Change Data Capture

CDC systems monitor a source of truth (like a blockchain's state) and emit events or deltas for every state change. This is achieved by:

• Indexing new blocks and transactions.
• Decoding on-chain data (e.g., smart contract logs).
• Streaming these changes as a continuous, ordered log of events. This creates a real-time feed, eliminating the need for inefficient polling of RPC nodes.

CDC enables a wide range of real-time Web3 applications:

• DeFi Dashboards & Wallets: Update portfolio balances and prices instantly.
• On-Chain Analytics: Power live dashboards tracking metrics like Total Value Locked (TVL) or trading volume.
• Automated Trading Bots: Execute strategies based on immediate on-chain events (e.g., large swaps, liquidations).
• Notification Services: Alert users for transactions, governance proposals, or NFT listings.

A typical Web3 CDC pipeline consists of:

• Blockchain Client/RPC: The primary data source.
• CDC Connector: Software (e.g., The Graph's Subgraphs, custom indexers) that subscribes to new blocks.
• Event Stream: The output, often delivered via WebSockets or message queues (e.g., Apache Kafka, Amazon Kinesis).
• Sink/Destination: Databases (PostgreSQL, TimescaleDB), data lakes, or application frontends that consume the stream.

While related, CDC and indexing serve different purposes:

• Change Data Capture (CDC): Focuses on the stream of changes. It answers "What just happened?" and is optimal for real-time applications.
• Indexing: Focuses on the final aggregated state. It answers "What is the current state?" and is optimized for complex historical queries. Many systems use CDC as the ingestion layer to populate and update their indexed databases.

Implementing robust CDC in Web3 presents unique hurdles:

• Chain Reorganizations (Reorgs): Handling blocks that are temporarily added and then removed from the canonical chain.
• Finality & Latency: Balancing speed (using the latest block) with data certainty (awaiting finality).
• Data Volume & Scaling: Processing high-throughput chains requires efficient event filtering and parallel processing.
• Schema Management: Evolving smart contract ABIs require versioned decoders to maintain a consistent event stream.

A CDC system is only as trustworthy as its data source. In blockchain, this means ensuring the indexer or oracle providing the data stream is reading from the correct, canonical chain state. Key risks include:

• Reorg Attacks: The source chain reorganizes, invalidating previously captured events.
• Malicious RPC Nodes: A compromised node could feed incorrect or manipulated event logs.
• Solution: Implement header verification and proof-of-inclusion checks for captured data, and use multiple, geographically distributed RPC endpoints.

Guaranteeing exactly-once, in-order processing of blockchain events is critical for reliability. Network latency and retry logic can cause:

• Duplicate Events: The same transaction may be captured multiple times if acknowledgments fail.
• Out-of-Order Events: Events from different blocks may arrive in a non-canonical sequence.
• Solution: Use idempotent handlers in downstream consumers and implement sequence tracking using block numbers and log indexes as a composite key.

Smart contract upgrades can introduce breaking changes to event signatures or data structures, causing CDC pipelines to fail or misinterpret data. Considerations include:

• Event Signature Hashing: A change to an event's emit statement alters its topic0, making it invisible to old listeners.
• Data Type Evolution: New event parameters or changed types can break parsing logic.
• Solution: Implement versioned schemas, monitor for unknown event logs, and design consumers to be resilient to schema evolution.

Blockchain activity is bursty (e.g., during NFT mints or token launches). A CDC system must handle sudden spikes in event volume without dropping data or becoming unresponsive.

• Queue Overload: Downstream processing can't keep up, causing memory overflow.
• Chain Congestion: High gas periods can delay event finality, complicating the "capture" point.
• Solution: Use durable, scalable message queues (e.g., Apache Kafka, Amazon Kinesis) with dead-letter queues for failed events and implement rate-limiting and backoff strategies.

CDC listeners can fall behind the chain head due to downtime or processing delays, creating a synchronization gap. A reliable system must safely catch up to the current state.

• Historical Data Gaps: Missing events during downtime must be replayed.
• State Reconstruction: Simply replaying events may not be sufficient if the current state depends on external data.
• Solution: Design listeners to persist checkpoint cursors (e.g., latest processed block) and implement idempotent historical backfill processes from archival nodes.

While blockchain data is public, a CDC system that aggregates, enriches, and streams data can inadvertently create privacy risks or expose sensitive patterns.

• Data Enrichment Leaks: Combining on-chain events with off-chain data (e.g., linking addresses to real identities) in a stream.
• Front-running Vectors: A public, low-latency event stream could be exploited for MEV if not properly secured.
• Solution: Apply access controls to the CDC stream output and consider data minimization principles. For sensitive applications, use private mempools or encrypted data layers.

The foundational mechanism for non-intrusive CDC. Transaction logs (e.g., Write-Ahead Logs (WAL), Binary Logs) are append-only files where databases record all changes to ensure durability and support replication. CDC tools read these logs to:

• Avoid polling and performance impact on the source database.
• Capture every INSERT, UPDATE, DELETE operation in the order they occurred.
• Provide a point-in-time recovery capability.

An architectural pattern where state changes are stored as a sequence of immutable events. While CDC captures changes from an existing system's state, Event Sourcing designs the system to emit events as the primary source of truth. They are complementary:

• CDC is often used to create an event stream from a legacy system.
• Event Sourcing natively produces an event stream for CQRS and audit trails.

The end-to-end system that moves and transforms data from source to destination. CDC is a critical ingestion component within modern data pipelines, enabling real-time ETL or ELT processes. A typical CDC-powered pipeline includes:

• Source Connector (e.g., Debezium for PostgreSQL).
• Message Broker (e.g., Apache Kafka, Amazon Kinesis) for streaming.
• Sink Connector to load data into data warehouses (Snowflake, BigQuery) or caches.

A database replication method that transmits data changes based on the logical content of the transaction log, rather than physical disk blocks. This is the core technology enabling CDC for databases like PostgreSQL. It allows for:

• Selective replication of specific tables or columns.
• Cross-version and cross-platform replication.
• Real-time data feeds to external systems beyond simple standby replicas.

A solution to the dual-write problem in microservices, ensuring reliable event publishing. Instead of writing to a database and a message broker separately, the service writes an event to a dedicated outbox table within the same database transaction. A CDC connector then reads this table and publishes the events, guaranteeing atomicity and consistency between the service's state and published events.