![]() This Consumer Offset is periodically persisted (to ZooKeeper or a special topic in Kafka itself), so it can survive Consumer crashes or unclean shutdowns and avoid re-consuming too much old data. That is, it keeps track of which data it has read. Just like Brokers keep track of their write position in each Partition, each Consumer keeps track of “read position” in each Partition whose data it is consuming. Each Partition has its own independent Latest Offset. This is called Latest Offset, also known as Log End Offset. As it writes data it keeps track of the last “write position” in each Partition. When writing data a Broker actually writes it into a specific Partition. Inside Brokers data is stored in one or more Topics, and each Topic consists of one or more Partitions. ![]() Kafka Consumers are applications that read messages from Kafka (Brokers). Kafka Producers are applications that write messages into Kafka (Brokers). A Broker is what actually stores and serves Kafka messages. You can think of a Kafka Broker as a Kafka server. When talking about Kafka, people typically refer to Kafka Brokers. Kafka Consumer Lag indicates how much lag there is between Kafka producers and consumers. In this blog post, we will learn how to monitor it. One of the most crucial metrics for Kafka and the systems using it is consumer lag. ![]() However, as with everything, we need to monitor it to ensure that everything works well and is healthy. One system that allows us to process large amounts of data is Apache Kafka – an open-source, distributed event streaming platform designed to stream massive amounts of data. And the more complex the systems become, the less visibility into its working we have – at least when not using proper tools. ![]() That’s why the technological stack grew from simple applications to whole processing pipelines. The same goes for modern applications and algorithms – the data is the fuel that allows them to function and provide useful features.Įven though such thinking is not new, what is new in recent years is the requirement of near-real-time processing of large quantities of events processed by our systems. Humans process the data – each sound we hear, each picture we see – everything is data for our brain. Avoid Consumer Lag with Sematext’s Kafka Monitoring Tools. ![]()
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