well the "hadoop" way is to save to a/b and a/c and read from a/* :)
On Tue, Feb 2, 2016 at 11:05 PM, Jerry Lam wrote:
> Hi Spark users and developers,
>
> anyone knows how to union two RDDs without the overhead of it?
>
> say rdd1.union(rdd2).saveTextFile(..)
> Th
Hi Spark users and developers,
anyone knows how to union two RDDs without the overhead of it?
say rdd1.union(rdd2).saveTextFile(..)
This requires a stage to union the 2 rdds before saveAsTextFile (2 stages).
Is there a way to skip the union step but have the contents of the two rdds
save to the
Just saw I'm not calling state.update() in my trackState function. I
guess that is the issue!
On Fri, Jan 29, 2016 at 9:36 AM, Sebastian Piu
wrote:
> Hi All,
>
> I'm playing with the new mapWithState functionality but I can't get it
> quite to work yet.
>
> I'm doing two print() calls on the
Hi All,
I'm playing with the new mapWithState functionality but I can't get it
quite to work yet.
I'm doing two print() calls on the stream:
1. after mapWithState() call, first batch shows results - next batches
yield empty
2. after stateSnapshots(), always yields an empty RDD
Any pointers on wh
Hi,
I am working with Spark in Java on top of a HDFS cluster. In my code two
RDDs are partitioned with the same partitioner (HashPartitioner with the
same number of partitions), so they are co-partitioned.
Thus same keys are on the same partitions' number but that does not mean
that both RDD
; a.reduceByKey(generateInvertedIndex)
>>>>>>> vectors:RDD.mapPartitions{
>>>>>>> iter =>
>>>>>>> val invIndex = invertedIndexes(samePartitionKey)
>>>>>>> iter.map(i
>>>>>> )
>>>>>> }
>>>>>>
>>>>>> How could I go about setting up the Partition such that the specific
>>>>>> data
>>>>>> structure I need will be pres
slow
>> down the process a lot)
>>
>> Another thought I am currently exploring is whether there is some way I
>> can
>> create a custom Partition or Partitioner that could hold the data
>> structure
>> (Although that might get too complicated and become pr
t; the
>>>>> extra overhead of sending over all values (which would happen if I
>>>>> were to
>>>>> make a broadcast variable).
>>>>>
>>>>> One thought I have been having is to store the objects in HDFS but I'm
>>
; (Although that might get too complicated and become problematic)
>
> Any thoughts on how I could attack this issue would be highly appreciated.
>
> thank you for your help!
>
>
>
> --
> View this message in context:
> htt
I were
>>>> to
>>>> make a broadcast variable).
>>>>
>>>> One thought I have been having is to store the objects in HDFS but I'm
>>>> not
>>>> sure if that would be a suboptimal solution (It seems like it could slow
>
; down the process a lot)
>>>
>>> Another thought I am currently exploring is whether there is some way I
>>> can
>>> create a custom Partition or Partitioner that could hold the data
>>> structure
>>> (Although that might get too complicated
tition or Partitioner that could hold the data
>> structure
>> (Although that might get too complicated and become problematic)
>>
>> Any thoughts on how I could attack this issue would be highly appreciated.
>>
>> thank you for your help!
>>
>>
>&g
that has been automatically cleaned, then you will run into
problems like shuffle fetch failures or broadcast variable not found etc,
which may fail your job.
Alternatively, Spark already automatically cleans up all variables that
have been garbage collected, including RDDs, shuffle dependencies
Is it possible to automatically unpersist RDDs which are not used for 24
hours?
ted and become problematic)
>
> Any thoughts on how I could attack this issue would be highly appreciated.
>
> thank you for your help!
>
>
>
> --
> View this message in context:
> http://apache-spark-user-list.1001560.n3.nabble.com/Sending-large-objects-to-specific-RDDs-tp
s issue would be highly appreciated.
thank you for your help!
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Sending-large-objects-to-specific-RDDs-tp25967.html
Sent from the Apache Spark User List mailing lis
utside the window length. A buffered
> writer is a good idea too, thanks.
>
>
>
> Thanks,
>
> Ewan
>
>
>
> *From:* Ashic Mahtab [mailto:as...@live.com]
> *Sent:* 31 December 2015 13:50
> *To:* Ewan Leith ; Apache Spark <
> user@spark.apache.org>
>
good
idea too, thanks.
Thanks,
Ewan
From: Ashic Mahtab [mailto:as...@live.com]
Sent: 31 December 2015 13:50
To: Ewan Leith ; Apache Spark
Subject: RE: Batch together RDDs for Streaming output, without delaying
execution of map or transform functions
Hi Ewan,
Transforms are definitions of what n
Hi Ewan,Transforms are definitions of what needs to be done - they don't
execute until and action is triggered. For what you want, I think you might
need to have an action that writes out rdds to some sort of buffered writer.
-Ashic.
From: ewan.le...@realitymine.com
To: user@spark.apach
Hi all,
I'm sure this must have been solved already, but I can't see anything obvious.
Using Spark Streaming, I'm trying to execute a transform function on a DStream
at short batch intervals (e.g. 1 second), but only write the resulting data to
disk using saveAsTextFiles in a larger batch after
e on the use case? It looks a little
>> bit like an abuse of Spark in general . Interactive queries that are not
>> suitable for in-memory batch processing might be better supported by ignite
>> that has in-memory indexes, concept of hot, warm, cold data etc. or hive on
>> tez
12/28/2015 5:22 PM (GMT-05:00)
To: Richard Eggert
Cc: Daniel Siegmann , Divya Gehlot
, "user @spark"
Subject: Re: DataFrame Vs RDDs ... Which one to use When ?
here's a good article that sums it up, in my opinion:
https://ogirardot.wordpress.com/2015/05/29/rdds-are-the-new
/2015 5:22 PM (GMT-05:00)
To: Richard Eggert
Cc: Daniel Siegmann , Divya Gehlot
, "user @spark"
Subject: Re: DataFrame Vs RDDs ... Which one to use When ?
here's a good article that sums it up, in my opinion:
https://ogirardot.wordpress.com/2015/05/29/rdds-are-the-new-byte
here's a good article that sums it up, in my opinion:
https://ogirardot.wordpress.com/2015/05/29/rdds-are-the-new-bytecode-of-apache-spark/
basically, building apps with RDDs is like building with apps with
primitive JVM bytecode. haha.
@richard: remember that even if you're current
ements or
JSON, then Spark SQL can automatically figure out how to convert it into an
RDD of Record objects (and therefore a DataFrame), but there's no way to
automatically go the other way (from DataFrame/Record back to custom types).
In general, you can ultimately do more with RDDs than DataFr
DataFrames are a higher level API for working with tabular data - RDDs are
used underneath. You can use either and easily convert between them in your
code as necessary.
DataFrames provide a nice abstraction for many cases, so it may be easier
to code against them. Though if you're us
Hi,
I am new bee to spark and a bit confused about RDDs and DataFames in Spark.
Can somebody explain me with the use cases which one to use when ?
Would really appreciate the clarification .
Thanks,
Divya
ll be one RDD in each batch.
>>
>> You could refer to the implementation of DStream#getOrCompute.
>>
>>
>> On Mon, Dec 21, 2015 at 11:04 AM, Arun Patel
>> wrote:
>>
>>> It may be simple question...But, I am struggling to understand this
>>&
run Patel
> wrote:
>
>> It may be simple question...But, I am struggling to understand this
>>
>> DStream is a sequence of RDDs created in a batch window. So, how do I
>> know how many RDDs are created in a batch?
>>
>> I am clear about the number of part
Normally there will be one RDD in each batch.
You could refer to the implementation of DStream#getOrCompute.
On Mon, Dec 21, 2015 at 11:04 AM, Arun Patel
wrote:
> It may be simple question...But, I am struggling to understand this
>
> DStream is a sequence of RDDs created i
It may be simple question...But, I am struggling to understand this
DStream is a sequence of RDDs created in a batch window. So, how do I know
how many RDDs are created in a batch?
I am clear about the number of partitions created which is
Number of Partitions = (Batch Interval
ap .
>
> > On 14 Dec 2015, at 17:19, Krishna Rao wrote:
> >
> > Hi all,
> >
> > What's the best way to run ad-hoc queries against a cached RDDs?
> >
> > For example, say I have an RDD that has been processed, and persisted to
> memory-only. I want
tez+llap .
> On 14 Dec 2015, at 17:19, Krishna Rao wrote:
>
> Hi all,
>
> What's the best way to run ad-hoc queries against a cached RDDs?
>
> For example, say I have an RDD that has been processed, and persisted to
> memory-only. I want to be
Hi all,
What's the best way to run ad-hoc queries against a cached RDDs?
For example, say I have an RDD that has been processed, and persisted to
memory-only. I want to be able to run a count (actually
"countApproxDistinct") after filtering by an, at compile time, unknown
(spe
Hi Mark,
Were you able to successfully store the RDD with Akhil's method? When you
read it back as an objectFile, you will also need to specify the correct
type.
You can find more information about integrating Spark and Tachyon on this
page: http://tachyon-project.org/documentation/Running-Spark-
t 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 is:
itemInBlocks
itemOutBlocks
Products
ratingBlocks
userInBlocks
user
with various model configurations. Ideally I would like
> to be able to run one job that trains the recommendation model with many
> different configurations to try to optimize for performance. A sample code
> in python is copied below.
>
> The issue I have is that each new model
us model configurations. Ideally I
would like to be able to run one job that trains the recommendation
model with many different configurations to try to optimize for
performance. A sample code in python is copied below.
The issue I have is that each new model which is trained caches a set
of
On Tue, Dec 1, 2015 at 10:57 AM, Shams ul Haque wrote:
> Thanks for the suggestion, i am going to try union.
...and please report your findings back.
> And what is your opinion on 2nd question.
Dunno. If you find a solution, let us know.
Jacek
har?promo=email_sig>
On Tue, Dec 1, 2015 at 3:34 PM, Sonal Goyal wrote:
> I think you should be able to join different rdds with same key. Have you
> tried that?
> On Dec 1, 2015 3:30 PM, "Praveen Chundi" wrote:
>
>> cogroup could be useful to you, since all t
I think you should be able to join different rdds with same key. Have you
tried that?
On Dec 1, 2015 3:30 PM, "Praveen Chundi" wrote:
> cogroup could be useful to you, since all three are PairRDD's.
>
>
> https://spark.apache.org/docs/
cogroup could be useful to you, since all three are PairRDD's.
https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions
Best Regards,
Praveen
On 01.12.2015 10:47, Shams ul Haque wrote:
Hi All,
I have made 3 RDDs of 3 different dataset, all RDD
help in your case.
>
> def union[T](rdds: Seq[RDD[T]])(implicit arg0: ClassTag[T]): RDD[T]
>
>
> http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.SparkContext
>
> Pozdrawiam,
> Jacek
>
> --
> Jacek Laskowski | https://medium.com/@jace
Hi,
Never done it before, but just yesterday I found out about
SparkContext.union method that could help in your case.
def union[T](rdds: Seq[RDD[T]])(implicit arg0: ClassTag[T]): RDD[T]
http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.SparkContext
Pozdrawiam,
Jacek
Hi All,
I have made 3 RDDs of 3 different dataset, all RDDs are grouped by
CustomerID in which 2 RDDs have value of Iterable type and one has signle
bean. All RDDs have id of Long type as CustomerId. Below are the model for
3 RDDs:
JavaPairRDD>
JavaPairRDD>
JavaPairRDD
Now, i have to mer
>
> In the near future, I guess GUI interfaces of Spark will be available
> soon. Spark users (e.g, CEOs) might not need to know what are RDDs at all.
> They can analyze their data by clicking a few buttons, instead of writing
> the programs. : )
That's not in the future.
compiler (i.e., Catalyst) will
optimize your programs. For most users, the SQL, DataFrame and Dataset APIs
are good enough to satisfy most requirements.
In the near future, I guess GUI interfaces of Spark will be available soon.
Spark users (e.g, CEOs) might not need to know what are RDDs at all. They
Thanks Michael, that helped me a lot!
On 23 November 2015 at 17:47, Michael Armbrust
wrote:
> Here is how I view the relationship between the various components of
> Spark:
>
> - *RDDs - *a low level API for expressing DAGs that will be executed in
> parallel by Spark workers
Here is how I view the relationship between the various components of Spark:
- *RDDs - *a low level API for expressing DAGs that will be executed in
parallel by Spark workers
- *Catalyst -* an internal library for expressing trees that we use to
build relational algebra and expression
Hi everyone,
I'm doing some reading-up on all the newer features of Spark such as
DataFrames, DataSets and Project Tungsten. This got me a bit confused on
the relation between all these concepts.
When starting to learn Spark, I read a book and the original paper on RDDs,
this lead
1- Is there any wat=y to either make the pair of RDDs from a Dstream-
Dstream ---> Dstream
so that i can use already defined corelation function in spark.
*Aim is to find auto-corelation value in spark .(As per my knowledge spark
streaming does not support this.)*
--
Thanks &
Hi,
I thought I understood RDDs and DataFrames, but one noob thing is bugging
me (because I'm seeing weird errors involving joins):
*What does Spark do when you pass a big dataframe as an argument to a
function? *
Are these dataframes included in the closure of the function, and is
ther
I think they are roughly of equal size.
On Fri, Nov 6, 2015 at 3:45 PM, Ted Yu wrote:
> Can you tell us a bit more about your use case ?
>
> Are the two RDDs expected to be of roughly equal size or, to be of vastly
> different sizes ?
>
> Thanks
>
> On Fri, Nov 6, 2015 a
Can you tell us a bit more about your use case ?
Are the two RDDs expected to be of roughly equal size or, to be of vastly
different sizes ?
Thanks
On Fri, Nov 6, 2015 at 3:21 PM, swetha wrote:
> Hi,
>
> What is the efficient way to join two RDDs? Would converting both the
Hi,
What is the efficient way to join two RDDs? Would converting both the RDDs
to IndexedRDDs be of any help?
Thanks,
Swetha
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/What-is-the-efficient-way-to-Join-two-RDDs-tp25310.html
Sent from the Apache Spark
I have seen that the problem is on the Geohash class that can not be
picked.. but in groupByKey i use an other custom class an there is no
problem...
2015-11-06 13:44 GMT+01:00 Iker Perez de Albeniz :
> Hi All,
>
> I am new at this list. Before sending this mail i have searched on archive
> but
Hi All,
I am new at this list. Before sending this mail i have searched on archive
but i have not found a solution for me.
i am using spark to process user locations based on RSSI. My spark script
look like this..
text_files = sc.textFile(','.join(files[startime]))
result = text_files.f
tu
>
>
> On Sat, Oct 31, 2015 at 11:18 PM, ayan guha wrote:
>
>> My java knowledge is limited, but you may try with a hashmap and put RDDs
>> in it?
>>
>> On Sun, Nov 1, 2015 at 4:34 AM, amit tewari
>> wrote:
>>
>>> Thanks Ayan thats somethi
Hi,
You should perform an action (e.g. count, take, saveAs*, etc. ) in order
for your RDDs to be cached since cache/persist are lazy functions. You
might also want to do coalesce instead of repartition to avoid shuffling.
Thanks,
Deng
On Mon, Nov 2, 2015 at 5:53 PM, Sushrut Ikhar
wrote:
>
Hi,
I need to split a RDD into 3 different RDD using filter-transformation.
I have cached the original RDD before using filter.
The input is lopsided leaving some executors with heavy load while others
with less; so I have repartitioned it.
*DAG-lineage I expected:*
I/P RDD --> MAP RDD --> SHUF
") )
..
;
Natu
On Sat, Oct 31, 2015 at 11:18 PM, ayan guha wrote:
> My java knowledge is limited, but you may try with a hashmap and put RDDs
> in it?
>
> On Sun, Nov 1, 2015 at 4:34 AM, amit tewari
> wrote:
>
>> Thanks Ayan thats something similar to what I am lo
My java knowledge is limited, but you may try with a hashmap and put RDDs
in it?
On Sun, Nov 1, 2015 at 4:34 AM, amit tewari wrote:
> Thanks Ayan thats something similar to what I am looking at but trying the
> same in Java is giving compile error:
>
> JavaRDD jRDD[] = n
> # In Driver
> fileList=["/file1.txt","/file2.txt"]
> rdds = []
> for f in fileList:
> rdd = jsc.textFile(f)
> rdds.append(rdd)
>
>
> On Sat, Oct 31, 2015 at 11:14 PM, ayan guha wrote:
>
>> Yes, this can be done. quick
Yes, this can be done. quick python equivalent:
# In Driver
fileList=["/file1.txt","/file2.txt"]
rdd = []
for f in fileList:
rdd = jsc.textFile(f)
rdds.append(rdd)
On Sat, Oct 31, 2015 at 11:09 PM, amit tewari
wrote:
> Hi
>
> I need the abil
Hi
I need the ability to be able to create RDDs programatically inside my
program (e.g. based on varaible number of input files).
Can this be done?
I need this as I want to run the following statement inside an iteration:
JavaRDD rdd1 = jsc.textFile("/file1.txt");
Thanks
Amit
I guess you can do a .saveAsObjectFiles and read it back as sc.objectFile
Thanks
Best Regards
On Fri, Oct 23, 2015 at 7:57 AM, mark wrote:
> I have Avro records stored in Parquet files in HDFS. I want to read these
> out as an RDD and save that RDD in Tachyon for any spark job that wants the
>
UpdateStateByKey automatically caches its RDDs.
On Tue, Oct 27, 2015 at 8:05 AM, diplomatic Guru
wrote:
>
> Hello All,
>
> When I checked my running Stream job on WebUI, I can see that some RDDs
> are being listed that were not requested to be cached. What more is that
> they
Hello All,
When I checked my running Stream job on WebUI, I can see that some RDDs are
being listed that were not requested to be cached. What more is that they
are growing! I've not asked them to be cached. What are they? Are they the
state (UpdateStateByKey)?
Only the rows in white are
I have Avro records stored in Parquet files in HDFS. I want to read these
out as an RDD and save that RDD in Tachyon for any spark job that wants the
data.
How do I save the RDD in Tachyon? What format do I use? Which RDD
'saveAs...' method do I want?
Thanks
Hello All,
I have two Cassandra RDDs. I am using joinWithCassandraTable which is
doing a cartesian join because of which we are getting unwanted rows.
How to perform inner join on Cassandra RDDs ? If I intend to use normal
join, i have to read entire table which is costly.
Is there any
hih...@gmail.com]
Sent: Monday, October 12, 2015 8:37 AM
To: Cheng, Hao
Cc: Richard Eggert; Subhajit Purkayastha; User
Subject: Re: Saprk 1.5 - How to join 3 RDDs in a SQL DF?
Some weekend reading:
http://stackoverflow.com/questions/20022196/are-left-outer-joins-associative
Cheers
On Sun, Oct 11, 2015 a
t [mailto:richard.egg...@gmail.com]
> *Sent:* Monday, October 12, 2015 5:12 AM
> *To:* Subhajit Purkayastha
> *Cc:* User
> *Subject:* Re: Saprk 1.5 - How to join 3 RDDs in a SQL DF?
>
>
>
> It's the same as joining 2. Join two together, and then join the third one
&g
A join B join C === (A join B) join C
Semantically they are equivalent, right?
From: Richard Eggert [mailto:richard.egg...@gmail.com]
Sent: Monday, October 12, 2015 5:12 AM
To: Subhajit Purkayastha
Cc: User
Subject: Re: Saprk 1.5 - How to join 3 RDDs in a SQL DF?
It's the same as join
It's the same as joining 2. Join two together, and then join the third one
to the result of that.
On Oct 11, 2015 2:57 PM, "Subhajit Purkayastha" wrote:
> Can I join 3 different RDDs together in a Spark SQL DF? I can find
> examples for 2 RDDs but not 3.
>
>
>
> Thanks
>
>
>
Can I join 3 different RDDs together in a Spark SQL DF? I can find examples
for 2 RDDs but not 3.
Thanks
The solution is to set 'spark.shuffle.io.preferDirectBufs' to 'false'.
Then it is working.
Cheers!
On Fri, Oct 9, 2015 at 3:13 PM, Ivan Héda wrote:
> Hi,
>
> I'm facing an issue with PySpark (1.5.1, 1.6.0-SNAPSHOT) running over Yarn
> (2.6.0-cdh5.4.4). Everything seems fine when working with d
Hi,
I'm facing an issue with PySpark (1.5.1, 1.6.0-SNAPSHOT) running over Yarn
(2.6.0-cdh5.4.4). Everything seems fine when working with dataframes, but
when i need RDD the workers start to fail. Like in the next code
table1 = sqlContext.table('someTable')
table1.count() ## OK ## cca 500 millions
Hi,
I have a couple of old application RDDs under /var/lib/spark/rdd that
haven't been properly cleaned up after themselves. Example:
# du -shx /var/lib/spark/rdd/*
44K /var/lib/spark/rdd/liblz4-java1011984124691611873.so
48K /var/lib/spark/rdd/snappy-1.0.5-libsnappyjava.so
2.3G /var/lib/
r 30, 2015 4:07 PM
> *To:* user@spark.apache.org
> *Subject:* Re: partition recomputation in big lineage RDDs
>
>
> Hi,
>
> In fact, my RDD will get a new version (a new RDD assigned to the same
> var) quite frequently, by merging bulks of 1000 events of events of last
>
rg
Subject: Re: partition recomputation in big lineage RDDs
Hi,
In fact, my RDD will get a new version (a new RDD assigned to the same var)
quite frequently, by merging bulks of 1000 events of events of last 10s.
But recomputation would be more efficient to do not by reading initial RDD
partit
Subject: partition recomputation in big lineage RDDs
Hi,
If I implement a manner to have an up-to-date version of my RDD by ingesting
some new events, called RDD_inc (from increment), and I provide a "merge"
function m(RDD, RDD_inc), which returns the RDD_new, it looks like I can e
Hi,
If I implement a manner to have an up-to-date version of my RDD by ingesting
some new events, called RDD_inc (from increment), and I provide a "merge"
function m(RDD, RDD_inc), which returns the RDD_new, it looks like I can evolve
the state of my RDD by constructing new RDDs al
:
Hi Zhiliang,
How about doing something like this?
val rdd3 = rdd1.zip(rdd2).map(p => p._1.zip(p._2).map(z => z._1 - z._2))
The first zip will join the two RDDs and produce an RDD of (Array[Float],
Array[Float]) pairs. On each pair, we zip the two Array[Float] components
together t
gt;>
>> On Tue, Sep 22, 2015 at 4:20 AM, Uthayan Suthakar <
>> uthayan.sutha...@gmail.com> wrote:
>>
>>>
>>> Hello All,
>>>
>>> We have a Spark Streaming job that reads data from DB (three tables) and
>>> cache them into memor
Hi Zhiliang,
How about doing something like this?
val rdd3 = rdd1.zip(rdd2).map(p =>
p._1.zip(p._2).map(z => z._1 - z._2))
The first zip will join the two RDDs and produce an RDD of (Array[Float],
Array[Float]) pairs. On each pair, we zip the two Array[Float] components
together to f
cache them into memory ONLY at the start then it will happily carry out the
>> incremental calculation with the new data. What we've noticed occasionally
>> is that one of the RDDs caches only 90% of the data. Therefore, at each
>> execution time the remaining 10% had to be r
there is matrix add API, might map rdd2 each row element to be negative , then
make rdd1 and rdd2 and call add ?
Or some more ways ...
On Wednesday, September 23, 2015 3:11 PM, Zhiliang Zhu
wrote:
Hi All,
There are two RDDs : RDD> rdd1, and RDD> rdd2,that
is to say, rd
Hi All,
There are two RDDs : RDD> rdd1, and RDD> rdd2,that
is to say, rdd1 and rdd2 are similar with DataFrame, or Matrix with same row
number and column number.
I would like to get RDD> rdd3, each element in rdd3 is the
subtract between rdd1 and rdd2 of thesame position, which i
) and
> cache them into memory ONLY at the start then it will happily carry out the
> incremental calculation with the new data. What we've noticed occasionally
> is that one of the RDDs caches only 90% of the data. Therefore, at each
> execution time the remaining 10% had to be r
one during actions stage.
--
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Sent from the Apache Spark User List mailing list archive at Nabble.com.
-
To u
In general, RDDs get partitioned automatically without programmer
intervention. You generally don't need to worry about them unless you need
to adjust the size/number of partitions or the partitioning scheme
according to the needs of your application. Partitions get redistributed
among
I'm always confused by the partitions. We may have many RDDs in the code. Do
we need to partition on all of them? Do the rdds get rearranged among all
the nodes whenever we do a partition? What is a wise way of doing
partitions?
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Hello All,
We have a Spark Streaming job that reads data from DB (three tables) and
cache them into memory ONLY at the start then it will happily carry out the
incremental calculation with the new data. What we've noticed occasionally
is that one of the RDDs caches only 90% of the data. Ther
Hi Romi,
Yes, you understand it correctly.And rdd1 keys are cross with rdd2 keys, that
is, there are lots of same keys between rdd1 and rdd2, and there are some keys
inrdd1 but not in rdd2, there are also some keys in rdd2 but not in rdd1.Then
rdd3 keys would be same with rdd1 keys, rdd3 will no
Hi,
If I understand correctly:
rdd1 contains keys (of type StringDate)
rdd2 contains keys and values
and rdd3 contains all the keys, and the values from rdd2?
I think you should make rdd1 and rdd2 PairRDD, and then use outer join.
Does that make sense?
On Mon, Sep 21, 2015 at 8:37 PM Zhiliang Zhu
Dear Romi, Priya, Sujt and Shivaram and all,
I have took lots of days to think into this issue, however, without any enough
good solution...I shall appreciate your all kind help.
There is an RDD rdd1, and another RDD rdd2,
(rdd2 can be PairRDD, or DataFrame with two columns as ).StringDate colum
ble
"GC task thread#7 (ParallelGC)" prio=10 tid=0x7f9adc02d000
nid=0x65d1 runnable
"VM Periodic Task Thread" prio=10 tid=0x7f9adc0c1800 nid=0x65d9
waiting on condition
JNI global references: 208
On 14/09/15 19:45, Sean Owen wrote:
There isn't a cycle
There isn't a cycle in your graph, since although you reuse reference
variables in your code called A and B you are in fact creating new
RDDs at each operation. You have some other problem, and you'd have to
provide detail on why you think something is deadlocked, like a thread
dump.
O
Hi all,
I am new to spark and I have writen a few spark programs mostly around
machine learning
applications.
I am trying to resolve a particular problem where there are two RDDs that
should be updated
by using elements of each other. More specifically, if the two pair RDDs are
called A and B
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