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https://github.com/confluentinc/ksql

The database purpose-built for stream processing applications.
https://github.com/confluentinc/ksql

event-streaming-database interactive kafka kafka-connect ksqldb ksqldb-documentation ksqldb-tutorials materialized-views real-time sql stream-processing streaming-queries

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The database purpose-built for stream processing applications.

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README 

        
# ![KSQL rocket](ksql-rocket.png) ksqlDB



### The database purpose-built for stream processing applications



# Overview



ksqlDB is a database for building stream processing applications on top of Apache Kafka. It is **distributed**, **scalable**, **reliable**, and **real-time**. ksqlDB combines the power of real-time stream processing with the approachable feel of a relational database through a familiar, lightweight SQL syntax. ksqlDB offers these core primitives:



* **[Streams](https://docs.ksqldb.io/en/latest/concepts/collections/streams/) and [tables](https://docs.ksqldb.io/en/latest/concepts/collections/tables/)** - Create relations with schemas over your Apache Kafka topic data

* **[Materialized views](https://docs.ksqldb.io/en/latest/concepts/materialized-views/)** - Define real-time, incrementally updated materialized views over streams using SQL

* **[Push queries](https://docs.ksqldb.io/en/latest/concepts/queries/push/)** - Continuous queries that push incremental results to clients in real time

* **[Pull queries](https://docs.ksqldb.io/en/latest/concepts/queries/pull/)** - Query materialized views on demand, much like with a traditional database

* **[Connect](https://docs.ksqldb.io/en/latest/concepts/connectors)** - Integrate with any [Kafka Connect](https://docs.confluent.io/current/connect/index.html) data source or sink, entirely from within ksqlDB



Composing these powerful primitives enables you to build a complete streaming app with just SQL statements, minimizing complexity and operational overhead. ksqlDB supports a wide range of operations including aggregations, joins, windowing, sessionization, and much more. You can find more ksqlDB tutorials and resources [here](https://developer.confluent.io/tutorials/use-cases.html).



# Getting Started



* Follow the [ksqlDB quickstart](https://ksqldb.io/quickstart.html) to get started in just a few minutes.

* Read through the [ksqlDB documentation](https://docs.ksqldb.io).

* Take a look at some [ksqlDB use case recipes](https://developer.confluent.io/tutorials/use-cases.html) for examples of common patterns.



# Documentation



See the [ksqlDB documentation](https://docs.ksqldb.io/) for the latest stable release.



# Use Cases and Examples



## Materialized views



ksqlDB allows you to define materialized views over your streams and tables. Materialized views are defined by what is known as a "persistent query". These queries are known as persistent because they maintain their incrementally updated results using a table.



```sql

CREATE TABLE hourly_metrics AS

  SELECT url, COUNT(*)

  FROM page_views

  WINDOW TUMBLING (SIZE 1 HOUR)

  GROUP BY url EMIT CHANGES;



```



Results may be **"pulled"** from materialized views on demand via `SELECT` queries. The following query will return a single row:



```sql

SELECT * FROM hourly_metrics

  WHERE url = 'http://myurl.com' AND WINDOWSTART = '2019-11-20T19:00';

```



Results may also be continuously **"pushed"** to clients via streaming `SELECT` queries. The following streaming query will push to the client all incremental changes made to the materialized view:



```sql

SELECT * FROM hourly_metrics EMIT CHANGES;

```



Streaming queries will run perpetually until they are explicitly terminated.



## Streaming ETL



Apache Kafka is a popular choice for powering data pipelines. ksqlDB makes it simple to transform data within the pipeline, readying messages to cleanly land in another system.



```sql

CREATE STREAM vip_actions AS

  SELECT userid, page, action

  FROM clickstream c

  LEFT JOIN users u ON c.userid = u.user_id

  WHERE u.level = 'Platinum' EMIT CHANGES;

```



## Anomaly Detection



ksqlDB is a good fit for identifying patterns or anomalies on real-time data. By processing the stream as data arrives you can identify and properly surface out of the ordinary events with millisecond latency.



```sql

CREATE TABLE possible_fraud AS

  SELECT card_number, count(*)

  FROM authorization_attempts

  WINDOW TUMBLING (SIZE 5 SECONDS)

  GROUP BY card_number

  HAVING count(*) > 3 EMIT CHANGES;

```



## Monitoring



Kafka's ability to provide scalable ordered records with stream processing make it a common solution for log data monitoring and alerting. ksqlDB lends a familiar syntax for tracking, understanding, and managing alerts.



```sql

CREATE TABLE error_counts AS

  SELECT error_code, count(*)

  FROM monitoring_stream

  WINDOW TUMBLING (SIZE 1 MINUTE)

  WHERE  type = 'ERROR'

  GROUP BY error_code EMIT CHANGES;

```



## Integration with External Data Sources and Sinks



ksqlDB includes native integration with [Kafka Connect](https://docs.ksqldb.io/en/latest/concepts/connectors) data sources and sinks, effectively providing a unified SQL interface over a [broad variety of external systems](https://www.confluent.io/hub).



The following query is a simple persistent streaming query that will produce all of its output into a topic named `clicks_transformed`:



```sql

CREATE STREAM clicks_transformed AS

  SELECT userid, page, action

  FROM clickstream c

  LEFT JOIN users u ON c.userid = u.user_id EMIT CHANGES;

```



Rather than simply send all continuous query output into a Kafka topic, it is often very useful to route the output into another datastore. ksqlDB's Kafka Connect integration makes this pattern very easy.



The following statement will create a Kafka Connect sink connector that continuously sends all output from the above streaming ETL query directly into Elasticsearch:



```sql

 CREATE SINK CONNECTOR es_sink WITH (

  'connector.class' = 'io.confluent.connect.elasticsearch.ElasticsearchSinkConnector',

  'key.converter'   = 'org.apache.kafka.connect.storage.StringConverter',

  'topics'          = 'clicks_transformed',

  'key.ignore'      = 'true',

  'schema.ignore'   = 'true',

  'type.name'       = '',

  'connection.url'  = 'http://elasticsearch:9200');

```





# Join the Community



For user help, questions or queries about ksqlDB please use our [user Google Group](https://groups.google.com/forum/#!forum/ksql-users)

or our public Slack channel #ksqldb in [Confluent Community Slack](https://slackpass.io/confluentcommunity). Everyone is welcome!



You can get help, learn how to contribute to ksqlDB, and find the latest news by [connecting with the Confluent community](https://www.confluent.io/contact-us-thank-you/).



For more general questions about the Confluent Platform please post in the [Confluent Google group](https://groups.google.com/forum/#!forum/confluent-platform).



# Contributing and building from source



Contributions to the code, examples, documentation, etc. are very much appreciated.



- Report issues and bugs directly in [this GitHub project](https://github.com/confluentinc/ksql/issues).

- Learn how to work with the ksqlDB source code, including building and testing ksqlDB as well as contributing code changes

  to ksqlDB by reading our [Development and Contribution guidelines](CONTRIBUTING.md).

- One good way to get started is by tackling a [newbie issue](https://github.com/confluentinc/ksql/labels/good%20first%20issue).



# License



The project is licensed under the [Confluent Community License](LICENSE).



*Apache, Apache Kafka, Kafka, and associated open source project names are trademarks of the [Apache Software Foundation](https://www.apache.org/).*