Task
A Kafka Streams task is an independently executable processing unit that processes assigned input partitions through a topology and its associated state stores.
Kafka Streams in Action, Second Edition
A StreamTask is a Kafka Streams runtime unit that processes records for assigned input partitions and maintains its associated local state.
- How it is used
- Frames Kafka Streams parallel processing and local-state ownership, while distinguishing Kafka Connect data-moving task instances.
- How deep it goes
- Explains task threading, partition-driven counts, state isolation, and repartitioning; defers broader runtime architecture details.
- Examples
- Uses stock aggregation state-store assignment, a 10-partition KStream repartition, and a stock-ticker source connector.
- What you can do after
- Operate/configure task parallelism by relating partitions and key distribution to task count, threads, and state ownership.
Read how Kafka Streams in Action, Second Edition explains task →
Apache Kafka in Action
A Kafka Streams task is the smallest processing unit, responsible for processing one or more assigned partitions and executing a topology.
- How it is used
- Uses tasks to parallelize Kafka Streams topologies according to their input partitions.
- How deep it goes
- Explains task creation, assignment, threading, rebalancing, and failure takeover, but does not descend into task implementation internals.
- Examples
- Demonstrates twelve filtering tasks, six-instance distribution, nine-task thread allocation, and failure-driven reassignment.
- What you can do after
- Apply task-based partitioning to reason about Kafka Streams scaling, thread configuration, instance capacity, and failure recovery.
Kafka for Architects
A Kafka Streams task is an independently executable processing unit containing assigned input partitions, state stores, and a graph topology.
- How it is used
- Uses tasks as Kafka Streams’ unit of distribution, parallel processing, state ownership, and failure recovery.
- How deep it goes
- Explains task composition, partition-based sizing, event processing, independence, and reassignment down to internal architecture.
- Examples
- Uses a maximum-partition-count formula and architecture diagrams showing partition, state-store, and task assignments.
- What you can do after
- Evaluate Kafka Streams parallelism, task isolation, state placement, and reassignment behavior during failures.
Related concepts
- state storehas part · A state store is a storage component that maintains application or processing state and provides mechanisms to access or update it.
- Kafka Streamspart of · Kafka Streams is a client library for building applications that continuously process data from and write data to Apache Kafka topics.
- connectorproduced by · A connector is a software element or mechanism that links components, applications, or resources to enable or represent their interaction, communication, or data exchange.
- shared-nothing architectureuses · Shared-nothing architecture is a design in which components independently manage resources without sharing mutable state, communicating through explicit interfaces.
- Kafka Connect workerpart of · A Kafka Connect worker is a process that manages connectors and runs their tasks within a Kafka Connect cluster.
- source connectorused by · A source connector is a Kafka Connect connector that reads data from an external source system and publishes records to Kafka.
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