if i use em client deduplicator will contacts be deleted

If you are writing files, exactly once is guaranteed as any retries by Dataflow workers in event of failure will overwrite the file. Depending on the sink, duplicates may be filtered out, over-written or appear as duplicates. It is the responsibility of the sink to detect these duplicates and handle them accordingly. You can read more about how this is achieved in this blog.Įach element can be retried multiple times by Dataflow workers and may produce duplicate writes. Dataflow does guarantee exactly once processing by leveraging checkpointing at each stage to ensure such duplicates are not reprocessed affecting state or output. However Dataflow ensures that only one of those tries wins and the processing from the other tries does not affect downstream fused stages. You can read more about this topic on one of our earlier blog under the section “Example source: Cloud Pub/Sub”ĭue to the distributed nature of processing in Dataflow each message may be retried multiple times on different Dataflow workers. Thus Dataflow filters out such duplicates originating from redelivery of the same message by Pub/Sub. However, because Pub/Sub provides each message with a unique message_id, Dataflow uses it to deduplicate messages by default if you use the built-in Apache Beam PubSubIO.

This can be monitored using metrics listed here. However this is best effort and there is a possibility that messages may be redelivered. If a message has not been acknowledged within its acknowledgement deadline, Dataflow attempts to maintain the lease on the message by repeatedly extending the acknowledgement deadline to prevent redelivery from Pub/Sub. A break in fusion happens when there is a shuffle which happens if you have transforms like GROUP BY, COMBINE or I/O transforms like BigQueryIO. Dataflow does optimization called fusion where multiple stages can be combined into a single fused stage. When Dataflow receives messages from Pub/Sub subscription, messages are acknowledged after they are successfully processed by the first fused stage. It is the responsibility of the subscribing system (for example Dataflow) to detect such duplicates and handle accordingly. In such scenarios the same message would be redelivered and subscribers may see duplicate data. The subscriber may acknowledge after the acknowledgement deadline or the acknowledgement may be lost due to transient network issues. This means that a message may be delivered more than once by the same subscription if Pub/Sub doesn’t receive acknowledgement within the acknowledgement deadline. Pub/Sub guarantees at least once delivery for every subscription. For Pub/Sub these are unique messages as they have different message_id. If retries happen, we may end up with duplicate messages with different message_id in Pub/Sub. If success of the publish is not observed for some reason (network delays, interruptions etc) the same message payload may be retried by the publisher.

Within a topic all messages have a unique message_id and no two messages have the same message_id. Each message is considered successfully published by the publisher when Pub/Sub returns an acknowledgement to the publisher. Pub/Sub automatically assigns a unique message_id to each message successfully published to a topic. Such duplicates are referred to as ‘publisher generated duplicates’. Your publisher when publishing messages to Pub/Sub can generate duplicates due to at-least-once publishing guarantees. This feature guarantees at-least-once delivery to Pub/Sub and can produce duplicate notifications. One example where this could happen is when you set trigger notifications from Google Cloud Storage to Pub/Sub in response to object changes to GCS buckets. Such duplicates are referred to as ‘source generated duplicates’.

There could be several reasons like network failure, system errors etc that can produce duplicate data. Your data source system may itself produce duplicate data.

If i use em client deduplicator will contacts be deleted