title: 'Change Data Capture (CDC)' sidebar_label: 'Change Data Capture' description: 'Learn how to use Change Data Capture (CDC) in Spice.' sidebar_position: 5 pagination_prev: null pagination_next: null

Change Data Capture (CDC) captures insert, update, and delete events from a database's transaction log and delivers them to consumers with low latency. This technique enables Spice to keep locally accelerated datasets synchronized with the source data in near real-time. CDC is efficient because it transfers only changed rows instead of re-fetching the entire dataset.

Benefits

Using locally accelerated datasets configured with CDC enables Spice to provide high-performance accelerated queries and efficient real-time updates.

Example Use Case

Consider a fraud detection application that needs to determine whether a pending transaction is likely fraudulent. The application queries a Spice-accelerated, real-time updated table of recent transactions to check if a pending transaction resembles known fraudulent ones. With CDC, the table is kept up-to-date, so the application can quickly identify potential fraud.

Considerations

When configuring datasets to be accelerated with CDC, ensure that the data connector supports CDC and can return a stream of row-level changes. See the Supported Data Connectors section for more information.

The startup time for CDC-accelerated datasets may be longer than for non-CDC-accelerated datasets due to the initial synchronization.

:::tip

It is recommended to use CDC-accelerated datasets with persistent data accelerator configurations (i.e., file mode for DuckDB/SQLite or PostgreSQL). This ensures that when Spice restarts, it can resume from the last known state of the dataset instead of re-fetching the entire dataset.

:::

Supported Data Connectors

Enabling CDC by setting refresh_mode: changes in the acceleration settings requires support from the data connector to provide a stream of row-level changes.

Spice currently supports streaming ingestion via:

• PostgreSQL Logical Replication — recommended for PostgreSQL sources. Spice connects directly to the source using Postgres' native logical replication protocol (wal_level=logical + pgoutput) and streams INSERT/UPDATE/DELETE events into the accelerator. No Kafka, no Debezium, no external services.
• DynamoDB Streams — for Amazon DynamoDB sources. Spice consumes the table's DynamoDB Streams directly and applies INSERT/UPDATE/DELETE events to the accelerator.
• MongoDB Change Streams — for MongoDB replica sets and sharded clusters. Spice opens a native Change Stream on the source collection and applies inserts, updates, replaces, and deletes to the accelerator.
• Apache Kafka — for event-streaming topics. Spice consumes records directly with refresh_mode: append for real-time, append-only acceleration (no separate CDC connector required).
• Debezium (over Kafka) — for sources where Debezium + Kafka is already deployed, or for databases without a native Spice CDC path (MySQL, SQL Server, etc.).

Example

See an example of configuring a dataset to use CDC with Debezium by following the recipe at Streaming changes in real-time with Debezium CDC.