Databricks Performance Tuning

Overview

Optimize Databricks cluster, Spark, and Delta Lake performance.

Prerequisites

• Access to cluster configuration
• Understanding of workload characteristics
• Query history access

Instructions

Step 1: Cluster Sizing

# Cluster sizing calculator
def recommend_cluster_size(
    data_size_gb: float,
    complexity: str = "medium",  # low, medium, high
    parallelism_need: str = "standard",  # standard, high
) -> dict:
    """
    Recommend cluster configuration based on workload.

    Args:
        data_size_gb: Estimated data size to process
        complexity: Query/transform complexity
        parallelism_need: Required parallelism level

    Returns:
        Recommended cluster configuration
    """
    # Memory per executor (standard DS3_v2 = 14GB)
    memory_per_worker = 14

    # Base calculation
    workers_by_data = max(1, int(data_size_gb / memory_per_worker / 2))

    # Adjust for complexity
    complexity_multiplier = {"low": 1, "medium": 1.5, "high": 2.5}
    workers = int(workers_by_data * complexity_multiplier.get(complexity, 1.5))

    # Adjust for parallelism
    if parallelism_need == "high":
        workers = max(workers, 8)

    return {
        "node_type_id": "Standard_DS3_v2",
        "num_workers": workers,
        "autoscale": {
            "min_workers": max(1, workers // 2),
            "max_workers": workers * 2,
        },
        "spark_conf": {
            "spark.sql.shuffle.partitions": str(workers * 4),
            "spark.default.parallelism": str(workers * 4),
        }
    }

Step 2: Spark Configuration Optimization

# Optimized Spark configurations by workload type
spark_configs = {
    "etl_batch": {
        # Memory and parallelism
        "spark.sql.shuffle.partitions": "200",
        "spark.default.parallelism": "200",
        "spark.sql.files.maxPartitionBytes": "134217728",  # 128MB

        # Delta Lake optimizations
        "spark.databricks.delta.optimizeWrite.enabled": "true",
        "spark.databricks.delta.autoCompact.enabled": "true",
        "spark.databricks.delta.properties.defaults.autoOptimize.optimizeWrite": "true",

        # Adaptive query execution
        "spark.sql.adaptive.enabled": "true",
        "spark.sql.adaptive.coalescePartitions.enabled": "true",
        "spark.sql.adaptive.skewJoin.enabled": "true",
    },

    "ml_training": {
        # Memory for ML workloads
        "spark.driver.memory": "16g",
        "spark.executor.memory": "16g",
        "spark.memory.fraction": "0.8",
        "spark.memory.storageFraction": "0.3",

        # Serialization for ML
        "spark.serializer": "org.apache.spark.serializer.KryoSerializer",
        "spark.kryoserializer.buffer.max": "1024m",
    },

    "streaming": {
        # Streaming configurations
        "spark.sql.streaming.schemaInference": "true",
        "spark.sql.streaming.checkpointLocation": "/mnt/checkpoints",
        "spark.databricks.delta.autoCompact.minNumFiles": "10",

        # Micro-batch tuning
        "spark.sql.streaming.forEachBatch.enabled": "true",
    },

    "interactive": {
        # Fast startup
        "spark.databricks.cluster.profile": "singleNode",
        "spark.master": "local[*]",

        # Caching
        "spark.sql.inMemoryColumnarStorage.compressed": "true",
        "spark.sql.inMemoryColumnarStorage.batchSize": "10000",
    }
}

Step 3: Delta Lake Optimization

# delta_optimization.py
from pyspark.sql import SparkSession

def optimize_delta_table(
    spark: SparkSession,
    table_name: str,
    z_order_columns: list[str] = None,
    vacuum_hours: int = 168,  # 7 days
) -> dict:
    """
    Optimize Delta table for query performance.

    Args:
        spark: SparkSession
        table_name: Fully qualified table name
        z_order_columns: Columns for Z-ordering (max 4)
        vacuum_hours: Retention period for vacuum

    Returns:
        Optimization results
    """
    results = {}

    # 1. Run OPTIMIZE with Z-ordering
    if z_order_columns:
        z_order_clause = ", ".join(z_order_columns[:4])  # Max 4 columns
        spark.sql(f"OPTIMIZE {table_name} ZORDER BY ({z_order_clause})")
        results["z_order"] = z_order_columns
    else:
        spark.sql(f"OPTIMIZE {table_name}")

    results["optimized"] = True

    # 2. Analyze table statistics
    spark.sql(f"ANALYZE TABLE {table_name} COMPUTE STATISTICS")
    results["statistics_computed"] = True

    # 3. Vacuum old files
    spark.sql(f"VACUUM {table_name} RETAIN {vacuum_hours} HOURS")
    results["vacuumed"] = True

    # 4. Get table metrics
    detail = spark.sql(f"DESCRIBE DETAIL {table_name}").first()
    results["metrics"] = {
        "num_files": detail.numFiles,
        "size_bytes": detail.sizeInBytes,
        "partitions": detail.partitionColumns,
    }

    return results

def enable_liquid_clustering(
    spark: SparkSession,
    table_name: str,
    cluster_columns: list[str],
) -> None:
    """
    Enable Liquid Clustering for automatic data layout optimization.

    Liquid Clustering replaces traditional partitioning and Z-ordering
    with automatic, incremental clustering.
    """
    columns = ", ".join(cluster_columns)
    spark.sql(f"""
        ALTER TABLE {table_name}
        CLUSTER BY ({columns})
    """)

def enable_predictive_optimization(
    spark: SparkSession,
    table_name: str,
) -> None:
    """Enable Databricks Predictive Optimization."""
    spark.sql(f"""
        ALTER TABLE {table_name}
        SET TBLPROPERTIES (
            'delta.enableDeletionVectors' = 'true',
            'delta.targetFileSize' = '104857600'
        )
    """)

Step 4: Query Performance Analysis

-- Find slow queries from query history
SELECT
    query_id,
    query_text,
    duration / 1000 as seconds,
    rows_produced,
    bytes_read,
    start_time
FROM system.query.history
WHERE duration > 60000  -- > 60 seconds
  AND start_time > current_timestamp() - INTERVAL 24 HOURS
ORDER BY duration DESC
LIMIT 20;

-- Analyze query plan
EXPLAIN FORMATTED
SELECT * FROM main.silver.orders
WHERE order_date > '2024-01-01'
  AND region = 'US';

-- Check table scan statistics
SELECT
    table_name,
    SUM(bytes_read) / 1024 / 1024 / 1024 as gb_read,
    SUM(rows_produced) as total_rows,
    COUNT(*) as query_count
FROM system.query.history
WHERE start_time > current_timestamp() - INTERVAL 7 DAYS
GROUP BY table_name
ORDER BY gb_read DESC;

Step 5: Caching Strategy

# Intelligent caching for repeated queries
from pyspark.sql import DataFrame
from functools import lru_cache

class CacheManager:
    """Manage Spark DataFrame caching."""

    def __init__(self, spark: SparkSession):
        self.spark = spark
        self._cache_registry = {}

    def cache_table(
        self,
        table_name: str,
        cache_level: str = "MEMORY_AND_DISK",
    ) -> DataFrame:
        """Cache table with specified storage level."""
        if table_name in self._cache_registry:
            return self._cache_registry[table_name]

        df = self.spark.table(table_name)

        if cache_level == "MEMORY_ONLY":
            df.cache()
        elif cache_level == "MEMORY_AND_DISK":
            from pyspark import StorageLevel
            df.persist(StorageLevel.MEMORY_AND_DISK)
        elif cache_level == "DISK_ONLY":
            from pyspark import StorageLevel
            df.persist(StorageLevel.DISK_ONLY)

        # Trigger caching
        df.count()

        self._cache_registry[table_name] = df
        return df

    def uncache_all(self):
        """Clear all cached DataFrames."""
        for df in self._cache_registry.values():
            df.unpersist()
        self._cache_registry.clear()
        self.spark.catalog.clearCache()

# Delta Cache (automatic)
# Enable in cluster config:
# "spark.databricks.io.cache.enabled": "true"
# "spark.databricks.io.cache.maxDiskUsage": "50g"

Step 6: Join Optimization

from pyspark.sql import DataFrame
from pyspark.sql.functions import broadcast

def optimize_join(
    df_large: DataFrame,
    df_small: DataFrame,
    join_key: str,
    small_table_threshold_mb: int = 100,
) -> DataFrame:
    """
    Optimize join based on table sizes.

    Uses broadcast join for small tables,
    sort-merge join for large tables.
    """
    # Estimate small table size
    small_size_mb = df_small.count() * 100 / 1024 / 1024  # rough estimate

    if small_size_mb < small_table_threshold_mb:
        # Broadcast join (small table fits in memory)
        return df_large.join(broadcast(df_small), join_key)
    else:
        # Sort-merge join with bucketing hint
        return df_large.join(df_small, join_key, "inner")

# Bucketed tables for frequent joins
def create_bucketed_table(
    spark: SparkSession,
    df: DataFrame,
    table_name: str,
    bucket_columns: list[str],
    num_buckets: int = 100,
):
    """Create bucketed table for join optimization."""
    (
        df.write
        .bucketBy(num_buckets, *bucket_columns)
        .sortBy(*bucket_columns)
        .saveAsTable(table_name)
    )

Output

• Optimized cluster configuration
• Tuned Spark settings
• Optimized Delta tables
• Improved query performance

Error Handling

Examples

Performance Benchmark

import time

def benchmark_query(spark, query: str, runs: int = 3) -> dict:
    """Benchmark query execution time."""
    times = []
    for _ in range(runs):
        spark.catalog.clearCache()
        start = time.time()
        spark.sql(query).collect()
        times.append(time.time() - start)

    return {
        "min": min(times),
        "max": max(times),
        "avg": sum(times) / len(times),
        "runs": runs,
    }

Resources

• Databricks Performance Guide
• Delta Lake Optimization
• Spark Tuning Guide

Next Steps

For cost optimization, see databricks-cost-tuning.