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
<[email protected]> 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 <[email protected]> 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
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