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Apache Flink vs Samza: What are the differences?
Introduction
Apache Flink and Samza are both stream processing systems that provide support for real-time data processing. While they share similarities in terms of their purpose, there are several key differences between the two.
Integration with Ecosystem: Apache Flink has a broader integration with various data sources and sinks, including Hadoop Distributed File System (HDFS), Apache Kafka, and others. Samza, on the other hand, has a more specific focus on integrating with Apache Kafka, making it a suitable choice for Kafka-based architectures.
Processing Model: Flink supports both batch processing and stream processing, offering a unified processing model. It provides a rich set of operators and an event time processing model, allowing for complex event-driven data processing. Samza, on the contrary, is primarily designed for stream processing and does not inherently support batch processing.
State Management: Flink provides built-in support for maintaining and managing state in stream processing applications. It includes features like stateful stream processing, fault-tolerant state checkpoints, and state recovery. Samza, on the other hand, does not have built-in state management capabilities and relies on external systems like Apache Kafka or Apache HBase for storing and managing the state.
Fault Tolerance: Flink offers robust fault-tolerance mechanisms, including exactly-once processing guarantees. It achieves this by maintaining consistent checkpoints of the operator states and providing recovery mechanisms in case of failures. Samza, on the other hand, focuses on at-least-once processing guarantees. It relies on Apache Kafka's offset-tracking mechanism for handling failures and ensuring data integrity.
Programming Model: Flink provides a high-level programming model with a SQL-like language called Flink SQL, as well as APIs in Java and Scala. It also supports complex event processing using CEP libraries and graph-based data processing using the Gelly library. Samza, on the other hand, primarily emphasizes a simple and lightweight programming model using the Apache Kafka Streams API.
Community and Maturity: Flink has a larger and more active community compared to Samza, resulting in a wider range of documentation, community support, and ecosystem integrations. Flink is also more mature and has been widely adopted in various industries. Samza, although still actively maintained, has a smaller community and is relatively less mature.
In summary, Apache Flink offers broader ecosystem integration, support for batch processing, built-in state management, and exactly-once processing guarantees. On the other hand, Samza focuses on integration with Apache Kafka, provides a lightweight programming model, relies on external systems for state management, and offers at-least-once processing guarantees.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Pros of Apache Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
Pros of Samza
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What is Apache Flink?
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