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Apache Spark vs KSQL: What are the differences?
Apache Spark vs KSQL
Apache Spark and KSQL are two popular technologies in the field of data processing and analytics. Although they both serve the purpose of analyzing and processing data, there are several key differences between the two.
Architecture: Apache Spark is a general-purpose distributed computing system that utilizes a cluster of machines to process large-scale data. It provides a flexible and scalable platform for performing complex data manipulations and transformations. On the other hand, KSQL is a streaming SQL engine for Apache Kafka, which allows for real-time processing and querying of data streams. KSQL is designed specifically for working with Kafka and is tightly integrated with its messaging system.
Data Processing Paradigm: Apache Spark follows a batch processing paradigm, where data is processed in batches or micro-batches. It is capable of processing both real-time and batch data, making it suitable for a variety of use cases. KSQL, on the other hand, is designed for real-time stream processing. It processes data as it arrives in a continuous stream, enabling users to react and make decisions in real-time.
Programming Languages: Apache Spark provides support for multiple programming languages, including Java, Scala, Python, and R. This flexibility allows developers to use the language of their choice for writing Spark applications. KSQL, on the other hand, is built on top of Apache Kafka, which primarily uses Java for writing processors.
Ease of Use: Apache Spark provides a rich set of high-level APIs and libraries, making it easier for developers to write complex data processing workflows. It also offers a built-in interactive shell, which enables users to explore and analyze data interactively. KSQL, on the other hand, is designed to provide a familiar SQL-like interface for working with data streams. It simplifies the process of writing streaming applications by abstracting away the complexities of low-level stream processing.
Ecosystem Integration: Apache Spark has a vast ecosystem of tools and libraries, allowing for integration with various data sources and systems. It can seamlessly work with Hadoop, Hive, HBase, and many other distributed systems. KSQL, on the other hand, is tightly integrated with the Apache Kafka ecosystem. It leverages the capabilities of Kafka for managing and processing data streams.
In Summary, Apache Spark is a general-purpose distributed computing system with support for batch and real-time processing, multiple programming languages, and a wide range of data sources. KSQL, on the other hand, is a streaming SQL engine specifically designed for real-time stream processing, tightly integrated with Apache Kafka, and provides a SQL-like interface for working with data streams.
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 KSQL
- Streamprocessing on Kafka3
- SQL syntax with windowing functions over streams2
- Easy transistion for SQL Devs0
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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Cons of KSQL
Cons of Apache Spark
- Speed4
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