@Richard I had pasted the two versions of the code below and I still
couldn't figure out why it wouldn't work without .collect ? Any help would
be great
*The code below doesn't work and sometime I also run into OutOfMemory
error.*
jsonMessagesDStream
.window(new Duration(6), new Duratio
Hi,
I am running spark sql job and it completes without any issues. I am
getting errors as
ERROR: SparkListenerBus has already stopped! Dropping event
SparkListenerExecutorMetricsUpdate after completion of job. could anyone
share your suggestions on how to avoid it.
Thanks,
Asmath
Thank you for the info, is there a way to get all keys of JSON, so that i
can create a dataframe with json keys, as below,
fieldsDataframe.withColumn("data" ,
functions.get_json_object($"RecordString", "$.id")) this is for appending
a single column in dataframe with id key.
I would like to aut
You can use get
On Thu, 7 Dec 2017 at 10:39 am, satyajit vegesna
wrote:
> Does spark support automatic detection of schema from a json string in a
> dataframe.
>
> I am trying to parse a json string and do some transofrmations on to it
> (would like append new columns to the dataframe) , from th
On Thu, 7 Dec 2017 at 11:37 am, ayan guha wrote:
> You can use get_json function
>
> On Thu, 7 Dec 2017 at 10:39 am, satyajit vegesna <
> satyajit.apas...@gmail.com> wrote:
>
>> Does spark support automatic detection of schema from a json string in a
>> dataframe.
>>
>> I am trying to parse a jso
Does spark support automatic detection of schema from a json string in a
dataframe.
I am trying to parse a json string and do some transofrmations on to it
(would like append new columns to the dataframe) , from the data i stream
from kafka.
But i am not very sure, how i can parse the json in str
Searching online with key word such as auto explode schema doesn't come up what
I am looking for, so ask here ...
I want to explode Dataset schema where the dataset schema are nested structure
from complicated XML and its structure changes a lot with high frequency. After
checking api doc, I no
I am sure that the other agents have plentiful enough resources, but I
don't know why Spark only scheduled executors on one single node, up to
that node's capacity ( it is a different node everytime I run btw ).
I checked the DEBUG log from Spark Driver, didn't see any mention of
decline. But from
Hi All,
I have the following snippets of the code and I wonder what is the
difference between these two and which one should I use? I am using spark
2.2.
Dataset df = sparkSession.readStream()
.format("kafka")
.load();
df.createOrReplaceTempView("table");
df.printSchema();
*Dataset resu
Hi All!
I have a very weird memory issue (which is what a lot of people will most
likely say ;-)) with Spark running in standalone mode inside a Docker
container. Our setup is as follows: We have a Docker container in which we have
a Spring boot application that runs Spark in standalone mode. T
I prepare a simple example (python) as follows to illustrate what I found:
- The code works well by calling a persist beforehand under all Spark
versions
- Without calling persist, the code works well under Spark 2.2.0 but doesn't
work under Spark 2.1.1 and Spark 2.1.2
- It really looks like a b
Hello Ji,
Spark will launch Executors round-robin on offers, so when the resources on
an agent get broken into multiple resource offers it's possible that many
Executrors get placed on a single agent. However, from your description,
it's not clear why your other agents do not get Executors schedul
Hi,
LogisticAggregator [1] scales every sample on every iteration. Without
scaling binaryUpdateInPlace could be rewritten using BLAS.dot and that
would significantly improve performance.
However, there is a comment [2] saying that standardization and
caching of the dataset before training will "cr
thanks
the machine where spark job was being submitted had SPARK_HOME pointing old
2.1.1 directory.
On Wed, Dec 6, 2017 at 1:35 PM, Qiao, Richard
wrote:
> Are you now building your app using spark 2.2 or 2.1?
>
>
>
> Best Regards
>
> Richard
>
>
>
>
>
> *From: *Imran Rajjad
> *Date: *Wednesd
the words: [app-id] will actually be [base-app-id]/[attempt-id], where
[base-app-id] is the YARN application ID. is not so correct. As after I
changed the [app-id] to [base-app-id], it works.
Maybe we need to fix the document?
>From the information of the spark job or the spark stages, I can't se
Hi Kant,
> but would your answer on .collect() change depending on running the
spark app in client vs cluster mode?
No, it should make no difference.
-kr, Gerard.
On Tue, Dec 5, 2017 at 11:34 PM, kant kodali wrote:
> @Richard I don't see any error in the executor log but let me run again to
Are you now building your app using spark 2.2 or 2.1?
Best Regards
Richard
From: Imran Rajjad
Date: Wednesday, December 6, 2017 at 2:45 AM
To: "user @spark"
Subject: unable to connect to connect to cluster 2.2.0
Hi,
Recently upgraded from 2.1.1 to 2.2.0. My Streaming job seems to have broken
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