spark-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Joseph Bradley <jos...@databricks.com>
Subject Re: Shrinking the DataFrame lineage
Date Sun, 12 Jun 2016 17:33:35 GMT
Sorry for the slow response.  I agree with Hamel on #1.
GraphFrames are mostly wrappers for GraphX algorithms.  There are a few
which are not:
* BFS: This is an iterative DataFrame alg.  Though it has unit tests, I
have not pushed it in scaling to see how far it can go.
* Belief Propagation example: This uses the conversion to and from an RDD.
Not great, but it's really just an example for now.

I definitely want to get this issue fixed ASAP!

On Sun, May 15, 2016 at 7:15 AM, Hamel Kothari <hamelkothari@gmail.com>
wrote:

> I don't know about the second one but for question #1:
> When you convert from a cached DF to an RDD (via a map function or the
> "rdd" value) the types are converted from the off-heap types to on-heap
> types. If your rows are fairly large/complex this can have a pretty big
> performance impact so I would watch out for that.
>
> On Fri, May 13, 2016 at 5:29 PM Ulanov, Alexander <
> alexander.ulanov@hpe.com> wrote:
>
>> Hi Joseph,
>>
>>
>>
>> Thank you for the link! Two follow up questions
>>
>> 1)Suppose I have the original DataFrame in Tungsen, i.e. catalyst types
>> and cached in off-heap store. It might be quite useful for iterative
>> workloads due to lower GC overhead. Then I convert it to RDD and then
>> backto DF. Will the resulting DF remain off-heap or it will be on heap as
>> regular RDD?
>>
>> 2)How is the mentioned problem handled in GraphFrames? Suppose, I want to
>> use aggregateMessages in the iterative loop, for implementing PageRank.
>>
>>
>>
>> Best regards, Alexander
>>
>>
>>
>> *From:* Joseph Bradley [mailto:joseph@databricks.com]
>> *Sent:* Friday, May 13, 2016 12:38 PM
>> *To:* Ulanov, Alexander <alexander.ulanov@hpe.com>
>> *Cc:* dev@spark.apache.org
>> *Subject:* Re: Shrinking the DataFrame lineage
>>
>>
>>
>> Here's a JIRA for it: https://issues.apache.org/jira/browse/SPARK-13346
>>
>>
>>
>> I don't have a great method currently, but hacks can get around it:
>> convert the DataFrame to an RDD and back to truncate the query plan lineage.
>>
>>
>>
>> Joseph
>>
>>
>>
>> On Wed, May 11, 2016 at 12:46 PM, Ulanov, Alexander <
>> alexander.ulanov@hpe.com> wrote:
>>
>> Dear Spark developers,
>>
>>
>>
>> Recently, I was trying to switch my code from RDDs to DataFrames in order
>> to compare the performance. The code computes RDD in a loop. I use
>> RDD.persist followed by RDD.count to force Spark compute the RDD and cache
>> it, so that it does not need to re-compute it on each iteration. However,
>> it does not seem to work for DataFrame:
>>
>>
>>
>> import scala.util.Random
>>
>> val rdd = sc.parallelize(1 to 10, 2).map(x => (Random(5), Random(5))
>>
>> val edges = sqlContext.createDataFrame(rdd).toDF("from", "to")
>>
>> val vertices =
>> edges.select("from").unionAll(edges.select("to")).distinct().cache()
>>
>> vertices.count
>>
>> [Stage 34:=================>                                     (65 + 4)
>> / 200]
>>
>> [Stage 34:========================>                              (90 + 5)
>> / 200]
>>
>> [Stage 34:==============================>                       (114 + 4)
>> / 200]
>>
>> [Stage 34:====================================>                 (137 + 4)
>> / 200]
>>
>> [Stage 34:==========================================>           (157 + 4)
>> / 200]
>>
>> [Stage 34:=================================================>    (182 + 4)
>> / 200]
>>
>>
>>
>> res25: Long = 5
>>
>> If I run count again, it recomputes it again instead of using the cached
>> result:
>>
>> scala> vertices.count
>>
>> [Stage 37:=============>                                         (49 + 4)
>> / 200]
>>
>> [Stage 37:==================>                                    (66 + 4)
>> / 200]
>>
>> [Stage 37:========================>                              (90 + 4)
>> / 200]
>>
>> [Stage 37:=============================>                        (110 + 4)
>> / 200]
>>
>> [Stage 37:===================================>                  (133 + 4)
>> / 200]
>>
>> [Stage 37:==========================================>           (157 + 4)
>> / 200]
>>
>> [Stage 37:================================================>     (178 + 5)
>> / 200]
>>
>> res26: Long = 5
>>
>>
>>
>> Could you suggest how to schrink the DataFrame lineage ?
>>
>>
>>
>> Best regards, Alexander
>>
>>
>>
>

Mime
View raw message