Change Data Capture (CDC) in Database Systems using Aternity, use Kafka topic to consume those changes into a .NET application

In the realm of database management, tracking changes to data is critical for various purposes, including auditing, analytics, and replication. Change Data Capture (CDC) is a feature offered by many relational database management systems (RDBMS) that facilitates the monitoring and capture of data modifications. In this blog post, we’ll delve into the concept of CDC, its benefits, and how it helps in database change tracking.

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What is Change Data Capture (CDC)?

Change Data Capture (CDC) is a feature of database management systems that captures and records changes made to data in a relational database. It provides a mechanism for tracking modifications such as inserts, updates, and deletes performed on tables within the database.

How CDC Works:

CDC operates by monitoring the database’s transaction log, also known as the redo log or WAL (Write-Ahead Log), which records all changes made to the database at the transaction level. By analyzing the transaction log, CDC identifies and captures the details of data modifications, including the affected rows, the type of operation (insert, update, delete), and the timestamp of the change.

Benefits of CDC:

1. Real-time Data Integration: CDC enables real-time or near-real-time data integration by capturing changes as they occur, allowing downstream systems to consume the latest data without delay.
2. Efficient Data Replication: CDC facilitates efficient and incremental data replication by capturing only the changes made to the database, reducing the overhead associated with full data refreshes.
3. Auditing and Compliance: CDC supports auditing and compliance requirements by providing a detailed record of data modifications, including who made the changes and when.
4. Business Intelligence and Analytics: CDC enables organizations to perform real-time analytics and reporting on changing data, leading to more informed decision-making.

Implementing CDC with SQL Server:

SQL Server provides built-in support for Change Data Capture (CDC), making it easy to enable and configure CDC for tables within a database. Let’s see how to implement CDC using SQL Server.

Enable CDC on the Database:

-- Enable CDC on the database
EXEC sys.sp_cdc_enable_db;

Enable CDC on a Table:

-- Enable CDC on a specific table
EXEC sys.sp_cdc_enable_table
     @source_schema = N'dbo',
     @source_name = N'MyTable',
     @role_name = NULL,
     @supports_net_changes = 1;

Query CDC Changes:

-- Query CDC changes for a specific table
SELECT * FROM cdc.dbo_MyTable_CT;

What is Aternity?

Aternity is a digital experience management platform that provides insights into user interactions with applications and services. Capturing Change Data Capture (CDC) changes in Aternity allows organizations to monitor and analyze data modifications in real-time, enabling proactive decision-making and performance optimization. In this section, we’ll explore how to integrate CDC changes into Aternity for enhanced visibility and analysis.

Integration Overview:

Integrating CDC changes into Aternity involves leveraging Aternity’s data ingestion capabilities to capture and process CDC data from the source database. This process typically includes configuring the CDC source, defining the data extraction method, and configuring Aternity to consume and analyze the CDC data.

Step-by-Step Integration Process:

Step 1: Configure CDC Source:

First, ensure that CDC is properly configured on the source database. This involves enabling CDC on the database and relevant tables, as outlined in the database documentation.

-- Enable CDC on the database
EXEC sys.sp_cdc_enable_db;

-- Enable CDC on a specific table
EXEC sys.sp_cdc_enable_table
    @source_schema = N'dbo',
    @source_name = N'MyTable',
    @role_name = NULL,
    @supports_net_changes = 1;

Step 2: Extract CDC Data:

Next, extract CDC data from the source database using an appropriate method. This may involve querying the CDC tables directly or using database connectors or middleware tools to retrieve the CDC changes.

public List<CDCChange> GetCDCChanges()
{
    List<CDCChange> changes = new List<CDCChange>();
    
    using (var connection = new SqlConnection(connectionString))
    {
        connection.Open();
        
        SqlCommand command = new SqlCommand("SELECT * FROM cdc.dbo_MyTable_CT", connection);
        SqlDataReader reader = command.ExecuteReader();
        
        while (reader.Read())
        {
            // Parse CDC change data and populate CDCChange object
            CDCChange change = new CDCChange();
            change.Operation = (CDCOperation)reader["__$operation"];
            change.TableName = (string)reader["__$tablename"];
            // Populate other CDC change properties
            // ...
            
            changes.Add(change);
        }
    }
    
    return changes;
}

Step 3: Transform and Enrich Data:

Transform and enrich the extracted CDC data as needed to meet the requirements of Aternity. This may involve mapping CDC changes to Aternity’s data schema, applying data transformations, and enriching the data with additional context or metadata.

public AternityData TransformCDCData(List<CDCChange> changes)
{
    AternityData aternityData = new AternityData();
    
    foreach (var change in changes)
    {
        // Transform CDC change into AternityData format
        AternityEntry entry = new AternityEntry();
        entry.OperationType = change.Operation.ToString();
        entry.TableName = change.TableName;
        // Map other properties
        
        aternityData.AddEntry(entry);
    }
    
    return aternityData;
}

Step 4: Ingest Data into Aternity:

Ingest the transformed CDC data into Aternity using the platform’s data ingestion capabilities. This typically involves configuring data connectors or APIs to receive and process the CDC data streams.

public async Task<bool> IngestDataIntoAternity(AternityData data)
{
    // Serialize AternityData object to JSON
    string jsonData = JsonConvert.SerializeObject(data);
    
    // Send HTTP POST request to Aternity API endpoint
    using (var httpClient = new HttpClient())
    {
        var response = await httpClient.PostAsync("https://aternity-api.example.com/ingest", 
                                                   new StringContent(jsonData, Encoding.UTF8, "application/json"));
        
        return response.IsSuccessStatusCode;
    }
}

Step 5: Analyze and Visualize Data:

Once the CDC data is ingested into Aternity, leverage the platform’s analytics and visualization tools to analyze and visualize the data. This may include creating dashboards, reports, and alerts to monitor CDC changes in real-time and identify performance trends and anomalies.

Benefits of Capturing CDC Changes in Aternity:

1. Real-time Insights: By capturing CDC changes in Aternity, organizations gain real-time insights into data modifications, enabling proactive monitoring and troubleshooting.
2. Performance Optimization: Analyzing CDC data in Aternity allows organizations to identify performance bottlenecks and optimize application and database performance.
3. Root Cause Analysis: Aternity’s analytics capabilities enable organizations to perform root cause analysis on CDC-related issues, facilitating rapid resolution and minimizing downtime.
4. Enhanced Visibility: Integrating CDC changes into Aternity provides comprehensive visibility into data modifications across the organization, ensuring data integrity and compliance.

What is Kafka?

Apache Kafka is an open-source distributed event streaming platform used for building real-time streaming data pipelines and applications. It is designed to handle high-throughput, fault-tolerant, and scalable streaming of data. Kafka is built around the concepts of topics, partitions, producers, and consumers.

To get Aternity data into a Kafka topic and consume those changes as messages into a .NET application, you’ll need to follow these steps:

Step 1: Configure Kafka Producer:

Configure a Kafka producer to send Aternity data to a Kafka topic. Below is an example of how to produce messages to a Kafka topic using the Confluent Kafka library in C#:

using Confluent.Kafka;
using Newtonsoft.Json;

public class KafkaProducer
{
    private readonly string bootstrapServers;

    public KafkaProducer(string bootstrapServers)
    {
        this.bootstrapServers = bootstrapServers;
    }

    public async Task ProduceAternityDataAsync(string topic, AternityData data)
    {
        var config = new ProducerConfig { BootstrapServers = bootstrapServers };

        using (var producer = new ProducerBuilder<string, string>(config).Build())
        {
            string jsonData = JsonConvert.SerializeObject(data);

            var message = new Message<string, string>
            {
                Key = Guid.NewGuid().ToString(),
                Value = jsonData
            };

            await producer.ProduceAsync(topic, message);
        }
    }
}

Step 2: Configure Kafka Consumer:

Configure a Kafka consumer in your .NET application to consume messages from the Kafka topic. Below is an example of how to consume messages from a Kafka topic using the Confluent Kafka library in C#:

using Confluent.Kafka;

public class KafkaConsumer
{
    private readonly string bootstrapServers;
    private readonly string groupId;

    public KafkaConsumer(string bootstrapServers, string groupId)
    {
        this.bootstrapServers = bootstrapServers;
        this.groupId = groupId;
    }

    public void ConsumeAternityData(string topic)
    {
        var config = new ConsumerConfig
        {
            BootstrapServers = bootstrapServers,
            GroupId = groupId,
            AutoOffsetReset = AutoOffsetReset.Earliest
        };

        using (var consumer = new ConsumerBuilder<Ignore, string>(config).Build())
        {
            consumer.Subscribe(topic);

            while (true)
            {
                var message = consumer.Consume();
                Console.WriteLine($"Consumed message: {message.Value}");
                // Process the consumed message
            }
        }
    }
}

Step 3: Integrate Aternity Data Ingestion and Consumption:

Integrate the Aternity data ingestion and consumption logic into your application. You can use the KafkaProducer to send Aternity data to the Kafka topic and the KafkaConsumer to consume messages from the same topic.

// Usage example
var producer = new KafkaProducer("localhost:9092");
var consumer = new KafkaConsumer("localhost:9092", "group-1");

// Produce Aternity data to Kafka topic
var aternityData = GetAternityData();
await producer.ProduceAternityDataAsync("aternity-topic", aternityData);

// Consume Aternity data from Kafka topic
consumer.ConsumeAternityData("aternity-topic");

Here’s a step-by-step guide with detailed code snippets on how to capture CDC changes, send them to Aternity, and then ingest them into a Kafka topic using a .NET application:

Step 1: Configure CDC in SQL Server:

Enable Change Data Capture (CDC) on the SQL Server database table that you want to track changes for. This allows SQL Server to capture and expose changes made to the table.

Step 2: Create a CDC Service:

Create a service in your .NET application to capture CDC changes from SQL Server. Use the System.Data.SqlClient namespace to connect to the SQL Server database and retrieve CDC changes.

public class CdcService
{
    private readonly string connectionString;

    public CdcService(string connectionString)
    {
        this.connectionString = connectionString;
    }

    public IEnumerable<CdcChange> GetChanges()
    {
        List<CdcChange> changes = new List<CdcChange>();

        using (var connection = new SqlConnection(connectionString))
        {
            connection.Open();

            SqlCommand command = new SqlCommand("SELECT * FROM cdc.fn_cdc_get_all_changes_dbo_TableName(@from_lsn, @to_lsn, 'all');", connection);
            // Replace 'TableName' with the name of your CDC-enabled table

            // Set the range of LSNs to capture changes
            command.Parameters.AddWithValue("@from_lsn", DBNull.Value);
            command.Parameters.AddWithValue("@to_lsn", DBNull.Value);

            using (var reader = command.ExecuteReader())
            {
                while (reader.Read())
                {
                    // Parse the CDC change record
                    CdcChange change = new CdcChange
                    {
                        Operation = reader.GetString(1),
                        // Parse other columns as needed
                    };
                    changes.Add(change);
                }
            }
        }

        return changes;
    }
}

Step 3: Send CDC Changes to Aternity:

Once CDC changes are captured, send them to Aternity for further processing and analysis. You can use HTTP requests or an SDK provided by Aternity to send the data.

public class AternityService
{
    private readonly HttpClient httpClient;
    private readonly string aternityApiUrl;

    public AternityService(string aternityApiUrl)
    {
        this.aternityApiUrl = aternityApiUrl;
        httpClient = new HttpClient();
    }

    public async Task SendChangesAsync(IEnumerable<CdcChange> changes)
    {
        foreach (var change in changes)
        {
            // Convert CDC change to JSON
            string json = JsonConvert.SerializeObject(change);

            // Send HTTP POST request to Aternity API
            var response = await httpClient.PostAsync(aternityApiUrl, new StringContent(json, Encoding.UTF8, "application/json"));
            response.EnsureSuccessStatusCode();
        }
    }
}

Step 4: Configure Kafka Producer:

Set up a Kafka producer in your .NET application to publish messages to a Kafka topic. Use the Confluent.Kafka NuGet package to interact with Kafka.

public class KafkaProducer
{
    private readonly IProducer<string, string> producer;

    public KafkaProducer(string bootstrapServers)
    {
        var config = new ProducerConfig { BootstrapServers = bootstrapServers };
        producer = new ProducerBuilder<string, string>(config).Build();
    }

    public async Task ProduceMessageAsync(string topic, string message)
    {
        await producer.ProduceAsync(topic, new Message<string, string> { Key = null, Value = message });
    }
}

Step 5: Ingest Aternity Data into Kafka:

Ingest the data received from Aternity into the Kafka topic using the Kafka producer. This allows other systems or applications to consume the data from the Kafka topic.

// Assuming Aternity data is already retrieved and stored in a collection called 'aternityData'
foreach (var data in aternityData)
{
    await kafkaProducer.ProduceMessageAsync("aternity-topic", JsonConvert.SerializeObject(data));
}

Step 6: Configure Kafka Consumer:

Create a Kafka consumer in your .NET application to consume messages from the Kafka topic. Use the Confluent.Kafka NuGet package to create the consumer and subscribe to the Kafka topic.

public class KafkaConsumer
{
    private readonly IConsumer<string, string> consumer;

    public KafkaConsumer(string bootstrapServers, string groupId, string topic)
    {
        var config = new ConsumerConfig
        {
            BootstrapServers = bootstrapServers,
            GroupId = groupId,
            AutoOffsetReset = AutoOffsetReset.Earliest
        };

        consumer = new ConsumerBuilder<string, string>(config).Build();
        consumer.Subscribe(topic);
    }

    public async Task<string> ConsumeMessageAsync()
    {
        try
        {
            var message = consumer.Consume();
            return message.Message.Value;
        }
        catch (ConsumeException e)
        {
            // Handle consume error
            return null;
        }
    }
}

Step 7: Process Kafka Messages:

Process the messages received from the Kafka topic. You can perform any necessary business logic or data transformations on the messages before further processing or storage.

// Inside a loop or event handler
var kafkaMessage = await kafkaConsumer.ConsumeMessageAsync();
if (kafkaMessage != null)
{
    // Process the Kafka message
    Console.WriteLine("Received message: " + kafkaMessage);
}

Step 8: Handle Errors and Retries:

Implement error handling and retry logic in your application to handle failures during CDC capture, data transmission to Aternity, and Kafka message processing. Use try-catch blocks and retry mechanisms to handle transient errors.

// Inside the CDC service class
public class CdcService
{
    private readonly string connectionString;
    private readonly int maxRetries = 3; // Maximum number of retries

    public CdcService(string connectionString)
    {
        this.connectionString = connectionString;
    }

    public async Task<IEnumerable<CdcChange>> GetChangesAsync()
    {
        List<CdcChange> changes = new List<CdcChange>();

        int retries = 0;

        while (retries < maxRetries)
        {
            try
            {
                using (var connection = new SqlConnection(connectionString))
                {
                    connection.Open();

                    SqlCommand command = new SqlCommand("SELECT * FROM cdc.fn_cdc_get_all_changes_dbo_TableName(@from_lsn, @to_lsn, 'all');", connection);
                    // Replace 'TableName' with the name of your CDC-enabled table

                    // Set the range of LSNs to capture changes
                    command.Parameters.AddWithValue("@from_lsn", DBNull.Value);
                    command.Parameters.AddWithValue("@to_lsn", DBNull.Value);

                    using (var reader = command.ExecuteReader())
                    {
                        while (reader.Read())
                        {
                            // Parse the CDC change record
                            CdcChange change = new CdcChange
                            {
                                Operation = reader.GetString(1),
                                // Parse other columns as needed
                            };
                            changes.Add(change);
                        }
                    }
                }

                // If changes are successfully fetched, break the loop
                break;
            }
            catch (Exception ex)
            {
                // Log the exception
                Console.WriteLine($"Error fetching CDC changes: {ex.Message}");

                // Increment the retry count
                retries++;

                // Add a delay before retrying
                await Task.Delay(1000); // 1 second delay
            }
        }

        return changes;
    }
}

Step 9: Deploy and Monitor:

Deploy your .NET application to a suitable environment and monitor its performance and health. Use logging and monitoring tools to track CDC capture, data transmission, Kafka message processing, and overall application health.

Monitoring: Monitor your deployed application using appropriate monitoring tools like Azure Monitor, AWS CloudWatch, or third-party tools like Prometheus and Grafana. Monitor key metrics such as CPU usage, memory usage, request latency, and error rates. Set up alerts to notify you of any critical issues or anomalies in the application’s behavior.

With these code snippets and deployment and monitoring steps, you can ensure robust error handling and retries in your CDC service and effectively deploy and monitor your .NET application in production.

Conclusion

Change Data Capture (CDC) is a powerful feature in database systems that facilitates the tracking and capture of data modifications. By leveraging CDC, organizations can achieve real-time data integration, efficient data replication, and enhanced auditing and compliance capabilities. SQL Server provides robust support for CDC, making it a valuable tool for implementing change tracking in database environments.

By following these steps and integrating the provided code snippets, you can get Aternity data into a Kafka topic and consume those changes as messages into your .NET application. Remember to adjust the configurations and code according to your specific Kafka setup and Aternity data structure.