The Tool Desk
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Start by defining what “accepted” and “lost” mean
Map the path of a record: producer, network, ingestion service, consumer, and destination. Identify which failure you are protecting against at each boundary—a producer crash, a lost acknowledgment, a broker or region failure, a worker restart, or a rejected write at the sink. A record can be safely stored by the ingestion service but still fail to reach the destination; conversely, a destination write can succeed even if the consumer crashes before recording its progress.
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Choose an explicit acceptance point. A producer call returning locally is not necessarily proof that the service has durably stored the record. Wait for the acknowledgment level that matches the intended durability. If a request times out before the acknowledgment arrives, its outcome is ambiguous: the service may have committed the record even though the producer did not receive confirmation. Retrying avoids treating an unconfirmed write as definitely absent, but can create a duplicate. Apache Kafka documents how producer acknowledgment settings affect this guarantee in its replication and durability design.
Set replication and acknowledgment thresholds deliberately
Replication reduces the chance that a single machine failure destroys data, but it only protects against failures covered by the configured replicas and the service’s failure model. More replicas and stricter acknowledgments can reduce write availability or performance, so the safety threshold is a deliberate trade-off rather than a universal “maximum durability” switch.
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Kafka: acknowledgments apply to the current in-sync replicas
Kafka’s version 3.5 Streams resiliency guidance recommends considering acks=all, replication factor 3, min.insync.replicas=2, and one standby replica. These are configuration recommendations, not universal defaults. The documentation says replication factor 3 uses three times the storage of replication factor 1; its stated ability to tolerate up to two broker failures is specifically for the internal Streams topic under the described configuration. See Kafka Streams 3.5 configuration guidance.
The acknowledgment setting alone does not mean every assigned replica has the record. With acks=all, the leader waits for the current in-sync replica set (ISR). If the ISR has shrunk to one replica, that acknowledgment can still succeed unless a minimum ISR prevents the write. Kafka’s design documentation explains that min.insync.replicas can reject writes when too few replicas remain, reducing the risk of acknowledging a single copy at the cost of write availability. Disabling unclean leader election favors consistency by avoiding election of a stale replica when all ISR members are unavailable. Kafka summarizes the trade-off as “This is a simple tradeoff between availability and consistency.”
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acks=0 provides no server-receipt guarantee. With acks=1, the leader may acknowledge before followers replicate, leaving a loss window if the leader fails immediately. With acks=all, Kafka says a record is protected while at least one in-sync replica remains; that is not a guarantee against failures outside this model.
Make consumer progress follow durable output
A checkpoint, offset, or acknowledgment tells the system where processing can resume. Advance it only after the corresponding output is durable. If a worker writes to an external system and crashes before checkpointing, it will resume from its earlier position and process that record again. If it checkpoints first and the output then fails, the record may be skipped. The safe ordering is therefore: complete the destination write, then commit progress.
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That ordering prevents a checkpoint from moving past unfinished output, but it does not prevent duplicate work after a crash between the write and checkpoint. Give events stable IDs or natural keys, and make the destination operation idempotent where possible—for example, use a uniqueness constraint, a versioned update, or deterministic output names. AWS describes both duplicate paths in its Kinesis guidance for handling duplicate records: producer retries after an uncertain acknowledgment and processor restarts from the last checkpoint. Its example writes output to deterministic S3 filenames based on shard and first sequence number, then checkpoints after upload, so repeating the operation targets the same path.
Keep a recovery path for outages and bad records
Durable retention gives operators time to restore a consumer and replay data rather than relying on an in-memory queue or a one-shot delivery. Set retention to cover the plausible detection, repair, and catch-up period for your workload; the appropriate duration depends on that workload and cannot be inferred from a platform limit alone. Plan separately for a temporary outage, where replay is appropriate, and a poison record, where repeated delivery may block progress and the record needs inspection or correction.
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| Service or approach | Documented durability and recovery behavior | Important boundary |
|---|---|---|
| Kafka | Producer acknowledgment, replication, ISR thresholds, and leader-election settings control the durability/availability trade-off. See Kafka’s replication design and Streams configuration guidance. | acks=all means the current ISR, not necessarily every assigned replica. |
| Amazon Kinesis | Consumers can restart from their last checkpoint; deterministic output paths and stable keys can make repeated work safe. See AWS duplicate-record guidance. | A producer timeout before acknowledgment leaves arrival uncertain; retrying may write the record twice. |
| Google Cloud Pub/Sub | The documented Kafka-migration guidance states unacknowledged messages are retained for up to 7 days by default and describes acknowledged-message retention for replay, also up to 7 days under the documented subscription behavior. Timestamp replay can mark later messages unacknowledged; subscription snapshots can help recover from erroneous acknowledgments. A dead-letter topic can retain repeatedly failing messages with a configurable delivery-attempt count. See Google Cloud’s migration documentation. | Pub/Sub delivers each published message at least once per subscription, so subscribers need to tolerate duplicates. The retention figures and features are service-specific, not general streaming defaults. |
| Databricks Zerobus Ingest | The SDK retries transient errors automatically; flush() waits until submitted records are acknowledged as durable. On terminal stream failure, operators can recover unacknowledged records. See Databricks recovery and retry patterns. |
recreate_stream() requeues records already accepted, but does not retry a payload that failed to enqueue. A schema break after data is durable but before publication sends Parquet data to a fallback directory; after fixing the schema, the documented recovery is COPY INTO followed by expected-row-count verification. |
| Apache Druid streaming ingestion | Druid documents an exactly-once stream-processing guarantee for each indexing service using Kafka or Kinesis as an external source. A continuously running supervisor manages indexing-task state, failures, handoffs, scaling, and replication requirements. See Druid streaming ingestion documentation. | The exactly-once claim applies to the documented Druid ingestion boundary; it should not be assumed for unverified external side effects or other pipeline components. |
Test recovery, not just steady-state delivery
A configuration is only useful if the recovery sequence works under failure. Exercise the points where a write can succeed but its acknowledgment or checkpoint can be lost, and verify that replay restores the expected output without silently skipping records.
- Test a producer crash or network interruption after sending a record but before receiving its acknowledgment; confirm retries do not cause harmful duplicate effects.
- Test a worker crash after destination output but before checkpointing; verify restart and replay are safe.
- For replicated services, simulate broker loss or a reduced replica set and verify both the acknowledgment behavior and the expected availability trade-off.
- Test schema rejection and poison records; confirm failed payloads have a monitored recovery route instead of being silently dropped or retried without limit.
- Replay a known range and verify output counts or stable event IDs against an independent expected result. For the documented Zerobus schema-recovery flow, verify the expected row counts after
COPY INTO. - Observe backlog catch-up after an outage, not only whether consumers restart; a consumer that resumes but cannot drain the backlog within the available retention window is not a complete recovery plan.
Monitor producer errors and unacknowledged records, consumer lag, checkpoint age, retry volume, dead-letter volume, and recovery completion. Alert on a growing backlog or stale checkpoint early enough to act before retained data expires.
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