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Big Data Technology & Architecture
Big Data Technology & Architecture
Oct 29, 2021 · Big Data

Dimension Table Join Strategies in Apache Flink: Preload, Distributed Cache, Hot Storage, Broadcast, and Temporal Table Function

The article explains various dimension‑table join approaches in Apache Flink, including preloading tables into memory, using distributed cache, leveraging hot storage with async I/O, broadcasting state, and temporal table function joins, and compares their trade‑offs for different data volumes and update frequencies.

Dimension TableFlinkStreaming
0 likes · 10 min read
Dimension Table Join Strategies in Apache Flink: Preload, Distributed Cache, Hot Storage, Broadcast, and Temporal Table Function
Big Data Technology & Architecture
Big Data Technology & Architecture
Sep 6, 2021 · Big Data

Comprehensive Guide to Flink Join Operations: Interval Join, Window Join, Broadcast, and Temporal Table Function

This article explains Flink's various join mechanisms—including interval‑based joins, window‑based joins, streaming SQL joins, and dimension‑table joins such as preload, hot‑storage, broadcast, and temporal‑table function—provides detailed code examples in Java, discusses state management and performance considerations, and summarizes the four main dimension‑table join patterns.

Broadcast StateFlinkJava
0 likes · 32 min read
Comprehensive Guide to Flink Join Operations: Interval Join, Window Join, Broadcast, and Temporal Table Function