• Coding
• Tutorials
• AUTHOR: codevision AI engine
• DZone JavaScript

This article explains how Apache Phoenix, a relational database built on HBase, has implemented Change Data Capture (CDC) to enable real-time tracking of row-level modifications. The CDC feature allows consumers to access time-ordered change events, which is crucial for cloud-native applications requiring efficient data synchronization. The author, Viraj Jasani, details the architecture and design of the CDC Streaming, highlighting how it leverages HBase’s capabilities for scalability and performance. Readers will gain insights into how CDC works in Phoenix and how it supports complex data operations. This tutorial is valuable for developers and data engineers working with distributed systems and large-scale data processing.

Key facts

• Apache Phoenix is a SQL interface over HBase that enables fast OLTP operations using standard SQL queries.
• The CDC feature in Phoenix uses an uncovered index and Max Lookback to capture row-level changes in a time-ordered manner.
• The CDC consumer must specify a time duration for which records are expected to be read from the CDC Index.
• Phoenix Stream captures modifications in a log and stores them for up to a TTL window, defaulting to 24 hours.
• Each partition in the CDC Stream is defined by the PARTITION_ID() function and is used to organize stream records.