flink-cdc-connectors | CDC Connectors for Apache Flink® | SQL Database library

by ververica Java Version: release-2.3.0 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | flink-cdc-connectors Summary

flink-cdc-connectors is a Java library typically used in Database, SQL Database, Kafka applications. flink-cdc-connectors has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

Flink CDC Connectors is a set of source connectors for Apache Flink, ingesting changes from different databases using change data capture (CDC). The Flink CDC Connectors integrates Debezium as the engine to capture data changes. So it can fully leverage the ability of Debezium. See more about what is Debezium. This README is meant as a brief walkthrough on the core features with Flink CDC Connectors. For a fully detailed documentation, please see Documentation.

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Support

flink-cdc-connectors has a medium active ecosystem.

It has 4051 star(s) with 1397 fork(s). There are 106 watchers for this library.

It had no major release in the last 12 months.

There are 641 open issues and 612 have been closed. On average issues are closed in 133 days. There are 104 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of flink-cdc-connectors is release-2.3.0

Quality

flink-cdc-connectors has 0 bugs and 0 code smells.

Security

flink-cdc-connectors has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

flink-cdc-connectors code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

flink-cdc-connectors is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

flink-cdc-connectors releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 15943 lines of code, 922 functions and 123 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed flink-cdc-connectors and discovered the below as its top functions. This is intended to give you an instant insight into flink-cdc-connectors implemented functionality, and help decide if they suit your requirements.

Starts the loop
Updates the offset context
Attempts to abandon old transactions
Checks if a log switch has been detected
Commits a transaction
Returns true if the provided DML event should be merged with the given transaction
Checks whether the provided SEL_LOB_LOCATOR event should be merged
Deserialization provider
Checks if the literal string contains regular meta characters
Create a dynamic table source
Deserialize a record
Serialize a row
Deserialize record
Serialize a single record
Runs the offset storage
Creates an OracleSourceConfig for the given subtask
Runs low watermark
Create dynamic table source
Converts a MongoDB value to a string
Submit a snapshot split
Returns the configured options
Create a dynamic table source
Handle a query event
Configures the offset storage
Polls records from the queue
Serialize the given split into bytes

Get all kandi verified functions for this library.

flink-cdc-connectors Key Features

No Key Features are available at this moment for flink-cdc-connectors.

flink-cdc-connectors Examples and Code Snippets

No Code Snippets are available at this moment for flink-cdc-connectors.

Community Discussions

Trending Discussions on flink-cdc-connectors

Difference between Flink mysql and mysql-cdc connector?

Flink: Event-Time Aggregations with CSV file

QUESTION

Difference between Flink mysql and mysql-cdc connector?

Asked 2022-Feb-08 at 08:52

In order to enrich the data stream, we are planning to connect the MySQL (MemSQL) server to our existing flink streaming application

As we can see that Flink provides a Table API with JDBC connector https://nightlies.apache.org/flink/flink-docs-master/docs/connectors/table/jdbc/

Additionally, I discovered another MySQL connector called Flink-CDC https://ververica.github.io/flink-cdc-connectors/master/content/about.html allowing to work with external database in a stream fashion

what is the difference between them? what is better to choose in my case?

...

ANSWER

Answered 2022-Feb-08 at 08:52

Change Data Capture (CDC) connectors capture all changes that are happening in one or more tables. The schema usually has a before and an after record. The Flink CDC connectors can be used directly in Flink in an unbounded mode (streaming), without the need for something like Kafka in the middle.

The normal JDBC connector can used in bounded mode and as a lookup table.

If you're looking to enrich you existing stream, you most likely want to use the lookup functionality. That allows you to query a table for a specific key (coming from your stream) and enrich the stream with data from your table. Keep in mind that from a performance perspective you're best off to use a temporal table join. See the example in https://nightlies.apache.org/flink/flink-docs-master/docs/connectors/table/jdbc/#how-to-create-a-jdbc-table

Source https://stackoverflow.com/questions/71025117

QUESTION

Flink: Event-Time Aggregations with CSV file

Asked 2020-Dec-30 at 21:32

I use Flink 1.11.3 with the SQL API and Blink planner. I work in streaming mode and consume a CSV file with the filesystem connector and CSV format. For a time column I generate watermarks and want to do window aggregations based on this time. Like fast-forward the past based on event-time.

...

ANSWER

Answered 2020-Dec-30 at 21:32

Yes, when running in streaming mode you run the risk of having late events, which will be dropped by the SQL API when doing event time windowing.

Since the input is a file, why not run the job in batch mode, and avoid this problem altogether? Otherwise your options include sorting the input (by time), or making sure that the watermarking is configured so as to avoid late events.

As for the ordering of events produced by the CDC connector, I don't know.

Source https://stackoverflow.com/questions/65504870

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install flink-cdc-connectors

You can download it from GitHub.
You can use flink-cdc-connectors like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the flink-cdc-connectors component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .