spark-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Srabasti Banerjee <srabast...@ymail.com.INVALID>
Subject Re: DataSourceV2 sync today
Date Thu, 15 Nov 2018 00:08:50 GMT
 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 <rblue@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 <rblue@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  
Mime
View raw message