Hi,
Have you tried
https://spark.apache.org/docs/latest/sql-performance-tuning.html#spliting-skewed-shuffle-partitions
?
Another way of handling the skew is to split the task into multiple(2 or
more) stages involving a random salt as key in the intermediate stages.
In the above case,
val maxSa
Hi,
What is the purpose for which you want to use repartition() .. to reduce
the number of files in delta?
Also note that there is an alternative option of using coalesce() instead
of repartition().
--
Raghavendra
On Thu, Oct 5, 2023 at 10:15 AM Shao Yang Hong
wrote:
> Hi all on user@spark:
>
>
Given, you are already stating the above can be imagined as a partition, I
can think of mapPartitions iterator.
val inputSchema = inputDf.schema
val outputRdd = inputDf.rdd.mapPartitions(rows => new SomeClass(rows))
val outputDf = sparkSession.createDataFrame(outputRdd,
inputSchema.add("coun
For simple array types setting encoder to ExpressionEncoder() should work.
--
Raghavendra
On Sun, Apr 23, 2023 at 9:20 PM Thomas Wang wrote:
> Hi Spark Community,
>
> I'm trying to implement a custom Spark Aggregator (a subclass to
> org.apache.spark.sql.expressions.Aggregator). Correct me if I
you can groupBy(country). and use mapPartitions method in which you can
iterate over all rows keeping 2 variables for maxPopulationSoFar and
corresponding city. Then return the city with max population.
I think as others suggested, it may be possible to use Bucketing, it would
give a more friendly
withColumn takes a column as the second argument, not string.
If you want formatting before show() you can use the round() function.
--
Raghavendra
On Mon, May 23, 2022 at 11:35 AM wilson wrote:
> hello
>
> how to add a column for percent for the current row of counted data?
>
> scala>
> df2.gr
What is optimal depends on the context of the problem.
Is the intent here to find the best solution for top n values with a group
by ?
Both the solutions look sub-optimal to me. Window function would be
expensive as it needs an order by (which a top n solution shouldn't need).
It would be best to
You could use expr() function to achieve the same.
.withColumn("newColumn",expr(s"case when score>3 then 'good' else 'bad'
end"))
--
Raghavendra
On Fri, Feb 11, 2022 at 5:59 PM frakass wrote:
> Hello
>
> I have a column whose value (Int type as score) is from 0 to 5.
> I want to query that, wh
You can add an extra id column and perform an inner join.
val df1_with_id = df1.withColumn("id", monotonically_increasing_id())
val df2_with_id = df2.withColumn("id", monotonically_increasing_id())
df1_with_id.join(df2_with_id, Seq("id"), "inner").drop("id").show()
+-+-+
|amoun
There should not be any need to explicitly make DF-2, DF-3 computation
parallel. Spark generates execution plans and it can decide what can run in
parallel (ideally you should see them running parallel in spark UI).
You need to cache DF-1 if possible (either in memory/disk), otherwise
computation
Spark provides multiple options for caching (including disk). Have you
tried caching to disk ?
--
Raghavendra
On Mon, Oct 19, 2020 at 11:41 PM Lalwani, Jayesh
wrote:
> I was caching it because I didn't want to re-execute the DAG when I ran
> the count query. If you have a spark application with
Hi,
I am working on writing spark events and application logs in the blob
storage. I am using a similar path for writing spark events and application
logs in blob storage.
For example: *spark.eventLog.dir =
wasb://@/logs* and *application log dir =
wasb://@/logs/app/*.
Since I'm using blob storag
I was wrong here.
I am using spark standalone cluster and I am not using YARN or MESOS. Is it
possible to track spark execution memory?.
On Mon, Oct 21, 2019 at 5:42 PM Sriram Ganesh wrote:
> I looked into this. But I found it is possible like this
>
> https://github.com/apache/s
Roman, wrote:
> Take a look in this thread
> <https://stackoverflow.com/questions/48768188/spark-execution-memory-monitoring#_=_>
>
> El lun., 21 oct. 2019 a las 13:45, Sriram Ganesh ()
> escribió:
>
>> Hi,
>>
>> I wanna monitor how much memory executor and
Hi,
I wanna monitor how much memory executor and task used for a given job. Is
there any direct method available for it which can be used to track this
metric?
--
*Sriram G*
*Tech*
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Has anyone worked on non-linear/curved regression lines with Apache
Spark? This seems to be such a trivial issue but I have given up after
experimenting for nearly two weeks.
The plot line is as below and the raw data in the table at the end.
I just can't get Spark ML to give decent predictio
is around 55 Gb of text data.
Ganesh
ed in 9.440 s
>> 16/09/19 18:52:52 INFO DAGScheduler: Job 19 failed: insertIntoJDBC at
>> sparkjob.scala:143, took 9.449118 s
>> 16/09/19 18:52:52 INFO ApplicationMaster: Final app status: SUCCEEDED,
>> exitCode: 0
>> 16/09/19 18:52
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