Hi Spark Team, I am interested in joining this meeting because I am interested in the data source v2 APIs. I couldn't find information about this meeting, so could someone please share the link?
Thanks, Jamison Bennett Cloudera Software Engineer jamison.benn...@cloudera.com 515 Congress Ave, Suite 1212 | Austin, TX | 78701 On Wed, Nov 14, 2018 at 1:51 AM Arun Mahadevan <ar...@apache.org> wrote: > 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 >> >
