I am currently building a spark structured streaming application where I am
doing a batch-stream join. And the source for the batch data gets updated
periodically.
So, I am planning to do a persist/unpersist of that batch data periodically.
Below is a sample code which I am using to persist
I experienced the below two cases when unpersisting or destroying broadcast
variables in pyspark. But the same works good in spark scala shell. Any clue
why this happens ? Is it a bug in pyspark?
***Case 1:***
>>> b1 = sc.broadcast([1,2,3])
>>> b1.value
[1, 2, 3]
>>> b1.destroy()
Is there a way to unpersist all data frames, data sets, and/or RDD in Spark
2.2 in a single call?
Thanks
--
Cesar Flores
Hi,
Please call "Graph#unpersist" that releases two RDDs, vertex and edge ones.
"Graph#unpersist" just invokes "Graph#unpersistVertices" and
"Graph#edges#unpersist";
"Graph#unpersistVertices" releases memory for vertices and
"Graph#edg
Hi, What is he best way to unpersist the RDD in graphx to release memory?
RDD.unpersist
or
RDD.unpersistVertices and RDD..edges.unpersist
I study the source code of Pregel.scala, Both of above were used between
line 148 and line 150. Can anyone please tell me what the different? In
addition, what
questions,
-Andrew
2016-01-13 11:36 GMT-08:00 Alexander Pivovarov <apivova...@gmail.com>:
> Is it possible to automatically unpersist RDDs which are not used for 24
> hours?
>
Is it possible to automatically unpersist RDDs which are not used for 24
hours?
Sean,
Thanks.
It's a developer API and doesn't appear to be exposed.
Ewan
On 07/12/15 15:06, Sean Owen wrote:
I'm not sure if this is available in Python but from 1.3 on you should
be able to call ALS.setFinalRDDStorageLevel with level "none" to ask
it to unpersist when it is don
how I
could unpersist the data so I could clean up the files, but I was
unsuccessful.
We're using Spark 1.5.0
Yours,
Ewan
On 16/07/15 23:18, Stahlman, Jonathan wrote:
Hello all,
I am running the Spark recommendation algorithm in MLlib and I have
been studying its output with various mo
I'm not sure if this is available in Python but from 1.3 on you should
be able to call ALS.setFinalRDDStorageLevel with level "none" to ask
it to unpersist when it is done.
On Mon, Dec 7, 2015 at 1:42 PM, Ewan Higgs <ewan.hi...@ugent.be> wrote:
> Jonathan,
> Did you
1. Spark Streaming will automatically unpersist outdated data (you already
mentioned about the configurations).
2. If streaming job is started, I think you may lose the control of the job,
when do you call this unpersist, how to call this unpersist (from another
thread)?
Thanks
Saisai
On Thu, N
usage scenario of DStream unpersisting?
>
> From my understanding:
>
> 1. Spark Streaming will automatically unpersist outdated data (you already
> mentioned about the configurations).
> 2. If streaming job is started, I think you may lose the control of the
> job, when do
> Hi Swetha,
>>
>> Would you mind elaborating your usage scenario of DStream unpersisting?
>>
>> From my understanding:
>>
>> 1. Spark Streaming will automatically unpersist outdated data (you
>> already mentioned about the configurations).
>> 2.
Hi,
How to unpersist a DStream in Spark Streaming? I know that we can persist
using dStream.persist() or dStream.cache. But, I don't see any method to
unPersist.
Thanks,
Swetha
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/How-to-unpersist-a-DStream
Hi Swetha,
Would you mind elaborating your usage scenario of DStream unpersisting?
>From my understanding:
1. Spark Streaming will automatically unpersist outdated data (you already
mentioned about the configurations).
2. If streaming job is started, I think you may lose the control of the
Hi,
DStream->Discretized Streams are made up of multiple RDDs
You can unpersist each RDD by accessing the individual RDD's using
dstreamrdd.foreachRDD
{
rdd.unpersist().
}
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/How-to-unpersist-a-DStr
Other than setting the following.
sparkConf.set("spark.streaming.unpersist", "true")
sparkConf.set("spark.cleaner.ttl", "7200s")
On Wed, Nov 4, 2015 at 5:03 PM, swetha <swethakasire...@gmail.com> wrote:
> Hi,
>
> How to unpersist a DStream
rdd.unpersist while 3. is still running
Questions:
* Will the computation in 3) finish properly even if unpersist was called
on the rdd while running?
* What happens if a part of the computation fails and the rdd needs to
reconstruct based on DAG lineage, will this still work even though
unpersist
So in order to not incur any performance issues I should really wait for
all usage of the rdd to complete before calling unpersist, correct?
On Wed, Sep 16, 2015 at 4:08 PM, Tathagata Das <tathagata.das1...@gmail.com>
wrote:
> unpredictable. I think it will be safe (as in nothing sh
Yes.
On Wed, Sep 16, 2015 at 1:12 PM, Paul Weiss <paulweiss@gmail.com> wrote:
> So in order to not incur any performance issues I should really wait for
> all usage of the rdd to complete before calling unpersist, correct?
>
> On Wed, Sep 16, 2015 at 4:08 PM, Tathagata Das
Hi Stahlman,
finalRDDStorageLevel is the storage level for the final user/item
factors. It is not common to set it to StorageLevel.NONE, unless you
want to save the factors directly to disk. So if it is NONE, we cannot
unpersist the intermediate RDDs (in/out blocks) because the final
user/item
of the ALS calculation, these RDDs are no longer needed nor
returned, so I would assume the logical choice would be to unpersist() these
RDDs. The strategy in the code seems to be set by finalRDDStorageLevel, which
for some reason only calls unpersist() on the intermediate RDDs if
finalRDDStorageLevel
Hi Jonathan,
I believe calling persist with StorageLevel.NONE doesn't do anything.
That's why the unpersist has an if statement before it.
Could you give more information about your setup please? Number of cores,
memory, number of partitions of ratings_train?
Thanks,
Burak
On Wed, Jul 22, 2015
...@capitalone.com]
Sent: Wednesday, July 22, 2015 01:42 PM Eastern Standard Time
To: user@spark.apache.org
Subject: Re: How to unpersist RDDs generated by ALS/MatrixFactorizationModel
Hello again,
In trying to understand the caching of intermediate RDDs by ALS, I looked into
the source code
() is being called with finalRDDStorageLevel =
StorageLevel.NONE, which I would understand to mean that the intermediate RDDs
will not be persisted. Looking here:
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala#L631
unpersist() is only
. A sample code in python is
copied below.
The issue I have is that each new model which is trained caches a set of RDDs
and eventually the executors run out of memory. Is there any way in Pyspark to
unpersist() these RDDs after each iteration? The names of the RDDs which I
gather from the UI
rdd's which are no longer required will be removed from memory by spark
itself (which you can consider as lazy?).
Thanks
Best Regards
On Wed, Jul 1, 2015 at 7:48 PM, Jem Tucker jem.tuc...@gmail.com wrote:
Hi,
The current behavior of rdd.unpersist() appears to not be lazily executed
and
Hi,
After running some tests it appears the unpersist is called as soon as it
is reached, so any tasks using this rdd later on will have to re calculate
it. This is fine for simple programs but when an rdd is created within a
function and its reference is then lost but children of it continue
Unpersist Lazy
Hi,
After running some tests it appears the unpersist is called as soon as it is
reached, so any tasks using this rdd later on will have to re calculate it.
This is fine for simple programs but when an rdd is created within a function
and its reference is then lost but children
Hi,
The current behavior of rdd.unpersist() appears to not be lazily executed
and therefore must be placed after an action. Is there any way to emulate
lazy execution of this function so it is added to the task queue?
Thanks,
Jem
In a case that memory cannot hold all the cached RDD, then BlockManager
will evict some older block for storage of new RDD block.
Hope that will helpful.
2015-06-24 13:22 GMT+08:00 bit1...@163.com bit1...@163.com:
I am kind of consused about when cached RDD will unpersist its data. I
know we
I am kind of consused about when cached RDD will unpersist its data. I know we
can explicitly unpersist it with RDD.unpersist ,but can it be unpersist
automatically by the spark framework?
Thanks.
bit1...@163.com
What is the data size? Have you tried increasing the driver memory??
Thanks
Best Regards
On Sat, Jan 17, 2015 at 1:01 PM, Kevin (Sangwoo) Kim kevin...@apache.org
wrote:
Hi experts,
I got an error during unpersist RDD.
Any ideas?
java.util.concurrent.TimeoutException: Futures timed out
(Sangwoo) Kim kevin...@apache.org
wrote:
Hi experts,
I got an error during unpersist RDD.
Any ideas?
java.util.concurrent.TimeoutException: Futures timed out after [30
seconds] at
scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219) at
scala.concurrent.impl.Promise
Hi experts,
I got an error during unpersist RDD.
Any ideas?
java.util.concurrent.TimeoutException: Futures timed out after [30 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219) at
scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223
I want to create a temporary variables in a spark code.
Can I do this?
for (i - num)
{
val temp = ..
{
do something
}
temp.unpersist()
}
Thank You
like this?
var temp = ...
for (i - num)
{
temp = ..
{
do something
}
temp.unpersist()
}
Thanks
Best Regards
On Thu, Sep 11, 2014 at 3:26 PM, Deep Pradhan pradhandeep1...@gmail.com
wrote:
I want to create a temporary variables in a spark code.
Can I do this?
for (i - num)
{
After every loop I want the temp variable to cease to exist
On Thu, Sep 11, 2014 at 4:33 PM, Akhil Das ak...@sigmoidanalytics.com
wrote:
like this?
var temp = ...
for (i - num)
{
temp = ..
{
do something
}
temp.unpersist()
}
Thanks
Best Regards
On Thu, Sep 11, 2014
Hello,
Can someone enlighten me regarding whether call unpersist on a rdd is
expensive? what is the best solution to uncache the cached rdd?
Thanks
Edwin
Maybe It would be nice that unpersist() ‘triggers’ the computations of other
rdds that depends on it but not yet computed.
The pseudo code can be as follows:
unpersist()
{
if (this rdd has not been persisted)
return;
for (all rdds that depends on this rdd but not yet computed
FYI: Here is a related discussion
http://apache-spark-user-list.1001560.n3.nabble.com/Persist-and-unpersist-td6437.html
about this.
On Thu, Jun 12, 2014 at 8:10 PM, innowireless TaeYun Kim
taeyun@innowireless.co.kr wrote:
Maybe It would be nice that unpersist() ‘triggers’ the computations
Currently I use rdd.count() for forceful computation, as Nick Pentreath
suggested.
I think that it will be nice to have a method that forcefully computes a rdd,
so that the unnecessary rdds are safely unpersist()ed.
Let’s think a case that a rdd_a is a parent of both:
(1) a short-term
(I¡¯ve clarified the statement (1) of my previous mail. See below.)
From: innowireless TaeYun Kim [mailto:taeyun@innowireless.co.kr]
Sent: Friday, June 13, 2014 10:05 AM
To: user@spark.apache.org
Subject: RE: Question about RDD cache, unpersist, materialization
Currently I use
If you want to force materialization use .count()
Also if you can simply don't unpersist anything, unless you really need to free
the memory
—
Sent from Mailbox
On Wed, Jun 11, 2014 at 5:13 AM, innowireless TaeYun Kim
taeyun@innowireless.co.kr wrote:
BTW, it is possible that rdd.first
Hi,
What I (seems to) know about RDD persisting API is as follows:
- cache() and persist() is not an action. It only does a marking.
- unpersist() is also not an action. It only removes a marking. But if the
rdd is already in memory, it is unloaded.
And there seems no API to forcefully
Subject: Question about RDD cache, unpersist, materialization
Hi,
What I (seems to) know about RDD persisting API is as follows:
- cache() and persist() is not an action. It only does a marking.
- unpersist() is also not an action. It only removes a marking. But if the
rdd is already in memory
I keep bumping into a problem with persisting RDDs. Consider this (silly)
example:
def everySecondFromBehind(input: RDD[Int]): RDD[Int] = {
val count = input.count
if (count % 2 == 0) {
return input.filter(_ % 2 == 1)
} else {
return input.filter(_ % 2 == 0)
}
}
The situation is
Daniel,
Is SPARK-1103 https://issues.apache.org/jira/browse/SPARK-1103 related to
your example? Automatic unpersist()-ing of unreferenced RDDs would be nice.
Nick
On Tue, May 27, 2014 at 12:28 PM, Daniel Darabos
daniel.dara...@lynxanalytics.com wrote:
I keep bumping into a problem
I think what's desired here is for input to be unpersisted automatically as
soon as result is materialized. I don't think there's currently a way to do
this, but the usual workaround is to force result to be materialized
immediately and then unpersist input:
input.cache()val count
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