One of the principal benefits of using ksqlDB for stream processing is that it enables you to define your workload declaratively. All ksqlDB stream processing logic is defined using SQL statements, which are internally transformed into execution plans that perform the necessary computations as efficiently as possible.
Abstracting away internal execution plans underneath a SQL layer enables rapid development of stream processing workloads, but when you want optimal performance, you need to know something about the internal system behavior of ksqlDB.
This document provides an overview of key factors affecting ksqlDB performance, so you can develop high-performance stream processing workloads using ksqlDB. A basic understanding of ksqlDB's key abstractions is assumed, so if you're new to ksqlDB, read through the core concepts documentation before proceeding.
These guidelines are recommendations for getting the best performance from your ksqlDB workload. For example, if your workload is unable to keep up with the rate at which your input data is being produced, these guidelines may help to increase your workload's throughput to keep it from falling behind.
Typically, a ksqlDB workload is composed of one or more persistent queries. These queries consume input continuously from Apache Kafka® topics and produce their output back into Kafka topics. Although this document describes how to define these persistent queries such that they are as efficient as possible, the number of persistent queries running on a ksqlDB deployment has system-wide performance implications, regardless of how simple or complex they may be.
Combine persistent queries¶
Combine as much work as possible into as few persistent queries as possible. Each individual persistent query within a ksqlDB workload incurs a significant amount of system resource overhead. As a result, you should structure your workload such that all required work is consolidated into as few persistent queries as possible. A workload consolidated into a single persistent query requires significantly less system-resource overhead as the same workload is spread across two persistent queries.
Minimizing the number of persistent queries that your workload uses is probably the single most impactful design pattern that you can leverage, so it's a recurring theme throughout this performance guide. You'll learn more about the specific benefits of running fewer persistent queries in the following sections.
ksqlDB workloads consume their input from Kafka topics and produce their output into Kafka topics, but some local state is also maintained on each ksqlDB node. This state effectively serves as a local cache of topic data that improves workload performance. Local ksqlDB state is managed by using a pluggable key-value storage interface. RocksDB is the default storage engine for local state.
In addition to being stored as local key-value stores, each ksqlDB state store is also backed by a changelog topic in Kafka. Storing a complete log of all changes made to each local state store enables ksqlDB to recover in the event of a node failure.
Minimize the size of your state stores¶
State store sizes are proportional to the size of each key-value record that they contain. Smaller state stores reduce the amount of disk read-and-write throughput performed by ksqlDB, resulting in better overall workload throughput. Also, smaller state stores minimize local and Kafka disk storage consumption. State store row sizes may be minimized by eliminating unused fields in your records.
Reduce the number of state stores¶
Minimize the number of state stores that your ksqlDB deployment uses. ksqlDB batches writes made to local state stores, so fewer state stores enables larger batches of writes to a given state store, which improves disk write throughput. Having fewer state stores minimizes both local and Kafka disk storage requirements.
Serialization and deserialization¶
Input and output records consumed and produced by ksqlDB workloads are serialized as Kafka messages, so each consumed record must be deserialized before being processed by ksqlDB. Similarly, each output record produced by ksqlDB must be serialized before being written to Kafka. As a result, record serialization is generally the most expensive aspect of any ksqlDB workload.
You should use the format that best suits your use case, but keep in mind that record serialization is usually the dominant cost for most workloads.
Avoid unnecessary serialization and deserialization¶
Avoid extraneous serialization and deserialization operations. Combine as much work as possible into as few persistent queries as your use case allows. For example, instead of performing a transformation with one persistent query and filtering its output with another persistent query, consider combining the transformation and filter into a single persistent query. In this case, serialization and deserialization overhead would be reduced approximately by half.
Reduce record complexity and size¶
Avoid unnecessary record complexity and size. Smaller, simpler records are more efficient to serialize and deserialize. If your workload doesn't require certain fields in your input messages, consider removing these fields as early as possible to improve serialization performance. In some cases, it may be preferable to use ksqlDB itself to perform this message simplification by selecting a subset of the messages' fields to prepare them for downstream processing.
Transformations and filters¶
A transformation is any reference to a column that is not a bare column reference. For example, a function call over a column, and an arithmetic expression involving one or more columns are both transformations. A filter is a Boolean expression invoked via a WHERE clause that excludes any record for which that expression evaluates to false. Transformations and filters are thus evaluated for each input record, and typically incur a negligible amount of performance overhead. This does not necessarily apply to user-defined function invocations.
Consolidate transformations and filters¶
Consolidate your transformations and filters into as few persistent queries as possible. Although transformations and filters generally aren't performance bottlenecks, each individual persistent query does incur a significant amount of system-resource overhead. For this reason, you should combine as many of your transformations and filters as possible into the fewest number of persistent queries that your workload allows. Additionally, for any transformations that reduce record size, we recommend that you perform these transformations as early as possible in your processing pipeline.
ksqlDB relies heavily on topic partitioning to spread work across a deployment. For aggregations and joins, it's especially important to understand partitioning semantics.
For aggregations, ksqlDB must ensure that each node in a deployment is always guaranteed to process the same subset of aggregation groupings, which enables each ksqlDB node to accumulate aggregate results independently of other nodes in the deployment.
For joins, both sides of the join must be partitioned using the same key, known as co-partitioning, to guarantee that records for both sides of the join are always processed by the same ksqlDB node.
ksqlDB re-partitions input topics automatically if they aren't partitioned in a way that satisfies these partitioning requirements for aggregations and joins.
Whenever ksqlDB repartitions a topic, it creates an internal topic to store the repartitioned data. These internal topics essentially duplicate their input topics using the required partitioning key.
Avoiding this data duplication can reduce the amount of topic storage consumed by your Kafka cluster. Also, it can reduce the volume of network round trips between ksqlDB and Kafka.
Key your streams and tables for your workload¶
Key your streams and tables explicitly to fit your workload. You may specify partitioning keys by using the KEY column qualifier or a PARTITION BY clause for persistent queries.
Key your join inputs on columns used downstream¶
Whenever possible, key your join input streams and tables on the columns that will be used in downstream joins. If input streams and tables are co-partitioned already, no internal repartitioning is required to join them.
Key your aggregation inputs on the grouping criteria¶
Whenever possible, key your aggregation inputs on the aggregation grouping criteria. If you're aggregating a stream using a single grouping column, consider keying the input stream on that column, if your workload permits. This avoids an internal repartitioning.
When aggregating using multiple grouping columns, ksqlDB generates a composite key derived from each grouping column, so repartitioning is more difficult to avoid.
Avoid unnecessary repartitioning¶
If repartitioning is unavoidable for some aspect of your workload, consider
sharing any repartitioned data in order to avoid extraneous repartitions. For
example, if you know that you'll be aggregating a stream over column
and also joining against
col1 somewhere else, keying the stream explicitly
col1 will avoid two repartitions. This is another instance in which
consolidating logic into as few persistent queries as possible can benefit your
workload's efficiency, by reducing repartitioning overhead.
To provide scalability and fault tolerance, ksqlDB parallelizes workloads across nodes in a deployment. This parallelism is achieved by leveraging topic partitions. For a given persistent query, each node in a ksqlDB deployment processes a subset of the persistent query's input partitions. This is an important consideration to take into account when designing a workload that leverages all of the available resources within a ksqlDB deployment.
Use an appropriate number of partitions¶
Spread your workload across your ksqlDB deployment by using an appropriate number of partitions. If a persistent query's input stream has two partitions, and your ksqlDB deployment has four nodes, two of these nodes aren't performing any work for the persistent query, and you get an unbalanced workload. For this reason, input streams and tables usually should have more partitions than there are nodes in a ksqlDB deployment.
Stateful persistent queries create one state store per partition, so too many partitions can result in an excessive number of state stores, each with a very thin shard. You need adequate partitions to parallelize your workload completely across all nodes in your cluster, but no more than that. Usually, a good balance is achieved with twice as many partitions as nodes in your cluster.
A given node in a ksqlDB deployment can handle more than one input partition, so partition counts don't need to be the same as the node count.
ksqlDB provides one of the easiest ways to compute aggregations over your topic data stored in Kafka. These aggregations are defined as persistent queries whose results are maintained within a ksqlDB table, optionally with a time window as a dimension. Since aggregations are inherently stateful, they rely heavily on local materialized state stores to accumulate ongoing aggregation results efficiently. These state stores maintain a local cache of current aggregate results for the subset of persistent query partitions that are assigned to a given node in a deployment.
Consolidate aggregation calls into as few persistent queries as possible¶
Frequently, aggregations aren't done in isolation, for example, a SUM call is often accompanied by a COUNT call. Whenever the aggregation grouping criteria, and, potentially, filtering criteria, are equivalent for multiple aggregations, you should combine these aggregate calls into the same persistent query. Aggregation consolidation can potentially avoid duplicate repartitioning in cases for which repartitioning is necessary, as well as minimize the number of state stores required for a given set of aggregations.
Avoid unnecessarily narrow window widths¶
Aggregations that use a WINDOW clause add dimensionality to the aggregation's grouping criteria. Think of the aggregation's WINDOW as an implicit grouping column. Each aggregation group requires local materialization, via state stores, of up to one row per grouping criteria, per window. For example, a window width of one hour will result in 24 times more windows than a window width of one day. As a result, the number of locally materialized rows required for a windowed aggregation is inversely proportional to the window's width. We recommend using aggregation windows with the widest width that your use case will allow.
ksqlDB provides rich support for joining together multiple streams and tables. While such join operations are relatively easy to express via SQL, in certain cases they can become expensive to execute internally.
Avoid unnecessarily wide join windows¶
Stream-stream joins require that you specify a window over which to perform the join. Records on each side of the join match only if they both occur within the specified window. The amount of local state required for a stream-stream join is directly proportional to the width of the join window. As a result, we recommend using stream-stream join window widths that are no wider than your use case permits.
Ensure both sides of a join are co-partitioned whenever possible¶
ksqlDB requires that both sides of a join operation are partitioned identically in order to guarantee that matching records are always processed by the same ksqlDB node. Whenever two input streams or tables are not co-partitioned, ksqlDB must perform an internal repartition, thereby duplicating topic data as well as network round trips issued by the persistent query that does the join.