A DAG Stage in Pyspark is divided into tasks based on the partitions of the data. How these partitions are decided?

We know a stage in Pyspark is divided into tasks based on the partitions of the data. But Big Question is How these partions of data is decided? This post is succesor to our DAG Scheduler in Spark: Detailed Explanation, How it is involved at architecture Level.

In Apache Spark, data is divided into partitions, which are the fundamental units of parallelism. The number and size of these partitions can significantly affect the performance of a Spark application. The decision on how data is partitioned is influenced by several factors, including the source of the data, transformations applied, and specific partitioning strategies employed. Here’s a detailed explanation of how data partitions are decided in Spark:

1. Source of Data

a. HDFS/Parquet/ORC Files:

• When reading data from HDFS or other distributed storage systems, the partitions are typically determined by the block size of the files. For example, if a file is 1 GB and the HDFS block size is 128 MB, Spark will create 8 partitions.

b. CSV/JSON Files:

• When reading text-based files, the number of partitions is influenced by the file size and the default parallelism setting in Spark.

c. Hive Tables:

• When reading from Hive tables, the partitions can be influenced by the Hive table’s partitioning scheme and the number of files stored in each partition.

d. Databases (JDBC Source):

• When reading from a database using JDBC, the number of partitions can be controlled by specifying the partitionColumn, lowerBound, upperBound, and numPartitions options.

2. Transformations

a. Narrow Transformations:

• Operations like map, filter, and flatMap do not change the number of partitions. The data within each partition is transformed independently.

b. Wide Transformations:

• Operations like groupByKey, reduceByKey, and join often involve shuffling data across partitions. The number of resulting partitions can be controlled by the numPartitions parameter in these transformations.

3. Partitioning Strategies

a. Default Parallelism:

• Spark uses the default parallelism setting to determine the number of partitions when creating RDDs. This is typically set to the number of cores in the cluster.

b. Repartitioning:

• You can explicitly control the number of partitions using the repartition() or coalesce() methods. repartition() increases or decreases the number of partitions, while coalesce() is more efficient for reducing the number of partitions without a full shuffle.

c. Custom Partitioning:

• For RDDs, you can define a custom partitioner using the partitionBy() method. For DataFrames, you can use the df.repartition() method with columns to partition by.

4.Shuffling and Partitioning

• Shuffling occurs when data needs to be redistributed across the network, typically in operations like groupByKey, reduceByKey, join, etc.
• During a shuffle, Spark may repartition data based on a hash function applied to keys. The number of output partitions can be controlled with the spark.sql.shuffle.partitions configuration (default is 200).

Example

Here is a detailed example illustrating how partitions are decided and controlled in Spark:

from pyspark.sql import SparkSession

# Initialize Spark session
spark = SparkSession.builder.appName("PartitioningExample").getOrCreate()

# Example 1: Reading from a CSV file
df_csv = spark.read.csv("hdfs://path/to/file.csv", header=True, inferSchema=True)
print("Number of partitions for CSV:", df_csv.rdd.getNumPartitions())

# Example 2: Reading from a Parquet file
df_parquet = spark.read.parquet("hdfs://path/to/file.parquet")
print("Number of partitions for Parquet:", df_parquet.rdd.getNumPartitions())

# Example 3: Reading from a JDBC source with custom partitioning
jdbc_url = "jdbc:mysql://hostname:port/database"
jdbc_properties = {
    "user": "username",
    "password": "password",
    "driver": "com.mysql.jdbc.Driver"
}
df_jdbc = spark.read.jdbc(
    url=jdbc_url,
    table="tablename",
    column="id",
    lowerBound=1,
    upperBound=1000,
    numPartitions=10,
    properties=jdbc_properties
)
print("Number of partitions for JDBC:", df_jdbc.rdd.getNumPartitions())

# Example 4: Transformations and repartitioning
df_transformed = df_csv.filter(df_csv["column"] > 0).repartition(5)
print("Number of partitions after transformation and repartitioning:", df_transformed.rdd.getNumPartitions())

# Example 5: Wide transformation with groupBy
df_grouped = df_csv.groupBy("column").count()
print("Number of partitions after groupBy:", df_grouped.rdd.getNumPartitions())

# Example 6: Coalesce to reduce partitions
df_coalesced = df_grouped.coalesce(2)
print("Number of partitions after coalesce:", df_coalesced.rdd.getNumPartitions())

Explanation

1. CSV and Parquet Files: The partitions are determined based on the file size and block size.
2. JDBC Source: Partitions are specified using numPartitions, partitionColumn, lowerBound, and upperBound.
3. Transformations: The number of partitions can be controlled using repartition() and coalesce(). Wide transformations like groupBy may involve shuffles and create new partitions based on the operation.