IMO, the currentOffset should not be optional. For continuous mode I assume this offset gets periodically check pointed (so mandatory) ? For the micro batch mode the currentOffset would be the start offset for a micro-batch.
And if the micro-batch could be executed without knowing the 'latest' offset (say until 'next' returns false), we only need the current offset (to figure out the offset boundaries of a micro-batch) and may be then the 'latest' offset is not needed at all. - Arun On Tue, 13 Nov 2018 at 16:01, Ryan Blue <rb...@netflix.com.invalid> wrote: > Hi everyone, > I just wanted to send out a reminder that there’s a DSv2 sync tomorrow at > 17:00 PST, which is 01:00 UTC. > > Here are some of the topics under discussion in the last couple of weeks: > > - Read API for v2 - see Wenchen’s doc > > <https://docs.google.com/document/d/1uUmKCpWLdh9vHxP7AWJ9EgbwB_U6T3EJYNjhISGmiQg/edit?ts=5be4868a#heading=h.2h7sf1665hzn> > - Capabilities API - see the dev list thread > > <https://mail-archives.apache.org/mod_mbox/spark-dev/201811.mbox/%3CCAO4re1%3Doizqo1oFfVViK3bKWCp7MROeATXcWAEUY5%2B8Vpf6GGw%40mail.gmail.com%3E> > - Using CatalogTableIdentifier to reliably separate v2 code paths - > see PR #21978 <https://github.com/apache/spark/pull/21978> > - A replacement for InternalRow > > I know that a lot of people are also interested in combining the source > API for micro-batch and continuous streaming. Wenchen and I have been > discussing a way to do that and Wenchen has added it to the Read API doc as > Alternative #2. I think this would be a good thing to plan on discussing. > > rb > > Here’s some additional background on combining micro-batch and continuous > APIs: > > The basic idea is to update how tasks end so that the same tasks can be > used in micro-batch or streaming. For tasks that are naturally limited like > data files, when the data is exhausted, Spark stops reading. For tasks that > are not limited, like a Kafka partition, Spark decides when to stop in > micro-batch mode by hitting a pre-determined LocalOffset or Spark can just > keep running in continuous mode. > > Note that a task deciding to stop can happen in both modes, either when a > task is exhausted in micro-batch or when a stream needs to be reconfigured > in continuous. > > Here’s the task reader API. The offset returned is optional so that a task > can avoid stopping if there isn’t a resumeable offset, like if it is in the > middle of an input file: > > interface StreamPartitionReader<T> extends InputPartitionReader<T> { > Optional<LocalOffset> currentOffset(); > boolean next() // from InputPartitionReader > T get() // from InputPartitionReader > } > > The streaming code would look something like this: > > Stream stream = scan.toStream() > StreamReaderFactory factory = stream.createReaderFactory() > > while (true) { > Offset start = stream.currentOffset() > Offset end = if (isContinuousMode) { > None > } else { > // rate limiting would happen here > Some(stream.latestOffset()) > } > > InputPartition[] parts = stream.planInputPartitions(start) > > // returns when needsReconfiguration is true or all tasks finish > runTasks(parts, factory, end) > > // the stream's current offset has been updated at the last epoch > } > > -- > Ryan Blue > Software Engineer > Netflix >
