PySpark’s map_keys function : Function used to retrieve the keys of a map column.

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PySpark provides, map_keys stands out when it comes to handling maps (dictionary-like structures in PySpark). In this article, we will delve deep into the map_keys function, understanding its use cases and advantages. The map_keys function in PySpark provides a powerful and scalable way to handle and analyze map columns in DataFrames.

What is map_keys in PySpark?

In PySpark’s DataFrame API, map_keys is a function used to retrieve the keys of a map column. Think of it as the equivalent of calling .keys() on a Python dictionary, but at a column-wide scale for your DataFrame.

When to use map_keys?

Analyzing key data: When you have a map column and want to analyze the distribution or presence of specific keys.

Transforming data: Before transforming keys into separate columns or rows.

Filtering based on keys: If you want to filter rows based on the presence or absence of certain keys in a map column.

Advantages of using map_keys:

Scalability: Leveraging the distributed nature of Spark, you can process large datasets efficiently.

Chainability: Can be easily chained with other DataFrame operations for streamlined data transformation and analysis.

Readability: Provides a clear intent in your PySpark code, making it more understandable.


To understand map_keys in action, let’s take a hardcoded example:

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

# Initialize Spark Session
spark = SparkSession.builder.appName("map_keys_example Learning @").getOrCreate()

# Sample DataFrame with a map column
data = [(1, {"a": 10, "b": 20}),
        (2, {"c": 30, "d": 40}),
        (3, {"e": 50, "a": 60})]
df = spark.createDataFrame(data, ["id", "attributes"])

# Use map_keys to get the keys of the map column
df_with_keys ="id", map_keys(df["attributes"]).alias("keys"))


| id|      attributes|
|  1|[a -> 10, b -> 20]|
|  2|[c -> 30, d -> 40]|
|  3|[e -> 50, a -> 60]|

| id|  keys|
|  1|[a, b]|
|  2|[c, d]|
|  3|[e, a]|

Spark important urls to refer

  1. Spark Examples
  2. PySpark Blogs
  3. Bigdata Blogs
  4. Spark Interview Questions
  5. Official Page
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