Apache Kafka stands as a well known open supply occasion retailer and stream processing platform. It has advanced into the de facto commonplace for knowledge streaming, as over 80% of Fortune 500 firms use it. All main cloud suppliers present managed knowledge streaming providers to satisfy this rising demand.
One key benefit of choosing managed Kafka providers is the delegation of duty for dealer and operational metrics, permitting customers to focus solely on metrics particular to purposes. On this article, Product Supervisor Uche Nwankwo offers steerage on a set of producer and shopper metrics that clients ought to monitor for optimum efficiency.
With Kafka, monitoring sometimes entails numerous metrics which can be associated to subjects, partitions, brokers and shopper teams. Customary Kafka metrics embrace info on throughput, latency, replication and disk utilization. Consult with the Kafka documentation and related monitoring instruments to know the particular metrics obtainable on your model of Kafka and easy methods to interpret them successfully.
Why is it vital to observe Kafka shoppers?
Monitoring your IBM® Occasion Streams for IBM Cloud® occasion is essential to make sure optimum performance and total well being of your knowledge pipeline. Monitoring your Kafka shoppers helps to establish early indicators of utility failure, akin to excessive useful resource utilization and lagging shoppers and bottlenecks. Figuring out these warning indicators early allows proactive response to potential points that reduce downtime and forestall any disruption to enterprise operations.
Kafka shoppers (producers and shoppers) have their very own set of metrics to observe their efficiency and well being. As well as, the Occasion Streams service helps a wealthy set of metrics produced by the server. For extra info, see Monitoring Event Streams metrics by using IBM Cloud Monitoring.
Consumer metrics to observe
Producer metrics
| Metric | Description |
| Document-error-rate | This metric measures the typical per-second variety of data despatched that resulted in errors. A excessive (or a rise in) record-error-rate would possibly point out a loss in knowledge or knowledge not being processed as anticipated. All these results would possibly compromise the integrity of the info you might be processing and storing in Kafka. Monitoring this metric helps to make sure that knowledge being despatched by producers is precisely and reliably recorded in your Kafka subjects. |
| Request-latency-avg | That is the typical latency for every produce request in ms. A rise in latency impacts efficiency and would possibly sign a difficulty. Measuring the request-latency-avg metric will help to establish bottlenecks inside your occasion. For a lot of purposes, low latency is essential to make sure a high-quality consumer expertise and a spike in request-latency-avg would possibly point out that you’re reaching the boundaries of your provisioned occasion. You’ll be able to repair the difficulty by altering your producer settings, for instance, by batching or scaling your plan to optimize efficiency. |
| Byte-rate | The typical variety of bytes despatched per second for a subject is a measure of your throughput. For those who stream knowledge often, a drop in throughput can point out an anomaly in your Kafka occasion. The Occasion Streams Enterprise plan begins from 150MB-per-second break up one-to-one between ingress and egress, and you will need to know the way a lot of that you’re consuming for efficient capability planning. Don’t go above two-thirds of the utmost throughput, to account for the attainable impression of operational actions, akin to inside updates or failure modes (for instance, the lack of an availability zone). |
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Shopper metrics
| Metric | Description |
| Fetch-rate fetch-size-avg | The variety of fetch requests per second (fetch-rate) and the typical variety of bytes fetched per request (fetch-size-avg) are key indicators for a way nicely your Kafka shoppers are performing. A excessive fetch-rate would possibly sign inefficiency, particularly over a small variety of messages, because it means inadequate (presumably no) knowledge is being acquired every time. The fetch-rate and fetch-size-avg are affected by three settings: fetch.min.bytes, fetch.max.bytes and fetch.max.wait.ms. Tune these settings to attain the specified total latency, whereas minimizing the variety of fetch requests and probably the load on the dealer CPU. Monitoring and optimizing each metrics ensures that you’re processing knowledge effectively for present and future workloads. |
| Commit-latency-avg | This metric measures the typical time between a dedicated file being despatched and the commit response being acquired. Just like the request-latency-avg as a producer metric, a secure commit-latency-avg signifies that your offset commits occur in a well timed method. A high-commit latency would possibly point out issues throughout the shopper that forestall it from committing offsets shortly, which straight impacts the reliability of knowledge processing. It’d result in duplicate processing of messages if a shopper should restart and reprocess messages from a beforehand uncommitted offset. A high-commit latency additionally means spending extra time in administrative operations than precise message processing. This concern would possibly result in backlogs of messages ready to be processed, particularly in high-volume environments. |
| Bytes-consumed-rate | This can be a consumer-fetch metric that measures the typical variety of bytes consumed per second. Just like the byte-rate as a producer metric, this needs to be a secure and anticipated metric. A sudden change within the anticipated pattern of the bytes-consumed-rate would possibly symbolize a difficulty together with your purposes. A low fee is perhaps a sign of effectivity in knowledge fetches or over-provisioned assets. A better fee would possibly overwhelm the shoppers’ processing functionality and thus require scaling, creating extra shoppers to stability out the load or altering shopper configurations, akin to fetch sizes. |
| Rebalance-rate-per-hour | The variety of group rebalances participated per hour. Rebalancing happens each time there’s a new shopper or when a shopper leaves the group and causes a delay in processing. This occurs as a result of partitions are reassigned making Kafka shoppers much less environment friendly if there are loads of rebalances per hour. A better rebalance fee per hour will be brought on by misconfigurations resulting in unstable shopper conduct. This rebalancing act could cause a rise in latency and would possibly end in purposes crashing. Be sure that your shopper teams are secure by monitoring a low and secure rebalance-rate-per-hour. |
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The metrics ought to cowl all kinds of purposes and use circumstances. Occasion Streams on IBM Cloud present a wealthy set of metrics which can be documented right here and can present additional helpful insights relying on the area of your utility. Take the subsequent step. Be taught extra about Event Streams for IBM Cloud.
What’s subsequent?
You’ve now received the information on important Kafka shoppers to observe. You’re invited to place these factors into apply and check out the totally managed Kafka providing on IBM Cloud. For any challenges in arrange, see the Getting Started Guide and FAQs.
Learn more about Kafka and its use cases
Provision an instance of Event Streams on IBM Cloud
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