Spark SQL - Difference between df.repartition and DataFrameWriter partitionBy?

问题

What is the difference between DataFrame repartition() and DataFrameWriter partitionBy() methods?

I hope both are used to \"partition data based on dataframe column\"? Or is there any difference?

回答1:

If you run repartition(COL) you change the partitioning during calculations - you will get spark.sql.shuffle.partitions (default: 200) partitions. If you then call .write you will get one directory with many files.

If you run .write.partitionBy(COL) then as the result you will get as many directories as unique values in COL. This speeds up futher data reading (if you filter by partitioning column) and saves some space on storage (partitioning column is removed from data files).

UPDATE: See @conradlee's answer. He explains in details not only how the directories structure will look like after applying different methods but also what will be resulting number of files in both scenarios.

回答2:

Watch out: I believe the accepted answer is not quite right! I'm glad you ask this question, because the behavior of these similarly-named functions differs in important and unexpected ways that are not well documented in the official spark documentation.

The first part of the accepted answer is correct: calling df.repartition(COL, numPartitions=k) will create a dataframe with k partitions using a hash-based partitioner. COL here defines the partitioning key--it can be a single column or a list of columns. The hash-based partitioner takes each input row's partition key, hashes it into a space of k partitions via something like partition = hash(partitionKey) % k. This guarantees that all rows with the same partition key end up in the same partition. However, rows from multiple partition keys can also end up in the same partition (when a hash collision between the partition keys occurs) and some partitions might be empty.

In summary, the unintuitive aspects of df.repartition(COL, numPartitions=k) are that

partitions will not strictly segregate partition keys
some of your k partitions may be empty, whereas others may contain rows from multiple partition keys

The behavior of df.write.partitionBy is quite different, in a way that many users won't expect. Let's say that you want your output files to be date-partitioned, and your data spans over 7 days. Let's also assume that df has 10 partitions to begin with. When you run df.write.partitionBy('day'), how many output files should you expect? The answer is 'it depends'. If each partition of your starting partitions in df contains data from each day, then the answer is 70. If each of your starting partitions in df contains data from exactly one day, then the answer is 10.

How can we explain this behavior? When you run df.write, each of the original partitions in df is written independently. That is, each of your original 10 partitions is sub-partitioned separately on the 'day' column, and a separate file is written for each sub-partition.

I find this behavior rather annoying and wish there were a way to do a global repartitioning when writing dataframes.

回答3:

repartition() is used to partition data in memory and partitionBy is used to partition data on disk. They're often used in conjunction as explained in this blog post.

Both repartition() and partitionBy can be used to "partition data based on dataframe column", but repartition() partitions the data in memory and partitionBy partitions the data on disk.

repartition()

Let's play around with some code to better understand partitioning. Suppose you have the following CSV data.

first_name,last_name,country
Ernesto,Guevara,Argentina
Vladimir,Putin,Russia
Maria,Sharapova,Russia
Bruce,Lee,China
Jack,Ma,China

df.repartition(col("country")) will repartition the data by country in memory.

Let's write out the data so we can inspect the contents of each memory partition.

val outputPath = new java.io.File("./tmp/partitioned_by_country/").getCanonicalPath
df.repartition(col("country"))
  .write
  .csv(outputPath)

Here's how the data is written out on disk:

partitioned_by_country/
  part-00002-95acd280-42dc-457e-ad4f-c6c73be6226f-c000.csv
  part-00044-95acd280-42dc-457e-ad4f-c6c73be6226f-c000.csv
  part-00059-95acd280-42dc-457e-ad4f-c6c73be6226f-c000.csv

Each file contains data for a single country - the part-00059-95acd280-42dc-457e-ad4f-c6c73be6226f-c000.csv file contains this China data for example:

Bruce,Lee,China
Jack,Ma,China

partitionBy()

Let's write out data to disk with partitionBy and see how the filesystem output differs.

Here's the code to write out the data to disk partitions.

val outputPath = new java.io.File("./tmp/partitionedBy_disk/").getCanonicalPath
df
  .write
  .partitionBy("country")
  .csv(outputPath)

Here's what the data looks like on disk:

partitionedBy_disk/
  country=Argentina/
    part-00000-906f845c-ecdc-4b37-a13d-099c211527b4.c000.csv
  country=China/
    part-00000-906f845c-ecdc-4b37-a13d-099c211527b4.c000
  country=Russia/
    part-00000-906f845c-ecdc-4b37-a13d-099c211527b4.c000

Why partition data on disk?

Partitioning data on disk can make certain queries run much faster, as explained in this blog post.

来源：https://stackoverflow.com/questions/40416357/spark-sql-difference-between-df-repartition-and-dataframewriter-partitionby

标签

apache-spark-sql

data-partitioning