Kafka -> Kafka

This guide is a copy/paste-ready starting point for loading data from Kafka into Kafka with dpone.

When to use this path

Use this path when Kafka is the system of record or ingestion boundary and Kafka is the landing, warehouse, event-log, or downstream replication target.

Copy/paste manifest

# yaml-language-server: $schema=../../src/dpone/schema/etl-batch-manifest.schema.json
kind: dpone.batch.v1

defaults:
  name: kafka_to_kafka_example
  source:
    type: kafka
    connection_id: kafka_source
    topic: orders.events
    options:
      group_id: dpone.orders.batch
      read_mode: max_records
      max_records: 50000
      offset_storage: dpone
      start_from: stored
      message_format: json
  sink:
    type: kafka
    connection_id: kafka_target
    topic: dwh.orders
    strategy:
      mode: incremental_append
    options:
      message_format: json
      envelope: dpone
      key:
        mode: hash_row
      delivery:
        mode: at_least_once

quality:
  gates:
    - id: source_target_rows
      type: row_count_reconciliation
      severity: error
      tolerance:
        mode: pct
        value: 0.1

schemas:
  kafka:
    tables:
      - orders_events

Run it locally:

dpone plan examples/source-sink/kafka-to-kafka.yaml --format md
dpone run examples/source-sink/kafka-to-kafka.yaml

The checked source file is examples/source-sink/kafka-to-kafka.yaml; CI compares its parsed YAML with this block. The example uses a finite max_records boundary; do not schedule an unbounded batch consume.

If you change the strategy to semantic full_refresh publication and empty output is invalid, row-count reconciliation is not enough: it can pass a 0 source / 0 target comparison. Add an explicit non-empty target gate:

quality:
  gates:
    - id: target_min_rows
      type: min_rows
      side: target
      threshold: 1
      severity: error

Supported load strategies

Kafka is append-only message publication. Strategy names describe event semantics; they do not mutate existing topic records or swap topic storage. These rows are runtime contracts, not exact-route certification.

Strategy	Status	Notes
full_refresh	Event publication	Publishes the bounded snapshot as new messages; existing messages remain.
incremental_append	Example	Publishes each bounded source row as a new message.
incremental_merge	Event publication	Publishes keyed upsert/delete events with merge_policy: event_upsert.
replace	Event publication	Publishes replacement-intent events; consumers decide how to materialize them.
partition_replace	Not supported	Kafka topics are append-only logs; use incremental_merge with merge_policy: event_upsert.
snapshot_diff	Event publication	Publishes keyed upsert/delete events derived from a complete source snapshot.

See Load strategies for the detailed algorithm for each strategy. Bounded Kafka offsets, timestamps, and max_records are source capabilities, not load strategies. dpone persists the consumed offset boundary only after Kafka delivery succeeds.

Runtime algorithm

This sink does not currently implement StagedLoadPort, so this route records governance_finalization=legacy_post_finalize. Blocking gates run only after messages have been published. A failure prevents source-state advancement but cannot retract delivered messages; inspect delivery evidence and consumer idempotency before retrying. See Load governance.

flowchart TD
    A["Resolve manifest and registry entries"] --> B["Create Kafka source"]
    B --> C["Plan bounded extract"]
    C --> D["Read through bounded offset, timestamp, or max-record Kafka batch consume"]
    D --> E["Emit ExtractResult with schema and artifact"]
    E --> F["Plan schema evolution"]
    F --> G["Create bounded Kafka producer batch"]
    G --> H["Publish messages and wait for delivery aggregation"]
    H --> J["Run quality and reconciliation checks (legacy_post_finalize)"]
    J --> K["Advance state only after success"]

Strategy behavior

• full_refresh: publish every row in the bounded snapshot as a new message; existing topic records remain.
• incremental_append: publish only the bounded incremental rows as new messages.
• incremental_merge: publish keyed upsert/delete events with merge_policy: event_upsert; requires unique_key.
• replace: publish replacement-intent events for a bounded window; no existing message is rewritten.
• snapshot_diff: publish keyed upsert/delete events derived from a complete source snapshot.
• partition_replace: not supported for Kafka sinks because a topic is an append-only event log.

Snapshot reconciliation is separate from the load strategy. Runtime planning reports that capability as reconciliation.mode=snapshot; in the official dpone.batch.v1 authoring schema, enable it with reconciliation: true.

Schema evolution and type mapping

Schema evolution is enabled by default and runs before bounded message publication:

1. Read source schema from ExtractResult.schema.
2. Introspect the Kafka target schema.
3. Apply safe additions and widening operations.
4. Fail breaking changes by default.
5. If configured, route incompatible type changes to __dpone__nc__<column>.

Use Schema evolution and Type mapping matrix when adding columns or changing source types.

Runbook

1. Start with dpone doctor --profile local and fix missing extras or native clients.
2. Run dpone plan <manifest> --format md and review source boundary, publication and delivery path, schema evolution, state, and quality gates.
3. Run a small bounded window first.
4. Inspect the run artifact under .dpone/runs/kafka_to_kafka.
5. For incremental jobs, verify state before enabling a schedule.
6. For delete-aware event semantics, review keyed tombstone/upsert behavior with consumers before publication.
7. Promote the manifest through GitOps after the plan and artifact are reviewed.

Cross-links

• Source -> sink matrix
• Load strategies
• Schema evolution
• Type mapping matrix
• Reconciliation and CDC
• Performance guide

Type contracts and physical design

This flow supports the shared dpone type-governance stack:

• Type inference for source metadata, sampled profiling, confidence, and empty string vs NULL behavior.
• Schema contracts for explicit logical column types, enforcement modes, and __dpone__nc__* variant columns.
• Physical design for target-specific DDL such as concrete SQL types, indexes, partitioning, compression, ClickHouse LowCardinality, and BigQuery clustering.

Use dpone schema infer --manifest ... and dpone schema physical-plan --manifest ... before enabling new table DDL in production.