Streaming databases have emerged as a way to handle the ingestion and processing of high-velocity data streams in real-time. By avoiding storing data at rest, streaming systems can analyze and derive insights from large volumes of incoming data with low latency. In this article, we'll first provide an overview of streaming architectures and their benefits. We'll then walk through an example implementation using PostgreSQL and Spring Boot.

Overview of Streaming Databases

A streaming database ingests and processes a continuous stream of data in real-time. Data is not stored, but rather processed on the fly as it arrives. This allows for faster analysis and insights from large volumes of incoming data.

Streaming databases leverage an architecture optimized for throughput over latency. They can handle very high volumes of writes per second. Data flows through the system rapidly, unlike traditional databases where data at rest.

Examples of streaming data include server logs, IoT sensor data, clickstreams, financial transactions, and geospatial data. Streaming databases are well-suited for appending-only data that doesn't need updates or deletions.

Benefits of Streaming Databases

• Real-time analytics - Streaming databases allow you to run analytics on data as soon as it arrives, with millisecond latency. This enables real-time dashboards, alerts, and pattern detection.
• High throughput - They easily handle hundreds of thousands to millions of writes per second. This makes them suitable for data-intensive apps.
• Efficient use of resources - Data is processed in-memory as it streams through the system. This requires fewer computing resources than recurrently querying and processing batch data.
• Flexible scaling - Streaming systems can scale out horizontally by adding more processing nodes. This lets you handle unpredictable workloads.

Example Use Cases

• Web/mobile analytics - Analyze clickstreams, transaction logs, and usage metrics in real-time to surface insights.
• Fraud detection - Detect credit card fraud by analyzing transactions as they occur vs. running daily batch jobs.
• Sensor data processing - Ingest telemetry from IoT devices and identify anomalies as the data streams in.
• Stock trading - Stream prices, trades, and ticker data to conduct real-time analysis for algorithmic trading.
• Customer engagement - Track user activity on a website and trigger customized interactions based on behavior.

Implementing Streaming Queries in PostgreSQL

For databases like PostgreSQL that don't natively support streaming, we can simulate streaming behavior through careful query design.

Let's walk through an example Spring Boot app that queries a large PostgreSQL table using a streaming approach.

First, add the pgjdbc-ng driver to enable streaming result sets:

<dependency>
  <groupId>io.pgjdbc</groupId>
  <artifactId>pgjdbc-ng</artifactId>
</dependency>

In the repository, use @QueryHint to configure streaming:

@QueryHints(...) 
@Query("SELECT * FROM large_table")
Stream<T> streamRows();

In the service layer, process the stream:

repository.streamRows()
  .forEach(row -> {  
    // analyze row
  });

We can also configure a fetch size to control how many rows are retrieved at a time:

@QueryHints({
  @QueryHint(name = "streamResults", value="true"),
  @QueryHint(name = "FetchSize", value="100")
})

This approach allows us to efficiently process result sets of any size and build real-time analytics into our Spring Boot application.

Key Takeaways

• Streaming databases allow low-latency analysis of high-velocity data
• PostgreSQL can simulate streaming behavior through careful query design
• Spring Data repositories can return result sets as a stream
• Configuring fetch size, parallelism, and partitioning allows scaling
• Streaming databases complement traditional stores for real-time use cases

By combining streaming queries with a technology like Spring Boot, we can bring real-time analytics to our data applications.