I have a Spark job that inner joins a large Hive table (5bn rows, 400MB x 1000 partitions, compressed parquet) with a much smaller table which is likely to contain less than a few hundred rows and on some/most weeks may be empty.
The data in the large table is not partitioned/bucketed by the join key and in any case the join key is very heavily skewed such that attempting a non-broadcast join causes some executors to exceed memory limits.
Luckily the smaller table size will always be way below the broadcast threshold so by using broadcast(rhs) I can avoid shuffling the large Dataset by the skewed key.
Now when the RHS is empty Spark still seems to do a fair amount of work when it seems fairly obvious the result will be an empty Dataset.
I can only assume Spark does not check for empty Datasets before (inner) joining because the check may be expensive but would appreciate a definitive answer.
In my case I know the RHS will be small so invoking rhs.rdd.count will be cheap and I can skip the join if unnecessary.
I have had to omit business sensitive code but the basic algorithm is:
// Note small and large tables are cached for later re-use
smallTable
// Complex DAG
// write to hive
.cache
largeTable
// read from hive
.cache
largeTable.as("l")
.join(broadcast(smallTable.as("r")), $"l.key" === $"r.key", "inner")
.select($"l.*")
.as[LargeTable]
.mapPartitions(mapPartitionsFunction)
Thanks for any insight.
Terry.
Related
I have a denormalization use case - one hive avro fact table to join with 14 smaller dimension tables and produce a denormalized parquet output table. Both the input fact table and output table are partitioned in the same way (Category=TEST1, YearMonthId=202101). And I do run historical processing, which means processing and loading several months for a given category at once.
I am using Spark 2.4.0/pyspark dataframe, broadcast join for all the table joins, dynamic partition inserts, using coalasce at the end to control the number of output files. (seeing a shuffle at the last stage probably because of dynamic partition inserts)
Would like to know the optimizations possible w.r.t to managing partitions - say maintain partitions consistently from input to output stage such that no shuffle is involved. Want to leverage the fact that the input and output storage tables are partitioned by the same columns.
I am also thinking about this - Use static partitions writes by determining the partitions and write to partitions parallelly - would this help in speeding-up or avoid shuffle?
Appreciate any help that would lead me in the right direction.
Couple of options below that I tried that improved the performance (both time + avoid small files).
Tried using repartition (instead of coalesce) in the data frame before doing a broadcast join, which minimized shuffle and hence the shuffle spill.
-- repartition(count, *PartitionColumnList, AnyOtherSaltingColumn) (Add salting column if the repartition is not even)
Make sure that the the base tables are properly compacted. This might even eliminate the need for #1 in some cases, and reduce # of tasks resulting in reduced overhead due to task scheduling.
I want to understand the concept of merge-sort join in Spark in depth.
I understand the overall idea: this is the same approach as in merge sort algorithm: Take 2 sorted datasets, compare first rows, write smallest one, repeat.
I also understand how I can implement distributed merge sort.
But I cannot get how it is implemented in Spark with respect to concepts of partitions and executors.
Here is my take.
Given I need to join 2 tables A and B. Tables are read from Hive via Spark SQL, if this matters.
By default Spark uses 200 partitions.
Spark then will calculate join key range (from minKey(A,B) to maxKey(A,B)
) and split it into 200 parts. Both datasets to be split by key
ranges into 200 parts: A-partitions and B-partitions.
Each A-partition and each B-partition that relate to same key are sent to same executor and are
sorted there separatelt from each other.
Now 200 executors can join 200 A-partitions with 200 B-partitions
with guarantee that they share same key range.
The join happes via merge-sort algo: take smallest key from
A-partition, compare with smallest key from B-partition, write
match, or iterate.
Finally, I have 200 partitions of my data which are joined.
Does it make sense?
Issues:
Skewed keys. If some key range comprises 50% of dataset keys, some executor would suffer, because too many rows would go to the same partition.
It can even fail with OOM, while trying to sort too big A-partition or B-partition in memory (I cannot get why Spark cannot sort with disk spill, as Hadoop does?..) Or maybe it fails because it tries to read both partitions into memory for joining?
So, this was my guess. Could you please correct me and help to understand the way Spark works?
This is a common problem with joins on MPP databases and Spark is no different. As you say, to perform a join, all the data for the same join key value must be colocated so if you have a skewed distribution on the join key, you have a skewed distribution of data and one node gets overloaded.
If one side of the join is small you could use a map side join. The Spark query planner really ought to do this for you but it is tunable - not sure how current this is but it looks useful.
Did you run ANALYZE TABLE on both tables?
If you have a key on both sides that won't break the join semantics you could include that in the join.
why Spark cannot sort with disk spill, as Hadoop does?
Spark merge-sort join does spill to disk. Taking a look at Spark SortMergeJoinExec class, it uses ExternalAppendOnlyUnsafeRowArray which is described as:
An append-only array for UnsafeRows that strictly keeps content in an in-memory array until numRowsInMemoryBufferThreshold is reached post which it will switch to a mode which would flush to disk after numRowsSpillThreshold is met (or before if there is excessive memory consumption)
This is consistent with the experience of seeing tasks spilling to disk during a join operation from the Web UI.
why [merge-sort join] can throw OOM?
From the Spark Memory Management overview:
Spark’s shuffle operations (sortByKey, groupByKey, reduceByKey, join, etc) build a hash table within each task to perform the grouping, which can often be large. The simplest fix here is to increase the level of parallelism, so that each task’s input set is smaller.
i.e. in the case of join, increase spark.sql.shuffle.partitions to reduce the size of the partitions and the resulting hash table and correspondingly reduce the risk of OOM.
Problem outline: Say I have 300+ GB of data being processed with spark on an EMR cluster in AWS. This data has three attributes used to partition on the filesystem for use in Hive: date, hour, and (let's say) anotherAttr. I want to write this data to a fs in such a way that minimizes the number of files written.
What I'm doing right now is getting the distinct combinations of date, hour, anotherAttr, and a count of how many rows make up combination. I collect them into a List on the driver, and iterate over the list, building a new DataFrame for each combination, repartitioning that DataFrame using the number of rows to guestimate file size, and writing the files to disk with DataFrameWriter, .orc finishing it off.
We aren't using Parquet for organizational reasons.
This method works reasonably well, and solves the problem that downstream teams using Hive instead of Spark don't see performance issues resulting from a high number of files. For example, if I take the whole 300 GB DataFrame, do a repartition with 1000 partitions (in spark) and the relevant columns, and dumped it to disk, it all dumps in parallel, and finishes in ~9 min with the whole thing. But that gets up to 1000 files for the larger partitions, and that destroys Hive performance. Or it destroys some kind of performance, honestly not 100% sure what. I've just been asked to keep the file count as low as possible. With the method I'm using, I can keep the files to whatever size I want (relatively close anyway), but there is no parallelism and it takes ~45 min to run, mostly waiting on file writes.
It seems to me that since there's a 1-to-1 relationship between some source row and some destination row, and that since I can organize the data into non-overlapping "folders" (partitions for Hive), I should be able to organize my code/DataFrames in such a way that I can ask spark to write all the destination files in parallel. Does anyone have suggestions for how to attack this?
Things I've tested that did not work:
Using a scala parallel collection to kick off the writes. Whatever spark was doing with the DataFrames, it didn't separate out the tasks very well and some machines were getting massive garbage collection problems.
DataFrame.map - I tried to map across a DataFrame of the unique combinations, and kickoff writes from inside there, but there's no access to the DataFrame of the data that I actually need from within that map - the DataFrame reference is null on the executor.
DataFrame.mapPartitions - a non-starter, couldn't come up with any ideas for doing what I want from inside mapPartitions
The word 'partition' is also not especially helpful here because it refers both to the concept of spark splitting up the data by some criteria, and to the way that the data will be organized on disk for Hive. I think I was pretty clear in the usages above. So if I'm imagining a perfect solution to this problem, it's that I can create one DataFrame that has 1000 partitions based on the three attributes for fast querying, then from that create another collection of DataFrames, each one having exactly one unique combination of those attributes, repartitioned (in spark, but for Hive) with the number of partitions appropriate to the size of the data it contains. Most of the DataFrames will have 1 partition, a few will have up to 10. The files should be ~3 GB, and our EMR cluster has more RAM than that for each executor, so we shouldn't see a performance hit from these "large" partitions.
Once that list of DataFrames is created and each one is repartitioned, I could ask spark to write them all to disk in parallel.
Is something like this possible in spark?
One thing I'm conceptually unclear on: say I have
val x = spark.sql("select * from source")
and
val y = x.where(s"date=$date and hour=$hour and anotherAttr=$anotherAttr")
and
val z = x.where(s"date=$date and hour=$hour and anotherAttr=$anotherAttr2")
To what extent is y is a different DataFrame than z? If I repartition y, what effect does the shuffle have on z, and on x for that matter?
We had the same problem (almost) and we ended up by working directly with RDD (instead of DataFrames) and implementing our own partitioning mechanism (by extending org.apache.spark.Partitioner)
Details: we are reading JSON messages from Kafka. The JSON should be grouped by customerid/date/more fields and written in Hadoop using Parquet format, without creating too many small files.
The steps are (simplified version):
a)Read the messages from Kafka and transform them to a structure of RDD[(GroupBy, Message)]. GroupBy is a case class containing all the fields that are used for grouping.
b)Use a reduceByKeyLocally transformation and obtain a map of metrics (no of messages/messages size/etc) for each group - eg Map[GroupBy, GroupByMetrics]
c)Create a GroupPartitioner that's using the previously collected metrics (and some input parameters like the desired Parquet size etc) to compute how many partitions should be created for each GroupBy object. Basically we are extending org.apache.spark.Partitioner and overriding numPartitions and getPartition(key: Any)
d)we partition the RDD from a) using the previously defined partitioner: newPartitionedRdd = rdd.partitionBy(ourCustomGroupByPartitioner)
e)Invoke spark.sparkContext.runJob with two parameters: the first one is the RDD partitioned at d), the second one is a custom function (func: (TaskContext, Iterator[T]) that will write the messages taken from Iterator[T] into Hadoop/Parquet
Let's say that we have 100 mil messages, grouped like that
Group1 - 2 mil
Group2 - 80 mil
Group3 - 18 mil
and we decided that we have to use 1.5 mil messages per partition to obtain Parquet files greater than 500MB. We'll end up with 2 partitions for Group1, 54 for Group2, 12 for Group3.
This statement:
I collect them into a List on the driver, and iterate over the list,
building a new DataFrame for each combination, repartitioning that
DataFrame using the number of rows to guestimate file size, and
writing the files to disk with DataFrameWriter, .orc finishing it off.
is completely off-beam where Spark is concerned. Collecting to driver is never a good approach, volumes and OOM issues and latency in your approach is high.
Use so the below so as to simplify and get parallelism of Spark benefits saving time and money for your boss:
df.repartition(cols...)...write.partitionBy(cols...)...
shuffle occurs via repartition, no shuffling ever with partitionBy.
That simple, with Spark's default parallelism utilized.
I have a data set extracted from Hbase, which is a long form of wide table, i.e has rowKey, columnQualifier and value columns. To get a form of pivot, I need to group by rowKey, which is a string UUID, into a collection and make an object out of the collection. The problem is that only group-by I manage to perform is count the number of elements in groups; other group-bys fail due to container being kill due to memory overflow beyond YARN container limits. I did experiment a lot with the memory sizes, including overhead, partitioning with and without sorting etc. I went even into a high number of partitions i.e. about 10 000 but the job dies the same. I tried both DataFrame groupBy and collect_list, as well as Dataset grouByKey and mapGroups.
The code works on a small data set but not on the larger one. The data set is about 500 GB in Parquet files. The data is not skewed as the largest group in group by have only 50 elements. Thus, by all known to me means the partitions should easily fit in memory as the aggregated data per one rowKey is not really large. The data keys and values are mostly strings and there are not long.
I am using Spark 2.0.2; the above computations were all done is Scala.
You're probably running into the dreaded groupByKey shuffle. Please read this Databricks article on avoiding groupByKey, which details the underlying differences between the two functions.
If you don't want the read the article, the short story is this: Though groupByKey and reduceByKey produce the same results, groupByKey instantiates a shuffle of ALL data, while reduceByKey tries to minimize data shuffle by reducing first. A bit like MapReduce Combiners, if you're familiar with that concept.
I have a Spark application that will need to make heavy use of unions whereby I'll be unioning lots of DataFrames together at different times, under different circumstances. I'm trying to make this run as efficiently as I can. I'm still pretty much brand-spanking-new to Spark, and something occurred to me:
If I have DataFrame 'A' (dfA) that has X number of partitions (numAPartitions), and I union that to DataFrame 'B' (dfB) which has Y number of partitions (numBPartitions), then what will the resultant unioned DataFrame (unionedDF) look like, with result to partitions?
// How many partitions will unionedDF have?
// X * Y ?
// Something else?
val unionedDF : DataFrame = dfA.unionAll(dfB)
To me, this seems like its very important to understand, seeing that Spark performance seems to rely heavily on the partitioning strategy employed by DataFrames. So if I'm unioning DataFrames left and right, I need to make sure I'm constantly managing the partitions of the resultant unioned DataFrames.
The only thing I can think of (so as to properly manage partitions of unioned DataFrames) would be to repartition them and then subsequently persist the DataFrames to memory/disk as soon as I union them:
val unionedDF : DataFrame = dfA.unionAll(dfB)
unionedDF.repartition(optimalNumberOfPartitions).persist(StorageLevel.MEMORY_AND_DISK)
This way, as soon as they are unioned, we repartition them so as to spread them over the available workers/executors properly, and then the persist(...) call tells to Spark to not evict the DataFrame from memory, so we can continue working on it.
The problem is, repartitioning sounds expensive, but it may not be as expensive as the alternative (not managing partitions at all). Are there generally-accepted guidelines about how to efficiently manage unions in Spark-land?
Yes, Partitions are important for spark.
I am wondering if you could find that out yourself by calling:
yourResultedRDD.getNumPartitions()
Do I have to persist, post union?
In general, you have to persist/cache an RDD (no matter if it is the result of a union, or a potato :) ), if you are going to use it multiple times. Doing so will prevent spark from fetching it again in memory and can increase the performance of your application by 15%, in some cases!
For example if you are going to use the resulted RDD just once, it would be safe not to do persist it.
Do I have to repartition?
Since you don't care about finding the number of partitions, you can read in my memoryOverhead issue in Spark
about how the number of partitions affects your application.
In general, the more partitions you have, the smaller the chunk of data every executor will process.
Recall that a worker can host multiple executors, you can think of it like the worker to be the machine/node of your cluster and the executor to be a process (executing in a core) that runs on that worker.
Isn't the Dataframe always in memory?
Not really. And that's something really lovely with spark, since when you handle bigdata you don't want unnecessary things to lie in the memory, since this will threaten the safety of your application.
A DataFrame can be stored in temporary files that spark creates for you, and is loaded in the memory of your application only when needed.
For more read: Should I always cache my RDD's and DataFrames?
Union just add up the number of partitions in dataframe 1 and dataframe 2. Both dataframe have same number of columns and same order to perform union operation. So no worries, if partition columns different in both the dataframes, there will be max m + n partitions.
You doesn't need to repartition your dataframe after join, my suggestion is to use coalesce in place of repartition, coalesce combine common partitions or merge some small partitions and avoid/reduce shuffling data within partitions.
If you cache/persist dataframe after each union, you will reduce performance and lineage is not break by cache/persist, in that case, garbage collection will clean cache/memory in case of some heavy memory intensive operation and recomputing will increase computation time for the same, may be this time partial computation is required for clear/removed data.
As spark transformation are lazy, i.e; unionAll is lazy operation and coalesce/repartition is also lazy operation and come in action at the time of first action, so try to coalesce unionall result after an interval like counter of 8 and reduce partition in resulting dataframe. Use checkpoints to break lineage and store data, if there is lots of memory intensive operation in your solution.