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Change data capture (CDC) lets an application react to database changes as events instead of repeatedly asking whether anything has changed. In my project, Kaptanto, I built that flow for PostgreSQL and MongoDB and exposed events through stdout as NDJSON, server-sent events (SSE), and gRPC. Those are implementation details I describe here, not independently verified product capabilities.
Why use change data capture instead of polling?
Polling checks for changes on a schedule. That can add delay between a database write and a downstream reaction, while repeated checks continue even when nothing has changed. Application-managed notifications avoid some repeated reads, but require every code path that writes data to remember to publish the corresponding event. CDC instead captures changes at the database boundary so downstream consumers can process them as events.
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For PostgreSQL, the approach I used relies on logical decoding: PostgreSQL extracts persistent table changes from its write-ahead log (WAL) into a form applications can interpret. A replication slot represents a replayable stream of changes for a consumer. PostgreSQL’s documentation explains the underlying mechanism at Logical Decoding.
How Kaptanto moves from existing rows to live changes
A new consumer needs both the database’s current state and changes that happen while that state is being read. Those two sources must be coordinated: a gap can lose changes, while an uncoordinated overlap can produce duplicates.
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My described Kaptanto sequence opens a replication slot, takes a consistent snapshot, emits the snapshot rows, and then applies buffered WAL changes relative to a watermark. As I put it, “The slot opens before the snapshot, so nothing is missed.” That sentence describes my design intent; PostgreSQL’s documentation of logical decoding does not independently establish Kaptanto’s snapshot algorithm or guarantee.
How I handled persistence, ordering, and failover
Persist events before advancing the source checkpoint
I describe writing each event to an embedded Badger log before advancing the PostgreSQL checkpoint. The ordering matters: if the source position moves ahead before the event is safely retained, a restart could leave the consumer without that change. This is my account of Kaptanto’s implementation, not an independently verified durability guarantee.
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Track order with log sequence numbers
I say Kaptanto uses PostgreSQL log sequence number (LSN) positions to preserve per-key ordering. Ordering is distinct from delivery durability: a consumer still needs a clear policy for retries and duplicates when it restarts or an event is delivered again.
Coordinate active and standby instances
For active/standby coordination, I report using a PostgreSQL advisory lock to elect an active instance. This describes the approach in my article; it is not a general claim that an advisory lock alone solves every failover or recovery concern.
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How this compares with Debezium’s documented PostgreSQL flow
Debezium’s official PostgreSQL connector documentation describes an initial consistent snapshot followed by streaming committed row-level inserts, updates, and deletes into Kafka topics. It also documents logical decoding and replication slots. That is a useful comparison for the broad snapshot-then-stream pattern, but does not establish that Kaptanto has equivalent behavior or operational guarantees. See the Debezium PostgreSQL connector documentation.
What my benchmark showed—and what it cannot show
In my 2026 article, I reported a benchmark against PostgreSQL 16 using an Apple M-series machine and Docker Desktop. The figures below are my reported results, not independently reproduced measurements. They apply to that stated setup, not to production hardware or CDC performance in general.
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| Tool as tested in the article | Steady rate | Large-batch rate |
|---|---|---|
| Kaptanto | 4,805 events/sec | 36,267 events/sec |
| kaptanto-rust | 3,559 events/sec | 31,883 events/sec |
| Debezium | 128 events/sec | 150 events/sec |
| Sequin | 220 events/sec | 324 events/sec |
I also reported that the Rust FFI version had lower throughput in this benchmark, despite lower p50 latency and recovery time. Those outcomes are also my measurements. Without a reproducible workload, full configuration details, and independent runs, these figures should not be treated as a neutral ranking or a forecast for another environment.
What to check when choosing a CDC approach
Throughput is only one part of the decision. Compare tools against the systems and recovery behavior your team actually needs:
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- Source support and capture interface: Verify which databases are supported and what privileges or database configuration the connector requires.
- Snapshot handoff: Understand how initial rows are coordinated with changes made during the snapshot, and how the connector resumes if interrupted.
- Durability and duplicates: Establish when an event is considered safely retained and whether consumers must tolerate replay or duplicate delivery.
- Ordering: Confirm the scope of any ordering guarantee—for example, per key versus across the entire stream.
- Failover and operations: Check how active instances are selected, how offsets or checkpoints are recovered, and what monitoring and dependencies are required.
- Outputs and workload performance: Match integrations to downstream systems, then benchmark with a documented, repeatable workload representative of your own data and hardware.
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