Section 1.1: Why Implement Change Data Capture?

CDC is essential for a variety of reasons:

• Real-Time Data Processing: It enables applications to handle data modifications as they occur.
• Data Replication: It supports the synchronization of data across various systems to maintain consistency.
• Audit and Compliance: It aids in preserving an audit trail of data modifications.
• Event-Driven Architectures: It facilitates the creation of event-driven systems where data changes initiate actions.
• Enhanced ETL Processes: It streamlines ETL (Extract, Transform, Load) processes by minimizing data integration latency.

Section 1.2: Steps for Implementing CDC in Rust

Implementing CDC in Rust requires several key steps:

1. Database Preparation: Ensure your database supports CDC. Popular options like PostgreSQL and MySQL offer built-in CDC functionalities.
2. Capturing Data Modifications: Utilize database-specific features or third-party tools to detect data changes.
3. Processing Data Changes: Develop Rust-based services to handle these modifications.
4. Integrating with Your Application: Ensure smooth integration of CDC within your Rust application.

Step 1: Configuring PostgreSQL for CDC

Begin by ensuring that your PostgreSQL database is configured for logical replication, which is crucial for CDC.

-- Enable logical replication

ALTER SYSTEM SET wal_level = logical;

ALTER SYSTEM SET max_replication_slots = 4;

ALTER SYSTEM SET max_wal_senders = 4;

SELECT pg_reload_conf();

Next, create a replication slot to capture changes:

SELECT * FROM pg_create_logical_replication_slot('rust_cdc_slot', 'pgoutput');

Step 2: Setting Up Change Publication

Create a publication for the tables you want to monitor:

CREATE PUBLICATION rust_publication FOR TABLE your_table;

Step 3: Configuring Rust to Process Changes

Include the necessary dependencies in your Cargo.toml:

[dependencies]

postgres = "0.19"

tokio = { version = "1", features = ["full"] }

serde = { version = "1", features = ["derive"] }

serde_json = "1.0"

log = "0.4"

env_logger = "0.9"

Now, implement the Rust code to connect to the database and capture changes:

use postgres::{Client, NoTls};

use serde_json::Value;

use tokio::stream::StreamExt;

use log::{info, error};

use std::fs::OpenOptions;

use std::io::Write;

#[tokio::main]

async fn main() {

env_logger::init();

let mut client = Client::connect("host=localhost user=postgres password=secret dbname=test_db", NoTls).unwrap();

let replication_query = "

START_REPLICATION SLOT rust_cdc_slot LOGICAL 0/0 (proto_version '1', publication_names 'rust_publication');

";

let mut stream = client.copy_both_simple(replication_query).unwrap();

while let Some(message) = stream.next().await {

match message {

Ok(replication_message) => {

let json: Value = serde_json::from_str(&replication_message.data).unwrap();

info!("Change detected: {:?}", json);

log_change(json);

},

Err(e) => error!("Error reading replication message: {}", e),

}

}

}

fn log_change(change: Value) {

let mut file = OpenOptions::new()

.create(true)

.append(true)

.open("changes.log")

.unwrap();

if let Err(e) = writeln!(file, "{:?}", change) {

eprintln!("Could not write to file: {}", e);

}

}

Step 4: Processing and Utilizing Data Changes

The code above logs the captured changes. You can customize it further to update caches, trigger additional processes, or synchronize with other systems.

Section 2.1: Use Cases for CDC in Rust Applications

• Microservices Architecture: Synchronize databases across microservices.
• Data Lakes: Continuously stream modifications to a data lake for analysis.
• Real-Time Analytics: Supply changes to analytics platforms for immediate insights.
• Event-Driven Systems: Initiate business workflows based on data changes.

Implementing Change Data Capture (CDC) in Rust applications can greatly enhance your capability to manage and respond to real-time data modifications. In this guide, we've examined the concept of CDC, configured PostgreSQL for logical replication, and created a Rust application to detect and process data changes. By utilizing PostgreSQL’s logical replication alongside Rust’s powerful concurrency features, you can develop efficient and scalable CDC solutions.

This example showcased a basic logging mechanism, but you can extend it to meet your specific needs, such as updating caches or integrating with other systems. Through CDC, you can ensure that your applications remain synchronized with your data, delivering real-time insights and enhancing overall responsiveness.

Feel free to explore and adapt the example to fit your requirements. For any queries or to share your experiences, please leave a comment below. Happy coding!

This video demonstrates how to develop an incremental pipeline using Change Data Capture (CDC) from Hudi to Aurora Postgres, showcasing practical implementations.

Watch this video to learn about driving real-time data capture and transformation in Delta Lake using Change Data Capture with Databricks.