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Global Row Number

Spark provides the SQL function row_number, which assigns each row a consecutive number, starting from 1. This function works on a Window. Assigning a row number over the entire Dataset will load the entire dataset into a single partition / executor. This does not scale.

Spark extensions provide the Dataset transformation withRowNumbers, which assigns a global row number while scaling:

val df = Seq((1, "one"), (2, "TWO"), (2, "two"), (3, "three")).toDF("id", "value")
df.show()
// +---+-----+
// | id|value|
// +---+-----+
// |  1|  one|
// |  2|  TWO|
// |  2|  two|
// |  3|three|
// +---+-----+

import uk.co.gresearch.spark._

df.withRowNumbers().show()
// +---+-----+----------+
// | id|value|row_number|
// +---+-----+----------+
// |  1|  one|         1|
// |  2|  two|         2|
// |  2|  TWO|         3|
// |  3|three|         4|
// +---+-----+----------+

In Java:

import uk.co.gresearch.spark.RowNumbers;

RowNumbers.of(df).show();
// +---+-----+----------+
// | id|value|row_number|
// +---+-----+----------+
// |  1|  one|         1|
// |  2|  two|         2|
// |  2|  TWO|         3|
// |  3|three|         4|
// +---+-----+----------+

In Python:

import gresearch.spark

df.with_row_numbers().show()
# +---+-----+----------+
# | id|value|row_number|
# +---+-----+----------+
# |  1|  one|         1|
# |  2|  two|         2|
# |  2|  TWO|         3|
# |  3|three|         4|
# +---+-----+----------+

Row number order

Row numbers are assigned in the current order of the Dataset. If you want a specific order, provide columns as follows:

df.withRowNumbers($"id".desc, $"value").show()
// +---+-----+----------+
// | id|value|row_number|
// +---+-----+----------+
// |  3|three|         1|
// |  2|  TWO|         2|
// |  2|  two|         3|
// |  1|  one|         4|
// +---+-----+----------+

In Java:

RowNumbers.withOrderColumns(df.col("id").desc(), df.col("value")).of(df).show();
// +---+-----+----------+
// | id|value|row_number|
// +---+-----+----------+
// |  3|three|         1|
// |  2|  TWO|         2|
// |  2|  two|         3|
// |  1|  one|         4|
// +---+-----+----------+

In Python:

df.with_row_numbers(order=[df.id.desc(), df.value]).show()
# +---+-----+----------+
# | id|value|row_number|
# +---+-----+----------+
# |  3|three|         1|
# |  2|  TWO|         2|
# |  2|  two|         3|
# |  1|  one|         4|
# +---+-----+----------+

Row number column name

The column name that contains the row number can be changed by providing the rowNumberColumnName argument:

df.withRowNumbers(rowNumberColumnName="row").show()
// +---+-----+---+
// | id|value|row|
// +---+-----+---+
// |  1|  one|  1|
// |  2|  TWO|  2|
// |  2|  two|  3|
// |  3|three|  4|
// +---+-----+---+

In Java:

RowNumbers.withRowNumberColumnName("row").of(df).show();
// +---+-----+---+
// | id|value|row|
// +---+-----+---+
// |  1|  one|  1|
// |  2|  TWO|  2|
// |  2|  two|  3|
// |  3|three|  4|
// +---+-----+---+

In Python:

df.with_row_numbers(row_number_column_name='row').show()
# +---+-----+---+
# | id|value|row|
# +---+-----+---+
# |  1|  one|  1|
# |  2|  TWO|  2|
# |  2|  two|  3|
# |  3|three|  4|
# +---+-----+---+

Cached / persisted intermediate Dataset

The withRowNumbers transformation requires the input Dataset to be cached / persisted, after adding an intermediate column. You can specify the level of persistence through the storageLevel parameter.

import org.apache.spark.storage.StorageLevel

val dfWithRowNumbers = df.withRowNumbers(storageLevel=StorageLevel.DISK_ONLY)

In Java:

import org.apache.spark.storage.StorageLevel;

Dataset<Row> dfWithRowNumbers = RowNumbers.withStorageLevel(StorageLevel.DISK_ONLY()).of(df);

In Python:

from pyspark.storagelevel import StorageLevel

df_with_row_numbers = df.with_row_numbers(storage_level=StorageLevel.DISK_ONLY)

Un-persist intermediate Dataset

If you want control over when to un-persist this intermediate Dataset, you can provide an UnpersistHandle and call it when you are done with the result Dataset:

import uk.co.gresearch.spark.UnpersistHandle

val unpersist = UnpersistHandle()
val dfWithRowNumbers = df.withRowNumbers(unpersistHandle=unpersist);

// after you are done with dfWithRowNumbers you may want to call unpersist()
unpersist(blocking=false)

In Java:

import uk.co.gresearch.spark.UnpersistHandle;

UnpersistHandle unpersist = new UnpersistHandle();
Dataset<Row> dfWithRowNumbers = RowNumbers.withUnpersistHandle(unpersist).of(df);

// after you are done with dfWithRowNumbers you may want to call unpersist()
unpersist.apply(true);

In Python:

unpersist = spark.unpersist_handle()
df_with_row_numbers = df.with_row_numbers(unpersist_handle=unpersist)

# after you are done with df_with_row_numbers you may want to call unpersist()
unpersist(blocking=True)

Spark warning

You will recognize that Spark logs the following warning:

WindowExec: No Partition Defined for Window operation! Moving all data to a single partition, this can cause serious performance degradation.

This warning is unavoidable, because withRowNumbers has to pull information about the initial partitions into a single partition. Fortunately, there are only 12 Bytes per input partition required, so this amount of data usually fits into a single partition and the warning can safely be ignored.

Known issues

Note that this feature is not supported in Python when connected with a Spark Connect server.