ReadMeFeed / Community knowledge
Real questions. Useful conversations. Find the people who know your stack.
Idempotent Producer Limits The Kafka idempotent producer, introduced in 0.11, guarantees that a message with a given producer ID and sequence number is written at most once to a partition. To preserve ordering, the feature requires that max.in.flight.requests.per.connection be set to 5 or less. Retry Handling and Partial Writes When a producer retries after
Decide between idempotent and transactional delivery in Kafka. Compare latency, operational complexity, and configurations for financial event processing.
Julia's Base.retry(f; delays, check) re-executes a zero-argument function on failure, but provides no built-in idempotency guarantee. When f performs a write operation, a retry after a partially successful attempt can duplicate the effect, particularly for append-style writes such as push!, logging, or message-queue produce. Two documented approaches exist f
Goal: Use IBM Event Streams idempotent producer to guarantee that retries from IBM Cloud Functions do not result in duplicate writes to downstream systems while maintaining exactly‑once semantics per partition. Constraints: Idempotence only suppresses duplicates within a single producer session; a new Producer ID is generated if the producer is recreated, wh
The constraint A producer must survive client-side retries (network timeouts, broker failovers) without writing duplicate records to a single-partition-per-key workload. Kafka documents two relevant mechanisms: the idempotent producer ( enable.idempotence=true with acks=all and retries>0 ), and full transactional writes via initTransactions() / commitTran
I'm designing a Spark Structured Streaming pipeline (assume Spark 3.4/3.5) that writes micro-batches to an external store via foreachBatch , with checkpointing and the write-ahead log enabled. The hard constraint is that when a batch fails partway and Spark retries it, the target must not end up with duplicate records. Two documented patterns seem applicable
The goal is to achieve exactly‑once write semantics when a Pulsar IO JDBC sink retries a message after a transient failure. Transactional producers bypass broker‑side deduplication, so relying on deduplicationEnabled=true does not prevent duplicate inserts on retry. The sink receives the same message again and, if the JDBC operation is a plain INSERT, each r
UML 2.5 activity diagrams model concurrent branches using fork and join nodes, and support compensation actions to undo partial work when errors occur. A precise design goal is to determine whether compensation handlers should be automatically retried following a write failure, or whether explicit user intervention is required to prevent duplicate original w
Goal Maintain exactly‑once semantics across all Structured Streaming sinks during retries, avoiding duplicate writes when a task restarts. Current Landscape Apache Spark relies on checkpointing and write‑ahead logs to recover from failures. Exactly‑once guarantees are only available for sinks that implement idempotent writes (Kafka, Delta Lake, HDFS/Parquet)
apachekafka