Is there spark conf property available to default the number of partitions after UnionAll operation in Spark.. In case joins and aggregations, spark.sql.shuffle.partitions value is used as default partition size and do we have similar property to restrict the number of partitions after UnionAll operation.. The problem which I see now is if I join dataframe df1 to df2, the number of resulting partition is df1.partitions + df2.partitions and I am looking for a solution to restrict the number of resulting partitions of all unions in my program..
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df=spark.read.parquet('path')
df.rdd.getNumPartitions() --->It gives the number of partitions but I would like to know information like size of each partition of this dataframe
How do I get it?
I read that in Apache Spark, GroupBy is a wide transformation meaning it requires data shuffle.My doubt is : Let's say I do df.groupBy(column).count(), so will the partitions first groupBy and count the values within their own partition and then share the result with the other partitions or will it be the case where data for similar keys are transfered to a common partition and then count operation would take place on each partition?
[New to Spark] Language - Scala
As per docs, RangePartitioner sorts and divides the elements into chunks and distributes the chunks to different machines. How would it work for below example.
Let's say we have a dataframe with 2 columns and one column (say 'A') has continuous values from 1 to 1000. There is another dataframe with same schema but the corresponding column has only 4 values 30, 250, 500, 900. (These could be any values, randomly selected from 1 to 1000)
If I partition both using RangePartitioner,
df_a.partitionByRange($"A")
df_b.partitionByRange($"A")
how will the data from both the dataframes be distributed across nodes ?
Assuming that the number of partitions is 5.
Also, if I know that second DataFrame has less number of values then will reducing number of partitions for it make any difference ?
What I am struggling to understand is that how Spark maps one partition of df_a to a partition of df_b and how it sends (if it does) both those partitions to same machine for processing.
A very detailed explanation of how RangePartitioner works internally is described here
Specific to your question, RangePartitioner samples the RDD at runtime, collects the statistics, and only then are the ranges (limits) evaluated. Note that there are 2 parameters here - ranges (logical), and partitions (physical). The number of partitions can be affected by many factors - number of input files, inherited number from parent RDD, 'spark.sql.shuffle.partitions' in case of shuffling, etc. The ranges evaluated according to the sampling. In any case, RangePartitioner ensures every range is contained in single partition.
how will the data from both the dataframes be distributed across nodes ? how Spark maps one partition of df_a to a partition of df_b
I assume you implicitly mean joining 'A' and 'B', otherwise the question does not make any sense. In that case, Spark would make sure to match partitions with ranges on both DataFrames, according to their statistics.
I am using repartition on columns to store the data in parquet. But
I see that the no. of parquet partitioned files are not same with the
no. of Rdd partitions. Is there no correlation between rdd partitions
and parquet partitions?
When I write the data to parquet partition and I use Rdd
repartition and then I read the data from parquet partition , is
there any condition when the rdd partition numbers will be same
during read / write?
How is bucketing a dataframe using a column id and repartitioning a
dataframe via the same column id different?
While considering the performance of joins in Spark should we be
looking at bucketing or repartitioning (or maybe both)
Couple of things here that you;re asking - Partitioning, Bucketing and Balancing of data,
Partitioning:
Partitioning data is often used for distributing load horizontally, this has performance benefit, and helps in organizing data in a logical fashion.
Partitioning tables changes how persisted data is structured and will now create subdirectories reflecting this partitioning structure.
This can dramatically improve query performance, but only if the partitioning scheme reflects common filtering.
In Spark, this is done by df.write.partitionedBy(column*) and groups data by partitioning columns into same sub directory.
Bucketing:
Bucketing is another technique for decomposing data sets into more manageable parts. Based on columns provided, the entire data is hashed into a user-defined number of buckets (files).
Synonymous to Hive's Distribute By
In Spark, this is done by df.write.bucketBy(n, column*) and groups data by partitioning columns into same file. number of files generated is controlled by n
Repartition:
It returns a new DataFrame balanced evenly based on given partitioning expressions into given number of internal files. The resulting DataFrame is hash partitioned.
Spark manages data on these partitions that helps parallelize distributed data processing with minimal network traffic for sending data between executors.
In Spark, this is done by df.repartition(n, column*) and groups data by partitioning columns into same internal partition file. Note that no data is persisted to storage, this is just internal balancing of data based on constraints similar to bucketBy
Tl;dr
1) I am using repartition on columns to store the data in parquet. But I see that the no. of parquet partitioned files are not same with the no. of Rdd partitions. Is there no correlation between rdd partitions and parquet partitions?
repartition has correlation to bucketBy not partitionedBy. partitioned files is governed by other configs like spark.sql.shuffle.partitions and spark.default.parallelism
2) When I write the data to parquet partition and I use Rdd repartition and then I read the data from parquet partition , is there any condition when the rdd partition numbers will be same during read / write?
during read time, the number of partitions will be equal to spark.default.parallelism
3) How is bucketing a dataframe using a column id and repartitioning a dataframe via the same column id different?
Working similar, except, bucketing is a write operation and is used for persistence.
4) While considering the performance of joins in Spark should we be looking at bucketing or repartitioning (or maybe both)
repartition of both datasets are in memory, if one or both the datasets are persisted, then look into bucketBy also.
How can I calculate the trade off between number of partitions and size of DataFrame in Spark whit spark.conf.set configuration?