PostgreSQL CDC: 5 Methods to Keep Track of Real-Time Data Changes in Your Database

Keeping track of data changes is crucial in a world where data is rapidly getting updated. PostgreSQL's Change Data Capture (CDC) feature, a tool that captures and replicates data alterations in real-time within the database, simplifies and enhances this process. CDC significantly improves data accuracy and maintains consistency across various systems, thereby safeguarding data integrity.

However, implementing CDC does not come without its challenges. This article addresses these issues, explores alternative methods, and guides you in making informed decisions for effective and modern data management!

What is PostgreSQL CDC?

PostgreSQL CDC is a method used in databases to track and record changes made to data. It captures modifications like inserts, updates, and deletes, and stores them for analysis or replication. CDC helps maintain data consistency across different systems by keeping track of alterations in real time.

Traditional methods of data synchronization

Traditional methods of data synchronization often fall short in a real-time environment. These methods typically involve batch-based data transfer, where data is extracted from the source and sent to the destination at scheduled intervals. This approach can be inefficient and prone to errors.

In a real-time environment, data needs to be synchronized instantly to ensure accuracy and timeliness. Traditional methods may not be able to keep up with the speed of changes, leading to outdated information and hindered decision-making processes. Moreover, these methods often come with issues such as data consistency across diverse systems and devices, handling concurrent updates to the same data, and dealing with high network latency.

Deep dive into CDC

CDC is a modern alternative to traditional data synchronization methods. It captures change events in real-time, keeping downstream systems, such as data warehouses, always in sync with PostgreSQL and enabling fully event-driven data architectures. Using CDC reduces the load on PostgreSQL since only relevant information, i.e., changes, are processed.

CDC enables the efficient implementation of use cases requiring access to change events of PostgreSQL, such as audit or changelogs, without modifying the application code. It facilitates the creation of architectures that support efficient data propagation, ensuring that updates, inserts, and deletes are accurately mirrored across different components or databases in real-time or near real-time. By incorporating CDC into their system design, developers can enhance data consistency, improve performance, and enable advanced functionalities like real-time analytics and reporting.

5 Methods for setting up PostgreSQL CDC

PostgreSQL CDC is a method used in databases to track and record changes made to data. It captures modifications like inserts, updates, and deletes, and stores them for analysis or replication. CDC helps maintain data consistency across different systems by keeping track of alterations in real time.

Logical replication

Logical replication involves mirroring database changes between two Postgres instances. It’s a write-ahead log on disk, which holds all events that change the data of the PostgreSQL database. Ensure that the wal_level=logical parameter is set in your PostgreSQL database. You can set this parameter in the postgresql.conf configuration file.

• Pros: Enables event-driven capturing of real-time data changes, detecting all change event types in PostgreSQL: INSERTs, UPDATEs, and DELETEs.
  
• Cons: Consuming events via logical replication directly accesses the file system, potentially impacting PostgreSQL database performance.

Triggers

Triggers in Postgres CDC work well for tracking changes in a table, recording them in a different table, and creating a log of every change. You can make audit triggers on your Postgres database that will track all events related to actions like INSERT, UPDATE, and DELETE.

• Pros: Triggers offer more control over capturing logic and can handle complex data transformations.
• Cons: They can impact source database performance, especially with high-frequency changes.
  Managing and maintaining triggers can also be challenging.

Query-based CDC

Query-based CDC involves periodically querying tables and comparing historical data to identify changes. You must actively query your Postgres database to identify any changes instead of relying on pre-configured triggers.

• Pros: Query-based CDC can detect and capture all types of changes (INSERT, UPDATE, and DELETE) to records.
• Cons: It can be resource-intensive and might not be the fastest option. It also requires a table schema change to include a timestamp column if it is not present in the original table.

Write-ahead logging (WAL)

PostgreSQL’s Write-Ahead Log (WAL) functionality records all database changes. Before modifying the data files, PostgreSQL writes the changes to a circular transaction log file called the “WAL” file.

• Pros: WAL increases PostgreSQL’s efficiency because it can delay random-access writes to disk, and just do sequential writes to the log for a long time.
• Cons: To limit WAL, Postgres requires forgoing WAL archival. The standby can fall too far behind meaning a new base backup would be needed.

Table differencing

Table differencing is a technique for identifying changes between snapshots of tables. It involves having a separate table that tracks the main table and has triggers on the second table. Syncing between the master and the secondary table can be done using the Postgres logical replication feature.

• Pros: Table differencing can provide a detailed record of changes over time.
• Cons: It can be resource-intensive and may not be suitable for tables with frequent updates.

How to choose the right PostgreSQL CDC method for your needs

To choose the right PostgreSQL CDC method, you need to first assess your organizational needs and their complexities, and then decide based on the functionalities provided by each CDC option. The table below assists you in comparing the key features, complexity, and ideal use cases for each PostgreSQL CDC method, to make a better decision:

Intelligent change data capture in PostgreSQL with CData Sync

CData Sync leverages the existing CDC configuration in PostgreSQL, implementing logical replication to efficiently replicate data. Instead of querying the source table, it identifies all change events by reading log files of the PostgreSQL database. The application then extracts these changes in near real-time for replication, storing the current log position for the next replication cycle.

In addition to PostgreSQL, CData Sync also supports unlimited data movement to any database or data warehouse you use, whether it’s on-premises to on-premises, cloud to cloud, on-premises to cloud, or cloud to on-premises.

If you’re looking to enhance operational reporting, connect data to analytics for BI and decision support, or maintain data archives for disaster recovery, CData Sync is the tool for you. Don’t wait—sign up today for a 30-day free trial of CData Sync and experience the ease of high-volume data replication and CDC.

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