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Change Data Capture (CDC)

Change Data Capture

Change Data Capture (CDC) is a design pattern that identifies and tracks changes in data so that action can be taken using this change data. It’s particularly crucial in data-driven architectures where it’s essential to promptly and reliably capture the modifications in the source data store and propagate them to downstream systems. CDC can be applied in various scenarios, including data replication, data warehousing, real-time analytics, and more.

Implementing Change Data Capture

CDC can be implemented using different approaches based on the source system and its capabilities:

  1. Log-Based CDC: This method utilizes the transaction logs generated by databases to capture changes. It offers high performance and minimal impact on the source system.

  2. Trigger-Based CDC: Here, triggers are set up on tables to detect changes, and when triggered, they capture the changes and push them to the target system. This approach is relatively easy to implement but might affect the source system’s performance.

  3. Replication-Based CDC: Some databases offer built-in replication features that can be used for CDC. This approach provides an efficient and integrated solution but may be limited to specific database technologies.

  4. API-Based CDC: For applications without built-in CDC support, custom APIs can be created to extract and propagate changes.

Example

Implementing Change Data Capture (CDC) in Python involves monitoring changes in a source database and capturing them for propagation to a target system. To demonstrate a simple example of CDC using Python, we will use a log-based approach to monitor an SQLite database and print the changes made to the database.

import sqlite3
import time

# Function to monitor and capture changes in the database
def capture_changes():
    connection = sqlite3.connect('your_database.db')
    cursor = connection.cursor()

    last_rowid = 0

    while True:
        # Get the maximum rowid from the source table
        cursor.execute("SELECT MAX(rowid) FROM your_source_table")
        max_rowid = cursor.fetchone()[0]

        # If the maximum rowid is greater than the last captured rowid, there are new changes
        if max_rowid > last_rowid:
            cursor.execute("SELECT * FROM your_source_table WHERE rowid > ?", (last_rowid,))
            changes = cursor.fetchall()

            print("Changes captured:")
            for change in changes:
                print(change)

            # Update the last captured rowid to the maximum rowid for the next iteration
            last_rowid = max_rowid

        # Wait for a short period before checking for new changes again
        time.sleep(1)

if __name__ == "__main__":
    capture_changes()

References