ot;com.databricks...
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From: jeff saremi <jeffsar...@hotmail.com>
Sent: Tuesday, September 12, 2017 3:38:00 PM
To: user@spark.apache.org
Subject: Multiple Sources found for csv
I have this line which works in the spark interactive console but it fails in
Intellij
Using Sp
I have this line which works in the spark interactive console but it fails in
Intellij
Using Spark 2.1.1 in both cases:
Exception in thread "main" java.lang.RuntimeException: Multiple sources found
for csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat,
thanks Suresh. it worked nicely
From: Suresh Thalamati <suresh.thalam...@gmail.com>
Sent: Tuesday, September 12, 2017 2:59:29 PM
To: jeff saremi
Cc: user@spark.apache.org
Subject: Re: Continue reading dataframe from file despite errors
Try the CSV Option
.scala:250)
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From: jeff saremi <jeffsar...@hotmail.com>
Sent: Tuesday, September 12, 2017 2:32:03 PM
To: user@spark.apache.org
Subject: Continue reading dataframe from file despite errors
I'm using a statement like the following to load my dataframe from some text
file
Upon encoun
I'm using a statement like the following to load my dataframe from some text
file
Upon encountering the first error, the whole thing throws an exception and
processing stops.
I'd like to continue loading even if that results in zero rows in my dataframe.
How can i do that?
thanks
thanks Vadim. yes this is a good option for us. thanks
From: Vadim Semenov <vadim.seme...@datadoghq.com>
Sent: Wednesday, August 2, 2017 6:24:40 PM
To: Suzen, Mehmet
Cc: jeff saremi; user@spark.apache.org
Subject: Re: How can i remove the need for calling
as
hoping for
From: Vadim Semenov <vadim.seme...@datadoghq.com>
Sent: Tuesday, August 1, 2017 12:05:17 PM
To: jeff saremi
Cc: user@spark.apache.org
Subject: Re: How can i remove the need for calling cache
You can use `.checkpoint()`:
```
val sc: SparkContext
sc.setCheckpoint
hen
duplication is already minimized even without an explicit cache call.
On Tue, Aug 1, 2017 at 11:05 AM, jeff saremi
<jeffsar...@hotmail.com<mailto:jeffsar...@hotmail.com>> wrote:
Calling cache/persist fails all our jobs (i have posted 2 threads on this).
And we're giving up hope i
Thanks Vadim. I'll try that
From: Vadim Semenov <vadim.seme...@datadoghq.com>
Sent: Tuesday, August 1, 2017 12:05:17 PM
To: jeff saremi
Cc: user@spark.apache.org
Subject: Re: How can i remove the need for calling cache
You can use `.checkpoint()`:
```
effect as in my sample code
without the use of cache().
If I use myrdd.count() would that be a good alternative?
thanks
From: lucas.g...@gmail.com <lucas.g...@gmail.com>
Sent: Tuesday, August 1, 2017 11:23:04 AM
To: jeff saremi
Cc: user@spark.apache.org
S
Calling cache/persist fails all our jobs (i have posted 2 threads on this).
And we're giving up hope in finding a solution.
So I'd like to find a workaround for that:
If I save an RDD to hdfs and read it back, can I use it in more than one
operation?
Example: (using cache)
// do a whole bunch
asking this on a tangent:
Is there anyway for the shuffle data to be replicated to more than one server?
thanks
From: jeff saremi <jeffsar...@hotmail.com>
Sent: Friday, July 28, 2017 4:38:08 PM
To: Juan Rodríguez Hortalá
Cc: user@spark.apache.org
Subje
etwork.timeout=1000s ^
From: Juan Rodríguez Hortalá <juan.rodriguez.hort...@gmail.com>
Sent: Friday, July 28, 2017 4:20:40 PM
To: jeff saremi
Cc: user@spark.apache.org
Subject: Re: Job keeps aborting because of
org.apache.spark.shuffle.FetchFailedException: Faile
. Thank you. Yes this is the same problem
however it looks like no one has come up with a solution for this problem yet
From: yohann jardin <yohannjar...@hotmail.com>
Sent: Friday, July 28, 2017 10:47:40 AM
To: jeff saremi; user@spark.apache.org
Subject: R
We have a not too complex and not too large spark job that keeps dying with
this error
I have researched it and I have not seen any convincing explanation on why
I am not using a shuffle service. Which server is the one that is refusing the
connection?
If I go to the server that is being
From: yohann jardin <yohannjar...@hotmail.com>
Sent: Thursday, July 27, 2017 11:15:39 PM
To: jeff saremi; user@spark.apache.org
Subject: Re: How to configure spark on Yarn cluster
Check the executor page of the Spark UI, to check if your storage level is
limiting.
Also, i
I have the simplest job which i'm running against 100TB of data. The job keeps
failing with ExecutorLostFailure's on containers killed by Yarn for exceeding
memory limits
I have varied the executor-memory from 32GB to 96GB, the
spark.yarn.executor.memoryOverhead from 8192 to 36000 and similar
Thank you. That helps
From: 周康 <zhoukang199...@gmail.com>
Sent: Monday, July 24, 2017 8:04:51 PM
To: jeff saremi
Cc: user@spark.apache.org
Subject: Re: How to list only erros for a stage
May be you can click Header Status cloumn of Task section,then faile
On the Spark status UI you can click Stages on the menu and see Active (and
completed stages). For the active stage, you can see Succeeded/Total and a
count of failed ones in paranthesis.
I'm looking for a way to go straight to the failed tasks and list the errors.
Currently I must go into
EXCEPT is not the same as EXCEPT ALL
Had they implemented EXCEPT ALL in SparkSQL one could have easily obtained
EXCEPT by adding a disctint() to the results
From: hareesh makam <makamhare...@gmail.com>
Sent: Thursday, July 6, 2017 12:48:18 PM
To: jeff sar
I tried this query in 1.6 and it failed:
SELECT * FROM Table1 EXCEPT ALL SELECT * FROM Table2
Exception in thread "main" java.lang.RuntimeException: [1.32] failure: ``(''
expected but `all' found
thanks
Jeff
ery annoying as
such forcing us to stay conservative and just make do without sql. I'm sure
we're not alone here.
From: Aaron Perrin <aper...@gravyanalytics.com>
Sent: Tuesday, June 27, 2017 4:50:25 PM
To: Ryan; jeff saremi
Cc: user@spark.apache.org
Subject: Re: What
.
From: Ryan <ryan.hd@gmail.com>
Sent: Sunday, June 25, 2017 7:18:32 PM
To: jeff saremi
Cc: user@spark.apache.org
Subject: Re: What is the equivalent of mapPartitions in SpqrkSQL?
Why would you like to do so? I think there's no need for us to explicitly ask
for a forEachPartition in spa
You can do a map() using a select and functions/UDFs. But how do you process a
partition using SQL?
never mind!
I has a space at the end of my data which was not showing up in manual testing.
thanks
From: jeff saremi <jeffsar...@hotmail.com>
Sent: Tuesday, June 20, 2017 2:48:06 PM
To: user@spark.apache.org
Subject: Bizzare diff in behavior between scal
I have this function which does a regex matching in scala. I test it in the
REPL I get expected results.
I use it as a UDF in sparkSQL i get completely incorrect results.
Function:
class UrlFilter (filters: Seq[String]) extends Serializable {
val regexFilters = filters.map(new Regex(_))
Thanks Sidney
From: Sidney Feiner <sidney.fei...@startapp.com>
Sent: Thursday, January 19, 2017 9:52 AM
To: jeff saremi
Cc: user@spark.apache.org
Subject: Re: Spark-submit: where do --files go?
Every executor creates a directory with your submitted
i wish someone added this to the documentation
From: jeff saremi <jeffsar...@hotmail.com>
Sent: Thursday, January 19, 2017 9:56 AM
To: Sidney Feiner
Cc: user@spark.apache.org
Subject: Re: Spark-submit: where do --files go?
Thanks
I'd like to know how -- From within Java/spark -- I can access the dependent
files which i deploy using "--files" option on the command line?
From: shixi...@databricks.com
To: jeffsar...@hotmail.com
CC: user@spark.apache.org
The broadcasted object is serialized in driver and sent to the executors. And
in the executor, it will deserialize the bytes to get the broadcasted object.
On Fri, Feb 19, 2016 at 5:54 AM, jeff saremi <jeff
could someone please comment on this? thanks
From: jeffsar...@hotmail.com
To: user@spark.apache.org
Subject: Access to broadcasted variable
Date: Thu, 18 Feb 2016 14:44:07 -0500
I'd like to know if the broadcasted object gets serialized when accessed by the
executor during the execution
I'd like to know if the broadcasted object gets serialized when accessed by the
executor during the execution of a task?
I know that it gets serialized from the driver to the worker. This question is
inside worker when executor JVM's are accessing it
thanks
Jeff
unnecessary overhead of creating Java objects. As you've pointed out, this is
at the expense of making the code more verbose when caching.
-Sandy
On Fri, Nov 13, 2015 at 10:29 AM, jeff saremi <jeffsar...@hotmail.com> wrote:
So we tried reading a sequencefile in Spark and realized that all our reco
So we tried reading a sequencefile in Spark and realized that all our records
have ended up becoming the same.
THen one of us found this:
Note: Because Hadoop's RecordReader class re-uses the same Writable object for
each record, directly caching the returned RDD or directly passing it to an
@spark.apache.org
To: jeffsar...@hotmail.com
Are you talking about package which is listed onhttp://spark-packages.org
The package should come with installation instructions, right ?
On Oct 4, 2015, at 8:55 PM, jeff saremi <jeffsar...@hotmail.com> wrote:
So that it is available even in o
So that it is available even in offline mode? I can't seem to be able to find
any notes on thatthanksjeff
There are executor logs and driver logs. Most of them are not intuitive enough
to mean anything to us.
Are there any notes, documents, talks on how to decipher these logs and
troubleshoot our applications' performance as a result?
thanks
Jeff
i've tried desperately to create an RDD from a matrix i have. Every combination
failed.
I have a sparse matrix returned from a call to
dv = DictVectorizer()sv_tf = dv.fit_transform(tf)
which is supposed to be a matrix of document terms and their frequencies.
I need to convert this to
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