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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Production patterns for optimizing Apache Spark jobs including partitioning strategies, memory management, shuffle optimization, and performance tuning.
resources/implementation-playbook.md.Driver Program
↓
Job (triggered by action)
↓
Stages (separated by shuffles)
↓
Tasks (one per partition)
| Factor |
|---|
| Impact |
|---|
| Solution |
|---|
| Shuffle | Network I/O, disk I/O | Minimize wide transformations |
| Data Skew | Uneven task duration | Salting, broadcast joins |
| Serialization | CPU overhead | Use Kryo, columnar formats |
| Memory | GC pressure, spills | Tune executor memory |
| Partitions | Parallelism | Right-size partitions |
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
# Create optimized Spark session
spark = (SparkSession.builder
.appName("OptimizedJob")
.config("spark.sql.adaptive.enabled", "true")
.config("spark.sql.adaptive.coalescePartitions.enabled", "true")
.config("spark.sql.adaptive.skewJoin.enabled", "true")
.config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.config("spark.sql.shuffle.partitions", "200")
.getOrCreate())
# Read with optimized settings
df = (spark.read
.format("parquet")
.option("mergeSchema", "false")
.load("s3://bucket/data/"))
# Efficient transformations
result = (df
.filter(F.col("date") >= "2024-01-01")
.select("id", "amount", "category")
.groupBy("category")
.agg(F.sum("amount").alias("total")))
result.write.mode("overwrite").parquet("s3://bucket/output/")
# 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/"))
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")
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
# 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")
# 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
# 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)
""")
# 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)")
# 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",
}
.count() for existence - Use .take(1) or .isEmpty()
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).