Hi All, As you might have already seen there is an effort tracked in FLINK-12005 [1] to support event time scale for state with time-to-live (TTL) [2]. While thinking about design, we realised that there can be multiple options for semantics of this feature, depending on use case. There is also sometimes confusion because of event time out-of-order nature in Flink. I am starting this thread to discuss potential use cases of this feature and their requirements for interested users and developers. There was already discussion thread asking about event time for TTL and it already contains some thoughts [3].
There are two semantical cases where we use time for TTL feature at the moment. Firstly, we store timestamp of state last access/update. Secondly, we use this timestamp and current timestamp to check expiration and garbage collect state at some point later. At the moment, Flink supports *only processing time* for both timestamps: state *last access and current timestamp*. It is basically current local system unix epoch time. When it comes to event time scale, we also need to define what Flink should use for these two timestamps. Here I will list some options and their possible pros&cons for discussion. There might be more depending on use case. *Last access timestamp (stored in backend with the actual state value):* - *Event timestamp of currently being processed record.* This seems to be the simplest option and it allows user-defined timestamps in state backend. The problem here might be instability of event time which can not only increase but also decrease if records come out of order. This can lead to rewriting the state timestamp to smaller value which is unnatural for the notion of time. - *Max event timestamp of records seen so far for this record key.* This option is similar to the previous one but it tries to fix the notion of time to make it always increasing. Maintaining this timestamp has also performance implications because the previous timestamp needs to be read out to decide whether to rewrite it. - *Last emitted watermark*. This is what we usually use for other operations to trigger some actions in Flink, like timers and windows but it can be unrelated to the record which actually triggers the state update. *Current timestamp to check expiration:* - *Event timestamp of last processed record.* Again quite simple but unpredictable option for out-of-order events. It can potentially lead to undesirable expiration of late buffered data in state without control. - *Max event timestamp of records seen so far for operator backend.* Again similar to previous one, more stable but still user does not have too much control when to expire state. - *Last emitted watermark*. Again, this is what we usually use for other operations to trigger some actions in Flink, like timers and windows. It also gives user some control to decide when state is expired (up to which point in event time) by emitting certain watermark. It is more flexible but complicated. If some watermark emitting strategy is already used for other operations, it might be not optimal for TTL and delay state cleanup. - *Current processing time.* This option is quite simple, It would mean that user just decides which timestamp to store but it will expire in real time. For data privacy use case, it might be better because we want state to be unavailable in particular real moment of time since the associated piece of data was created in event time. For long term approximate garbage collection, it might be not a problem as well. For quick expiration, the time skew between event and processing time can lead again to premature deletion of late data and user cannot delay it. We could also make this behaviour configurable. Another option is to make time provider pluggable for users. The interface can give users context (currently processed record, watermark etc) and ask them which timestamp to use. This is more complicated though. Looking forward for your feedback. Best, Andrey [1] https://issues.apache.org/jira/browse/FLINK-12005 [2] https://docs.google.com/document/d/1SI_WoXAfOd4_NKpGyk4yh3mf59g12pSGNXRtNFi-tgM [3] http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/State-TTL-in-Flink-1-6-0-td22509.html
