Streams Replication Supervisor Prefixless Replication

Replication is an important functionality in distributed methods to handle challenges associated to fault tolerance, excessive availability, load balancing, scalability, knowledge locality, community effectivity, and knowledge sturdiness. It varieties a foundational component for constructing sturdy and dependable distributed architectures. Additionally it is vital to have a number of choices (like regular and prefixless replication) to do the replication course of, since each resolution has its personal benefits.

Streams Replication Supervisor (SRM) is an enterprise-grade replication resolution that permits fault tolerant, scalable, and sturdy cross-cluster Kafka matter replication. SRM replicates knowledge at excessive efficiency and retains matter properties in sync throughout clusters. Replication may be dynamically enabled for matters and shopper teams. SRM additionally delivers customized extensions that facilitate set up, administration, and monitoring, making SRM an entire replication resolution that’s constructed for mission-critical workloads. 


Kafka as an occasion streaming element may be utilized to all kinds of use circumstances. SRM offers cross-cluster Kafka matter replication to make it extra fault tolerant and sturdy. SRM relies on the Mirror Maker 2 (MM2) element of Kafka, which is the improved model of Mirror Maker (MM1). MM1 has been used for years in large-scale manufacturing environments, however not with out a number of limitationsthat’s the reason MM2 was launched.

These are a few of the MM1 limitations that MM2 addresses:

  • Matters are created with default configuration, typically wanted to be repartitioned manually.
  • ACL and configuration modifications should not synced throughout mirrored clusters. This makes it troublesome to handle a number of clusters.
  • Data are repartitioned with DefaultPartitioner. Semantic partitioning could also be misplaced.
  • Any configuration change means the cluster should be bounced. This contains including new matters to the whitelist, which can be a frequent operation.
  • No mechanism emigrate producers or customers between mirrored clusters.
  • No help for precisely as soon as supply. Data could also be duplicated throughout replication.
  • Rebalancing causes latency spikes, which can set off additional rebalances.

When SRM replicates a subject, it renames the subject within the goal cluster by prefixing the identify of the subject with the alias (identify) of the supply cluster. This differs from the best way replication labored in MM1, the place the goal matters had the identical identify because the supply (thus “prefixless”). The MM1 conduct is essential for some use-cases. For instance, cluster migration situations can’t be appropriately carried out with the default replication conduct of SRM, the MM1 conduct is a should. Up till now, any such replication was not obtainable or absolutely supported. Furthermore, MM1 was deprecated in one of many newer releases of Kafka (Kafka 3.0.0) and its use is now not advisable. 

To deal with this, Cloudera launched a brand new MM1-compatible mode in SRM. Beginning with Cloudera Knowledge Platform (CDP) Personal Cloud Base 7.1.9, prefixless replication is usually obtainable with replication monitoring help in SRM. This makes it attainable emigrate cluster replication workloads from the deprecated MM1 to SRM with out change within the replicated matter names.

Replicated matter names

The naming of the replicated matters is outlined by the replication coverage that SRM is configured to make use of. By default, SRM makes use of the DefaultReplicationPolicy, which provides the supply cluster alias as a prefix to the identify of replicated matters. Previously, this was the one coverage obtainable natively in SRM and the design of the replication monitoring options within the service was based mostly on the idea that each replicated matter would all the time have a prefix. Due to this fact, SRM service function cases have been solely in a position to monitor replication flows that used a replication coverage that makes use of prefixes, such because the DefaultReplicationPolicy.

As soon as the IdentityReplicationPolicy was launched, customers have been in a position to replicate matters with out having prefixes added to the replicated matter names. Because of the design of the SRM service although, these replications couldn’t be monitored till the discharge of CDP Personal Cloud Base 7.1.9. 

Word: SRM helps customized matter naming insurance policies via a plugin known as replication coverage. There are two totally different Replication coverage varieties shipped with SRM by default:

  • DefaultReplicationPolicy – default coverage. Prefixes matter names with “<source_cluster>.”
  • IdentityReplicationPolicy – coverage which doesn’t change matter names throughout replication. (with this coverage, replication monitoring doesn’t work till CDP 7.1.9 launch)

Distant matter discovery

SRM wants to have the ability to know which matters are replicas and what are their respective supply matters. It depends on the replication coverage and the subject naming conventions to find reproduction matters by default. The method lists all the matter names of a cluster, then detects the supply cluster identify. When utilizing the DefaultReplicationPolicy, SRM is aware of {that a} matter is a reproduction when it has a prefix that may be a legitimate cluster alias (<cluster_alias>.). The reproduction matter identify accommodates the alias of the supply cluster and identify of the supply matter. For example, the subject identify may be source-cluster.topic-name. On this case source-cluster would be the alias of the supply cluster, whereas topic-name would be the identify of the subject within the supply cluster.

This discovery process has some limitations, because it depends on matter naming conventions to supply supply cluster data. When the IdentityReplicationPolicy is used, the supply cluster can’t be recognized by this technique. Moreover, the present state of the replication (stopped, lively, and many others.) has no reference to the reproduction matter detectionif a subject has been faraway from the SRM replication configuration, the logic will nonetheless detect the prefixed matter as a reproduction matter.

The above shortcomings have been addressed within the CDP Personal Cloud Base 7.1.9. On this launch, SRM is shipped with a brand new property Use Inner Subject For Distant Matters Discovery, which is enabled for brand spanking new installations. For upgraded clusters, this characteristic might be disabled by default to make sure that current SRM deployments will proceed to work with out modifications in conduct.

When Use Inner Subject For Distant Matters Discovery is enabled, SRM drivers will write the checklist of supply mattergoal matter pairs that need to be replicated to an inner, compacted matter (srm-meta.inner), saved on the goal cluster. SRM drivers will periodically verify which matters have to be replicated and can write updates to the inner matter as wanted.

Shoppers making an attempt to find reproduction matters are in a position to scan the “srm-meta.inner” matter, and devour the most recent messagewhich lists the at the moment replicated matters. This knowledge additionally accommodates the source-target matter identify mappings. It makes the characteristic unbiased of the ReplicationPolicy that’s in use.

Prefixless replication

From CDP 7.1.9, SRM helps knowledge replication, checkpointing, and monitoring with the IdentityReplicationPolicy. Identification replication, or prefixless replication, implies that reproduction matters’ names would be the similar as on the supply cluster (MM1-compatible mode, however with the benefits of MM2). The IdentityReplicationPolicy will also be used for matter aggregation use circumstances, the place the identical matter on a number of clusters are replicated to the identical identically-named “aggregated matter” on a unique cluster. After all, aggregation may be averted if DefaultReplicationPolicy is in use or if the separate supply clusters have totally different matter names.

To allow prefixless replication for SRM, you solely want to pick the “Allow Prefixless Replication” property within the SRM service configuration.

When “Allow Prefixless Replication” is chosen, SRM should additionally allow the “Use Inner Subject For Distant Matters Discovery” characteristic because of the limitations of reproduction discovery talked about beforehand on this weblog. Luckily, Cloudera Supervisor handles this mechanically, so if a person allows the “Allow Prefixless Replication” possibility, Cloudera Supervisor will override the configuration of “Use Inner Subject For Distant Matters Discovery” to allow it.

Prefixless replication just isn’t freed from limitations or caveats. Pay attention to the next:

  • Replication loop detection just isn’t supported

In consequence, you have to be sure that matters should not replicated in a loop between your supply and goal clusters. You’ll be able to guarantee this by organising your matter permit and deny lists (also called matter filters) in a method that’s applicable in your use case.

For instance, assume you have got two replications that replicate matters between two clusters, however in numerous instructions. If each replications embrace topic_1, they have to by no means be enabled on the similar time.

  • All SRM companies should use the identical replication coverage

For instance, if you wish to use prefixless replication then all the SRM companies ought to use IdentityReplicationPolicy. In case of prefixed replication DefaultReplicationPolicy ought to be used in every single place. Clusters linked by replication flows, whatever the variety of SRM companies, ought to solely use one ReplicationPolicy. In any other case, replications might be combined up and undesirable unintended effects can occur.

  • Group offset sync ought to be disabled 

SRM makes a mapping about Kafka message offsets of the supply and goal clusters. Offset checkpoints are saved within the supply clusters and they are going to be interpreted provided that the message is coming from the present supply cluster. If extra supply clusters have the identical group offsets, then they’ll intervene with one another, so group offset sync ought to be disabled.

  • Not all REST API endpoints and SMM UI options are supported
    • The /v2/topic-metrics/{goal}/{downstreamTopic}/{metric} endpoint of the SRM Service v2 API doesn’t work correctly with prefixless replication. Use the /v2/topic-metrics/{supply}/{goal}/{upstreamTopic}/{metric} endpoint as a substitute.
    • The replication metric graphs proven on the Subject Particulars web page of the SMM UI don’t work with prefixless replication. The graph just isn’t displayed.


Prefixless replication allows you to use MM1-like replication conduct in CDP whereas getting access to the various enterprise prepared options that SRM offers. Whereas aggregation is the principle use case for prefixless replication, it will also be used to construct conventional replication pipelines that present a security internet in your Kafka knowledge if issues go amiss. Higher but, prefixless replication can also be an ideal device emigrate that outdated Kafka deployment working on CDH, HDP, or HDF to CDP.

As well as, the modifications and enhancements to distant matter discovery that have been launched alongside prefixless replication make SRM extra sturdy than ever as some core options inside SRM, like replication monitoring, now not must depend on matter prefixes to perform. 

If you wish to be taught extra about  SRM and Kafka in CDP Personal Cloud Base, jump over to Cloudera’s doc portal and see Streams Messaging Ideas, Streams Messaging How Tos, and/or the Streams Messaging Migration Information. That is the primary of a two-blog sequence, to proceed your journey on Streams Replication, click on right here.

To get arms on with SRM, obtain Cloudera Stream Processing Neighborhood version right here.

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