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Apache Flink vs KSQL: What are the differences?
Introduction
Apache Flink and KSQL are both powerful technologies used for stream processing in real-time. They have their unique features and capabilities that make them suitable for different use cases and requirements. Here, we will discuss the key differences between Apache Flink and KSQL.
Language and Query Flexibility: One of the major differences between Apache Flink and KSQL is the language and query flexibility they offer. Apache Flink provides a more general-purpose stream processing framework, where developers can write complex stream processing algorithms using APIs or customizable functions. On the other hand, KSQL is a SQL-like language that simplifies real-time stream processing by allowing users to write stream processing queries using SQL syntax.
Support for Complex Event Processing: Apache Flink provides native support for complex event processing (CEP) through its CEP library. This allows developers to define complex patterns in streams and perform operations like windowing, filtering, and aggregations on those patterns. KSQL, on the other hand, lacks native support for complex event processing, thus making it more suitable for simpler stream processing tasks.
Scalability and Fault Tolerance: Apache Flink is known for its distributed processing capabilities and excellent scalability. It can handle large-scale data processing and supports fault tolerance through its distributed runtime architecture. KSQL, on the other hand, is built on top of Kafka Streams and inherits its scalability and fault-tolerance features. However, it may not be as scalable as Apache Flink in extremely high-volume scenarios.
Connectivity and Integration: Apache Flink provides built-in connectors for various data sources and sinks, including messaging systems like Kafka, databases, and file systems, making it highly versatile in terms of data connectivity and integration. KSQL, on the other hand, is tightly integrated with Apache Kafka and works seamlessly with Kafka topics, but it may require additional connectors or customization for interacting with other systems.
Advanced Analytics and Machine Learning: Apache Flink offers extensive support for advanced analytics and machine learning tasks. It provides libraries and tools for performing tasks like batch processing, graph processing, and machine learning in addition to stream processing. On the other hand, KSQL focuses primarily on stream processing and lacks the advanced analytics and machine learning features provided by Apache Flink.
Community and Ecosystem: Apache Flink has a large and active community, with a wide range of contributors and users, leading to a mature ecosystem. It has a rich set of connectors, libraries, and tools developed by the community, making it easier to integrate with other systems and extend its functionalities. KSQL, being a part of the Apache Kafka ecosystem, also benefits from the wider Kafka community, but it may not have the same level of community and ecosystem support as Apache Flink.
In Summary, Apache Flink offers more language and query flexibility, complex event processing support, scalability, and advanced analytics capabilities compared to KSQL. However, KSQL provides simplicity, easy integration with Kafka, and a SQL-like language for stream processing tasks. The choice between the two depends on the specific requirements and use cases of the stream processing application.
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 KSQL
- Streamprocessing on Kafka3
- SQL syntax with windowing functions over streams2
- Easy transistion for SQL Devs0
