If we compare file-stream source with other streaming sources such as Kafka, the current behavior is indeed incomplete. Starting the streaming from a custom offset/particular point of time is something that is missing. Typically filestream sources don't have auto-deletion of the older data/files. In kafka we can define the retention period. So even if we use "Earliest" we won't end up reading from the time when the Kafka topic was created. On the other hand, streaming sources can hold very old files. It's very valid use-cases to read the bulk of the old files using a batch job until a particular timestamp. And then use streaming jobs for real-time updates.
So having support where we can specify a timestamp. and we would consider files created post that timestamp can be useful. Another concern which we need to consider is the listing cost. is there any way we can avoid listing the entire base directory and then filtering out the new files. if the data is organized as partitions using date, will it help to list only those partitions where new files were added? On Thu, Jul 30, 2020 at 11:22 AM Jungtaek Lim <kabhwan.opensou...@gmail.com> wrote: > bump, is there any interest on this topic? > > On Mon, Jul 20, 2020 at 6:21 AM Jungtaek Lim <kabhwan.opensou...@gmail.com> > wrote: > >> (Just to add rationalization, you can refer the original mail thread on >> dev@ list to see efforts on addressing problems in file stream source / >> sink - >> https://lists.apache.org/thread.html/r1cd548be1cbae91c67e5254adc0404a99a23930f8a6fde810b987285%40%3Cdev.spark.apache.org%3E >> ) >> >> On Mon, Jul 20, 2020 at 6:18 AM Jungtaek Lim < >> kabhwan.opensou...@gmail.com> wrote: >> >>> Hi devs, >>> >>> As I have been going through the various issues on metadata log growing, >>> it's not only the issue of sink, but also the issue of source. >>> Unlike sink metadata log which entries should be available to the >>> readers, the source metadata log is only for the streaming query starting >>> from the checkpoint, hence in theory it should only memorize about >>> minimal entries which prevent processing multiple times on the same file. >>> >>> This is not applied to the file stream source, and I think it's because >>> of the existence of the "latestFirst" option which I haven't seen from any >>> sources. The option works as reading files in "backward" order, which means >>> Spark can read the oldest file and latest file together in a micro-batch, >>> which ends up having to memorize all files previously read. The option can >>> be changed during query restart, so even if the query is started with >>> "latestFirst" being false, it's not safe to apply the logic of minimizing >>> entries to memorize, as the option can be changed to true and then we'll >>> read files again. >>> >>> I'm seeing two approaches here: >>> >>> 1) apply "retention" - unlike "maxFileAge", the option would apply to >>> latestFirst as well. That said, if the retention is set to 7 days, the >>> files older than 7 days would never be read in any way. With this approach >>> we can at least get rid of entries which are older than retention. The >>> issue is how to play nicely with existing "maxFileAge", as it also plays >>> similar with the retention, though it's being ignored when latestFirst is >>> turned on. (Change the semantic of "maxFileAge" vs leave it to "soft >>> retention" and introduce another option.) >>> >>> (This approach is being proposed under SPARK-17604, and PR is available >>> - https://github.com/apache/spark/pull/28422) >>> >>> 2) replace "latestFirst" option with alternatives, which no longer read >>> in "backward" order - this doesn't say we have to read all files to move >>> forward. As we do with Kafka, start offset can be provided, ideally as a >>> timestamp, which Spark will read from such timestamp and forward order. >>> This doesn't cover all use cases of "latestFirst", but "latestFirst" >>> doesn't seem to be natural with the concept of SS (think about watermark), >>> I'd prefer to support alternatives instead of struggling with "latestFirst". >>> >>> Would like to hear your opinions. >>> >>> Thanks, >>> Jungtaek Lim (HeartSaVioR) >>> >>
