How PySpark automatically optimizes the job execution by breaking it down into stages and tasks based on data dependencies. can explain with an example

by | Jun 25, 2024 | Uncategorized

Apache Spark, including PySpark, automatically optimizes job execution by breaking it down into stages and tasks based on data dependencies. This process is facilitated by Spark’s Directed Acyclic Graph (DAG) Scheduler, which helps in optimizing the execution plan for efficiency. Let’s break this down with a detailed example and accompanying numbers to illustrate the process.

In Apache Spark, including PySpark, the Directed Acyclic Graph (DAG) is a fundamental concept that represents the sequence of computations that need to be performed on data. Understanding how Spark breaks down a job into stages and tasks within this DAG is crucial for grasping how Spark optimizes and executes jobs.

Understanding DAG in PySpark

A DAG in Spark is a logical representation of the operations to be executed. It is built when an action (like collect, save, count, etc.) is called on an RDD, DataFrame, or Dataset. The DAG contains all the transformations (like map, filter, groupBy, etc.) that Spark must perform on the data.

Breakdown of a Job into Stages and Tasks

  1. Logical Plan:
    • When you define transformations, Spark constructs a logical plan. This plan captures the sequence of transformations on the data.
  2. Physical Plan:
    • Spark’s Catalyst optimizer converts the logical plan into a physical plan, which specifies how the operations will be executed.
  3. DAG of Stages:
    • The physical plan is divided into stages. Each stage consists of tasks that can be executed in parallel. Stages are determined by shuffle boundaries (i.e., points where data needs to be redistributed across the cluster).
  4. Tasks within Stages:
    • Each stage is further divided into tasks. A task is the smallest unit of work and is executed on a single partition of the data. Tasks within a stage are executed in parallel across the cluster.

Example: Complex ETL Pipeline

Scenario: Process a large dataset of user activity logs to compute user session statistics, filter erroneous data, and generate aggregated reports.

Steps in the Pipeline:

  1. Data Ingestion:
    • Read raw logs from HDFS.
  2. Data Cleaning:
    • Filter out records with missing or malformed fields.
  3. Sessionization:
    • Group records by user and time interval to form sessions.
  4. Aggregation:
    • Compute session statistics such as total time spent and number of pages visited.

PySpark Code Example:

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, window, sum as _sum

# Initialize Spark session
spark = SparkSession.builder.appName("Complex ETL Pipeline").getOrCreate()

# Data Ingestion
df = spark.read.csv("hdfs:///path/to/input/*.csv", header=True, inferSchema=True)

# Data Cleaning
df_cleaned = df.filter(col("user_id").isNotNull() & col("timestamp").isNotNull())

# Sessionization
df_sessions = df_cleaned.groupBy("user_id", window("timestamp", "30 minutes")).agg({"page": "count"})

# Aggregation
df_aggregated = df_sessions.groupBy("user_id").agg(
_sum("count(page)").alias("total_pages"),
_sum("session_duration").alias("total_duration")
)

# Write results to HDFS
df_aggregated.write.parquet("hdfs:///path/to/output/")

How PySpark Optimizes Job Execution:

  1. Logical Plan:
    • When transformations are applied (e.g., filter, groupBy), Spark builds a logical plan. This plan is an abstract representation of the operations to be performed.
  2. Physical Plan:
    • Spark’s Catalyst optimizer converts the logical plan into a physical plan. This plan is optimized for execution by selecting the most efficient strategies for operations.
  3. DAG Scheduler:
    • The physical plan is translated into a DAG of stages and tasks. Each stage consists of tasks that can be executed in parallel.

Breaking Down into Stages and Tasks:

  • Stage 1:
    • Tasks: Reading data from HDFS and filtering (Data Cleaning).
    • Operations: df.filter(...)
    • Number of Tasks: Equal to the number of HDFS blocks (e.g., 100 blocks -> 100 tasks).
    • Memory Usage: Depends on the size of the data being read and filtered.
  • Stage 2:
    • Tasks: Grouping data by user and session window (Sessionization).
    • Operations: df_sessions = df_cleaned.groupBy("user_id", window(...))
    • Number of Tasks: Determined by the number of unique keys (users and time intervals) and partitioning strategy (e.g., 200 partitions).
    • Memory Usage: Depends on the intermediate shuffle data size.
  • Stage 3:
    • Tasks: Aggregating session data (Aggregation).
    • Operations: df_aggregated = df_sessions.groupBy("user_id").agg(...)
    • Number of Tasks: Again determined by the number of unique keys (users) and partitioning strategy (e.g., 50 partitions).
    • Memory Usage: Depends on the size of the aggregated data.
  • Stage 4:
    • Tasks: Writing results to HDFS.
    • Operations: df_aggregated.write.parquet(...)
    • Number of Tasks: Typically determined by the number of output partitions (e.g., 50 tasks).
    • Memory Usage: Depends on the size of the data being written.

Example with Numbers:

Let’s assume we have a dataset of 1 TB, distributed across 100 HDFS blocks.

  1. Stage 1 (Data Cleaning):
    • Tasks: 100 (one per HDFS block)
    • Data Read: 1 TB (distributed across tasks)
    • Intermediate Data: 900 GB (after filtering out 10% erroneous data)
  2. Stage 2 (Sessionization):
    • Tasks: 200 (partitioned by user and time interval)
    • Intermediate Data: 900 GB
    • Shuffled Data: 900 GB (data shuffled across tasks for grouping)
  3. Stage 3 (Aggregation):
    • Tasks: 50 (grouped by user)
    • Intermediate Data: 50 GB (assuming significant aggregation)
    • Shuffled Data: 50 GB
  4. Stage 4 (Write to HDFS):
    • Tasks: 50 (one per output partition)
    • Output Data: 50 GB (final aggregated data)

Memory Management:

  • Executor Memory: Configured based on the size of the data and the number of tasks. E.g., each executor allocated 8 GB memory.
  • Caching and Persistence: Intermediate results (e.g., df_cleaned and df_sessions) can be cached in memory to avoid recomputation.
  • Shuffle Memory: Managed dynamically to balance between storage and execution needs.

Execution Log Example:

plaintextCopy codeINFO DAGScheduler: Final stage: ResultStage 4 (parquet at ETL_Pipeline.py:24)
INFO DAGScheduler: Parents of final stage: List(ShuffleMapStage 3, ShuffleMapStage 2, ShuffleMapStage 1)
INFO DAGScheduler: Missing parents: List(ShuffleMapStage 3, ShuffleMapStage 2, ShuffleMapStage 1)
INFO DAGScheduler: Submitting ShuffleMapStage 1 (MapPartitionsRDD[4] at filter at ETL_Pipeline.py:10), which has no missing parents
INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 4.1 KB, free 2004.6 MB)
INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on executor_1 (size: 4.1 KB, free: 2004.6 MB)
INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on executor_2 (size: 4.1 KB, free: 2004.6 MB)
INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on executor_3 (size: 4.1 KB, free: 2004.6 MB)
INFO DAGScheduler: Submitting 100 missing tasks from ShuffleMapStage 1 (MapPartitionsRDD[4] at filter at ETL_Pipeline.py:10) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14))
INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 0, executor 1, partition 0, PROCESS_LOCAL, 7987 bytes)
INFO TaskSetManager: Starting task 1.0 in stage 1.0 (TID 1, executor 2, partition 1, PROCESS_LOCAL, 7987 bytes)
INFO TaskSetManager: Starting task 2.0 in stage 1.0 (TID 2, executor 3, partition 2, PROCESS_LOCAL, 7987 bytes)
...
INFO BlockManagerInfo: Added rdd_4_0 in memory on executor_1 (size: 45.6 MB, free: 1958.6 MB)
INFO BlockManagerInfo: Added rdd_4_1 in memory on executor_2 (size: 45.6 MB, free: 1958.6 MB)
INFO BlockManagerInfo: Added rdd_4_2 in memory on executor_3 (size: 45.6 MB, free: 1958.6 MB)
...
INFO DAGScheduler: Submitting 200 missing tasks from ShuffleMapStage 2 (MapPartitionsRDD[7] at groupBy at ETL_Pipeline.py:14) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14))
INFO TaskSetManager: Starting task 0.0 in stage 2.0 (TID 100, executor 1, partition 0, PROCESS_LOCAL, 7987 bytes)
...
INFO BlockManagerInfo: Added rdd_7_0 in memory on executor_1 (size: 10.2 MB, free: 1948.4 MB)
INFO BlockManagerInfo: Added rdd_7_1 in memory on executor_2 (size: 10.2 MB, free: 1948.4 MB)
INFO BlockManagerInfo: Added rdd_7_2 in memory on executor_3 (size: 10.2 MB, free: 1948.4 MB)
...
INFO DAGScheduler: Submitting 50 missing tasks from ResultStage 4 (ShuffledRDD[10] at write at ETL_Pipeline.py:24) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14))
INFO TaskSetManager: Starting task 0.0 in stage 4.0 (TID 300, executor 1, partition 0, PROCESS_LOCAL, 7987 bytes)
...
INFO BlockManagerInfo: Removed broadcast_0_piece0 on executor_1 in memory (size: 4.1 KB, free: 1958.6 MB)
INFO BlockManagerInfo: Removed broadcast_0_piece0 on executor_2 in memory (size: 4.1 KB, free: 1958.6 MB)
INFO BlockManagerInfo: Removed broadcast_0_piece0 on executor_3 in memory (size: 4.1 KB, free: 1958.6 MB)

PySpark optimizes job execution by breaking it down into stages and tasks based on data dependencies. The DAG Scheduler orchestrates the process, ensuring efficient memory management and parallel execution. By caching intermediate results and leveraging in-memory computation, PySpark minimizes disk I/O and improves overall performance. This approach contrasts with the more disk-intensive Hadoop MapReduce, highlighting PySpark’s advantages in handling complex data pipelines.

Written by HintsToday Team

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