the data between them. The main cost of running EKS in AWS is attributed to the EC2 machines that form the worker nodes for the Kubernetes cluster. Apache Kafka is a publish-subscribe messaging system which lets you send messages between processes, applications, and servers. After connecting to any broker, you will be connected to the entire cluster. The traditional Kubernetes Horizontal Pod Scaling (HPA) that uses basic CPU and/or memory metrics is not suitable for scaling Kafka consumers. What is the meaning of the verb Its subject and object? In a typical deployment of Kafka with many topics and partitions, scaling the Kafka consumer efficiently is one of the important tasks in maintaining overall smooth Kafka operations. 464), How APIs can take the pain out of legacy system headaches (Ep.
From the above cases, we observed different Kafka consumers exhibit different scaling results with Kubernetes native HPA. It calculates the right number of consumer replicas based on predicted workload and target KPI metrics such as the desired latency to determine the capabilities of consumer pods. Current offset is the offset from which next new record will be fetched. Applying ML on your Data in Motion with AWS and Confluent | Joseph Morais, Co Ciscos E-Commerce Transformation Using Kafka, Redis and Kafka - Advanced Microservices Design Patterns Simplified. Data Modeling with Kafka? I agree with David. The zookeepers maintain the offset of the last message sent by a producer for a topic, and the offset of the last committed message notified by a consumer for a topic. Because records are fetched and processed by the same thread, they are processed in the same order as they were written to the partition. Love podcasts or audiobooks? A Kafka-based platform to process medical prescriptions of Germanys health i Be A Great Product Leader (Amplify, Oct 2019), Trillion Dollar Coach Book (Bill Campbell). However, a Multi-threaded model may cause some undesirable effects: Multi-threaded model is useful when the other system can accept such high load and when there is no dependency on the order of processing. What do I need to do and repair where these 3M strips pulled off. In a Kafka-based application, messages for specific topics are generated from some producers, and sent to the Kafka brokers. Partitions are spread across these brokers. Just pick a number of partitions that matches how much maximum parallelization you want. It kind of affects the order of the messages we had before! Hence, our first action to drive cost optimisation was to include Spot instances in our worker node group. Each stream processing pod (the smallest unit of a pipelines deployment) has three top level components: We initially architected our Kubernetes setup around horizontal pod autoscaling (HPA), which scales the number of pods per deployment based on CPU and memory usage. We wanted our pods to scale up and down as the load increases or decreases without any manual intervention. We examine the applications with three different CPU utilization characteristics, which are denoted as Consumer 1, 2, and 3 representing different consumer groups under similar producer workloads. We can also use vertical scaling for consumers but we need to be careful of the change of processing order if we choose multithreading. Changing the partition for an existing topic is really not recommended as It could cause issues. If you are dealing with high volume data or your consumer is doing expensive calculation, this can be a common question for you. It is difficult to determine the right parameters of consumer lag targets and other configuration parameters. Topic is made up of partitions.
Zookeeper manages brokers, helps in performing leader election for the partition.
Committed offsets is the last committed offset for the given partition. How do I use multiple consumers in Kafka? Do you get it? The following shows the test results with Federator.ai under the similar workloads. http://www.vinsguru.com/kafka-scaling-consumers-out-for-a-consumer-group/, Code completion isnt magic; it just feels that way (Ep. In this article, we will dive deeper into our Kubernetes infrastructure setup for our stream processing framework. Transform Your Mainframe and IBM i Data for the Cloud with Precisely and Apac Real-time Adaptation of Financial Market Events with Kafka | Cliff Cheng and Transform Your Mainframe Data for the Cloud with Precisely and Apache Kafka. Want to join us in our mission to revolutionize transportation? Software Engineer working on building big data & machine learning platform. Its known to not be very susceptible to quick load changes as it trains its model to monitor the deployments load trend over a period of time before recommending an optimal resource. Each consumer in a group read from mutually exclusive partitions. This process ensures the optimal resource allocation for our pipelines considering the historic trends on throughput.
The problem with more partitions than consumers is the consumers are no more uniformly loaded as we scale them. Lets first review some of the key concepts in Kafka. Topics consist of one or more partitions, ordered, immutable sequences of messages to which Kafka appends new messages. Ming Sheu Prior to joining ProphetStor, he spent 13 years with Ruckus/CommScope in development of large scale WiFi Controller and Cloud-based network management service. ProphetStor Data Services, Inc., a leader in the Intelligent Data Platform, provides AI-enabled federated data services to help both enterprises and cloud service providers to build agile, automated, cost-effective, intelligent and orchestrated IT and Cloud infrastructures. We broadly classify our workloads as latency sensitive (critical) and latency tolerant (non-critical). Multithreading is the ability of a central processing unit (CPU) (or a single core in a multi-core processor) to provide multiple threads of execution concurrently, supported by the operating system. In situations where the work can be divided into smaller units, which can be run in parallel, without negative effects on data consistency, multithreading can be used to improve application performance. If you are still limited to a single consumer reading and processing the data, your application may be unable to keep up with the rate of incoming messages.
