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Apache Spark Data Processing

manutej/luxor-claude-marketplace

  • 345 installs
  • 61 repo stars
  • Updated June 13, 2026
  • manutej/luxor-claude-marketplace

apache-spark-data-processing is a Claude Code skill that implements distributed Spark jobs for developers who need large-scale transforms, aggregations, and feature pipelines with sound partitioning and performance tunin

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apache-spark-data-processing is a data engineering skill from manutej/luxor-claude-marketplace that guides developers implementing Apache Spark jobs for large-scale data processing. It covers distributed transforms, aggregations, and feature pipelines with emphasis on partitioning strategies and performance tuning for production workloads. Developers reach for apache-spark-data-processing when datasets exceed single-node capacity and require Spark DataFrame operations, shuffle optimization, and pipeline design for analytics or ML feature stores. The skill fits backend and data engineers building ETL flows, batch analytics, or ML preprocessing stages where correct partitioning and job configuration directly affect cluster cost and runtime.

  • Distributed transform patterns
  • Partitioning and shuffle optimization
  • Batch and structured streaming jobs
  • Data lake and warehouse I/O
  • Cluster sizing and cost control

Apache Spark Data Processing by the numbers

  • 345 all-time installs (skills.sh)
  • +20 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #546 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
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Repositorymanutej/luxor-claude-marketplace ↗

How do you build performant Apache Spark ETL jobs?

Implement distributed Spark jobs for large-scale transforms, aggregations, and feature pipelines with sound partitioning and performance tuning.

Who is it for?

Data engineers implementing large-scale Spark ETL, aggregations, or ML feature pipelines that need partitioning and performance tuning.

Skip if: Developers processing small datasets comfortably handled by pandas or single-node SQL without distributed compute.

When should I use this skill?

A developer asks to implement Spark transforms, aggregations, or feature pipelines with partitioning and performance optimization.

What you get

Distributed Spark job code, tuned partitioning configs, and production-ready transform and aggregation pipelines.

  • Spark job implementations
  • Partitioning and tuning configurations

Files

SKILL.mdMarkdownGitHub ↗

Apache Spark Data Processing

A comprehensive skill for mastering Apache Spark data processing, from basic RDD operations to advanced streaming, SQL, and machine learning workflows. Learn to build scalable, distributed data pipelines and analytics systems.

When to Use This Skill

Use Apache Spark when you need to:

  • Process Large-Scale Data: Handle datasets too large for single-machine processing (TB to PB scale)
  • Perform Distributed Computing: Execute parallel computations across cluster nodes
  • Real-Time Stream Processing: Process continuous data streams with low latency
  • Complex Data Analytics: Run sophisticated analytics, aggregations, and transformations
  • Machine Learning at Scale: Train ML models on massive datasets
  • ETL/ELT Pipelines: Build robust data transformation and loading workflows
  • Interactive Data Analysis: Perform exploratory analysis on large datasets
  • Unified Data Processing: Combine batch and streaming workloads in one framework

Not Ideal For:

  • Small datasets (<100 GB) that fit in memory on a single machine
  • Simple CRUD operations (use traditional databases)
  • Ultra-low latency requirements (<10ms) where specialized stream processors excel
  • Workflows requiring strong ACID transactions across distributed data

Core Concepts

Resilient Distributed Datasets (RDDs)

RDDs are Spark's fundamental data abstraction - immutable, distributed collections of objects that can be processed in parallel.

Key Characteristics:

  • Resilient: Fault-tolerant through lineage tracking
  • Distributed: Partitioned across cluster nodes
  • Immutable: Transformations create new RDDs, not modify existing ones
  • Lazy Evaluation: Transformations build computation graph; actions trigger execution
  • In-Memory Computing: Cache intermediate results for iterative algorithms

RDD Operations:

  • Transformations: Lazy operations that return new RDDs (map, filter, flatMap, reduceByKey)
  • Actions: Operations that trigger computation and return values (collect, count, reduce, saveAsTextFile)

When to Use RDDs:

  • Low-level control over data distribution and partitioning
  • Custom partitioning schemes required
  • Working with unstructured data (text files, binary data)
  • Migrating legacy code from early Spark versions

Prefer DataFrames/Datasets when possible - they provide automatic optimization via Catalyst optimizer.

DataFrames and Datasets

DataFrames are distributed collections of data organized into named columns - similar to a database table or pandas DataFrame, but with powerful optimizations.

DataFrames:

  • Structured data with schema
  • Automatic query optimization (Catalyst)
  • Cross-language support (Python, Scala, Java, R)
  • Rich API for SQL-like operations

Datasets (Scala/Java only):

  • Typed DataFrames with compile-time type safety
  • Best performance in Scala due to JVM optimization
  • Combine RDD type safety with DataFrame optimizations

Key Advantages Over RDDs:

  • Query Optimization: Catalyst optimizer rewrites queries for efficiency
  • Tungsten Execution: Optimized CPU and memory usage
  • Columnar Storage: Efficient data representation
  • Code Generation: Compile-time bytecode generation for faster execution

Lazy Evaluation

Spark uses lazy evaluation to optimize execution:

1. Transformations build a Directed Acyclic Graph (DAG) of operations 2. Actions trigger execution of the DAG 3. Spark's optimizer analyzes the entire DAG and creates an optimized execution plan 4. Work is distributed across cluster nodes

Benefits:

  • Minimize data movement across network
  • Combine multiple operations into single stage
  • Eliminate unnecessary computations
  • Optimize memory usage

Partitioning

Data is divided into partitions for parallel processing:

  • Default Partitioning: Typically based on HDFS block size or input source
  • Hash Partitioning: Distribute data by key hash (used by groupByKey, reduceByKey)
  • Range Partitioning: Distribute data by key ranges (useful for sorted data)
  • Custom Partitioning: Define your own partitioning logic

Partition Count Considerations:

  • Too few partitions: Underutilized cluster, large task execution time
  • Too many partitions: Scheduling overhead, small task execution time
  • General rule: 2-4 partitions per CPU core in cluster
  • Use repartition() or coalesce() to adjust partition count

Caching and Persistence

Cache frequently accessed data in memory for performance:

# Cache DataFrame in memory
df.cache()  # Shorthand for persist(StorageLevel.MEMORY_AND_DISK)

# Different storage levels
df.persist(StorageLevel.MEMORY_ONLY)       # Fast but may lose data if evicted
df.persist(StorageLevel.MEMORY_AND_DISK)   # Spill to disk if memory full
df.persist(StorageLevel.DISK_ONLY)         # Store only on disk
df.persist(StorageLevel.MEMORY_ONLY_SER)   # Serialized in memory (more compact)

# Unpersist when done
df.unpersist()

When to Cache:

  • Data used multiple times in workflow
  • Iterative algorithms (ML training)
  • Interactive analysis sessions
  • Expensive transformations reused downstream

When Not to Cache:

  • Data used only once
  • Very large datasets that exceed cluster memory
  • Streaming applications with continuous new data

Spark SQL

Spark SQL allows you to query structured data using SQL or DataFrame API:

  • Execute SQL queries on DataFrames and tables
  • Register DataFrames as temporary views
  • Join structured and semi-structured data
  • Connect to Hive metastore for table metadata
  • Support for various data sources (Parquet, ORC, JSON, CSV, JDBC)

Performance Features:

  • Catalyst Optimizer: Rule-based and cost-based query optimization
  • Tungsten Execution Engine: Whole-stage code generation, vectorized processing
  • Adaptive Query Execution (AQE): Runtime optimization based on statistics
  • Dynamic Partition Pruning: Skip irrelevant partitions during execution

Broadcast Variables and Accumulators

Shared variables for efficient distributed computing:

Broadcast Variables:

  • Read-only variables cached on each node
  • Efficient for sharing large read-only data (lookup tables, ML models)
  • Avoid sending large data with every task
# Broadcast a lookup table
lookup_table = {"key1": "value1", "key2": "value2"}
broadcast_lookup = sc.broadcast(lookup_table)

# Use in transformations
rdd.map(lambda x: broadcast_lookup.value.get(x, "default"))

Accumulators:

  • Write-only variables for aggregating values across tasks
  • Used for counters and sums in distributed operations
  • Only driver can read final accumulated value
# Create accumulator
error_count = sc.accumulator(0)

# Increment in tasks
rdd.foreach(lambda x: error_count.add(1) if is_error(x) else None)

# Read final value in driver
print(f"Total errors: {error_count.value}")

Spark SQL Deep Dive

DataFrame Creation

Create DataFrames from various sources:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("SparkSQLExample").getOrCreate()

# From structured data
data = [("Alice", 1), ("Bob", 2), ("Charlie", 3)]
columns = ["name", "id"]
df = spark.createDataFrame(data, columns)

# From files
df_json = spark.read.json("path/to/file.json")
df_parquet = spark.read.parquet("path/to/file.parquet")
df_csv = spark.read.option("header", "true").csv("path/to/file.csv")

# From JDBC sources
df_jdbc = spark.read \
    .format("jdbc") \
    .option("url", "jdbc:postgresql://host:port/database") \
    .option("dbtable", "table_name") \
    .option("user", "username") \
    .option("password", "password") \
    .load()

DataFrame Operations

Common DataFrame transformations:

# Select columns
df.select("name", "age").show()

# Filter rows
df.filter(df.age > 21).show()
df.where(df["age"] > 21).show()  # Alternative syntax

# Add/modify columns
from pyspark.sql.functions import col, lit
df.withColumn("age_plus_10", col("age") + 10).show()
df.withColumn("country", lit("USA")).show()

# Aggregations
df.groupBy("department").count().show()
df.groupBy("department").agg({"salary": "avg", "age": "max"}).show()

# Sorting
df.orderBy("age").show()
df.orderBy(col("age").desc()).show()

# Joins
df1.join(df2, df1.id == df2.user_id, "inner").show()
df1.join(df2, "id", "left_outer").show()

# Unions
df1.union(df2).show()

SQL Queries

Execute SQL on DataFrames:

# Register DataFrame as temporary view
df.createOrReplaceTempView("people")

# Run SQL queries
sql_result = spark.sql("SELECT name FROM people WHERE age > 21")
sql_result.show()

# Complex queries
result = spark.sql("""
    SELECT
        department,
        COUNT(*) as employee_count,
        AVG(salary) as avg_salary,
        MAX(age) as max_age
    FROM people
    WHERE age > 25
    GROUP BY department
    HAVING COUNT(*) > 5
    ORDER BY avg_salary DESC
""")
result.show()

Data Sources

Spark SQL supports multiple data formats:

Parquet (Recommended for Analytics):

  • Columnar storage format
  • Excellent compression and query performance
  • Schema embedded in file
  • Supports predicate pushdown and column pruning
# Write
df.write.parquet("output/path", mode="overwrite", compression="snappy")

# Read with partition pruning
df = spark.read.parquet("output/path").filter(col("date") == "2025-01-01")

ORC (Optimized Row Columnar):

  • Similar to Parquet with slightly better compression
  • Preferred for Hive integration
  • Built-in indexes for faster queries
df.write.orc("output/path", mode="overwrite")
df = spark.read.orc("output/path")

JSON (Semi-Structured Data):

  • Human-readable but less efficient
  • Schema inference on read
  • Good for nested/complex data
# Read with explicit schema
from pyspark.sql.types import StructType, StructField, StringType, IntegerType

schema = StructType([
    StructField("name", StringType(), True),
    StructField("age", IntegerType(), True)
])

df = spark.read.schema(schema).json("data.json")

CSV (Legacy/Simple Data):

  • Widely compatible but slow
  • Requires header inference or explicit schema
  • Minimal compression benefits
df.write.csv("output.csv", header=True, mode="overwrite")
df = spark.read.option("header", "true").option("inferSchema", "true").csv("data.csv")

Window Functions

Advanced analytics with window functions:

from pyspark.sql.window import Window
from pyspark.sql.functions import row_number, rank, dense_rank, lag, lead, sum, avg

# Define window specification
window_spec = Window.partitionBy("department").orderBy(col("salary").desc())

# Ranking functions
df.withColumn("rank", rank().over(window_spec)).show()
df.withColumn("row_num", row_number().over(window_spec)).show()
df.withColumn("dense_rank", dense_rank().over(window_spec)).show()

# Aggregate functions over window
df.withColumn("dept_avg_salary", avg("salary").over(window_spec)).show()
df.withColumn("running_total", sum("salary").over(window_spec.rowsBetween(Window.unboundedPreceding, Window.currentRow))).show()

# Offset functions
df.withColumn("prev_salary", lag("salary", 1).over(window_spec)).show()
df.withColumn("next_salary", lead("salary", 1).over(window_spec)).show()

User-Defined Functions (UDFs)

Create custom transformations:

from pyspark.sql.functions import udf
from pyspark.sql.types import StringType, IntegerType

# Python UDF (slower due to serialization overhead)
def categorize_age(age):
    if age < 18:
        return "Minor"
    elif age < 65:
        return "Adult"
    else:
        return "Senior"

categorize_udf = udf(categorize_age, StringType())
df.withColumn("age_category", categorize_udf(col("age"))).show()

# Pandas UDF (vectorized, faster for large datasets)
from pyspark.sql.functions import pandas_udf
import pandas as pd

@pandas_udf(IntegerType())
def square(series: pd.Series) -> pd.Series:
    return series ** 2

df.withColumn("age_squared", square(col("age"))).show()

UDF Performance Tips:

  • Use built-in Spark functions when possible (always faster)
  • Prefer Pandas UDFs over Python UDFs for better performance
  • Use Scala UDFs for maximum performance (no serialization overhead)
  • Cache DataFrames before applying UDFs if used multiple times

Transformations and Actions

Common Transformations

map: Apply function to each element

# RDD
rdd = sc.parallelize([1, 2, 3, 4, 5])
squared = rdd.map(lambda x: x * 2)  # [2, 4, 6, 8, 10]

# DataFrame (use select with functions)
from pyspark.sql.functions import col
df.select(col("value") * 2).show()

filter: Select elements matching predicate

# RDD
rdd.filter(lambda x: x > 2).collect()  # [3, 4, 5]

# DataFrame
df.filter(col("age") > 25).show()

flatMap: Map and flatten results

# RDD - Split text into words
lines = sc.parallelize(["hello world", "apache spark"])
words = lines.flatMap(lambda line: line.split(" "))  # ["hello", "world", "apache", "spark"]

reduceByKey: Aggregate values by key

# Word count example
words = sc.parallelize(["apple", "banana", "apple", "cherry", "banana", "apple"])
word_pairs = words.map(lambda word: (word, 1))
word_counts = word_pairs.reduceByKey(lambda a, b: a + b)
# Result: [("apple", 3), ("banana", 2), ("cherry", 1)]

groupByKey: Group values by key (avoid when possible - use reduceByKey instead)

# Less efficient than reduceByKey
word_pairs.groupByKey().mapValues(list).collect()
# Result: [("apple", [1, 1, 1]), ("banana", [1, 1]), ("cherry", [1])]

join: Combine datasets by key

# RDD join
users = sc.parallelize([("user1", "Alice"), ("user2", "Bob")])
orders = sc.parallelize([("user1", 100), ("user2", 200), ("user1", 150)])
users.join(orders).collect()
# Result: [("user1", ("Alice", 100)), ("user1", ("Alice", 150)), ("user2", ("Bob", 200))]

# DataFrame join (more efficient)
df_users.join(df_orders, "user_id", "inner").show()

distinct: Remove duplicates

# RDD
rdd.distinct().collect()

# DataFrame
df.distinct().show()
df.dropDuplicates(["user_id"]).show()  # Drop based on specific columns

coalesce/repartition: Change partition count

# Reduce partitions (no shuffle, more efficient)
df.coalesce(1).write.parquet("output")

# Increase/decrease partitions (involves shuffle)
df.repartition(10).write.parquet("output")
df.repartition(10, "user_id").write.parquet("output")  # Partition by column

Common Actions

collect: Retrieve all data to driver

results = rdd.collect()  # Returns list
# WARNING: Only use on small datasets that fit in driver memory

count: Count elements

total = df.count()  # Number of rows

first/take: Get first N elements

first_elem = rdd.first()
first_five = rdd.take(5)

reduce: Aggregate all elements

total_sum = rdd.reduce(lambda a, b: a + b)

foreach: Execute function on each element

# Side effects only (no return value)
rdd.foreach(lambda x: print(x))

saveAsTextFile: Write to file system

rdd.saveAsTextFile("hdfs://path/to/output")

show: Display DataFrame rows (action)

df.show(20, truncate=False)  # Show 20 rows, don't truncate columns

Structured Streaming

Process continuous data streams using DataFrame API.

Core Concepts

Streaming DataFrame:

  • Unbounded table that grows continuously
  • Same operations as batch DataFrames
  • Micro-batch processing (default) or continuous processing

Input Sources:

  • File sources (JSON, Parquet, CSV, ORC, text)
  • Kafka
  • Socket (for testing)
  • Rate source (for testing)
  • Custom sources

Output Modes:

  • Append: Only new rows added to result table
  • Complete: Entire result table written every trigger
  • Update: Only updated rows written

Output Sinks:

  • File sinks (Parquet, ORC, JSON, CSV, text)
  • Kafka
  • Console (for debugging)
  • Memory (for testing)
  • Foreach/ForeachBatch (custom logic)

Basic Streaming Example

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.appName("StreamingExample").getOrCreate()

# Read stream from JSON files
input_stream = spark.readStream \
    .format("json") \
    .schema(schema) \
    .option("maxFilesPerTrigger", 1) \
    .load("input/directory")

# Transform streaming data
processed = input_stream \
    .filter(col("value") > 10) \
    .select("id", "value", "timestamp")

# Write stream to Parquet
query = processed.writeStream \
    .format("parquet") \
    .option("path", "output/directory") \
    .option("checkpointLocation", "checkpoint/directory") \
    .outputMode("append") \
    .start()

# Wait for termination
query.awaitTermination()

Stream-Static Joins

Join streaming data with static reference data:

# Static DataFrame (loaded once)
static_df = spark.read.parquet("reference/data")

# Streaming DataFrame
streaming_df = spark.readStream.format("kafka").load()

# Inner join (supported)
joined = streaming_df.join(static_df, "type")

# Left outer join (supported)
joined = streaming_df.join(static_df, "type", "left_outer")

# Write result
joined.writeStream \
    .format("parquet") \
    .option("path", "output") \
    .option("checkpointLocation", "checkpoint") \
    .start()

Windowed Aggregations

Aggregate data over time windows:

from pyspark.sql.functions import window, col, count

# 10-minute tumbling window
windowed_counts = streaming_df \
    .groupBy(
        window(col("timestamp"), "10 minutes"),
        col("word")
    ) \
    .count()

# 10-minute sliding window with 5-minute slide
windowed_counts = streaming_df \
    .groupBy(
        window(col("timestamp"), "10 minutes", "5 minutes"),
        col("word")
    ) \
    .count()

# Write to console for debugging
query = windowed_counts.writeStream \
    .outputMode("complete") \
    .format("console") \
    .option("truncate", "false") \
    .start()

Watermarking for Late Data

Handle late-arriving data with watermarks:

from pyspark.sql.functions import window

# Define watermark (10 minutes tolerance for late data)
windowed_counts = streaming_df \
    .withWatermark("timestamp", "10 minutes") \
    .groupBy(
        window(col("timestamp"), "10 minutes"),
        col("word")
    ) \
    .count()

# Data arriving more than 10 minutes late will be dropped

Watermark Benefits:

  • Limit state size by dropping old aggregation state
  • Handle late data within tolerance window
  • Improve performance by not maintaining infinite state

Session Windows

Group events into sessions based on inactivity gaps:

from pyspark.sql.functions import session_window, when

# Dynamic session window based on user
session_window_spec = session_window(
    col("timestamp"),
    when(col("userId") == "user1", "5 seconds")
    .when(col("userId") == "user2", "20 seconds")
    .otherwise("5 minutes")
)

sessionized_counts = streaming_df \
    .withWatermark("timestamp", "10 minutes") \
    .groupBy(session_window_spec, col("userId")) \
    .count()

Stateful Stream Processing

Maintain state across micro-batches:

from pyspark.sql.functions import expr

# Deduplication using state
deduplicated = streaming_df \
    .withWatermark("timestamp", "1 hour") \
    .dropDuplicates(["user_id", "event_id"])

# Stream-stream joins (stateful)
stream1 = spark.readStream.format("kafka").option("subscribe", "topic1").load()
stream2 = spark.readStream.format("kafka").option("subscribe", "topic2").load()

joined = stream1 \
    .withWatermark("timestamp", "10 minutes") \
    .join(
        stream2.withWatermark("timestamp", "20 minutes"),
        expr("stream1.user_id = stream2.user_id AND stream1.timestamp >= stream2.timestamp AND stream1.timestamp <= stream2.timestamp + interval 15 minutes"),
        "inner"
    )

Checkpointing

Ensure fault tolerance with checkpoints:

# Checkpoint location stores:
# - Stream metadata (offsets, configuration)
# - State information (for stateful operations)
# - Write-ahead logs

query = streaming_df.writeStream \
    .format("parquet") \
    .option("path", "output") \
    .option("checkpointLocation", "checkpoint/dir")  # REQUIRED for production \
    .start()

# Recovery: Restart query with same checkpoint location
# Spark will resume from last committed offset

Checkpoint Best Practices:

  • Always set checkpointLocation for production streams
  • Use reliable distributed storage (HDFS, S3) for checkpoints
  • Don't delete checkpoint directory while stream is running
  • Back up checkpoints for disaster recovery

Machine Learning with MLlib

Spark's scalable machine learning library.

Core Components

MLlib Features:

  • ML Pipelines: Chain transformations and models
  • Featurization: Vector assemblers, scalers, encoders
  • Classification & Regression: Linear models, tree-based models, neural networks
  • Clustering: K-means, Gaussian Mixture, LDA
  • Collaborative Filtering: ALS (Alternating Least Squares)
  • Dimensionality Reduction: PCA, SVD
  • Model Selection: Cross-validation, train-test split, parameter tuning

ML Pipelines

Chain transformations and estimators:

from pyspark.ml import Pipeline
from pyspark.ml.feature import VectorAssembler, StandardScaler
from pyspark.ml.classification import LogisticRegression

# Load data
df = spark.read.format("libsvm").load("data/sample_libsvm_data.txt")

# Define pipeline stages
assembler = VectorAssembler(
    inputCols=["feature1", "feature2", "feature3"],
    outputCol="features"
)

scaler = StandardScaler(
    inputCol="features",
    outputCol="scaled_features",
    withStd=True,
    withMean=True
)

lr = LogisticRegression(
    featuresCol="scaled_features",
    labelCol="label",
    maxIter=10,
    regParam=0.01
)

# Create pipeline
pipeline = Pipeline(stages=[assembler, scaler, lr])

# Split data
train_df, test_df = df.randomSplit([0.8, 0.2], seed=42)

# Train model
model = pipeline.fit(train_df)

# Make predictions
predictions = model.transform(test_df)
predictions.select("label", "prediction", "probability").show()

Feature Engineering

Transform raw data into features:

from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler, MinMaxScaler

# Categorical encoding
indexer = StringIndexer(inputCol="category", outputCol="category_index")
encoder = OneHotEncoder(inputCol="category_index", outputCol="category_vec")

# Numerical scaling
scaler = MinMaxScaler(inputCol="features", outputCol="scaled_features")

# Assemble features
assembler = VectorAssembler(
    inputCols=["category_vec", "numeric_feature1", "numeric_feature2"],
    outputCol="features"
)

# Text processing
from pyspark.ml.feature import Tokenizer, HashingTF, IDF

tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashing_tf = HashingTF(inputCol="words", outputCol="raw_features", numFeatures=10000)
idf = IDF(inputCol="raw_features", outputCol="features")

Streaming Linear Regression

Train models on streaming data:

from pyspark.mllib.regression import LabeledPoint
from pyspark.streaming import StreamingContext
from pyspark.streaming.ml import StreamingLinearRegressionWithSGD

# Create StreamingContext
ssc = StreamingContext(sc, batchDuration=1)

# Define data streams
training_stream = ssc.textFileStream("training/data/path")
testing_stream = ssc.textFileStream("testing/data/path")

# Parse streams into LabeledPoint objects
def parse_point(line):
    values = [float(x) for x in line.strip().split(',')]
    return LabeledPoint(values[0], values[1:])

parsed_training = training_stream.map(parse_point)
parsed_testing = testing_stream.map(parse_point)

# Initialize model
num_features = 3
model = StreamingLinearRegressionWithSGD(initialWeights=[0.0] * num_features)

# Train and predict
model.trainOn(parsed_training)
predictions = model.predictOnValues(parsed_testing.map(lambda lp: (lp.label, lp.features)))

# Print predictions
predictions.pprint()

# Start streaming
ssc.start()
ssc.awaitTermination()

Model Evaluation

Evaluate model performance:

from pyspark.ml.evaluation import BinaryClassificationEvaluator, MulticlassClassificationEvaluator, RegressionEvaluator

# Binary classification
binary_evaluator = BinaryClassificationEvaluator(
    labelCol="label",
    rawPredictionCol="rawPrediction",
    metricName="areaUnderROC"
)
auc = binary_evaluator.evaluate(predictions)
print(f"AUC: {auc}")

# Multiclass classification
multi_evaluator = MulticlassClassificationEvaluator(
    labelCol="label",
    predictionCol="prediction",
    metricName="accuracy"
)
accuracy = multi_evaluator.evaluate(predictions)
print(f"Accuracy: {accuracy}")

# Regression
regression_evaluator = RegressionEvaluator(
    labelCol="label",
    predictionCol="prediction",
    metricName="rmse"
)
rmse = regression_evaluator.evaluate(predictions)
print(f"RMSE: {rmse}")

Hyperparameter Tuning

Optimize model parameters with cross-validation:

from pyspark.ml.tuning import ParamGridBuilder, CrossValidator
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator

# Define model
rf = RandomForestClassifier(labelCol="label", featuresCol="features")

# Build parameter grid
param_grid = ParamGridBuilder() \
    .addGrid(rf.numTrees, [10, 20, 50]) \
    .addGrid(rf.maxDepth, [5, 10, 15]) \
    .addGrid(rf.minInstancesPerNode, [1, 5, 10]) \
    .build()

# Define evaluator
evaluator = MulticlassClassificationEvaluator(metricName="accuracy")

# Cross-validation
cv = CrossValidator(
    estimator=rf,
    estimatorParamMaps=param_grid,
    evaluator=evaluator,
    numFolds=5,
    parallelism=4
)

# Train
cv_model = cv.fit(train_df)

# Best model
best_model = cv_model.bestModel
print(f"Best numTrees: {best_model.getNumTrees}")
print(f"Best maxDepth: {best_model.getMaxDepth()}")

# Evaluate on test set
predictions = cv_model.transform(test_df)
accuracy = evaluator.evaluate(predictions)
print(f"Test Accuracy: {accuracy}")

Distributed Matrix Operations

MLlib provides distributed matrix representations:

from pyspark.mllib.linalg.distributed import RowMatrix, IndexedRowMatrix, CoordinateMatrix
from pyspark.mllib.linalg import Vectors

# RowMatrix: Distributed matrix without row indices
rows = sc.parallelize([
    Vectors.dense([1.0, 2.0, 3.0]),
    Vectors.dense([4.0, 5.0, 6.0]),
    Vectors.dense([7.0, 8.0, 9.0])
])
row_matrix = RowMatrix(rows)

# Compute statistics
print(f"Rows: {row_matrix.numRows()}")
print(f"Cols: {row_matrix.numCols()}")
print(f"Column means: {row_matrix.computeColumnSummaryStatistics().mean()}")

# IndexedRowMatrix: Matrix with row indices
from pyspark.mllib.linalg.distributed import IndexedRow
indexed_rows = sc.parallelize([
    IndexedRow(0, Vectors.dense([1.0, 2.0, 3.0])),
    IndexedRow(1, Vectors.dense([4.0, 5.0, 6.0]))
])
indexed_matrix = IndexedRowMatrix(indexed_rows)

# CoordinateMatrix: Sparse matrix using (row, col, value) entries
from pyspark.mllib.linalg.distributed import MatrixEntry
entries = sc.parallelize([
    MatrixEntry(0, 0, 1.0),
    MatrixEntry(0, 2, 3.0),
    MatrixEntry(1, 1, 5.0)
])
coord_matrix = CoordinateMatrix(entries)

Stratified Sampling

Sample data while preserving class distribution:

# Scala/Java approach
data = [("a", 1), ("b", 2), ("a", 3), ("b", 4), ("a", 5), ("c", 6)]
rdd = sc.parallelize(data)

# Define sampling fractions per key
fractions = {"a": 0.5, "b": 0.5, "c": 0.5}

# Approximate sample (faster, one pass)
sampled_rdd = rdd.sampleByKey(withReplacement=False, fractions=fractions)

# Exact sample (slower, guaranteed exact counts)
exact_sampled = rdd.sampleByKeyExact(withReplacement=False, fractions=fractions)

print(sampled_rdd.collect())

Performance Tuning

Memory Management

Memory Breakdown:

  • Execution Memory: Used for shuffles, joins, sorts, aggregations
  • Storage Memory: Used for caching and broadcast variables
  • User Memory: Used for user data structures and UDFs
  • Reserved Memory: Reserved for Spark internal operations

Configuration:

spark = SparkSession.builder \
    .appName("MemoryTuning") \
    .config("spark.executor.memory", "4g") \
    .config("spark.driver.memory", "2g") \
    .config("spark.memory.fraction", "0.6")  # Fraction for execution + storage \
    .config("spark.memory.storageFraction", "0.5")  # Fraction of above for storage \
    .getOrCreate()

Memory Best Practices:

  • Monitor memory usage via Spark UI
  • Use appropriate storage levels for caching
  • Avoid collecting large datasets to driver
  • Increase executor memory for memory-intensive operations
  • Use kryo serialization for better memory efficiency

Shuffle Optimization

Shuffles are expensive operations - minimize them:

Causes of Shuffles:

  • groupByKey, reduceByKey, aggregateByKey
  • join, cogroup
  • repartition, coalesce (with increase)
  • distinct, intersection
  • sortByKey

Optimization Strategies:

# 1. Use reduceByKey instead of groupByKey
# Bad: groupByKey shuffles all data
word_pairs.groupByKey().mapValues(sum)

# Good: reduceByKey combines locally before shuffle
word_pairs.reduceByKey(lambda a, b: a + b)

# 2. Broadcast small tables in joins
from pyspark.sql.functions import broadcast
large_df.join(broadcast(small_df), "key")

# 3. Partition data appropriately
df.repartition(200, "user_id")  # Partition by key for subsequent aggregations

# 4. Coalesce instead of repartition when reducing partitions
df.coalesce(10)  # No shuffle, just merge partitions

# 5. Tune shuffle partitions
spark.conf.set("spark.sql.shuffle.partitions", 200)  # Default is 200

Shuffle Configuration:

spark = SparkSession.builder \
    .config("spark.sql.shuffle.partitions", 200) \
    .config("spark.default.parallelism", 200) \
    .config("spark.sql.adaptive.enabled", "true")  # Enable AQE \
    .config("spark.sql.adaptive.coalescePartitions.enabled", "true") \
    .getOrCreate()

Partitioning Strategies

Partition Count Guidelines:

  • Too few: Underutilized cluster, OOM errors
  • Too many: Task scheduling overhead
  • Sweet spot: 2-4x number of CPU cores
  • For large shuffles: 100-200+ partitions

Partition by Column:

# Partition writes by date for easy filtering
df.write.partitionBy("date", "country").parquet("output")

# Read with partition pruning (only reads relevant partitions)
spark.read.parquet("output").filter(col("date") == "2025-01-15").show()

Custom Partitioning:

from pyspark.rdd import portable_hash

# Custom partitioner for RDD
def custom_partitioner(key):
    return portable_hash(key) % 100

rdd.partitionBy(100, custom_partitioner)

Caching Strategies

When to Cache:

# Iterative algorithms (ML)
training_data.cache()
for i in range(num_iterations):
    model = train_model(training_data)

# Multiple aggregations on same data
base_df.cache()
result1 = base_df.groupBy("country").count()
result2 = base_df.groupBy("city").avg("sales")

# Interactive analysis
df.cache()
df.filter(condition1).show()
df.filter(condition2).show()
df.groupBy("category").count().show()

Storage Levels:

from pyspark import StorageLevel

# Memory only (fastest, but may lose data)
df.persist(StorageLevel.MEMORY_ONLY)

# Memory and disk (spill to disk if needed)
df.persist(StorageLevel.MEMORY_AND_DISK)

# Serialized in memory (more compact, slower access)
df.persist(StorageLevel.MEMORY_ONLY_SER)

# Disk only (slowest, but always available)
df.persist(StorageLevel.DISK_ONLY)

# Replicated (fault tolerance)
df.persist(StorageLevel.MEMORY_AND_DISK_2)  # 2 replicas

Broadcast Joins

Optimize joins with small tables:

from pyspark.sql.functions import broadcast

# Automatic broadcast (tables < spark.sql.autoBroadcastJoinThreshold)
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 10 * 1024 * 1024)  # 10 MB

# Explicit broadcast hint
large_df.join(broadcast(small_df), "key")

# Benefits:
# - No shuffle of large table
# - Small table sent to all executors once
# - Much faster for small dimension tables

Adaptive Query Execution (AQE)

Enable runtime query optimization:

spark.conf.set("spark.sql.adaptive.enabled", "true")
spark.conf.set("spark.sql.adaptive.coalescePartitions.enabled", "true")
spark.conf.set("spark.sql.adaptive.skewJoin.enabled", "true")

# AQE Benefits:
# - Dynamically coalesce partitions after shuffle
# - Handle skewed joins by splitting large partitions
# - Optimize join strategy at runtime

Data Format Selection

Performance Comparison: 1. Parquet (Best for analytics): Columnar, compressed, fast queries 2. ORC (Best for Hive): Similar to Parquet, slightly better compression 3. Avro (Best for row-oriented): Good for write-heavy workloads 4. JSON (Slowest): Human-readable but inefficient 5. CSV (Legacy): Compatible but slow and no schema

Recommendation:

  • Use Parquet for most analytics workloads
  • Enable compression (snappy, gzip, lzo)
  • Partition by commonly filtered columns
  • Use columnar formats for read-heavy workloads

Catalyst Optimizer

Understand query optimization:

# View physical plan
df.explain(mode="extended")

# Optimizations include:
# - Predicate pushdown: Push filters to data source
# - Column pruning: Read only required columns
# - Constant folding: Evaluate constants at compile time
# - Join reordering: Optimize join order
# - Partition pruning: Skip irrelevant partitions

Production Deployment

Cluster Managers

Standalone:

  • Simple, built-in cluster manager
  • Easy setup for development and small clusters
  • No resource sharing with other frameworks
# Start master
$SPARK_HOME/sbin/start-master.sh

# Start workers
$SPARK_HOME/sbin/start-worker.sh spark://master:7077

# Submit application
spark-submit --master spark://master:7077 app.py

YARN:

  • Hadoop's resource manager
  • Share cluster resources with MapReduce, Hive, etc.
  • Two modes: cluster (driver on YARN) and client (driver on local machine)
# Cluster mode (driver runs on YARN)
spark-submit --master yarn --deploy-mode cluster app.py

# Client mode (driver runs locally)
spark-submit --master yarn --deploy-mode client app.py

Kubernetes:

  • Modern container orchestration
  • Dynamic resource allocation
  • Cloud-native deployments
spark-submit \
  --master k8s://https://k8s-master:443 \
  --deploy-mode cluster \
  --name spark-app \
  --conf spark.executor.instances=5 \
  --conf spark.kubernetes.container.image=spark:latest \
  app.py

Mesos:

  • General-purpose cluster manager
  • Fine-grained or coarse-grained resource sharing

Application Submission

Basic spark-submit:

spark-submit \
  --master yarn \
  --deploy-mode cluster \
  --driver-memory 4g \
  --executor-memory 8g \
  --executor-cores 4 \
  --num-executors 10 \
  --conf spark.sql.shuffle.partitions=200 \
  --py-files dependencies.zip \
  --files config.json \
  application.py

Configuration Options:

  • --master: Cluster manager URL
  • --deploy-mode: Where to run driver (client or cluster)
  • --driver-memory: Memory for driver process
  • --executor-memory: Memory per executor
  • --executor-cores: Cores per executor
  • --num-executors: Number of executors
  • --conf: Spark configuration properties
  • --py-files: Python dependencies
  • --files: Additional files to distribute

Resource Allocation

General Guidelines:

  • Driver Memory: 1-4 GB (unless collecting large results)
  • Executor Memory: 4-16 GB per executor
  • Executor Cores: 4-5 cores per executor (diminishing returns beyond 5)
  • Number of Executors: Fill cluster capacity, leave resources for OS/other services
  • Parallelism: 2-4x total cores

Example Calculations:

Cluster: 10 nodes, 32 cores each, 128 GB RAM each

Option 1: Many small executors
  - 30 executors (3 per node)
  - 10 cores per executor
  - 40 GB memory per executor
  - Total: 300 cores

Option 2: Fewer large executors (RECOMMENDED)
  - 50 executors (5 per node)
  - 5 cores per executor
  - 24 GB memory per executor
  - Total: 250 cores

Dynamic Allocation

Automatically scale executors based on workload:

spark = SparkSession.builder \
    .appName("DynamicAllocation") \
    .config("spark.dynamicAllocation.enabled", "true") \
    .config("spark.dynamicAllocation.minExecutors", 2) \
    .config("spark.dynamicAllocation.maxExecutors", 100) \
    .config("spark.dynamicAllocation.initialExecutors", 10) \
    .config("spark.dynamicAllocation.executorIdleTimeout", "60s") \
    .getOrCreate()

Benefits:

  • Better resource utilization
  • Automatic scaling for varying workloads
  • Reduced costs in cloud environments

Monitoring and Logging

Spark UI:

  • Web UI at http://driver:4040
  • Stages, tasks, storage, environment, executors
  • SQL query plans and execution details
  • Identify bottlenecks and performance issues

History Server:

# Start history server
$SPARK_HOME/sbin/start-history-server.sh

# Configure event logging
spark.conf.set("spark.eventLog.enabled", "true")
spark.conf.set("spark.eventLog.dir", "hdfs://namenode/spark-logs")

Metrics:

# Enable metrics collection
spark.conf.set("spark.metrics.conf.*.sink.console.class", "org.apache.spark.metrics.sink.ConsoleSink")
spark.conf.set("spark.metrics.conf.*.sink.console.period", 10)

Logging:

# Configure log level
spark.sparkContext.setLogLevel("WARN")  # ERROR, WARN, INFO, DEBUG

# Custom logging
import logging
logger = logging.getLogger(__name__)
logger.info("Custom log message")

Fault Tolerance

Automatic Recovery:

  • Task failures: Automatically retry failed tasks
  • Executor failures: Reschedule tasks on other executors
  • Driver failures: Restore from checkpoint (streaming)
  • Node failures: Recompute lost partitions from lineage

Checkpointing:

# Set checkpoint directory
spark.sparkContext.setCheckpointDir("hdfs://namenode/checkpoints")

# Checkpoint RDD (breaks lineage for very long chains)
rdd.checkpoint()

# Streaming checkpoint (required for production)
query = streaming_df.writeStream \
    .option("checkpointLocation", "hdfs://namenode/streaming-checkpoint") \
    .start()

Speculative Execution:

# Enable speculative execution for slow tasks
spark.conf.set("spark.speculation", "true")
spark.conf.set("spark.speculation.multiplier", 1.5)
spark.conf.set("spark.speculation.quantile", 0.75)

Data Locality

Optimize data placement for performance:

Locality Levels: 1. PROCESS_LOCAL: Data in same JVM as task (fastest) 2. NODE_LOCAL: Data on same node, different process 3. RACK_LOCAL: Data on same rack 4. ANY: Data on different rack (slowest)

Improve Locality:

# Increase locality wait time
spark.conf.set("spark.locality.wait", "10s")
spark.conf.set("spark.locality.wait.node", "5s")
spark.conf.set("spark.locality.wait.rack", "3s")

# Partition data to match cluster topology
df.repartition(num_nodes * cores_per_node)

Best Practices

Code Organization

1. Modular Design: Separate data loading, transformation, and output logic 2. Configuration Management: Externalize configuration (use config files) 3. Error Handling: Implement robust error handling and logging 4. Testing: Unit test transformations, integration test pipelines 5. Documentation: Document complex transformations and business logic

Performance

1. Avoid Shuffles: Use reduceByKey instead of groupByKey 2. Cache Wisely: Only cache data reused multiple times 3. Broadcast Small Tables: Use broadcast joins for small reference data 4. Partition Appropriately: 2-4x CPU cores, partition by frequently filtered columns 5. Use Parquet: Columnar format for analytical workloads 6. Enable AQE: Leverage adaptive query execution for runtime optimization 7. Tune Memory: Balance executor memory and cores 8. Monitor: Use Spark UI to identify bottlenecks

Development Workflow

1. Start Small: Develop with sample data locally 2. Profile Early: Monitor performance from the start 3. Iterate: Optimize incrementally based on metrics 4. Test at Scale: Validate with production-sized data before deployment 5. Version Control: Track code, configurations, and schemas

Data Quality

1. Schema Validation: Enforce schemas on read/write 2. Null Handling: Explicitly handle null values 3. Data Validation: Check for expected ranges, formats, constraints 4. Deduplication: Remove duplicates based on business logic 5. Audit Logging: Track data lineage and transformations

Security

1. Authentication: Enable Kerberos for YARN/HDFS 2. Authorization: Use ACLs for data access control 3. Encryption: Encrypt data at rest and in transit 4. Secrets Management: Use secure credential providers 5. Audit Trails: Log data access and modifications

Cost Optimization

1. Right-Size Resources: Don't over-provision executors 2. Dynamic Allocation: Scale executors based on workload 3. Spot Instances: Use spot/preemptible instances in cloud 4. Data Compression: Use efficient formats (Parquet, ORC) 5. Partitioning: Prune unnecessary data reads 6. Auto-Shutdown: Terminate idle clusters

Common Patterns

ETL Pipeline Pattern

def etl_pipeline(spark, input_path, output_path):
    # Extract
    raw_df = spark.read.parquet(input_path)

    # Transform
    cleaned_df = raw_df \
        .dropDuplicates(["id"]) \
        .filter(col("value").isNotNull()) \
        .withColumn("processed_date", current_date())

    # Enrich
    enriched_df = cleaned_df.join(broadcast(reference_df), "key")

    # Aggregate
    aggregated_df = enriched_df \
        .groupBy("category", "date") \
        .agg(
            count("*").alias("count"),
            sum("amount").alias("total_amount"),
            avg("value").alias("avg_value")
        )

    # Load
    aggregated_df.write \
        .partitionBy("date") \
        .mode("overwrite") \
        .parquet(output_path)

Incremental Processing Pattern

def incremental_process(spark, input_path, output_path, checkpoint_path):
    # Read last processed timestamp
    last_timestamp = read_checkpoint(checkpoint_path)

    # Read new data
    new_data = spark.read.parquet(input_path) \
        .filter(col("timestamp") > last_timestamp)

    # Process
    processed = transform(new_data)

    # Write
    processed.write.mode("append").parquet(output_path)

    # Update checkpoint
    max_timestamp = new_data.agg(max("timestamp")).collect()[0][0]
    write_checkpoint(checkpoint_path, max_timestamp)

Slowly Changing Dimension (SCD) Pattern

def scd_type2_upsert(spark, dimension_df, updates_df):
    # Mark existing records as inactive if updated
    inactive_records = dimension_df \
        .join(updates_df, "business_key") \
        .select(
            dimension_df["*"],
            lit(False).alias("is_active"),
            current_date().alias("end_date")
        )

    # Add new records
    new_records = updates_df \
        .withColumn("is_active", lit(True)) \
        .withColumn("start_date", current_date()) \
        .withColumn("end_date", lit(None))

    # Union unchanged, inactive, and new records
    result = dimension_df \
        .join(updates_df, "business_key", "left_anti") \
        .union(inactive_records) \
        .union(new_records)

    return result

Window Analytics Pattern

def calculate_running_metrics(df):
    from pyspark.sql.window import Window
    from pyspark.sql.functions import row_number, lag, sum, avg

    # Define window
    window_spec = Window.partitionBy("user_id").orderBy("timestamp")

    # Calculate metrics
    result = df \
        .withColumn("row_num", row_number().over(window_spec)) \
        .withColumn("prev_value", lag("value", 1).over(window_spec)) \
        .withColumn("running_total", sum("value").over(window_spec.rowsBetween(Window.unboundedPreceding, Window.currentRow))) \
        .withColumn("moving_avg", avg("value").over(window_spec.rowsBetween(-2, 0)))

    return result

Troubleshooting

Out of Memory Errors

Symptoms:

  • java.lang.OutOfMemoryError
  • Executor failures
  • Slow garbage collection

Solutions:

# Increase executor memory
spark.conf.set("spark.executor.memory", "8g")

# Increase driver memory (if collecting data)
spark.conf.set("spark.driver.memory", "4g")

# Reduce memory pressure
df.persist(StorageLevel.MEMORY_AND_DISK)  # Spill to disk
df.coalesce(100)  # Reduce partition count
spark.conf.set("spark.sql.shuffle.partitions", 400)  # Increase shuffle partitions

# Avoid collect() on large datasets
# Use take() or limit() instead
df.take(100)

Shuffle Performance Issues

Symptoms:

  • Long shuffle read/write times
  • Skewed partition sizes
  • Task stragglers

Solutions:

# Increase shuffle partitions
spark.conf.set("spark.sql.shuffle.partitions", 400)

# Handle skew with salting
df_salted = df.withColumn("salt", (rand() * 10).cast("int"))
result = df_salted.groupBy("key", "salt").agg(...)

# Use broadcast for small tables
large_df.join(broadcast(small_df), "key")

# Enable AQE for automatic optimization
spark.conf.set("spark.sql.adaptive.enabled", "true")
spark.conf.set("spark.sql.adaptive.skewJoin.enabled", "true")

Streaming Job Failures

Symptoms:

  • Streaming query stopped
  • Checkpoint corruption
  • Processing lag increasing

Solutions:

# Increase executor memory for stateful operations
spark.conf.set("spark.executor.memory", "8g")

# Tune watermark for late data
.withWatermark("timestamp", "15 minutes")

# Increase trigger interval to reduce micro-batch overhead
.trigger(processingTime="30 seconds")

# Monitor lag and adjust parallelism
spark.conf.set("spark.sql.shuffle.partitions", 200)

# Recover from checkpoint corruption
# Delete checkpoint directory and restart (data loss possible)
# Or implement custom state recovery logic

Data Skew

Symptoms:

  • Few tasks take much longer than others
  • Unbalanced partition sizes
  • Executor OOM errors

Solutions:

# 1. Salting technique (add random prefix to keys)
from pyspark.sql.functions import concat, lit, rand

df_salted = df.withColumn("salted_key", concat(col("key"), lit("_"), (rand() * 10).cast("int")))
result = df_salted.groupBy("salted_key").agg(...)

# 2. Repartition by skewed column
df.repartition(200, "skewed_column")

# 3. Isolate skewed keys
skewed_keys = df.groupBy("key").count().filter(col("count") > threshold).select("key")
skewed_df = df.join(broadcast(skewed_keys), "key")
normal_df = df.join(broadcast(skewed_keys), "key", "left_anti")

# Process separately
skewed_result = process_with_salting(skewed_df)
normal_result = process_normally(normal_df)
final = skewed_result.union(normal_result)

Context7 Code Integration

This skill integrates real-world code examples from Apache Spark's official repository. All code snippets in the EXAMPLES.md file are sourced from Context7's Apache Spark library documentation, ensuring production-ready patterns and best practices.

Version and Compatibility

  • Apache Spark Version: 3.x (compatible with 2.4+)
  • Python: 3.7+
  • Scala: 2.12+
  • Java: 8+
  • R: 3.5+

References

  • Official Documentation: https://spark.apache.org/docs/latest/
  • API Reference: https://spark.apache.org/docs/latest/api.html
  • GitHub Repository: https://github.com/apache/spark
  • Databricks Blog: https://databricks.com/blog
  • Context7 Library: /apache/spark

---

Skill Version: 1.0.0 Last Updated: October 2025 Skill Category: Big Data, Distributed Computing, Data Engineering, Machine Learning Context7 Integration: /apache/spark with 8000 tokens of documentation

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How it compares

Choose apache-spark-data-processing when data volume requires distributed Spark execution rather than single-node pandas or SQL scripts.

FAQ

What workloads does apache-spark-data-processing cover?

apache-spark-data-processing covers distributed transforms, aggregations, and feature pipelines on Apache Spark. The skill emphasizes sound partitioning and performance tuning for large-scale datasets that exceed single-node capacity.

When should developers use apache-spark-data-processing?

apache-spark-data-processing fits backend data engineering when batch ETL, analytics, or ML feature stores require Spark cluster jobs. Use it when partitioning and shuffle optimization materially affect runtime and cost.

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