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Apache Kafka & Stream Processing Fundamentals · 课时

Kafka 流分析中的 KSQL 简介

探索 KSQL：用于在 Kafka 上进行实时流处理的类 SQL 接口，可快速转换和查询数据

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Kafka 流分析中的 KSQL 简介 是 CoddyKit 上的免费 Apache Kafka & Stream Processing Fundamentals 课时。 这是第 3 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Apache Kafka & Stream Processing Fundamentals 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Apache Kafka & Stream Processing Fundamentals 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Meet KSQL: Stream Processing with SQL

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.

Why KSQL? The Benefits

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.

KSQLDB: The Stream Processing Engine

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.

Interacting with KSQLDB CLI

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:

ksql http://localhost:8088

Streams: Continuous Data Flows

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:

CREATE STREAM pageviews (
  viewtime BIGINT,
  userid VARCHAR,
  pageid VARCHAR
)
WITH (
  kafka_topic='pageviews_topic',
  value_format='JSON'
);

Tables: Materialized Views of Streams

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.

CREATE TABLE users_by_region AS
SELECT
  regionid,
  COUNT(userid) AS user_count
FROM users_stream
GROUP BY regionid
EMIT CHANGES;

Querying Streams Continuously

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:

SELECT userid, pageid
FROM pageviews
EMIT CHANGES
LIMIT 5;

Basic Data Transformations

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:

SELECT userid, pageid
FROM pageviews
WHERE pageid LIKE '%home%'
EMIT CHANGES;

Persistent Queries for ETL

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.

CREATE STREAM high_value_pageviews AS
SELECT userid, pageid
FROM pageviews
WHERE userid IN ('user_premium', 'user_gold');

KSQL Concepts Check

Test your understanding of KSQL fundamentals.

在交互式课程中回答

KSQL: SQL for Your Data Streams

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!

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常见问题解答

「Kafka 流分析中的 KSQL 简介」课时是免费的吗？

是的 — 「Kafka 流分析中的 KSQL 简介」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Apache Kafka & Stream Processing Fundamentals 课程的其余内容，请升级到 CoddyKit PRO。 Apache Kafka & Stream Processing Fundamentals 课程共包含 4 节课。

「Kafka 流分析中的 KSQL 简介」这节课中我会学到什么？

探索 KSQL：用于在 Kafka 上进行实时流处理的类 SQL 接口，可快速转换和查询数据 你通过在浏览器中直接运行的动手代码来练习 Apache Kafka & Stream Processing Fundamentals，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Apache Kafka & Stream Processing Fundamentals 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Apache Kafka & Stream Processing Fundamentals 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 3 节课，共 4 节。

「Kafka 流分析中的 KSQL 简介」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Apache Kafka & Stream Processing Fundamentals 课中编写并运行代码吗？

能。每节 Apache Kafka & Stream Processing Fundamentals 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. Kafka Streams 中的窗口操作
  2. 流连接与聚合
  3. Kafka 流分析中的 KSQL 简介
  4. 交互式查询与状态存储
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