Note when testing consumer group replication: By default, MirrorMaker does not replicate consumer groups created with the kafka-console-consumer.sh tool, allowing you to test your MirrorMaker configuration from the command line. If you also want to replicate these consumer groups, set the groups.exclude configuration accordingly (default: groups.exclude = console-consumer-.*, connect-.*, __.*). Don`t forget to update the configuration again after the test is complete. Kafka administrators can define data flows that cross the boundaries of individual Kafka clusters, datacenters, or geographic regions. For more information, see Geo-replication. The administrator can also use the kafka-configs.sh to validate the affected configurations. Two throttling configuration pairs are used to manage the throttling process. The first pair refers to the value of the accelerator itself. This is configured at the broker level using dynamic properties: while many teams unfamiliar with Kafka overestimate hardware requirements, the solution actually has low overhead and horizontal scalability.

This makes it possible to use low-cost core hardware while running Kafka successfully: Limited scaling (non-disruptive): Adding Openshift/Kubernetes master nodes requires Openshift/Kubernetes to be (almost) configured from scratch. (Note that we expect to only need to add additional master nodes for very large cluster installations.) Client quotas: Kafka supports different types of client quotas (per primary user). Because a customer`s quotas apply regardless of what topics the customer writes or reads about, they are a convenient and effective tool for allocating resources in a multi-tenant cluster. For example, request rate quotas help limit a user`s impact on broker CPU utilization by limiting the time a broker spends on the request processing path for that user, after which throttling begins. In many situations, isolating users with request rate quotas in multi-tenant clusters has a greater impact than setting incoming/outbound network bandwidth quotas, because excessive use of the broker`s CPU to process requests reduces the actual bandwidth that the broker can serve. In addition, administrators can also set quotas for topic operations, such as Create, delete, and modify to prevent Kafka clusters from being overloaded with highly simultaneous topic operations (see KIP-599 and the controller_mutations_rate quota type). Note about preventing replication loops (where topics are initially replicated from A to B, then replicated topics are replicated again from B to A, and so on): As long as you define the above streams in the same MirrorMaker configuration file, you do not need to explicitly add topics.exclude parameters to prevent replication loops between the two clusters. You need to choose a modern processor with multiple cores. Common clusters use 24 back-end computers. In this case, the two processes share the configuration between cluster B, resulting in a conflict. Depending on which of the two processes is the chosen „leader”, the result will be that the topic foo or subject bar is replicated, but not both. By default, kafka-reassign-partitions.sh applies the Leader limitation to all replicas that existed before the redistribution, each of which can be a Leader disk.

It will apply the follower accelerator to all motion targets. Therefore, if there is a partition with replicas on broker 101,102 that is remapped to 102,103, a leader limitation for that partition is applied to 101,102 and a trailing throttling is applied to only 103. The four configuration values are automatically assigned by kafka-reassign-partitions.sh (see below). For example, define two primary and secondary cluster aliases, including their login information. Finally, as is the case with Kafka`s hardware requirements, you provide ZooKeeper with the highest possible network bandwidth. Using the best disks, storing logs separately, isolating the ZooKeeper process, and disabling exchanges also reduce latency. Kafka administrators running a multi-tenant cluster typically need to define user scopes for each client. For the purposes of this section, „user areas” are a collection of topics grouped under the management of a single entity or user. Multi-tenant clusters typically need to be configured with quotas that protect users (tenants) from excessive consumption of cluster resources, such as when trying to write or read very large amounts of data, or when making demands to brokers at an excessively high rate. This can lead to network saturation, monopolize broker resources, and affect other clients, which you should avoid in a shared environment. In Kafka, the main unit of data is the theme.

Users can create and name any topic. You can also delete them, but it is not possible to rename a topic directly. Instead, to rename a topic, the user must create a new topic, move messages from the original topic to the new topic, and then delete the original topic. With this in mind, it is recommended to define logical spaces according to a hierarchical subject naming structure. This configuration can then be combined with security features such as previous ACLs to isolate different zones and tenants while minimizing the administrative overhead of backing up data in the cluster. The Apache Kafka website also includes a section dedicated to hardware and operating system configuration with valuable recommendations. Finding your optimal partition settings is as simple as calculating the throughput you want to achieve for your hardware and then calculating it to determine how many partitions you need. According to a conservative estimate, a partition can provide 10 MB/s for a single topic, and by extrapolating from this estimate, you can determine the total throughput required. Another method that goes straight to testing is to use one partition per broker and per topic, then review the results and double the partitions if additional throughput is needed.

Data contracts: You may need to define data contracts between data producers and consumers in a cluster using event schemas. This ensures that events written in Kafka can be read correctly over and over again, and prevents erroneous or corrupted events from being written. The best way to do this is to deploy a schema registry next to the cluster. (Kafka does not include schema saving, but third-party implementations are available.) A schema registry manages event schemas and associates schemas with topics so that producers know which topics accept which types of events (schemas) and consumers know how to read and analyze events in a topic. Some registry implementations provide additional functionality, such as schema development, storing a history of all schemas, and schema compatibility settings. You should avoid clusters that span multiple datacenters, even if the datacenters are nearby. and avoid clusters that span large geographical distances. Kafka clusters assume that all nodes are equal. Higher latencies can exacerbate problems in distributed systems and make debugging and resolution more difficult When securing a multi-tenant Kafka environment, the most common administrative task is the third category (permission), which is managing user/client permissions that grant or deny access to certain topics and therefore to data stored by users within a cluster. This task is primarily performed by defining access control lists (ACLs).

In particular, administrators of multi-tenant environments benefit from a hierarchical topic naming structure, as described in a previous section, because they can easily control access to topics through previous ACLs (–resource-pattern-type prefix).