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by Seth Hobsonwshobson/agents40k stars
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Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

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spark-optimization — detailed patterns and worked examples

Patterns

Pattern 1: Optimal Partitioning

# Calculate optimal partition count
def calculate_partitions(data_size_gb: float, partition_size_mb: int = 128) -> int:
    """
    Optimal partition size: 128MB - 256MB
    Too few: Under-utilization, memory pressure
    Too many: Task scheduling overhead
    """
    return max(int(data_size_gb * 1024 / partition_size_mb), 1)

# Repartition for even distribution
df_repartitioned = df.repartition(200, "partition_key")

# Coalesce to reduce partitions (no shuffle)
df_coalesced = df.coalesce(100)

# Partition pruning with predicate pushdown
df = (spark.read.parquet("s3://bucket/data/")
    .filter(F.col("date") == "2024-01-01"))  # Spark pushes this down

# Write with partitioning for future queries
(df.write
    .partitionBy("year", "month", "day")
    .mode("overwrite")
    .parquet("s3://bucket/partitioned_output/"))

Pattern 2: Join Optimization

from pyspark.sql import functions as F
from pyspark.sql.types import *

# 1. Broadcast Join - Small table joins
# Best when: One side < 10MB (configurable)
small_df = spark.read.parquet("s3://bucket/small_table/")  # < 10MB
large_df = spark.read.parquet("s3://bucket/large_table/")  # TBs

# Explicit broadcast hint
result = large_df.join(
    F.broadcast(small_df),
    on="key",
    how="left"
)

# 2. Sort-Merge Join - Default for large tables
# Requires shuffle, but handles any size
result = large_df1.join(large_df2, on="key", how="inner")

# 3. Bucket Join - Pre-sorted, no shuffle at join time
# Write bucketed tables
(df.write
    .bucketBy(200, "customer_id")
    .sortBy("customer_id")
    .mode("overwrite")
    .saveAsTable("bucketed_orders"))

# Join bucketed tables (no shuffle!)
orders = spark.table("bucketed_orders")
customers = spark.table("bucketed_customers")  # Same bucket count
result = orders.join(customers, on="customer_id")

# 4. Skew Join Handling
# Enable AQE skew join optimization
spark.conf.set("spark.sql.adaptive.skewJoin.enabled", "true")
spark.conf.set("spark.sql.adaptive.skewJoin.skewedPartitionFactor", "5")
spark.conf.set("spark.sql.adaptive.skewJoin.skewedPartitionThresholdInBytes", "256MB")

# Manual salting for severe skew
def salt_join(df_skewed, df_other, key_col, num_salts=10):
    """Add salt to distribute skewed keys"""
    # Add salt to skewed side
    df_salted = df_skewed.withColumn(
        "salt",
        (F.rand() * num_salts).cast("int")
    ).withColumn(
        "salted_key",
        F.concat(F.col(key_col), F.lit("_"), F.col("salt"))
    )

    # Explode other side with all salts
    df_exploded = df_other.crossJoin(
        spark.range(num_salts).withColumnRenamed("id", "salt")
    ).withColumn(
        "salted_key",
        F.concat(F.col(key_col), F.lit("_"), F.col("salt"))
    )

    # Join on salted key
    return df_salted.join(df_exploded, on="salted_key", how="inner")

Pattern 3: Caching and Persistence

from pyspark import StorageLevel

# Cache when reusing DataFrame multiple times
df = spark.read.parquet("s3://bucket/data/")
df_filtered = df.filter(F.col("status") == "active")

# Cache in memory (MEMORY_AND_DISK is default)
df_filtered.cache()

# Or with specific storage level
df_filtered.persist(StorageLevel.MEMORY_AND_DISK_SER)

# Force materialization
df_filtered.count()

# Use in multiple actions
agg1 = df_filtered.groupBy("category").count()
agg2 = df_filtered.groupBy("region").sum("amount")

# Unpersist when done
df_filtered.unpersist()

# Storage levels explained:
# MEMORY_ONLY - Fast, but may not fit
# MEMORY_AND_DISK - Spills to disk if needed (recommended)
# MEMORY_ONLY_SER - Serialized, less memory, more CPU
# DISK_ONLY - When memory is tight
# OFF_HEAP - Tungsten off-heap memory

# Checkpoint for complex lineage
spark.sparkContext.setCheckpointDir("s3://bucket/checkpoints/")
df_complex = (df
    .join(other_df, "key")
    .groupBy("category")
    .agg(F.sum("amount")))
df_complex.checkpoint()  # Breaks lineage, materializes

Pattern 4: Memory Tuning

# Executor memory configuration
# spark-submit --executor-memory 8g --executor-cores 4

# Memory breakdown (8GB executor):
# - spark.memory.fraction = 0.6 (60% = 4.8GB for execution + storage)
#   - spark.memory.storageFraction = 0.5 (50% of 4.8GB = 2.4GB for cache)
#   - Remaining 2.4GB for execution (shuffles, joins, sorts)
# - 40% = 3.2GB for user data structures and internal metadata

spark = (SparkSession.builder
    .config("spark.executor.memory", "8g")
    .config("spark.executor.memoryOverhead", "2g")  # For non-JVM memory
    .config("spark.memory.fraction", "0.6")
    .config("spark.memory.storageFraction", "0.5")
    .config("spark.sql.shuffle.partitions", "200")
    # For memory-intensive operations
    .config("spark.sql.autoBroadcastJoinThreshold", "50MB")
    # Prevent OOM on large shuffles
    .config("spark.sql.files.maxPartitionBytes", "128MB")
    .getOrCreate())

# Monitor memory usage
def print_memory_usage(spark):
    """Print current memory usage"""
    sc = spark.sparkContext
    for executor in sc._jsc.sc().getExecutorMemoryStatus().keySet().toArray():
        mem_status = sc._jsc.sc().getExecutorMemoryStatus().get(executor)
        total = mem_status._1() / (1024**3)
        free = mem_status._2() / (1024**3)
        print(f"{executor}: {total:.2f}GB total, {free:.2f}GB free")

Pattern 5: Shuffle Optimization

# Reduce shuffle data size
spark.conf.set("spark.sql.shuffle.partitions", "auto")  # With AQE
spark.conf.set("spark.shuffle.compress", "true")
spark.conf.set("spark.shuffle.spill.compress", "true")

# Pre-aggregate before shuffle
df_optimized = (df
    # Local aggregation first (combiner)
    .groupBy("key", "partition_col")
    .agg(F.sum("value").alias("partial_sum"))
    # Then global aggregation
    .groupBy("key")
    .agg(F.sum("partial_sum").alias("total")))

# Avoid shuffle with map-side operations
# BAD: Shuffle for each distinct
distinct_count = df.select("category").distinct().count()

# GOOD: Approximate distinct (no shuffle)
approx_count = df.select(F.approx_count_distinct("category")).collect()[0][0]

# Use coalesce instead of repartition when reducing partitions
df_reduced = df.coalesce(10)  # No shuffle

# Optimize shuffle with compression
spark.conf.set("spark.io.compression.codec", "lz4")  # Fast compression

Pattern 6: Data Format Optimization

# Parquet optimizations
(df.write
    .option("compression", "snappy")  # Fast compression
    .option("parquet.block.size", 128 * 1024 * 1024)  # 128MB row groups
    .parquet("s3://bucket/output/"))

# Column pruning - only read needed columns
df = (spark.read.parquet("s3://bucket/data/")
    .select("id", "amount", "date"))  # Spark only reads these columns

# Predicate pushdown - filter at storage level
df = (spark.read.parquet("s3://bucket/partitioned/year=2024/")
    .filter(F.col("status") == "active"))  # Pushed to Parquet reader

# Delta Lake optimizations
(df.write
    .format("delta")
    .option("optimizeWrite", "true")  # Bin-packing
    .option("autoCompact", "true")  # Compact small files
    .mode("overwrite")
    .save("s3://bucket/delta_table/"))

# Z-ordering for multi-dimensional queries
spark.sql("""
    OPTIMIZE delta.`s3://bucket/delta_table/`
    ZORDER BY (customer_id, date)
""")

Pattern 7: Monitoring and Debugging

# Enable detailed metrics
spark.conf.set("spark.sql.codegen.wholeStage", "true")
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")

# Explain query plan
df.explain(mode="extended")
# Modes: simple, extended, codegen, cost, formatted

# Get physical plan statistics
df.explain(mode="cost")

# Monitor task metrics
def analyze_stage_metrics(spark):
    """Analyze recent stage metrics"""
    status_tracker = spark.sparkContext.statusTracker()

    for stage_id in status_tracker.getActiveStageIds():
        stage_info = status_tracker.getStageInfo(stage_id)
        print(f"Stage {stage_id}:")
        print(f"  Tasks: {stage_info.numTasks}")
        print(f"  Completed: {stage_info.numCompletedTasks}")
        print(f"  Failed: {stage_info.numFailedTasks}")

# Identify data skew
def check_partition_skew(df):
    """Check for partition skew"""
    partition_counts = (df
        .withColumn("partition_id", F.spark_partition_id())
        .groupBy("partition_id")
        .count()
        .orderBy(F.desc("count")))

    partition_counts.show(20)

    stats = partition_counts.select(
        F.min("count").alias("min"),
        F.max("count").alias("max"),
        F.avg("count").alias("avg"),
        F.stddev("count").alias("stddev")
    ).collect()[0]

    skew_ratio = stats["max"] / stats["avg"]
    print(f"Skew ratio: {skew_ratio:.2f}x (>2x indicates skew)")

Configuration Cheat Sheet

# Production configuration template
spark_configs = {
    # Adaptive Query Execution (AQE)
    "spark.sql.adaptive.enabled": "true",
    "spark.sql.adaptive.coalescePartitions.enabled": "true",
    "spark.sql.adaptive.skewJoin.enabled": "true",

    # Memory
    "spark.executor.memory": "8g",
    "spark.executor.memoryOverhead": "2g",
    "spark.memory.fraction": "0.6",
    "spark.memory.storageFraction": "0.5",

    # Parallelism
    "spark.sql.shuffle.partitions": "200",
    "spark.default.parallelism": "200",

    # Serialization
    "spark.serializer": "org.apache.spark.serializer.KryoSerializer",
    "spark.sql.execution.arrow.pyspark.enabled": "true",

    # Compression
    "spark.io.compression.codec": "lz4",
    "spark.shuffle.compress": "true",

    # Broadcast
    "spark.sql.autoBroadcastJoinThreshold": "50MB",

    # File handling
    "spark.sql.files.maxPartitionBytes": "128MB",
    "spark.sql.files.openCostInBytes": "4MB",
}

Source: SKILL.md on GitHub

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Provenance

Signed by skilld at be57c0b. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 3 days ago.

Activeupdated 4 months ago

Topics

  • apache-spark
  • performance-tuning
  • partitioning
  • shuffle-optimization
  • memory-management
  • data-skew
  • pyspark
  • distributed-computing

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What it does

Optimizes Apache Spark jobs through partitioning strategies, memory tuning, shuffle reduction, and data skew mitigation. Targets production Spark pipelines and includes configuration patterns for adaptive query execution, serialization, and broadcast joins.

Generated from the current SKILL.md.

Frequently asked

Does this skill cover Spark on Kubernetes or just standalone/YARN clusters?
The skill focuses on general Spark optimization patterns (partitioning, caching, shuffle, memory tuning) that apply across all cluster managers. Kubernetes-specific deployment or resource allocation is not covered.
Does this include Spark Streaming or structured streaming optimization?
No. The skill targets batch Spark jobs and SQL workloads. Streaming-specific patterns like micro-batch tuning or backpressure handling are not included.
What Python and Spark versions does this assume?
The skill uses PySpark and assumes Spark 3.x with Adaptive Query Execution (AQE) available. Specific version pinning is not mentioned in the documentation.
Does this cover Delta Lake optimization or just Parquet?
The skill recommends Delta Lake and Parquet as columnar formats but does not include Delta-specific optimization patterns like Z-order clustering or merge/upsert tuning.
Are GPU or machine learning workloads covered?
No. The skill is limited to CPU-based distributed data processing optimization. GPU acceleration and ML-specific tuning are out of scope.

Generated from the current SKILL.md. These answers refresh after source changes.