This publish was written by Eunice Aguilar and Francisco Rodera from REA Group.
Enterprises that have to share and entry giant quantities of information throughout a number of domains and providers have to construct a cloud infrastructure that scales as want adjustments. REA Group, a digital enterprise that makes a speciality of actual property property, solved this drawback utilizing Amazon Managed Streaming for Apache Kafka (Amazon MSK) and a knowledge streaming platform referred to as Hydro.
REA Group’s staff of greater than 3,000 individuals is guided by our objective: to alter the best way the world experiences property. We assist individuals with all points of their property expertise—not simply shopping for, promoting, and renting—via the richest content material, information and insights, valuation estimates, and residential financing options. We ship unparalleled worth to our prospects, Australia’s actual property brokers, by offering entry to the most important and most engaged viewers of property seekers.
To attain this, the completely different technical merchandise inside the firm usually want to maneuver information throughout domains and providers effectively and reliably.
Throughout the Knowledge Platform staff, we’ve constructed a knowledge streaming platform referred to as Hydro to offer this functionality throughout the entire group. Hydro is powered by Amazon MSK and different instruments with which groups can transfer, remodel, and publish information at low latency utilizing event-driven architectures. This kind of construction is foundational at REA for constructing microservices and well timed information processing for real-time and batch use circumstances like time-sensitive outbound messaging, personalization, and machine studying (ML).
On this publish, we share our strategy to MSK cluster capability planning.
The issue
Hydro manages a large-scale Amazon MSK infrastructure by offering configuration abstractions, permitting customers to deal with delivering worth to REA with out the cognitive overhead of infrastructure administration. As the usage of Hydro grows inside REA, it’s essential to carry out capability planning to satisfy consumer calls for whereas sustaining optimum efficiency and cost-efficiency.
Hydro makes use of provisioned MSK clusters in growth and manufacturing environments. In every surroundings, Hydro manages a single MSK cluster that hosts a number of tenants with differing workload necessities. Correct capability planning makes certain the clusters can deal with excessive visitors and supply all customers with the specified degree of service.
Actual-time streaming is a comparatively new know-how at REA. Many customers aren’t but aware of Apache Kafka, and precisely assessing their workload necessities may be difficult. Because the custodians of the Hydro platform, it’s our duty to discover a approach to carry out capability planning to proactively assess the influence of the consumer workloads on our clusters.
Objectives
Capability planning entails figuring out the suitable measurement and configuration of the cluster primarily based on present and projected workloads, in addition to contemplating components resembling information replication, community bandwidth, and storage capability.
With out correct capability planning, Hydro clusters can turn out to be overwhelmed by excessive visitors and fail to offer customers with the specified degree of service. Due to this fact, it’s crucial to us to take a position time and assets into capability planning to verify Hydro clusters can ship the efficiency and availability that fashionable functions require.
The capability planning strategy we comply with for Hydro covers three most important areas:
- The fashions used for the calculation of present and estimated future capability wants, together with the attributes used as variables in them
- The fashions used to evaluate the approximate anticipated capability required for a brand new Hydro workload becoming a member of the platform
- The tooling accessible to operators and custodians to evaluate the historic and present capability consumption of the platform and, primarily based on them, the accessible headroom
The next diagram exhibits the interplay of capability utilization and the precalculated most utilization.
Though we don’t have this functionality but, the objective is to take this strategy one step additional sooner or later and predict the approximate useful resource depletion time, as proven within the following diagram.
To verify our digital operations are resilient and environment friendly, we should keep a complete observability of our present capability utilization. This detailed oversight permits us not solely to grasp the efficiency limits of our current infrastructure, but additionally to determine potential bottlenecks earlier than they influence our providers and customers.
By proactively setting and monitoring well-understood thresholds, we are able to obtain well timed alerts and take crucial scaling actions. This strategy makes certain our infrastructure can meet demand spikes with out compromising on efficiency, finally supporting a seamless consumer expertise and sustaining the integrity of our system.
Answer overview
The MSK clusters in Hydro are configured with a PER_TOPIC_PER_BROKER degree of monitoring, which offers metrics on the dealer and matter ranges. These metrics assist us decide the attributes of the cluster utilization successfully.
Nevertheless, it wouldn’t be sensible to show an extreme variety of metrics on our monitoring dashboards as a result of that would result in much less readability and slower insights on the cluster. It’s extra helpful to decide on probably the most related metrics for capability planning reasonably than displaying quite a few metrics.
Cluster utilization attributes
Based mostly on the Amazon MSK greatest practices tips, we’ve recognized a number of key attributes to evaluate the well being of the MSK cluster. These attributes embody the next:
- In/out throughput
- CPU utilization
- Disk area utilization
- Reminiscence utilization
- Producer and client latency
- Producer and client throttling
For extra data on right-sizing your clusters, see Finest practices for right-sizing your Apache Kafka clusters to optimize efficiency and value, Finest practices for Customary brokers, Monitor CPU utilization, Monitor disk area, and Monitor Apache Kafka reminiscence.
The next desk accommodates the detailed record of all of the attributes we use for MSK cluster capability planning in Hydro.
| Attribute Title | Attribute Sort | Models | Feedback |
|---|---|---|---|
| Bytes in | Throughput | Bytes per second | Depends on the combination Amazon EC2 community, Amazon EBS community, and Amazon EBS storage throughput |
| Bytes out | Throughput | Bytes per second | Depends on the combination Amazon EC2 community, Amazon EBS community, and Amazon EBS storage throughput |
| Shopper latency | Latency | Milliseconds | Excessive or unacceptable latency values often point out consumer expertise degradation earlier than reaching precise useful resource (for instance, CPU and reminiscence) depletion |
| CPU utilization | Capability limits | % CPU consumer + CPU system | Ought to keep underneath 60% |
| Disk area utilization | Persistent storage | Bytes | Ought to keep underneath 85% |
| Reminiscence utilization | Capability limits | % Reminiscence in use | Ought to keep underneath 60% |
| Producer latency | Latency | Milliseconds | Excessive or unacceptable sustained latency values often point out consumer expertise degradation earlier than reaching precise capability limits or precise useful resource (for instance, CPU or reminiscence) depletion |
| Throttling | Capability limits | Milliseconds, bytes, or messages | Excessive or unacceptable sustained throttling values point out capability limits are being reached earlier than precise useful resource (for instance, CPU or reminiscence) depletion |
By monitoring these attributes, we are able to shortly consider the efficiency of the clusters as we add extra workloads to the platform. We then match these attributes to the related MSK metrics accessible.
Cluster capability limits
In the course of the preliminary capability planning, our MSK clusters weren’t receiving sufficient visitors to offer us with a transparent concept of their capability limits. To handle this, we used the AWS efficiency testing framework for Apache Kafka to guage the theoretical efficiency limits. We carried out efficiency and capability exams on the take a look at MSK clusters that had the identical cluster configurations as our growth and manufacturing clusters. We obtained a extra complete understanding of the cluster’s efficiency by conducting these numerous take a look at situations. The next determine exhibits an instance of a take a look at cluster’s efficiency metrics.
To carry out the exams inside a selected timeframe and price range, we centered on the take a look at situations that would effectively measure the cluster’s capability. For example, we carried out exams that concerned sending high-throughput visitors to the cluster and creating subjects with many partitions.
After each take a look at, we collected the metrics of the take a look at cluster and extracted the utmost values of the important thing cluster utilization attributes. We then consolidated the outcomes and decided probably the most acceptable limits of every attribute. The next screenshot exhibits an instance of the exported take a look at cluster’s efficiency metrics.
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Capability monitoring dashboards
As a part of our platform administration course of, we conduct month-to-month operational opinions to take care of optimum efficiency. This entails analyzing an automatic operational report that covers all of the techniques on the platform. In the course of the assessment, we consider the service degree aims (SLOs) primarily based on choose service degree indicators (SLIs) and assess the monitoring alerts triggered from the earlier month. By doing so, we are able to determine any points and take corrective actions.
To help us in conducting the operational opinions and to offer us with an outline of the cluster’s utilization, we developed a capability monitoring dashboard, as proven within the following screenshot, for every surroundings. We constructed the dashboard as infrastructure as code (IaC) utilizing the AWS Cloud Growth Package (AWS CDK). The dashboard is generated and managed mechanically as a part of the platform infrastructure, together with the MSK cluster.
By defining the utmost capability limits of the MSK cluster in a configuration file, the boundaries are mechanically loaded into the capability dashboard as annotations within the Amazon CloudWatch graph widgets. The capability limits annotations are clearly seen and supply us with a view of the cluster’s capability headroom primarily based on utilization.
We decided the capability limits for throughput, latency, and throttling via the efficiency testing. Capability limits of the opposite metrics, resembling CPU, disk area, and reminiscence, are primarily based on the Amazon MSK greatest practices tips.
In the course of the operational opinions, we proactively assess the capability monitoring dashboards to find out if extra capability must be added to the cluster. This strategy permits us to determine and handle potential efficiency points earlier than they’ve a major influence on consumer workloads. It’s a preventative measure reasonably than a reactive response to a efficiency degradation.
Preemptive CloudWatch alarms
We’ve carried out preemptive CloudWatch alarms along with the capability monitoring dashboards. These alarms are configured to alert us earlier than a selected capability metric reaches its threshold, notifying us when the sustained worth reaches 80% of the capability restrict. This technique of monitoring allows us to take rapid motion as a substitute of ready for our month-to-month assessment cadence.
Worth added by our capability planning strategy
As operators of the Hydro platform, our strategy to capability planning has offered a constant approach to assess how far we’re from the theoretical capability limits of all our clusters, no matter their configuration. Our capability monitoring dashboards are a key observability instrument that we assessment frequently; they’re additionally helpful whereas troubleshooting efficiency points. They assist us shortly inform if capability constraints could possibly be a possible root explanation for any ongoing points. Which means we are able to use our present capability planning strategy and tooling each proactively or reactively, relying on the scenario and wish.
One other advantage of this strategy is that we calculate the theoretical most utilization values {that a} given cluster with a selected configuration can face up to from a separate cluster with out impacting any precise customers of the platform. We spin up short-lived MSK clusters via our AWS CDK primarily based automation and carry out capability exams on them. We do that very often to evaluate the influence, if any, that adjustments made to the cluster’s configurations have on the identified capability limits. In response to our present suggestions loop, if these newly calculated limits change from the beforehand identified ones, they’re used to mechanically replace our capability dashboards and alarms in CloudWatch.
Future evolution
Hydro is a platform that’s continuously bettering with the introduction of latest options. One in all these options contains the power to conveniently create Kafka consumer functions. To satisfy the growing demand, it’s important to remain forward of capability planning. Though the strategy mentioned right here has served us effectively to this point, it’s under no circumstances the ultimate stage , and there are capabilities that we have to lengthen and areas we have to enhance on.
Multi-cluster structure
To help essential workloads, we’re contemplating utilizing a multi-cluster structure utilizing Amazon MSK, which might additionally have an effect on our capability planning. Sooner or later, we plan to profile workloads primarily based on metadata, cross-check them with capability metrics, and place them within the acceptable MSK cluster. Along with the present provisioned MSK clusters, we’ll consider how the Amazon MSK Serverless cluster kind can complement our platform structure.
Utilization developments
We’ve added CloudWatch anomaly detection graphs to our capability monitoring dashboards to trace any uncommon developments. Nevertheless, as a result of the CloudWatch anomaly detection algorithm solely evaluates as much as 2 weeks of metric information, we’ll reassess its usefulness as we onboard extra workloads. Except for figuring out utilization developments, we’ll discover choices to implement an algorithm with predictive capabilities to detect when MSK cluster assets degrade and deplete.
Conclusion
Preliminary capability planning lays a stable basis for future enhancements and offers a protected onboarding course of for workloads. To attain optimum efficiency of our platform, we should ensure that our capability planning technique evolves according to the platform’s progress. Consequently, we keep a detailed collaboration with AWS to repeatedly develop further options that meet our enterprise wants and are in sync with the Amazon MSK roadmap. This makes certain we keep forward of the curve and might ship the very best expertise to our customers.
We advocate all Amazon MSK customers not miss out on maximizing their cluster’s potential and to start out planning their capability. Implementing the methods listed on this publish is a superb first step and can result in smoother operations and vital financial savings in the long term.
Concerning the Authors
Eunice Aguilar is a Workers Knowledge Engineer at REA. She has labored in software program engineering in numerous industries all through the years and just lately for property information. She’s additionally an advocate for ladies considering transitioning into tech, together with the well-versed who she takes inspiration from.
Francisco Rodera is a Workers Programs Engineer at REA. He has in depth expertise constructing and working large-scale distributed techniques. His pursuits are automation, observability, and making use of SRE practices to business-critical providers and platforms.
Khizer Naeem is a Technical Account Supervisor at AWS. He makes a speciality of Environment friendly Compute and has a deep ardour for Linux and open-source applied sciences, which he leverages to assist enterprise prospects modernize and optimize their cloud workloads.