kafka-source-connector
Parent: mongodb-kafka-connector · Published reference · snapshot 2026-09-08 · skill mongodb-operations-expert/references/mongodb-kafka-connector.md
↓ Facts as markdownall context files
The MongoDB Connector for Apache Kafka is a Kafka Connect plugin that bridges MongoDB and Kafka in both directions:
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Overview
- The MongoDB Connector for Apache Kafka is a Kafka Connect plugin that bridges MongoDB and Kafka in both directions: [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#overview]
- Source connector: MongoDB change streams → Kafka topics (CDC pipeline) [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#overview]
- Sink connector: Kafka topics → MongoDB collections (event consumer) [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#overview]
- Supports Confluent Platform, Confluent Cloud, Amazon MSK, and self-managed Kafka. [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#overview]
Basic Change Stream Source
- This publishes change events to topic mongo.mydb.orders (format: <prefix>.<db>.<collection>). [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#basic-change-stream-source]
Resume Token Persistence
- The connector automatically persists the resume token in a Kafka Connect offsets topic. On restart, it resumes from the saved token. [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#resume-token-persistence]
- If the token expires (oplog window exceeded during connector downtime): [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#resume-token-persistence]
Dead Letter Queue (DLQ)
- Configure DLQ to route failed messages instead of stopping the connector: [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#dead-letter-queue-dlq]
- DLQ messages include headers with error context. Process DLQ messages with a separate consumer for alerting or manual replay. [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#dead-letter-queue-dlq]
Source Connector Errors
- ChangeStreamHistoryLost (error code 286): [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#source-connector-errors]
- The oplog has been truncated past the resume token. Resolution: [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#source-connector-errors]
- Set startup.mode=timestamp to start from a recent time [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#source-connector-errors]
- Or re-snapshot with startup.mode=copy_existing [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#source-connector-errors]
- Increase oplog size to prevent future occurrences [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#source-connector-errors]
- InvalidResumeToken: Resume token is corrupted or from an incompatible MongoDB version. Resolution: clear stored offset and restart connector. [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#source-connector-errors]
Sink Connector Errors
- DuplicateKey (11000): Configure ReplaceOneDefaultStrategy instead of InsertOneDefaultStrategy to make sink idempotent. [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#sink-connector-errors]
- DocumentValidationFailure (121): Kafka messages don't match MongoDB $jsonSchema validator. Check message schema vs collection validator. [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#sink-connector-errors]
Sink Connector
- Worker parallelism: Set tasks.max equal to the number of Kafka partitions for the topic. [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#sink-connector]
CDC Pipeline Pattern: MongoDB → Kafka → Downstream
- For near-real-time with low latency: [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#cdc-pipeline-pattern-mongodb-kafka-downstream]
- Use poll.await.time.ms: 100 (shorter poll interval) [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#cdc-pipeline-pattern-mongodb-kafka-downstream]
- Monitor consumer lag on the Kafka topic [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#cdc-pipeline-pattern-mongodb-kafka-downstream]
- Keep poll.max.batch.size small (100-500) for lower latency at cost of throughput [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#cdc-pipeline-pattern-mongodb-kafka-downstream]
Anti-Patterns
- Single-partition topics with multiple sink tasks: Multiple sink tasks on a single partition = contention; match tasks.max to partition count [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#anti-patterns]
- Not configuring DLQ: Connector stops on first unprocessable message; always configure DLQ in production [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#anti-patterns]
- InsertOneDefaultStrategy for idempotent pipelines: Insert fails on duplicate; use ReplaceOneDefaultStrategy or BulkWriteStrategy for idempotent sinks [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#anti-patterns]
- Monitoring consumer lag but not resume token age: Consumer lag tells you about Kafka backlog; resume token age tells you about oplog risk (if token becomes invalid = full resync) [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#anti-patterns]
- Not increasing oplog for high-volume CDC: Connector outage exceeding the oplog window = full resync required; size oplog to cover expected maintenance windows [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#anti-patterns]
References
- MongoDB Kafka Connector Documentation [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#references]
- Source Connector Configuration [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#references]
- Sink Connector Configuration [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#references]
- Write Model Strategies [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#references]
- Kafka Connector GitHub [archived source: https://llms-explorer.com/sources/mdb-context-hub/mongodb-kafka-connector/#references]
Where this helps
- Streaming MongoDB change events into Kafka topics for downstream consumers, instead of polling MongoDB directly from every consuming service. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Building a near-real-time CDC pipeline where consumer lag and latency matter, tuning poll.await.time.ms and poll.max.batch.size to trade throughput for lower latency. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Recovering a source connector after extended downtime caused the oplog window to be exceeded and the stored resume token to expire. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Deciding between resuming from a timestamp versus a full re-snapshot (copy_existing) after a ChangeStreamHistoryLost error. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
Project ideas
- Stand up a basic MongoDB change-stream source connector publishing to a topic named mongo.<db>.<collection>, then simulate an outage long enough to exceed the oplog window and practice recovering with startup.mode=timestamp. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Build a low-latency CDC pipeline by setting poll.await.time.ms to 100 and poll.max.batch.size to a small value between 100 and 500, then measure the consumer-lag improvement against default settings. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Monitor both Kafka consumer lag and resume token age side by side on a running source connector, since they signal different kinds of risk. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Trigger and resolve an InvalidResumeToken error by clearing the stored offset and restarting the connector against a fresh resume point. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
Common mistakes
- Monitoring only Kafka consumer lag and not resume token age — consumer lag tells you about the Kafka-side backlog, but resume token age tells you whether the oplog window is at risk of invalidating the token entirely. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Not sizing the oplog large enough to cover expected connector downtime or maintenance windows, forcing a full resync when a routine outage runs long. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Treating ChangeStreamHistoryLost (error code 286) as unrecoverable instead of resolving it with startup.mode=timestamp or a copy_existing re-snapshot. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Leaving the connector on default poll settings for a use case that actually needs low latency, instead of deliberately tuning poll.await.time.ms and poll.max.batch.size. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
Known issues
- If the resume token expires because the oplog window was exceeded during connector downtime, the connector can't just pick back up — it needs either a timestamp-based restart or a full copy_existing re-snapshot. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- InvalidResumeToken can occur not just from oplog truncation but from a resume token that's corrupted or from an incompatible MongoDB version, and the only resolution is clearing the stored offset and restarting. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- Change events publish to a fixed topic-naming convention (<prefix>.<db>.<collection>), which constrains downstream topic routing and consumer subscription design. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
- The source connector persists its resume token in a Kafka Connect offsets topic, so recovery behavior is tied to that offsets topic's own durability and retention, not just to MongoDB's oplog. [source] — AI-suggested, synthesized from this pack's existing facts — not extracted from a source document.
Children
- change-stream-resume-token (frontier)
- heartbeat-configuration (frontier)
- startup-mode-copy-existing (frontier)
Frontier under this node: change-stream-resume-token, heartbeat-configuration, startup-mode-copy-existing