This put up was written by Eunice Aguilar and Francisco Rodera from REA Group.
Enterprises that have to share and entry giant quantities of knowledge throughout a number of domains and companies have to construct a cloud infrastructure that scales as want adjustments. REA Group, a digital enterprise that focuses on actual property property, solved this drawback utilizing Amazon Managed Streaming for Apache Kafka (Amazon MSK) and an information streaming platform known as Hydro.
REA Group’s group of greater than 3,000 individuals is guided by our goal: to alter the best way the world experiences property. We assist individuals with all facets of their property expertise—not simply shopping for, promoting, and renting—by means of the richest content material, knowledge 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 biggest and most engaged viewers of property seekers.
To attain this, the totally different technical merchandise throughout the firm repeatedly want to maneuver knowledge throughout domains and companies effectively and reliably.
Inside the Information Platform group, we now have constructed an information streaming platform known as Hydro to supply this functionality throughout the entire group. Hydro is powered by Amazon MSK and different instruments with which groups can transfer, remodel, and publish knowledge at low latency utilizing event-driven architectures. This kind of construction is foundational at REA for constructing microservices and well timed knowledge processing for real-time and batch use circumstances like time-sensitive outbound messaging, personalization, and machine studying (ML).
On this put up, we share our method to MSK cluster capability planning.
The issue
Hydro manages a large-scale Amazon MSK infrastructure by offering configuration abstractions, permitting customers to give attention to 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 atmosphere, Hydro manages a single MSK cluster that hosts a number of tenants with differing workload necessities. Correct capability planning makes positive the clusters can deal with excessive site visitors and supply all customers with the specified stage of service.
Actual-time streaming is a comparatively new expertise at REA. Many customers aren’t but conversant in Apache Kafka, and precisely assessing their workload necessities could be difficult. Because the custodians of the Hydro platform, it’s our accountability to discover a technique to carry out capability planning to proactively assess the affect of the consumer workloads on our clusters.
Targets
Capability planning includes figuring out the suitable dimension and configuration of the cluster based mostly on present and projected workloads, in addition to contemplating components reminiscent of knowledge replication, community bandwidth, and storage capability.
With out correct capability planning, Hydro clusters can turn out to be overwhelmed by excessive site visitors and fail to supply customers with the specified stage of service. Due to this fact, it’s essential to us to speculate time and assets into capability planning to ensure Hydro clusters can ship the efficiency and availability that trendy functions require.
The capability planning method we comply with for Hydro covers three fundamental 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, based mostly 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 aim is to take this method 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 preserve a complete observability of our present capability utilization. This detailed oversight permits us not solely to know the efficiency limits of our present infrastructure, but additionally to determine potential bottlenecks earlier than they affect our companies and customers.
By proactively setting and monitoring well-understood thresholds, we are able to obtain well timed alerts and take mandatory scaling actions. This method makes positive our infrastructure can meet demand spikes with out compromising on efficiency, finally supporting a seamless consumer expertise and sustaining the integrity of our system.
Resolution overview
The MSK clusters in Hydro are configured with a PER_TOPIC_PER_BROKER stage of monitoring, which supplies metrics on the dealer and matter ranges. These metrics assist us decide the attributes of the cluster utilization successfully.
Nonetheless, it wouldn’t be smart 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 beneficial to decide on probably the most related metrics for capability planning slightly than displaying quite a few metrics.
Cluster utilization attributes
Primarily based on the Amazon MSK greatest practices tips, we now have recognized a number of key attributes to evaluate the well being of the MSK cluster. These attributes embrace 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 Normal 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 | Items | Feedback |
|---|---|---|---|
| Bytes in | Throughput | Bytes per second | Depends on the mixture Amazon EC2 community, Amazon EBS community, and Amazon EBS storage throughput |
| Bytes out | Throughput | Bytes per second | Depends on the mixture Amazon EC2 community, Amazon EBS community, and Amazon EBS storage throughput |
| Shopper latency | Latency | Milliseconds | Excessive or unacceptable latency values normally 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 normally 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 site visitors to supply us with a transparent thought of their capability limits. To deal with this, we used the AWS efficiency testing framework for Apache Kafka to judge the theoretical efficiency limits. We carried out efficiency and capability exams on the check 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 check eventualities. The next determine exhibits an instance of a check cluster’s efficiency metrics.
To carry out the exams inside a selected time-frame and price range, we targeted on the check eventualities that would effectively measure the cluster’s capability. As an example, we carried out exams that concerned sending high-throughput site visitors to the cluster and creating matters with many partitions.
After each check, we collected the metrics of the check 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 check 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 critiques to keep up optimum efficiency. This includes analyzing an automatic operational report that covers all of the methods on the platform. In the course of the evaluation, we consider the service stage targets (SLOs) based mostly on choose service stage 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 critiques and to supply us with an outline of the cluster’s utilization, we developed a capability monitoring dashboard, as proven within the following screenshot, for every atmosphere. We constructed the dashboard as infrastructure as code (IaC) utilizing the AWS Cloud Improvement Package (AWS CDK). The dashboard is generated and managed routinely as a element of the platform infrastructure, together with the MSK cluster.
By defining the utmost capability limits of the MSK cluster in a configuration file, the bounds are routinely 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 based mostly on utilization.
We decided the capability limits for throughput, latency, and throttling by means of the efficiency testing. Capability limits of the opposite metrics, reminiscent of CPU, disk area, and reminiscence, are based mostly on the Amazon MSK greatest practices tips.
In the course of the operational critiques, we proactively assess the capability monitoring dashboards to find out if extra capability must be added to the cluster. This method permits us to determine and deal with potential efficiency points earlier than they’ve a major affect on consumer workloads. It’s a preventative measure slightly than a reactive response to a efficiency degradation.
Preemptive CloudWatch alarms
We’ve got applied 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 permits us to take instant motion as an alternative of ready for our month-to-month evaluation cadence.
Worth added by our capability planning method
As operators of the Hydro platform, our method to capability planning has offered a constant technique 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 evaluation regularly; they’re additionally helpful whereas troubleshooting efficiency points. They assist us shortly inform if capability constraints might be a possible root explanation for any ongoing points. Which means that we are able to use our present capability planning method and tooling each proactively or reactively, relying on the state of affairs and wish.
One other advantage of this method is that we calculate the theoretical most utilization values {that a} given cluster with a selected configuration can stand up to from a separate cluster with out impacting any precise customers of the platform. We spin up short-lived MSK clusters by means of our AWS CDK based mostly automation and carry out capability exams on them. We do that very often to evaluate the affect, if any, that adjustments made to the cluster’s configurations have on the identified capability limits. Based on our present suggestions loop, if these newly calculated limits change from the beforehand identified ones, they’re used to routinely replace our capability dashboards and alarms in CloudWatch.
Future evolution
Hydro is a platform that’s consistently enhancing with the introduction of latest options. One in every of these options contains the power to conveniently create Kafka shopper functions. To satisfy the growing demand, it’s important to remain forward of capability planning. Though the method mentioned right here has served us nicely up to now, it’s on no account the ultimate stage , and there are capabilities that we have to prolong and areas we have to enhance on.
Multi-cluster structure
To help important 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 based mostly on metadata, cross-check them with capability metrics, and place them within the acceptable MSK cluster. Along with the prevailing provisioned MSK clusters, we are going to consider how the Amazon MSK Serverless cluster kind can complement our platform structure.
Utilization traits
We’ve got added CloudWatch anomaly detection graphs to our capability monitoring dashboards to trace any uncommon traits. Nonetheless, as a result of the CloudWatch anomaly detection algorithm solely evaluates as much as 2 weeks of metric knowledge, we are going to reassess its usefulness as we onboard extra workloads. Other than figuring out utilization traits, we are going to 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 supplies a protected onboarding course of for workloads. To attain optimum efficiency of our platform, we should be sure that our capability planning technique evolves consistent with the platform’s progress. Consequently, we preserve a detailed collaboration with AWS to repeatedly develop extra options that meet our enterprise wants and are in sync with the Amazon MSK roadmap. This makes positive we keep forward of the curve and may ship the very best expertise to our customers.
We suggest 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 put up 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 Employees Information Engineer at REA. She has labored in software program engineering in numerous industries all through the years and lately for property knowledge. She’s additionally an advocate for ladies keen on transitioning into tech, together with the well-versed who she takes inspiration from.
Francisco Rodera is a Employees Methods Engineer at REA. He has intensive expertise constructing and working large-scale distributed methods. His pursuits are automation, observability, and making use of SRE practices to business-critical companies and platforms.
Khizer Naeem is a Technical Account Supervisor at AWS. He focuses on 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.
