Apache Flink Community China
FollowIn this article, we discuss several ways to improve the speed and stability of checkpointing with generic log-based incremental checkpoints.
We introduce Apache Flink's adaptive batch scheduler and detail how it can automatically decide parallelism of Flink batch jobs.
This article focuses on the processing logic of Flink CDC.
Part 5 of this 5-part series explains how to use Flink CDC and Doris Flink Connector to monitor data from MySQL databases and store data in the tables in real-time.
Part 4 of this 5-part series shares the details of the Flink CDC version 2.1 trial process, including troubleshooting experiences and internal execution principles.
Part 3 of this 5-part series shows how to use Flink CDC to build a real-time database and handle database and table shard merge synchronization.
Part 2 of this 5-part series explains how to realize Flink MongoDB CDC Connector through MongoDB Change Streams features based on Flink CDC.
Part 1 of this 5-part series explains how to use Flink CDC to simplify the entry of real-time data into the database.
This article is compiled from the topic "Exploration of Advanced Functions in Pravega Flink Connector Table API," shared by Zhou Yumin in Flink Forward Asia 2021.
This article mainly explains which dependencies need to be introduced and which need to be packaged into the job JAR during the job development.
Mowen discusses the future of Apache Flink regarding its core capabilities of stream computing and improving the processing standards of the entire industry.
This article is compiled from the presentation of JD search and recommendation algorithm engineers Zhang Ying and Liu Lu at Flink Forward Asia 2021.
This article introduces the research and development background and the design and use of Flink Remote Shuffle.
This tutorial explains how to quickly build streaming ETL for MySQL and Postgres with Flink CDC.
This article discusses scheduler performance improvements for large-scale jobs in Flink 1.13 and 1.14.
This article explains thoroughly how iQiyi (a Chinese online video platform) utilizes Apache Flink.
The article mainly introduces two applications of real-time big data based on Flink.
Part 2 of this 2-part series will give you insight into some core design considerations and implementation details of the sort-based blocking shuffle in Flink.
Part 1 of this 2-part series will introduce the sort-based blocking shuffle, present benchmark results, and provide guidelines on how to use this new feature.
This article introduces the real-time data warehouse architecture built by Kwai based on Flink and offers solutions to some difficult problems.
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