How to choose the number oftopics/partitions in a Kafka cluster?

如何为一个kafka集群选择topics/partitions的数量？

 

This is a common question asked by many Kafka users.The goal of this post is to explain a few important determining factors andprovide a few simple formulas.

这是许多kafka使用者经常会问到的一个问题。本文的目的是介绍与本问题相关的一些重要决策因素，并提供一些简单的计算公式。

More Partitions Lead to HigherThroughput

越多的分区可以提供更高的吞吐量

The first thing to understand is that a topic partition is the unit ofparallelism in Kafka. On both the producer and the broker side, writes todifferent partitions can be done fully in parallel. So expensive operationssuch as compression can utilize more hardware resources. On the consumer side,Kafka always gives a single partition's data to one consumer thread. Thus, thedegree of parallelism in the consumer (within a consumer group) is bounded bythe number of partitions being consumed. Therefore, in general, the morepartitions there are in a Kafka cluster, the higher the throughput one canachieve.

首先我们需要明白以下事实：在kafka中，单个patition是kafka并行操作的最小单元。

在producer和broker端，向每一个分区写入数据是可以完全并行化的，此时，可以通过加大硬件资源的利用率来提升系统的吞吐量，例如对数据进行压缩。

在consumer段，kafka只允许单个partition的数据被一个consumer线程消费。因此，在consumer端，每一个Consumer Group内部的consumer并行度完全依赖于被消费的分区数量。

综上所述，通常情况下，在一个Kafka集群中，partition的数量越多，意味着可以到达的吞吐量越大。

A rough formula for picking the number of partitionsis based on throughput. You measure the throughout that you can achieve on asingle partition for production (call itp) and consumption (call it c).Let's say your target throughput ist. Then you need to have at least max(t/p,t/c) partitions. The per-partition throughput that one can achieve on theproducer depends on configurations such as the batching size, compressioncodec, type of acknowledgement, replication factor, etc. However, in general,one can produce at 10s of MB/sec on just a single partition as shown in thisbenchmark. The consumer throughput is oftenapplication dependent since it corresponds to how fast the consumer logic canprocess each message. So, you really need to measure it.

我们可以粗略地通过吞吐量来计算kafka集群的分区数量。假设对于单个partition，producer端的可达吞吐量为p，Consumer端的可达吞吐量为c，期望的目标吞吐量为t，那么集群所需要的partition数量至少为max(t/p,t/c)。在producer端，单个分区的吞吐量大小会受到批量大小、数据压缩方法、 确认类型（同步/异步）、复制因子等配置参数的影响。经过测试，在producer端，单个partition的吞吐量通常是在10MB/s左右。在consumer端，单个partition的吞吐量依赖于consumer端每个消息的应用逻辑处理速度。因此，我们需要对consumer端的吞吐量进行测量。

Although it's possible to increase the number ofpartitions over time, one has to be careful if messages are produced with keys.When publishing a keyed message, Kafka deterministically maps the message to apartition based on the hash of the key. This provides a guarantee that messages with the same key are alwaysrouted to the same partition. This guarantee can be important for certainapplications since messages within a partition are always delivered in order tothe consumer. If the number of partitions changes, such a guarantee may nolonger hold. To avoid this situation, a common practice is to over-partition abit. Basically, you determine the number of partitions based on a future targetthroughput, say for one or two years later. Initially, you can just have asmall Kafka cluster based on your current throughput. Over time, you can addmore brokers to the cluster and proportionally move a subset of the existingpartitions to the new brokers (which can be done online). This way, you cankeep up with the throughput growth without breaking the semantics in theapplication when keys are used.

虽然随着时间的推移，我们能够对分区的数量进行添加，但是对于基于Key来生成的这一类消息需要我们重点关注。当producer向kafka写入基于key的消息时，kafka通过key的hash值来确定消息需要写入哪个具体的分区。通过这样的方案，kafka能够确保相同key值的数据可以写入同一个partition。kafka的这一能力对于一部分应用是极为重要的，例如对于同一个key的所有消息，consumer需要按消息的顺序进行有序消费。如果partition的数量发生改变，那么上面的有序性保证将不复存在。为了避免上述情况发生，通常的解决办法是多分配一些分区，以满足未来的需求。通常情况下，我们需要根据未来1到2年的目标吞吐量来设计kafka的分区数量。

一开始，我们可以基于当前的业务吞吐量为kafka集群分配较小的broker数量，随着时间的推移，我们可以向集群中增加更多的broker，然后在线方式将适当比例的partition转移到新增加的broker中去。通过这样的方法，我们可以在满足各种应用场景（包括基于key消息的场景）的情况下，保持业务吞吐量的扩展性。

In addition to throughput, there are a few otherfactors that are worth considering when choosing the number of partitions. Asyou will see, in some cases, having too many partitions may also have negativeimpact.

在设计分区数时，除了吞吐量，还有一些其他因素值得考虑。正如我们后面即将看到的，对于一些应用场景，集群拥有过的分区将会带来负面的影响。

More Partitions Requires More OpenFile Handles

越多的分区需要打开更多地文件句柄

Each partition maps to a directory in the file systemin the broker. Within that log directory, there will be two files (one for theindex and another for the actual data) per log segment. Currently, in Kafka,each broker opens a file handle of both the index and the data file of everylog segment. So, the more partitions, the higher that one needs to configurethe open file handle limit in the underlying operating system. This is mostlyjust a configuration issue. We have seen production Kafka clusters running withmore than 30 thousand open file handles per broker.

在kafka的broker中，每个分区都会对照着文件系统的一个目录。在kafka的数据日志文件目录中，每个日志数据段都会分配两个文件，一个索引文件和一个数据文件。当前版本的kafka，每个broker会为每个日志段文件打开一个index文件句柄和一个数据文件句柄。因此，随着partition的增多，需要底层操作系统配置更高的文件句柄数量限制。这更多的是一个配置问题。我们曾经见到过，在生产环境Kafka集群中，每个broker打开的文件句柄数量超过30,000。

More Partitions May IncreaseUnavailability

更多地分区会导致更高的不可用性

Kafka supports intra-cluster replication, which provides higher availabilityand durability. A partition can have multiple replicas, each stored on adifferent broker. One of the replicas is designated as the leader and the restof the replicas are followers. Internally, Kafka manages all those replicasautomatically and makes sure that they are kept in sync. Both the producer andthe consumer requests to a partition are served on the leader replica. When abroker fails, partitions with a leader on that broker become temporarilyunavailable. Kafka will automatically move the leader of those unavailablepartitions to some other replicas to continue serving the client requests. Thisprocess is done by one of the Kafka brokers designated as the controller. Itinvolves reading and writing some metadata for each affected partition inZooKeeper. Currently, operations to ZooKeeper are done serially in thecontroller.

Kafka通过多副本复制技术，实现kafka集群的高可用和稳定性。每个partition都会有多个数据副本，每个副本分别存在于不同的broker。所有的数据副本中，有一个数据副本为Leader，其他的数据副本为follower。在kafka集群内部，所有的数据副本皆采用自动化的方式进行管理，并且确保所有的数据副本的数据皆保持同步状态。不论是producer端还是consumer端发往partition的请求，皆通过leader数据副本所在的broker进行处理。当broker发生故障时，对于leader数据副本在该broker的所有partition将会变得暂时不可用。Kafka将会自动在其他数据副本中选择出一个leader，用于接收客户端的请求。这个过程由kafka controller节点broker自动完成，主要是从Zookeeper读取和修改受影响partition的一些元数据信息。在当前的kafka版本实现中，对于zookeeper的所有操作都是由kafka controller来完成的（serially的方式）。

In the common case when a broker is shut downcleanly, the controller will proactively move the leaders off the shutting downbroker one at a time. The moving of a single leader takes only a fewmilliseconds. So, from the clients perspective, there is only a small window ofunavailability during a clean broker shutdown.

在通常情况下，当一个broker有计划地停止服务时，那么controller会在服务停止之前，将该broker上的所有leader一个个地移走。由于单个leader的移动时间大约只需要花费几毫秒，因此从客户层面看，有计划的服务停机只会导致系统在很小时间窗口中不可用。（注：在有计划地停机时，系统每一个时间窗口只会转移一个leader，其他leader皆处于可用状态。）

However, when a broker is shut down uncleanly (e.g.,kill -9), the observed unavailability could be proportional to the number ofpartitions. Suppose that a broker has a total of 2000 partitions, each with 2replicas. Roughly, this broker will be the leader for about 1000 partitions.When this broker fails uncleanly, all those 1000 partitions become unavailableat exactly the same time. Suppose that it takes 5 ms to elect a new leader fora single partition. It will take up to 5 seconds to elect the new leader forall 1000 partitions. So, for some partitions, their observed unavailability canbe 5 seconds plus the time taken to detect the failure.

然而，当broker非计划地停止服务时（例如，kill -9方式)，系统的不可用时间窗口将会与受影响的partition数量有关。假如，一个2节点的kafka集群中存在2000个partition，每个partition拥有2个数据副本。当其中一个broker非计划地宕机，所有1000个partition同时变得不可用。假设每一个partition恢复时间是5ms，那么1000个partition的恢复时间将会花费5秒钟。因此，在这种情况下，用户将会观察到系统存在5秒钟的不可用时间窗口。

If one is unlucky, the failed broker may be thecontroller. In this case, the process of electing the new leaders won't startuntil the controller fails over to a new broker. The controller failoverhappens automatically, but requires the new controller to read some metadatafor every partition from ZooKeeper during initialization. For example, if thereare 10,000 partitions in the Kafka cluster and initializing the metadata fromZooKeeper takes 2 ms per partition, this can add 20 more seconds to the unavailabilitywindow.

更不幸的情况发生在宕机的broker恰好是controller节点时。在这种情况下，新leader节点的选举过程在controller节点恢复到新的broker之前不会启动。Controller节点的错误恢复将会自动地进行，但是新的controller节点需要从zookeeper中读取每一个partition的元数据信息用于初始化数据。例如，假设一个kafka集群存在10,000个partition，从zookeeper中恢复元数据时每个partition大约花费2ms，则controller的恢复将会增加约20秒的不可用时间窗口。

In general, unclean failures are rare. However, ifone cares about availability in those rare cases, it's probably better to limitthe number of partitions per broker to two to four thousand and the totalnumber of partitions in the cluster to low tens of thousand.

通常情况下，非计划的宕机事件发生的情况是很少的。如果系统可用性无法容忍这些少数情况的场景，我们最好是将每个broker的partition数量限制在2,000到4,000，每个kafka集群中partition的数量限制在10,000以内。

More Partitions May Increase End-to-endLatency

越多的分区可能增加端对端的延迟

The end-to-end latency in Kafka is defined by thetime from when a message is published by the producer to when the message isread by the consumer. Kafka only exposes a message to a consumer after it hasbeen committed, i.e., when the message is replicated to all the in-syncreplicas. So, the time to commit a message can be a significant portion of theend-to-end latency. By default, a Kafka broker only uses a single thread toreplicate data from another broker, for all partitions that share replicas betweenthe two brokers. Our experiments show that replicating 1000 partitions from onebroker to another can add about 20 ms latency, which implies that theend-to-end latency is at least 20 ms. This can be too high for some real-timeapplications.

Kafka端对端延迟定义为producer端发布消息到consumer端接收消息所需要的时间。即consumer接收消息的时间减去producer发布消息的时间。Kafka只有在消息提交之后，才会将消息暴露给消费者。例如，消息在所有in-sync副本列表同步复制完成之后才暴露。因此，in-sync副本复制所花时间将是kafka端对端延迟的最主要部分。在默认情况下，每个broker从其他broker节点进行数据副本复制时，该broker节点只会为此工作分配一个线程，该线程需要完成该broker所有partition数据的复制。经验显示，将1000个partition从一个broker到另一个broker所带来的时间延迟约为20ms，这意味着端对端的延迟至少是20ms。这样的延迟对于一些实时应用需求来说显得过长。

Note that this issue is alleviated on a largercluster. For example, suppose that there are 1000 partition leaders on a brokerand there are 10 other brokers in the same Kafka cluster. Each of the remaining10 brokers only needs to fetch 100 partitions from the first broker on average.Therefore, the added latency due to committing a message will be just a few ms,instead of tens of ms.

注意，上述问题可以通过增大kafka集群来进行缓解。例如，将1000个分区leader放到一个broker节点和放到10个broker节点，他们之间的延迟是存在差异的。在10个broker节点的集群中，每个broker节点平均需要处理100个分区的数据复制。此时，端对端的延迟将会从原来的数十毫秒变为仅仅需要几毫秒。

As a rule of thumb, if you care about latency, it'sprobably a good idea to limit the number of partitions per broker to100 x bx r, where b is the number of brokers in a Kafka cluster andris the replication factor.

根据经验，如果你十分关心消息延迟问题，限制每个broker节点的partition数量是一个很好的主意：对于b各broker节点和复制因子为r的kafka集群，整个kafka集群的partition数量最好不超过100*b*r个，即单个partition的leader数量不超过100.

More Partitions May Require MoreMemory In the Client

越多的partition意味着需要客户端需要更多的内存

In the most recent 0.8.2 release which we ship withthe Confluent Platform 1.0, we have developed a more efficientJava producer. One of the nice features of the new producer is that it allowsusers to set an upper bound on the amount of memory used for buffering incomingmessages. Internally, the producer buffers messages per partition. After enoughdata has been accumulated or enough time has passed, the accumulated messagesare removed from the buffer and sent to the broker.

在最新发布的0.8.2版本的kafka中，我们开发了一个更加高效的Java producer。新版producer拥有一个比较好的特征，他允许用户为待接入消息存储空间设置内存大小上限。在内部实现层面，producer按照每一个partition来缓存消息。在数据积累到一定大小或者足够的时间时，积累的消息将会从缓存中移除并发往broker节点。

If one increases the number of partitions, messagewill be accumulated in more partitions in the producer. The aggregate amount ofmemory used may now exceed the configured memory limit. When this happens, theproducer has to either block or drop any new message, neither of which isideal. To prevent this from happening, one will need to reconfigure theproducer with a larger memory size.

如果partition的数量增加，消息将会在producer端按更多的partition进行积累。众多的partition所消耗的内存汇集起来，有可能会超过设置的内容大小限制。当这种情况发生时，producer必须通过消息堵塞或者丢失一些新消息的方式解决上述问题，但是这两种做法都不理想。为了避免这种情况发生，我们必须重新将produder的内存设置得更大一些。

As a rule of thumb, to achieve good throughput, oneshould allocate at least a few tens of KB per partition being produced in theproducer and adjust the total amount of memory if the number of partitionsincreases significantly.

根据经验，为了达到较好的吞吐量，我们必须在producer端为每个分区分配至少几十KB的内存，并且在分区数量显著增加时调整可以使用的内存数量。

A similar issue exists in the consumer as well. Theconsumer fetches a batch of messages per partition. The more partitions that aconsumer consumes, the more memory it needs. However, this is typically only anissue for consumers that are not real time.

类似的事情对于consumer端依然有效。Consumer端每次从kafka按每个分区取出一批消息进行消费。消费的分区数越多，需要的内存数量越大。尽管如此，上述方式主要运用于非实时的应用场景。

Summary

总结

In general, more partitions in a Kafka cluster leadsto higher throughput. However, one does have to be aware of the potentialimpact of having too many partitions in total or per broker on things likeavailability and latency. In the future, we do plan to improve some of thoselimitations to make Kafka more scalable in terms of the number of partitions.

通常情况下，kafka集群中越多的partition会带来越高的吞吐量。但是，我们必须意识到集群的partition总量过大或者单个broker节点partition过多，都会对系统的可用性和消息延迟带来潜在的影响。未来，我们计划对这些限制进行一些改进，让kafka在分区数量方面变得更加可扩展。