With the relentless pace of data production today, it's critical to extract value instantly. Unlike traditional methods where data must be stored in databases before analysis, Streaming SQL allows for instant analysis as data streams, eliminating delays and the risk of data becoming outdated. Streaming SQL enables immediate data-driven decisions by processing data flows in real-time.
In this blog post, we'll dive deep into what Streaming SQL is, explore its significant benefits, and showcase various use cases that demonstrate its transformative impact across industries. Additionally, we'll answer some frequently asked questions to help you better understand how Streaming SQL can revolutionize your data processing strategies.
What is Streaming SQL?
To grasp the essence of Streaming SQL, it's crucial to dissect its components - streaming and SQL - before fusing them into a comprehensive explanation.
Understanding streaming
Streaming, also known as event streams, refers to continuously generated data flows utilized by applications capable of handling this data. Platforms like Apache Flink, Hadoop, Pulsar, and Kafka were developed to manage these data streams, allowing users to create, collect, store, and process data in motion with event streams. The act of processing this data while it's in motion is often called stream processing. Event streams and stream processing are useful for various types of interactions within applications, servers, IoT, and traditional databases.
Understanding SQL
SQL, or Structured Query Language, serves as the backbone for querying Relational Database Management Systems (RDBMS). It traditionally operates on static data residing in database tables. However, the exponential growth of data and the demand for real-time analysis necessitate an evolution of SQL to accommodate data in motion.
Understanding Streaming SQL
Enter Streaming SQL, an adaptation of SQL that supports stream processing, enabling real-time data querying. Unlike its traditional counterpart, Streaming SQL incorporates language constructs and query semantics tailored to real-time and stationary data, leveraging materialized views, windows, and event tables to facilitate up-to-the-minute data analysis.
Windows and event tables are crucial components of streaming SQL. They trigger actions whenever data changes. Aggregate queries are recalculated when a window is updated, resulting in outputs like sums over micro-batches.
Streaming systems enable organizations to input large volumes of various types of data—such as reference, context, or historical data—into event tables from files, databases, and other sources. These systems allow users to write SQL-like queries for streaming data, eliminating the need to write code.
Beyond Standard SQL
Streaming SQL extends the capabilities of standard SQL, introducing advanced features such as window functions and watermarks. These additions are essential for real-time data processing.
Window Functions
Window functions allow you to perform operations on each segment of data that falls into a particular window or time frame. This is crucial for analyzing data streams that are continuously generated.
There are different types of window functions, but let's focus on two common ones: tumbling windows and hopping windows.
Tumbling window
A tumbling window is a fixed-size, non-overlapping, and contiguous time interval. It is used to group events that arrive within a specified time period.
For example, if you want to calculate the amount changes every minute, you can use a tumbling window of one minute.
CREATE MATERIALIZED VIEW product_quantity_changes_per_minute AS
SELECT
window_start,
window_end,
product_id,
SUM(CASE WHEN event_type = 'add' THEN quantity ELSE -quantity END) as quantity_changes
FROM TUMBLE(events_stream, event_time, INTERVAL '1' MINUTE)
GROUP BY window_start, window_end, product_id;
In this example, the query will calculate the quantity_changes for each one-minute tumbling window.
Hopping window
A hopping window is a fixed-size time interval that hops by a specified hop size. The windows can overlap if the window size is larger than the hop size.
For example, if you want to calculate the quantity changes every minute, but update the result every 30 seconds, you can use a hopping window of one minute with a hop size of 30 seconds.
CREATE MATERIALIZED VIEW product_quantity_changes_per_minute AS
SELECT
window_start,
window_end,
product_id,
SUM(CASE WHEN event_type = 'add' THEN quantity ELSE -quantity END) as quantity_changes
FROM HOP(events_stream, event_time, INTERVAL '30' SECOND, INTERVAL '1' MINUTE)
GROUP BY window_start, window_end, product_id;
In this example, the query will calculate the quantity_changes for each one-minute window, but the result will be updated every 30 seconds.
Watermarks
Watermarks are a crucial feature in streaming SQL that helps handle late events or out-of-order events in data streams.
A watermark is a metadata associated with events in a data stream that represents the point in time up to which the events are complete. It is used to specify how late an event can arrive relative to the current processing time.
For example, if you want to calculate the quantity changes every minute, but allow events to arrive up to 30 seconds late, you can specify a watermark of 30 seconds.
CREATE TABLE event_stream (
event_id int,
cart_id int,
product_id int,
event_type varchar,
quantity int,
event_time timestamptz,
watermark for event_time as event_time - INTERVAL '30' SECOND
) APPEND ONLY;
CREATE MATERIALIZED VIEW product_quantity_changes_per_minute AS
SELECT
window_start,
window_end,
product_id,
SUM(CASE WHEN event_type = 'add' THEN quantity ELSE -quantity END) as quantity_changes
FROM HOP(events_stream, event_time, INTERVAL '30' SECOND, INTERVAL '1' MINUTE)
GROUP BY window_start, window_end, product_id;
In this example, the query will calculate the quantity_change for each one-minute tumbling window, but allow events to arrive up to 30 seconds late.
Leading open-source streaming processors
Streaming SQL has been adopted by leading open-source streaming processors like Kafka Streams, RisingWave, Storm, Flink, Structured Streaming, and Samza.
Kafka Streams
Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.
RisingWave
RisingWave is a distributed SQL streaming database that enables simple, efficient, and reliable processing of streaming data. RisingWave SQL is RisingWave’s dialect of SQL for data streams. As RisingWave is PostgreSQL-compatible, many of the statements are similar to those in PostgreSQL.
Apache Flink
Flink is a unified stream and batch data processing framework. Flink SQL is an ANSI standard compliant SQL engine that is built on Flink. It provides users with a declarative way to express data transformations and analytics on streams of data.
Structured Streaming
Structured Streaming, a high-level API for stream processing, became production-ready with Spark 2.2. It enables the same operations performed in batch mode using Spark's structured APIs to run in a streaming manner. This can reduce latency and facilitate incremental processing.
The key benefit of Structured Streaming is its ability to extract value from streaming systems rapidly with minimal code changes. It is also easy to understand; you can prototype your batch job then convert it into a streaming job. This process works by incrementally processing the data.
FAQs
What is streaming database?
A streaming database is specifically designed to process large volumes of real-time streaming data. In contrast to traditional databases, which store data in batches before processing, a streaming database processes data immediately upon generation, enabling real-time insights and analysis. Furthermore, unlike traditional stream processing engines that do not persist data, a streaming database can store data and cater to user data access requests. Streaming databases are ideal for latency-sensitive applications like real-time analytics, fraud detection, network monitoring, and the Internet of Things (IoT). They also help simplify the technology stack.
What is streaming ETL?
Streaming ETL (Extract, Transform, Load) is the processing and movement of real-time data from one place to another. ETL is short for the database functions extract, transform, and load.
How Does Streaming SQL Work?
Streaming SQL allows SQL-like queries for real-time data stream analysis. It simplifies data stream operations for those familiar with SQL.
Here's the process:
- Data Ingestion: Data from sources like sensors or APIs is ingested into a streaming platform.
- Data Processing: Streaming SQL queries process and analyze the data in real-time, performing actions like filtering or aggregating the data.
- Data Output: The processed data is sent to sinks like databases or dashboards for further analysis or visualization.
Streaming SQL extends the capabilities of standard SQL with advanced features like window functions and watermarks that are essential for real-time data processing and analysis. It performs incremental computations on data streams, rather than full computations on static data, making it a powerful and flexible tool for real-time data processing and analysis. Whether you are monitoring the stock market, analyzing social media trends, or optimizing supply chains, Streaming SQL can help you process and analyze data streams in real-time, providing timely insights and supporting data-driven decision-making.
