CODEWITHSUNDEEP
pythonapache-sparkapache-spark-sqlpyspark
emax
emax

Reputation: 7245

Pyspark: how to split a dataframe into chunks and save them?

I need to split a pyspark dataframe df and save the different chunks.

This is what I am doing: I define a column id_tmp and I split the dataframe based on that.

  chunk = 10000
  id1 = 0
  id2 = chunk
  df = df.withColumn('id_tmp', row_number().over(Window.orderBy(monotonically_increasing_id())) - 1)
  c = df.count()
  while id1 < c:
    stop_df = df.filter( (tmp.id_tmp < id2) & (tmp.id_tmp >= id1))
    stop_df.write.format('com.databricks.spark.csv').save('myFolder/')
    id1+=chunk
    id2+=chunk

Is there a more efficient way without defining the column id_tmp

Upvotes: 5

Views: 24298

Answers (1)

Emer
Emer

Reputation: 3824

I suggest you to use the partitionBy method from the DataFrameWriter interface built-in Spark (docs). Here is an example.

Given the df DataFrame, the chuck identifier needs to be one or more columns. In my example id_tmp. The following snippet generates a DF with 12 records with 4 chunk ids.

import pyspark.sql.functions as F
df = spark.range(0, 12).withColumn("id_tmp", F.col("id") % 4).orderBy("id_tmp")
df.show()

Returns:

+---+------+
| id|id_tmp|
+---+------+
|  8|     0|
|  0|     0|
|  4|     0|
|  1|     1|
|  9|     1|
|  5|     1|
|  6|     2|
|  2|     2|
| 10|     2|
|  3|     3|
| 11|     3|
|  7|     3|
+---+------+

To save each chunk indepedently you need:

(df
 .repartition("id_tmp")
 .write
 .partitionBy("id_tmp")
 .mode("overwrite")
 .format("csv")
 .save("output_folder"))

repartition will shuffle the records so that each node has a complete set of records for one "id_tmp" value. Then each chunk is written to one file with the partitionBy.

Resulting folder structure:

output_folder/
output_folder/._SUCCESS.crc
output_folder/id_tmp=0
output_folder/id_tmp=0/.part-00000-eba244a4-ce95-4f4d-b9b8-8e5f972b144f.c000.csv.crc
output_folder/id_tmp=0/part-00000-eba244a4-ce95-4f4d-b9b8-8e5f972b144f.c000.csv
output_folder/id_tmp=1
output_folder/id_tmp=1/.part-00000-eba244a4-ce95-4f4d-b9b8-8e5f972b144f.c000.csv.crc
output_folder/id_tmp=1/part-00000-eba244a4-ce95-4f4d-b9b8-8e5f972b144f.c000.csv
output_folder/id_tmp=2
output_folder/id_tmp=2/.part-00000-eba244a4-ce95-4f4d-b9b8-8e5f972b144f.c000.csv.crc
output_folder/id_tmp=2/part-00000-eba244a4-ce95-4f4d-b9b8-8e5f972b144f.c000.csv
output_folder/id_tmp=3
output_folder/id_tmp=3/.part-00000-eba244a4-ce95-4f4d-b9b8-8e5f972b144f.c000.csv.crc
output_folder/id_tmp=3/part-00000-eba244a4-ce95-4f4d-b9b8-8e5f972b144f.c000.csv
output_folder/_SUCCESS

The size and number of partitions are quite important for Spark's performance. Don't partition the dataset too much and have reasonable file sizes (like 1GB per file) especially if you are using cloud storage services. It is also advised to use the partition variables if you want to filter the data when loading (i.e.: year=YYYY/month=MM/day=DD)

Upvotes: 4

Related Questions