Apache Flink vs KSQL

Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

KSQL

Stacks57

Followers126

Votes5

GitHub Stars256

Forks1.0K

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.

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Advice on Apache Flink, KSQL

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

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Comments

Detailed Comparison

Apache Flink	KSQL
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.	KSQL is an open source streaming SQL engine for Apache Kafka. It provides a simple and completely interactive SQL interface for stream processing on Kafka; no need to write code in a programming language such as Java or Python. KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time.
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance	Real-time; Kafka-native; Simple constructs for building streaming apps
Statistics
GitHub Stars 25.4K	GitHub Stars 256
GitHub Forks 13.7K	GitHub Forks 1.0K
Stacks 534	Stacks 57
Followers 879	Followers 126
Votes 38	Votes 5
Pros & Cons
Pros 16 Unified batch and stream processing 8 Out-of-the box connector to kinesis,s3,hdfs 8 Easy to use streaming apis 4 Open Source 2 Low latency	Pros 3 Streamprocessing on Kafka 2 SQL syntax with windowing functions over streams 0 Easy transistion for SQL Devs
Integrations
YARN Hadoop Hadoop HBase Kafka	Kafka

What are some alternatives to Apache Flink, KSQL?

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Storm

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Related Comparisons

Apache Flink vs KSQL: What are the differences?

Introduction

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.

Apache Flink vs KSQL

Overview