Observed Signal · Apr 22, 2026 · Product Launch · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Developer Builds Kaptanto: Lightweight Postgres & MongoDB CDC
A developer describes building Kaptanto, an open-source Change Data Capture (CDC) tool that taps Postgres WAL logical replication and MongoDB Change Streams to emit ordered, durable insert/update/delete events with before/after values. Kaptanto normalizes different sources into a single event schema, implements watermark-coordinated backfills to avoid missed or duplicated changes, and ensures durability by writing events to an embedded Badger log before advancing source checkpoints. It enforces per-key ordering using WAL LSNs and achieves high-availability via a Postgres advisory lock. Implemented primarily in Go, the project includes a Rust FFI decoder experiment (kaptanto-ffi). Benchmarks in the post claim Kaptanto outperforms Debezium and Sequin on the author’s test hardware. The project is at v0.1.0 and available as an open-source binary with multiple output options (stdout, SSE, gRPC).
Open-source lightweight CDC tooling and pragmatic engineering patterns (snapshot + replication slot backfill, durable embedded log, LSN ordering) are useful to teams building real-time data pipelines and can reduce reliance on heavier CDC stacks, but this is a niche developer tool rather than an industry-shifting platform announcement.
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Key Takeaways & Evidence Grounding
- Kaptanto is an open-source CDC tool supporting PostgreSQL (WAL logical replication) and MongoDB (Change Streams).
- Kaptanto normalizes events into a unified schema containing operation type, table/collection, before, and after states.
- Durability is implemented by writing every event to an embedded Badger log and advancing the source checkpoint only after durable storage.
- High-availability uses a Postgres advisory lock tied to the replication slot; per-key ordering is enforced using WAL LSN positions.
- Benchmarks reported: kaptanto steady throughput 4,805 eps and large-batch 36,267 eps; Debezium reported 128 eps steady and 150 eps large-batch.
Connected Companies & Entities
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Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Move from Databases to Kafka for Robust Data Pipelines
The article explains why direct database listeners (e.g., PostgreSQL LISTEN/NOTIFY) are fragile and do not scale in microservices architectures, and presents Apache Kafka combined with Change Data Capture (CDC) as a robust alternative. It describes the difference between traditional message queues (RabbitMQ) and distributed logs (Kafka), and recommends using Debezium to tail a database Write-Ahead Log (WAL) so changes are published to Kafka topics for downstream consumers (cache invalidators, search indexers, analytics). The piece outlines an architecture where the main app writes to the primary database and Debezium/Kafka reliably propagate every insert/update/delete to multiple independent services.
PostgreSQL vs MongoDB vs Cassandra: Multi‑Node Differences
This technical explainer compares PostgreSQL, MongoDB, and Cassandra in multi-node deployments, focusing on replication, scaling, consistency, and transactional behavior. PostgreSQL is a single-node-first system with streaming WAL replication, synchronous/asynchronous trade-offs, and CP behavior; horizontal write scaling requires external tooling or extensions like Citus and incurs two‑phase commit costs for cross-shard transactions. MongoDB provides native replica sets and a logical oplog, configurable per-operation consistency via writeConcern/readPreference, and a built-in sharding architecture (mongos, config servers, shard replica sets) but advises avoiding cross-shard transactions where possible. Cassandra was designed for distribution from day one, using a consistent-hashing ring with vnodes, leaderless replication, tunable per-query consistency (ONE/QUORUM/ALL), hinted handoff, and limits on multi-partition transactions (LWT for single-partition conditional writes). The author concludes with a decision framework and recommends PostgreSQL as the honest default for new products unless specific scale or availability needs dictate otherwise.
Open-source apitap moves 10M rows in 9.9s
The author published apitap, an open-source Rust-core with Python bindings engine that copies whole tables between databases with no config. Using the pip-distributed wheel on a 16-core machine, apitap moved 10 million rows from Postgres to ClickHouse in 9.9 seconds and sustained 100M rows / 46 GB in 139 seconds (1.2 TB/hour) in later runs. apitap supports Postgres→Postgres, Postgres→ClickHouse, and MySQL→ClickHouse/Postgres routes, uses bounded streaming memory to run in very small containers (0.5 vCPU / 256 MB), stages into shadow tables for atomic swaps, and includes a reproducible benchmark comparing it to dlt and ingestr. The project is MIT-licensed and open for contributors; additional connectors and platforms are on the roadmap.
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