Kafka Streams | Part I

Kafka Connectors

Kafka Streams is a powerful library for building stream-processing applications using Apache Kafka. It provides a high-level DSL (Domain-Specific Language) and APIs for processing, transforming, and analyzing continuous streams of records. Here are some key features of Kafka Streams:

1. Stream Processing: Kafka Streams enable real-time processing of streams of records. It allows you to consume input data from Kafka topics, apply operations and transformations on the data, and produce results back to Kafka topics. Stream processing can be performed on individual records or in windowed aggregations, enabling near real-time analytics, monitoring, and data enrichment.
2. Event-Time Processing: Kafka Streams provides support for event-time processing, allowing you to handle out-of-order records by using timestamps associated with the records. It offers windowing operations based on event-time semantics, enabling time-based aggregations, sessionization, and window joins.
3. Stateful Processing: Kafka Streams allows you to maintain and update the state during stream processing. It provides built-in support for state stores, which are key-value stores that can be queried and updated within a processing topology. Stateful operations enable advanced stream processing tasks such as joins, aggregations, and anomaly detection.
4. Exactly-Once Processing: Kafka Streams provides end-to-end exactly-once processing semantics, ensuring that each record is processed exactly once, even in the presence of failures. This is achieved by leveraging the strong durability guarantees and transactional capabilities of Apache Kafka.
5. Windowing and Time-Based Operations: Kafka Streams offers a range of windowing operations, allowing you to perform computations over fixed time windows, tumbling windows, sliding windows, or session windows. Windowed operations enable time-based aggregations, time-sensitive analysis, and event-based triggers.
6. Join Operations: Kafka Streams provides join operations to combine data from multiple streams or tables based on key matching. It supports inner joins, left joins, and outer joins, allowing you to perform powerful data integrations and enrichments in real time.
7. Interactive Queries: Kafka Streams enables interactive querying of the state stores within a stream processing application. This allows applications to respond to ad-hoc queries by serving the latest state or aggregated results, making it suitable for building interactive real-time dashboards and applications.
8. Integration with Kafka Ecosystem: Kafka Streams seamlessly integrates with Apache Kafka, leveraging Kafka’s distributed storage, scalability, and fault tolerance. It also integrates with other components of the Kafka ecosystem, such as Kafka Connect for easy data integration and Kafka’s built-in security and authentication mechanisms.
9. Developer-Friendly APIs: Kafka Streams provides high-level DSL and APIs that are designed to be developer-friendly and easy to use. It offers a functional programming model with operators and fluent API syntax, making it accessible for developers to express complex stream processing logic in a concise and readable manner.

Kafka Streams is a versatile and robust stream processing library that allows you to build scalable, fault-tolerant, and real-time applications for processing continuous streams of data. Its features empower developers to implement sophisticated stream processing tasks, enabling real-time analytics, data transformations, event-driven architectures, and more.

Where are Kstream values stored?

In Apache Kafka’s Streams library, the values of a KStream are not stored in Kafka itself. Instead, the values are processed and transformed in-memory within the stream processing application.

When you define a KStream in your Kafka Streams application, it represents an abstraction over the input topic(s) from which the stream is consumed. The stream processing operations defined on the KStream, such as filtering, mapping, aggregating, or joining, are applied to the records as they are consumed.

The processed records and intermediate results are stored in memory within the stream processing application. The Kafka Streams library provides mechanisms to manage and maintain this in-memory state across multiple instances or threads of the application. The state can include windowed state, key-value stores, or any other stateful data structures used by the application for stream processing.

The in-memory state allows the application to maintain context and store the necessary information required for processing, such as aggregations, join results, or windowed computations. This state is constantly updated as new records are processed, and it is used to generate the output records or further process the stream.

It’s important to note that the in-memory state is transient and exists only within the lifetime of the stream processing application. If the application is restarted or stops processing, the state is lost, and the application would need to rebuild the state from the input topics or any external data sources upon restart.

However, the input and output records of the KStream can be stored in Kafka topics if desired. The processed records can be written to new topics using the Kafka producer, and the results can be consumed from these output topics by other applications or downstream processes.

So, while Kafka is the underlying messaging system used for input and output topics, the actual values of a KStream in the Kafka Streams library are stored and processed in-memory within the stream processing application.

Then what makes Kafka Streams fault-tolerant?

While Kafka Streams processes data in-memory, it still provides fault tolerance through several mechanisms to ensure data integrity and resiliency in the event of failures. Here are some key aspects that make Kafka Streams fault-tolerant:

1. Input Topic Replication: Kafka itself provides fault tolerance through topic replication. Input topics can be configured with a replication factor greater than one, meaning that multiple replicas of each partition are maintained across different brokers. If a broker or partition becomes unavailable, Kafka automatically redirects consumers to the available replicas, ensuring continuous data ingestion.
2. Stateful Processing and Changelog Topics: Kafka Streams maintains the necessary state information for stream processing. Intermediate results, aggregations, and stateful operations are stored in internal state stores. These state stores are also backed by Kafka topics called changelog topics, which record all updates to the state stores. This allows the state to be reconstructed in case of failures or application restarts.
3. Offset Management: Kafka Streams tracks the offsets of consumed records and periodically commits them to Kafka. This enables the library to resume processing from the last committed offset in case of failures or restarts. It ensures that the application can pick up from where it left off without reprocessing previously processed records.
4. Application Rebalancing: Kafka Streams leverages Kafka’s consumer group mechanism for fault tolerance. If an instance of the stream processing application fails or new instances are added, Kafka Streams automatically triggers a rebalancing process. During rebalancing, partitions and tasks are redistributed among the active instances to ensure even workload distribution and maintain fault tolerance.
5. Exactly-Once Semantics: Kafka Streams provides support for exactly-once processing semantics, which guarantees that each record will be processed exactly once, even in the face of failures or retries. This is achieved through the combination of transactional producer and consumer operations, along with the use of internal Kafka topics for storing offsets and maintaining state.

These fault-tolerance mechanisms in Kafka Streams ensure that data integrity is maintained, stateful processing is resilient to failures, and the application can continue processing from the last known state in case of disruptions. This allows stream processing applications built with Kafka Streams to handle failures gracefully and provide reliable and fault-tolerant processing of data streams.