Introduction

Spark SQL provides built-in standard map functions defined in the DataFrame API, which come in handy when you need to perform operations on map (MapType) columns. All these functions accept input as a map column and several other arguments based on the functions.

Spark SQL map functions are grouped as “collection_funcs” in Spark SQL along with several array functions. These map functions are useful when you want to concatenate two or more map columns, convert arrays of StructType entries to a map column, etc.

| Function | Description | | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | map_keys | Returns an array containing the keys of the map. | | map_values | Returns an array containing the values of the map. | | map_concat | Merges maps specified in the arguments. | | map_from_entries | Returns a map from the given array of StructType entries. | | map_entries | Returns an array of all StructType in the given map. | | explode() | Creates a new row for every key-value pair in the map by ignoring null & empty values. It creates two new columns, one for key and one for value. | | explode_outer() | Creates a new row for every key-value pair in the map, including null & empty values. It creates two new columns, one for key and one for value. | | posexplode() | Creates a new row for each key-value pair in a map by ignoring null & empty values. It also creates 3 columns: “pos” to hold the position of the map element, and “key” and “value” columns for every row. | | posexplode_outer() | Creates a new row for each key-value pair in a map, including null & empty values. It also creates 3 columns: “pos” to hold the position of the map element, and “key” and “value” columns for every row. | | transform_keys() | Transforms a map by applying functions to every key-value pair and returns a transformed map. | | transform_value() | Transforms a map by applying functions to every key-value pair and returns a transformed map. | | map_zip_with() | Merges two maps into a single map. | | element_at() | Returns a value of a key in a map. | | size() | Returns the length of a map column. |

Example: Using map_keys and map_values

from pyspark.sql import SparkSession
from pyspark.sql.functions import map_keys, map_values

spark = SparkSession.builder.master("local").appName("example").getOrCreate()

data = [
  (1, {"key1": "value1", "key2": "value2"}),
  (2, {"key3": "value3", "key4": "value4"})
]

df = spark.createDataFrame(data, ["id", "map"])
df.select("id", map_keys("map").alias("keys"), map_values("map").alias("values")).show(truncate=False)

+---+----------+----------+
|id |keys      |values    |
+---+----------+----------+
|1  |[key1,key2]|[value1,value2]|
|2  |[key3,key4]|[value3,value4]|
+---+----------+----------+

Example: Using map_concat

from pyspark.sql.functions import map_concat

df1 = spark.createDataFrame([
  (1, {"key1": "value1"}),
  (2, {"key2": "value2"})
], ["id", "map1"])

df2 = spark.createDataFrame([
  (1, {"key3": "value3"}),
  (2, {"key4": "value4"})
], ["id", "map2"])

df = df1.join(df2, "id")
df.withColumn("map_concat", map_concat("map1", "map2")).show(truncate=False)

+---+----------+----------+--------------------------+
|id |map1      |map2      |map_concat                |
+---+----------+----------+--------------------------+
|1  |{key1: value1}|{key3: value3}|{key1: value1, key3: value3}|
|2  |{key2: value2}|{key4: value4}|{key2: value2, key4: value4}|
+---+----------+----------+--------------------------+

Example: Using map_from_entries

from pyspark.sql.functions import map_from_entries

data = [
  (1, [("key1", "value1"), ("key2", "value2")]),
  (2, [("key3", "value3"), ("key4", "value4")])
]

df = spark.createDataFrame(data, ["id", "entries"])
df.withColumn("map", map_from_entries("entries")).show(truncate=False)

+---+------------------------+-------------------------+
|id |entries                 |map                      |
+---+------------------------+-------------------------+
|1  |[[key1,value1],[key2,value2]]|{key1: value1, key2: value2}|
|2  |[[key3,value3],[key4,value4]]|{key3: value3, key4: value4}|
+---+------------------------+-------------------------+

Example: Using posexplode

from pyspark.sql.functions import posexplode

data = [
  (1, {"key1": "value1", "key2": "value2"}),
  (2, {"key3": "value3", "key4": "value4"})
]

df = spark.createDataFrame(data, ["id", "map"])
df.select("id", posexplode("map")).show(truncate=False)

+---+---+----+------+
|id |pos|key |value |
+---+---+----+------+
|1  |0  |key1|value1|
|1  |1  |key2|value2|
|2  |0  |key3|value3|
|2  |1  |key4|value4|
+---+---+----+------+