Here[1] it is, please review
[1] https://issues.apache.org/jira/browse/SPARK-31854
On 20/05/27 10:21PM, Xiao Li wrote:
> Thanks for reporting it. Please open a JIRA with a test case.
>
> Cheers,
>
> Xiao
>
> On Wed, May 27, 2020 at 1:42 PM Pasha Finkelshteyn <
> pavel.finkelsht...@gmail.com> wr
Thanks Sean, got it.
Thanks,
Elango
On Thu, May 28, 2020, 9:04 PM Sean Owen wrote:
> I don't think so, that data is inherently ambiguous and incorrectly
> formatted. If you know something about the structure, maybe you can rewrite
> the middle column manually to escape the inner quotes and then
I can't reproduce the issue with my simple code:
```scala
spark.streams.addListener(new StreamingQueryListener {
override def onQueryProgress(event:
StreamingQueryListener.QueryProgressEvent): Unit = {
println(event.progress.id + " is on progress")
println(s"My accu is ${
Hi,
We have a Spark 2.4 job failed on Checkpoint recovery every few hours with
the following errors (from the Driver Log):
driver spark-kubernetes-driver ERROR 20:38:51 ERROR MicroBatchExecution:
Query impressionUpdate [id = 54614900-4145-4d60-8156-9746ffc13d1f, runId =
3637c2f3-49b6-40c2-b6d0-7e
Hi, anyone knows the behavior of dropping managed tables in case of
external hive meta store:
Deletion of the data (e.g. from object store) happens from Spark sql or,
the external hive metastore ?
Confused by local mode and remote mode codes.
I am assuming StateUpdateTask is your application specific class. Does it
have 'updateState' method or something? I googled but couldn't find any
documentation about doing it this way. Can you please direct me to some
documentation. Thanks.
On Thu, May 28, 2020 at 4:43 AM Srinivas V wrote:
> yes
You can’t play much if it is a streaming job. But in case of batch jobs,
sometimes teams will copy their S3 data to HDFS in prep for the next run :D
From: randy clinton
Date: Thursday, May 28, 2020 at 5:50 AM
To: Dark Crusader
Cc: Jörn Franke , user
Subject: Re: Spark dataframe hdfs vs s3
See
I don't think so, that data is inherently ambiguous and incorrectly
formatted. If you know something about the structure, maybe you can rewrite
the middle column manually to escape the inner quotes and then reparse.
On Thu, May 28, 2020 at 10:25 AM elango vaidyanathan
wrote:
> Is there any way I
Is there any way I can handle it in code?
Thanks,
Elango
On Thu, May 28, 2020, 8:52 PM Sean Owen wrote:
> Your data doesn't escape double-quotes.
>
> On Thu, May 28, 2020 at 10:21 AM elango vaidyanathan
> wrote:
>
>>
>> Hi team,
>>
>> I am loading an CSV. One column contains a json value. I am
Your data doesn't escape double-quotes.
On Thu, May 28, 2020 at 10:21 AM elango vaidyanathan
wrote:
>
> Hi team,
>
> I am loading an CSV. One column contains a json value. I am unable to
> parse that column properly. Below is the details. Can you please check once?
>
>
>
> val df1=spark.read.opt
Hi team,
I am loading an CSV. One column contains a json value. I am unable to parse
that column properly. Below is the details. Can you please check once?
val df1=spark.read.option("inferSchema","true").
option("header","true").option("quote", "\"")
.option("escape", "\"").csv("/FileStore/tab
Giving the code below:
//accumulators is a class level variable in driver.
sparkSession.streams().addListener(new StreamingQueryListener() {
@Override
public void onQueryStarted(QueryStartedEvent queryStarted) {
logger.info("Query started: " + queryStarted.
May I get how the accumulator is accessed in the method `onQueryProgress()`?
AFAICT, the accumulator is incremented well. There is a way to verify that
in cluster like this:
```
// Add the following while loop before invoking awaitTermination
while (true) {
println("My acc: " + myAcc
See if this helps
"That is to say, on a per node basis, HDFS can yield 6X higher read
throughput than S3. Thus, *given that the S3 is 10x cheaper than HDFS, we
find that S3 is almost 2x better compared to HDFS on performance per
dollar."*
*https://databricks.com/blog/2017/05/31/top-5-reasons-for-
yes, I am using stateful structured streaming. Yes similar to what you do.
This is in Java
I do it this way:
Dataset productUpdates = watermarkedDS
.groupByKey(
(MapFunction) event ->
event.getId(), Encoders.STRING())
.mapGroupsWithState(
Thanks Fabiano. I am building one myself. Will surely use yours as quick
starter.
On Wed, 27 May 2020, 18:00 Gaetano Fabiano,
wrote:
> I have no idea.
>
> I compiled a docker image that you can find on docker hub and you can do
> some experiments with it composing a cluster.
>
> https://hub.dock
Ok, thanks for the update Sean.
Can I also track RC vote?
On Wed, 27 May 2020, 18:12 Sean Owen, wrote:
> No firm dates; it always depends on RC voting. Another RC is coming soon.
> It is however looking pretty close to done.
>
> On Wed, May 27, 2020 at 3:54 AM ARNAV NEGI SOFTWARE ARCHITECT <
>
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