Thanks for the info Ryan :-) Will join on Hangouts! Warm Regards,Srabasti Banerjee
On Wednesday, 14 November, 2018, 4:13:32 PM GMT-8, Ryan Blue <rb...@netflix.com.INVALID> wrote: Srabasti, It looks like the live stream only works within the host's domain. Everyone should just join the meet/hangout. On Wed, Nov 14, 2018 at 4:08 PM Srabasti Banerjee <srabast...@ymail.com> wrote: Hi All, I am trying to view using gmail and see following message as below. Anyone getting the same error? Are there any alternate options? Any number I can dial in or Webex that I can attend? Thanks for your help in advance :-) Warm Regards,Srabasti Banerjee On Wednesday, 14 November, 2018, 9:44:11 AM GMT-8, Ryan Blue <rb...@netflix.com.INVALID> wrote: The live stream link for this is https://stream.meet.google.com/stream/6be59d80-04c7-44dc-9042-4f3b597fc8ba Some people said that it didn't work last time. I'm not sure why that would happen, but I don't use these much so I'm no expert. If you can't join the live stream, then feel free to join the meet up. I'll also plan on joining earlier than I did last time, in case we the meet/hangout needs to be up for people to view the live stream. rb On Tue, Nov 13, 2018 at 4:00 PM Ryan Blue <rb...@netflix.com> 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 - Capabilities API - see the dev list thread - Using CatalogTableIdentifier to reliably separate v2 code paths - see PR #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 BlueSoftware EngineerNetflix -- Ryan BlueSoftware EngineerNetflix -- Ryan BlueSoftware EngineerNetflix