What is the exact difference between Spark dataframe writer partitionBy and delta/hive create table partition by. Which one will be faster and why?
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?
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..
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.
Suppose there is a dataset with some number of rows.
I need to find out the Heterogeneity i.e.
distinct number of rows divide by total number of rows.
Please help me with spark query to execute the same.
Dataset and dataframe supports distinct function which finds distinct rows in the dataset.
So essentially you need to do
val heterogeneity = dataset.distinct.count / dataset.count
Only thing is if the dataset is big the distinct could be expensive and you might need to set the spark shuffle partition correctly.