Community

Blog Events Webinars Tutorials Forum
  1. Blog
  2. Events
  3. Webinars
  4. Tutorials
  5. Forum
Create Account
×
Community Blog Understand Flink SQL: Real-Time SQL Query Execution for Stream and Batch Data

Understand Flink SQL: Real-Time SQL Query Execution for Stream and Batch Data

Discover Flink SQL, the high-level API for executing SQL queries across streaming and batch data sets in Apache Flink.

What is Flink SQL?

Flink SQL is a powerful high-level API for running SQL queries on streaming and batch datasets in Apache Flink. It allows developers to process and analyze large volumes of data in real time using standard SQL syntax, without writing low-level code.
Key features of Flink SQL include:

  • ANSI SQL 2011 compliance makes learning easy for those familiar with SQL.
  • Support for both streaming and batch processing, enabling a unified approach to data processing.
  • Ability to operate on tables representing data streams, treating them as append-only logs or changelog streams.
  • Automatic handling of time attributes and watermarks for out-of-order data in streaming use cases.
  • Integration with various data sources and sinks like Apache Kafka, Elasticsearch, JDBC databases, etc.
  • Built-in support for common operations like filtering, aggregations, joins, UDFs, etc.

Flink SQL queries operate on tables, which can be defined using SQL DDL statements. Tables can be backed by various connectors, like Kafka topics, databases, filesystems, or any other system supported by Flink. Queries in Flink SQL are continuously executed, consuming new data as it arrives and producing results in real time. This enables building streaming applications using familiar SQL syntax without the need for low-level programming.

Integration with Other Flink APIs

Flink SQL is deeply integrated with Flink's other APIs, ensuring seamless interoperability among them. These APIs cater to developers with various levels of expertise and accommodate a range of application complexities. Here's how Flink SQL connects with other key Flink APIs:

Flink SQL represents the highest level of abstraction within Flink, similar to the Table API in terms of semantics and expressiveness but formulated as SQL query expressions. It closely interacts with the Table API, allowing SQL queries to be executed over tables defined within it.

The Table API is a language-integrated query API for Java, Scala, and Python that allows composing queries from relational operators like selection, filter and join. Flink SQL closely interacts with the Table API, and SQL queries can be executed over tables defined in the Table API.

The DataStream API offers a lower-level abstraction for stateful and timely stream processing, which is embedded via the Process Function1. Flink SQL integrates seamlessly with the DataStream API, allowing easy switching between the APIs15.

The DataSet API: While primarily focused on bounded data sets offering primitives like loops and iterations, the DataSet API, although not directly linked with Flink SQL, complements it in batch processing scenarios.

Source: Apache Flink's documentation

Integrating Batch and Stream Processing

Flink SQL, seamlessly integrated with the Table API, represents the top layer of abstraction within the Apache Flink ecosystem. This SQL interface interacts directly with tables in Apache Flink, making batch and stream processing uniformly accessible through high-level constructs. This integration ensures that whether the underlying operation is batch or stream processing, the approach remains consistent, producing identical results from similar queries.
The transformative journey of Flink SQL began earnestly with the merge of Blink into Apache Flink 1.9. This significant update marked the start of rapid development phases, culminating in a more refined and feature-complete Flink SQL by version 1.12. However, as of the latest release, Apache Flink 1.19, Flink SQL continues to evolve, with frequent updates and adjustments to its API usage.

Core Concepts of Flink SQL

Dynamic Tables and Continuous Queries

Apache Flink introduces the concept of dynamic tables for stream processing. Unlike static tables used in batch processing, Dynamic Tables evolve, reflecting continuous data streams. This dynamic nature allows Flink SQL to perform Continuous Queries—queries that don't simply execute once but continuously update as new data arrives. Each data event triggers a "snapshot" of the current table, leading to batch-like processing of these snapshots, seamlessly stringing together the flow of data like frames in a movie.

Converting Streams into Tables

In Apache Flink, a stream can be conceptualized as a continuously updating table where each data event corresponds to an insertion. This perspective allows the application of relational operations such as inserts, updates, and deletes, albeit structured in a way native to streaming data flows.

Querying with Flink SQL

Flink SQL facilitates the creation of complex data processing pipelines through simple SQL queries. This ease of use belies the sophisticated operations occurring underneath—transforming streams to dynamic tables, applying continuous queries, and emitting results as either streams or tables. For instance, an Update Query in Apache Flink might continuously adjust the counts of URLs accessed by users, reflecting each new piece of data as either an insertion or an update.

Time Attributes and Window Operations

A unique feature in Flink SQL is its handling of time attributes, crucial for defining windows in stream processing. Apache Flink allows two types of time attributes:

  • Event Time: Marks the time at which events actually occur.
  • Processing Time: Indicates the time at which events are processed by the system.

Users can specify event time by including timestamps in their tables and defining watermarks, which help manage event time skew and ensure completeness of data.

The Future of Flink SQL

As Flink SQL continues to evolve, it promises to simplify more complex use cases and bring real-time data processing capabilities to a broader audience.
Some key updates and future plans for Flink SQL include:

Apache Flink 1.18 and 1.19 Updates

  • Support for TRUNCATE TABLE, CREATE OR REPLACE TABLE AS SELECT, ALTER TABLE ADD/DROP PARTITION, and time-traveling queries in Apache Flink 1.183
  • Custom parallelism for Table/SQL sources, configurable state TTLs, and mini-batch optimization for joins in Apache Flink 1.19

Future Plans

  • Improving compatibility with Apache Hive, with ~94% of Hive SQL statements now running on Apache Flink.
  • Introducing pluggable SQL dialects to support other SQL flavors like Spark SQL and PostgreSQL.
  • Unifying streaming and batch data processing with the concept of "Streaming Lakehouses" via the Apache Paimon project.
  • Removing long-deprecated APIs like DataSet, Scala APIs, legacy SinkV1, and TableSource/TableSink in Apache Flink 2.0.

By bridging the gap between traditional database management systems and modern stream processing technologies, Flink SQL is enabling businesses to harness the true potential of their data in real-time.

Conclusion

Flink SQL is more than just a query language; it's a gateway to building scalable, flexible, and efficient data-driven applications. Whether you're managing high-throughput data streams or performing complex transformations on batch data, Flink SQL stands out as a formidable tool in the data engineer's toolkit.

SQL API stream flink Big Data Data Analytics Stream Processing Apache flink Table API Flink SQL Datastream Batch Data
0 1 0
Share on

Read previous post:

Flink CDC 3.0｜ A Next-generation Real-time Data Integration Framework

Read next post:

Data Lake for Stream Computing: The Evolution of Apache Paimon

Apache Flink Community

150 posts | 43 followers

You may also like

Comments

Apache Flink Community

150 posts | 43 followers

Related Products

More Posts by Apache Flink Community

See All