스트림 분석을 위한 KSQL 소개

Welcome to KSQL! It's an SQL-like language that lets you process and query data directly from Apache Kafka topics in real-time. Think of it as SQL for your data streams.

KSQL simplifies building stream processing applications, making it accessible even if you're not an expert in programming languages like Java or Scala.

KSQL brings several advantages to your Kafka ecosystem:

• Real-time Analytics: Query live data streams as they arrive.
• Simplified ETL: Easily transform, filter, and aggregate data to create new topics.
• Rapid Development: Write complex stream processing logic with simple SQL queries.
• Operational Insights: Monitor your Kafka data flows with continuous queries.

At the heart of KSQL is KSQLDB, a distributed streaming database built on Kafka. It acts as the engine that executes your KSQL queries.

When you run a KSQL query, KSQLDB translates it into a Kafka Streams application, which then runs continuously on your Kafka cluster to process data.

You can interact with KSQLDB using its command-line interface (CLI). This allows you to define streams, tables, and run queries directly.

First, you'll start the KSQLDB server, then connect to it using the CLI. Here's how you might connect:

In KSQL, a stream represents an unbounded, ordered sequence of events. It's like a direct view into a Kafka topic, where each message is an event.

You define a stream by mapping its fields to the data in a Kafka topic. Here's an example of creating a stream for website page views:

A table in KSQL represents a changelog stream, where each record is an update to a row in the table. It's a continuously updated, materialized view of your data.

Tables are useful for stateful operations, like counting events or storing the latest value for a key. Imagine a table of user profiles, always showing the most current data.

One of KSQL's most powerful features is the ability to run continuous queries on streams. Unlike traditional SQL which runs once and exits, KSQL queries keep running and output results as new data arrives.

You can use familiar SELECT statements. Add EMIT CHANGES to see new results and LIMIT for interactive exploration:

KSQL allows you to easily transform your stream data using standard SQL clauses like WHERE for filtering and selecting specific columns.

This is great for creating new, refined data streams or for simple real-time analytics. For example, let's filter page views to only show those related to the 'home' page:

Beyond interactive queries, KSQL allows you to define persistent queries. These are queries that run continuously in the background and write their results back to new Kafka topics.

You create persistent queries using CREATE STREAM AS SELECT or CREATE TABLE AS SELECT. They are perfect for building real-time ETL (Extract, Transform, Load) pipelines.

In this lesson, you've been introduced to KSQL, an incredibly powerful SQL-like interface for real-time stream processing on Apache Kafka.

• You learned about KSQLDB, the engine powering KSQL.
• You understood the difference between KSQL streams and tables.
• You saw how to write basic continuous queries and create persistent queries for ETL.

KSQL opens up a world of real-time analytics and data transformation directly on your Kafka topics!