Comparing ksqlDB, Spark SQL, and Flink SQL

Learn the differences between three leading SQL engines so you can choose the right one to process big data

8 min readJun 7, 2023

Real-time stream processing enables modern data enterprises to take quick action before data loses its value. Stream processing involves data computation on the fly to provide users with rapid results with minimum latency.

There are some widely popular streaming frameworks like Apache Kafka®, Apache Flink, Apache Storm, Apache Samza, and Apache Spark that various enterprises have adopted. While most streaming tools have core APIs in Java or Scala, some provide abstractions for running SQL queries.

SQL has become the universal language for performing data analysis. It’s used by all kinds of “data” personas, including data engineers, data analysts, data scientists, and business users. Applying SQL support to streaming technologies makes the complex activity of streaming computation much simpler.

Some stream-processing tools have leveraged these SQL capabilities to provide a simple framework with easy adaptability and a lesser learning curve. In this article, you will learn about three of them: ksqlDB, Spark SQL, and Flink SQL.

This post compares these frameworks using the following crucial parameters that can help you better understand each tool’s capabilities and limitations:

SQL structure
Language support
Developer experience
UI/UX for SQL interface
SerDe support
Fault tolerance
Performance
Advanced analysis support
Licensing
Latency

Let’s get started!

What is ksqlDB?

ksqlDB is an event-streaming database purpose-built for stream processing applications and is licensed under the Confluent Community License Agreement. It’s built on top of Kafka Streams and provides a simple query layer for stream processing by abstracting complex programming operations.

ksqlDB features

ksqlDB uses a dialect inspired by ANSI SQL with additional constructs to support querying streaming data. These queries can be executed from the ksqlDB CLI using the official Java client or community-supported clients like .NET, Go, and Python. Open-source tools like UI for Apache Kafka are also available, providing a better user experience.

ksqlDB supports creating objects like streams (for processing unbounded, immutable, append-only events) and tables (a mutable, partitioned collection representing the most up-to-date state for individual keys). Tables can be created by calculating the latest state of events based on the sequence of events and events keys from streams, while streams can be constructed by deriving the event state changes (like change logs) from tables. This close relationship between streams and tables, known as stream-table duality, is one of the unique features supported by ksqlDB.

Along with native integration with Kafka, ksqlDB supports popular serialization formats like delimited, JSON, Avro, and Protobuf. It also supports features like fault tolerance and high availability. ksqlDB creates a changelog of every event it processes, which can be replayed to recreate any lost data in case of node failures. You can also add servers to the cluster that can be configured for high availability by creating replicas to avoid downtime and data loss.

ksqlDB’s high availability, fault tolerance, horizontal scaling, and low latency make it an excellent choice for implementing stream processing applications.

ksqlDB pros and cons

ksqlDB provides a Kafka-native stream processing framework that helps to leverage existing Apache Kafka infrastructure. This is one of the most significant advantages of using ksqlDB compared to other stream processing frameworks.

Its benefits include:

Easy integration with Apache Kafka, as compared to Flink SQL and Spark SQL.
Can be used along with Apache Kafka streams for implementing event-streaming applications.
ANSI-SQL-like dialect makes it easy for developers to learn it quickly and enables them to write stream-processing applications in a language-neutral manner.
Highly scalable and fault-tolerant, allowing developers to focus more on functional logic and less on the infrastructure side of things.
Supports low latency levels. With push queries, analytics results can be seen in real time.
Supports pull queries for asynchronous applications like request/response flows to return available data at the time of execution.

Along with these advantages, it does have some key limitations for implementing streaming analytics use cases. Here are a few to keep in mind:

It’s not licensed under Apache 2.0, which can imply restrictions for specific use cases.
Unlike Spark SQL and Flink SQL, ksqlDB does not support checkpointing.
It has limited ETL/analytics capabilities as compared to Spark SQL and Flink SQL.

ksqlDB’s Kafka-native stream processing capabilities and SQL-like dialect make it an excellent choice for processing Kafka messages in real time. You should consider its benefits and limitations carefully before implementing your streaming use cases.

What is Spark SQL?

Spark SQL is part of Apache Spark, a distributed, in-memory processing framework licensed under Apache License 2.0. It provides an abstraction layer over Spark Core APIs for writing SQL queries.

Like tables in relational database management systems (RDBMSs), Spark has DataFrames, a distributed collection of data organized into columns. Spark SQL and DataFrames are the building blocks of Spark Structured Streaming, a distributed stream processing framework with SQL-like syntax that provides high throughput, exactly-once semantics, and fault-tolerance capabilities.

Spark SQL features

Spark SQL queries are aligned with ANSI SQL’s style and provide options to make their behavior ANSI- or Hive-compliant. Spark SQL and the DataFrame API can be embedded within programs written in Java, Scala, Python, and R. It provides native SerDe for delimited files and supports custom SerDe for Avro, ORC, and Parquet.

While interactive queries can be executed using the SQL CLI client, there are commercial products like Databricks that provide a web UI for creating interactive notebooks to execute streaming queries.

Spark’s ability to easily integrate with various sources like RDBMSs, files, Kafka, Cloud Object Storage, and socket connections provides a solid foundation for hydrating data lakes by providing the necessary integration points to build data pipelines. It also supports fault tolerance by implementing checkpointing to record the source offset to replay a specific message.

Spark SQL pros and cons

Spark SQL’s Structured Streaming is one of the most widely adopted distributed processing frameworks, with excellent community support compared to ksqlDB or Flink SQL. Abstractions provided by Spark SQL and DataFrames help developers quickly implement real-time use cases. Ease of use, SQL support, and a variety of connectors make it the most popular among all stream processing frameworks. Furthermore, its support for Python attracts more data engineers to use it via PySpark.

Benefits include:

Exactly-once semantics using checkpointing.
Easy to execute the same streaming code as batch/micro-batch for cost optimization.
Supports table caching for improved performance.
Provides easy integration with the Delta Lake storage format. Such easy integration is not available for ksqlDB or Flink SQL, which makes Spark SQL the default choice for lakehouse architectures built on Delta Lake.

Here’s an overview of its key limitations to be aware of:

Spark SQL is not a native streaming engine, since it’s based on micro-batch processing to provide its near real-time capabilities. (It does support a continuous processing mode, but as an experimental feature that supports limited operations, sources, and sinks.)
It does not support single-digit millisecond latency.
It does not offer direct SQL support for querying Kafka topics (you need to create a DataFrame). ksqlDB has a more straightforward approach, as it can directly create streams/tables on top of the Kafka topic, as discussed earlier in this article.

Spark SQL provides the capability for structured data processing. Its ability to perform streaming analytics using simple SQL queries makes it popular in the data community. It’s a great option if your use case does not have low latency requirements or other limitations as detailed above.

Flink SQL

Apache Flink is a distributed stream processing engine licensed under Apache License 2.0 that can perform computations in-memory and at scale. Flink provides a Table API and SQL API that support writing queries in SQL to abstract Flink’s core building blocks like streams, time, and state.

Flink SQL features

Flink SQL is based on Apache Calcite, which implements the SQL standard. You can create dynamic tables and continuously query them to perform stream processing. Flink SQL queries can be embedded within programs written in Java or Scala, as well as in ones written in Python using the PyFlink Table API.

While Flink SQL queries can be executed using the Flink SQL Client, there are also commercial products like Ververica that provide a web-based SQL editor for a better user experience.

Flink SQL supports multiple connectors, including FileSystem, Elasticsearch/OpenSearch, Apache Kafka, Amazon DynamoDB/Kinesis Data Streams/Firehose, JDBC, HBase, and Hive. It offers SerDe support for Java primitives, Avro, Protobuf, and Apache Thrift. It also provides fault tolerance for any hardware, network, or program failures by taking a state snapshot and storing it in the state backend.

Flink SQL pros and cons

Flink SQL provides a good combination of real-time processing with excellent analytics capabilities and low latency in milliseconds. It can do streaming and batch processing with the same programming model, making it easy to switch between the execution behavior of your application based on the use case.

It also has built-in performance optimization techniques like MiniBatch aggregation and Local-Global aggregation that help with faster streaming analytics.

Flink’s benefits include:

Real-time processing capabilities with reduced latency as compared to Spark’s Structured Streaming.
Support for a wide variety of built-in operators and functions used in stream processing.
A low learning curve, as developers need to learn only a single API for static and stream data.
Better support for windowing and state management as compared to Spark SQL.

However, Flink SQL doesn’t have the same level of community support as Spark does. Here are its key limitations:

Compared to Spark, Flink’s community is still growing — while Spark has been around for almost a decade, enjoys strong community support, and is much more mature.
Hadoop/Hive integration is a bit more challenging in Flink SQL and it has different SQL syntax like reserved keywords and literals. Spark SQL, on the other hand, can be easily used to read data from existing Hive installations and there is no major syntax gap with Hive Query Language.

These benefits and limitations should be weighed against factors like latency SLAs, analytics requirements, and source connectivity when deciding on stream processing technologies. While Spark SQL is more widely adopted, Flink SQL is also an excellent choice for real-time use cases with minimum latency.

Conclusion

ksqlDB, Flink SQL, and Spark SQL’s Structured Streaming are some of the leading stream processing frameworks. While they all have capabilities for implementing real-time processing, each has a different approach and some unique features:

ksqlDB can be a great choice if you already have Apache Kafka infrastructure and your Kafka messages need to be processed in real time.
For scenarios where latency requirements are within a few seconds, you can go for Spark SQL’s Structured Streaming with its micro-batch architecture for near-real-time processing.
To implement a real-time/stream processing solution with good analytical capabilities and low latencies, you can consider Flink SQL.

Based on your use case, you can select any of them to use with a streaming data platform like Redpanda to build your application.

Redpanda is a developer-first streaming data platform that is API-compatible with Apache Kafka — just ten times faster. It integrates with all the stream processing technologies listed in this article and can be a source/sink for these streaming engines. You don’t need a new connector — you can reuse Kafka connectors to build a real-time application.

To learn more about Redpanda, check out our documentation and browse the Redpanda blog for tutorials and guides on how to easily integrate with Redpanda. For a more hands-on approach, take Redpanda for a free spin!

If you get stuck, have a question, or want to chat with our solution architects, core engineers, and fellow Redpanda users, join our Redpanda Community on Slack!