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  
---------------------------------------------------------------------
To unsubscribe e-mail: dev-unsubscr...@spark.apache.org

Reply via email to