A Guide to Standard SQL vs. Streaming SQL: Why Do We Need Both?

Understanding the Differences Between Standard SQL and Streaming SQL

SQL has long been a foundational tool for querying databases. Traditional SQL queries are typically run against static, historical data, generating a snapshot of results at a single point in time. However, the rise of real-time data processing, driven by applications like IoT, financial transactions, security monitoring/intrusion, and social media, has led to the evolution of Streaming SQL. This variant extends traditional SQL capabilities, offering features specifically designed for real-time, continuous data streams.

Standard SQL and Streaming SQL Key Differences

1. Point-in-Time vs. Continuous Queries

In standard SQL, queries are typically run once and return results based on a snapshot of data. For instance, when you query a traditional database to get the sum of all sales, it reflects only the state of data up until the moment of the query.

In contrast, Streaming SQL works with data that continuously flows in, updating queries in real-time. The same query can be run in streaming SQL, but instead of receiving a one-time result, the query is maintained in a materialized view that updates as new data arrives. This is especially useful for use cases like dashboards or monitoring systems, where the data needs to stay current.

2. Real-Time Processing with Window Functions

Streaming SQL introduces window functions , allowing users to segment a data stream into windows for aggregation or analysis. For example, a tumbling window is a fixed-length window (such as one minute) that collects data for aggregation over that time frame. In contrast, a hopping window is a fixed-size time interval that will hop by a specified length. That means if you want to calculate the current inventory every two minutes but update the results every minute, the hopping window would then be two minutes, and the hop size would be a minute.

Windowing in traditional SQL is static and backward-looking, whereas in streaming SQL, real-time streams are processed continuously, updating aggregations within the described window.

3. Watermarks for Late Data Handling

In streaming environments, data can arrive late or out of order. To manage this, Streaming SQL introduces watermarks. Watermarks mark the point in time up to which the system expects to have received data. For instance, if an event is delayed by a minute, a watermark ensures it’s still processed if it arrives within that window, making streaming SQL robust for real-world, unpredictable data flows. Conventional SQL has no ability or need to address this scenario.

4. Continuous Materialization

One of the unique aspects of Streaming SQL is the ability to materialize views incrementally. Unlike traditional databases that recompute queries when data changes, streaming SQL continuously maintains these views as new data flows in. This approach dramatically improves performance for real-time analytics by avoiding expensive re-computations.

Use Cases for Streaming SQL

The rise of streaming SQL has been a game-changer across industries. Common applications include:

• Real-time analytics dashboards, such as stock trading platforms or retail systems where quick insights are needed to make rapid decisions.
• Event-driven applications where alerts and automations are triggered by real-time data, such as fraud detection or IoT sensor monitoring.
• Real-time customer personalization, where user actions or preferences update in real-time to deliver timely recommendations.

Conclusion

While Standard SQL excels in querying static, historical datasets, Streaming SQL is optimized for real-time data streams, offering powerful features like window functions, watermarks, and materialized views. These advancements handle fast-changing data with low latency, offering immediate insights and automation. This article at Datanami in July 2023 pegged 177% growth in streaming adoption in the previous 12 months. As more industries rely on real-time decision-making, streaming SQL is becoming a critical tool for modern data infrastructures.

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