Introduction
The SPARK004 – Shuffle Read Failure (Insufficient Disk Space) error occurs when Spark runs out of disk space while performing shuffle operations. This can lead to job failures, slow performance, and cluster instability in Databricks.
🚨 Common causes of shuffle read failures due to insufficient disk space:
- Insufficient local disk space on worker nodes.
- Large shuffle operations exceeding available storage.
- Inefficient partitioning leading to excessive data spilling to disk.
- Improper cluster configurations limiting disk usage.
This guide provides troubleshooting steps, fixes, and best practices to prevent shuffle read failures in Databricks.
Understanding Shuffle Operations in Spark
Shuffle occurs when data is redistributed across partitions, especially during:
- Joins (
JOIN
) – Large dataset joins. - GroupBy/Aggregations (
GROUP BY
) – Operations requiring re-partitioning. - Sort (
ORDER BY
) – Large dataset sorting.
💡 When shuffle data exceeds available memory, Spark spills it to disk. If disk space is insufficient, jobs fail with SPARK004 errors.
Common Causes and Fixes for SPARK004 Shuffle Read Failures
1. Insufficient Disk Space on Worker Nodes
Symptoms:
- Error: “SPARK004 – Shuffle Read Failure: Insufficient Disk Space”.
- Worker nodes crash due to low disk space.
- Jobs run out of space even when memory is available.
Causes:
- Shuffle data spilling to local disk (instead of memory) due to large datasets.
- Ephemeral disk storage in Databricks runs out of space.
- High parallelism leads to excessive shuffle writes.
Fix:
✅ Increase worker disk space by using a larger instance type (AWS, Azure, GCP):
- AWS: Choose EBS-backed instances for larger disk sizes.
- Azure: Use Premium Disks for worker nodes.
✅ Check available disk space on worker nodes:
df -h
✅ Use instance storage instead of root disk for shuffle storage (Databricks Runtime 11+):
{
"spark.local.dir": "/local_disk0/tmp"
}
✅ Reduce shuffle data using optimized query plans.
2. Large Shuffle Data Exceeding Disk Capacity
Symptoms:
- Jobs fail when performing large joins or aggregations.
- Shuffle files consume too much disk space.
- Slow execution times due to excessive disk spilling.
Causes:
- Large shuffle files cannot fit into available disk space.
- Partition skew leads to uneven data distribution.
- Broadcast joins are not used efficiently.
Fix:
✅ Use Broadcast Joins to Reduce Shuffle Size (for small tables):
from pyspark.sql.functions import broadcast
df_large = df_large.join(broadcast(df_small), "id")
✅ Optimize partitions to balance shuffle size:
df = df.repartition(100)
✅ Check shuffle size in Spark UI and reduce partitions if needed.
3. Inefficient Partitioning Leading to Excessive Shuffle Spills
Symptoms:
- Jobs fail during shuffle-heavy operations like joins, aggregations, and sorting.
- Spark UI shows excessive disk spills in the Shuffle tab.
- Execution time increases significantly.
Causes:
- Too many small partitions, increasing shuffle overhead.
- Skewed partitions causing large data transfers.
- Suboptimal Spark configurations for shuffle operations.
Fix:
✅ Manually adjust shuffle partitions for efficient processing:
spark.conf.set("spark.sql.shuffle.partitions", "200")
✅ Use Skew Join Optimization to handle large partitions:
df = df.repartitionByRange(100, "column_name")
✅ Monitor partition size using:
df.rdd.glom().map(len).collect()
4. Improper Cluster Configuration for Shuffle Operations
Symptoms:
- Clusters run out of space during shuffle operations.
- Shuffle data accumulates in temporary storage and causes failures.
- Performance degrades over time due to unoptimized configurations.
Causes:
- Default shuffle configurations do not match workload size.
- Shuffle writes are not optimized for disk storage.
- Databricks clusters use insufficient shuffle memory settings.
Fix:
✅ Set the local shuffle directory to a high-capacity storage path:
{
"spark.local.dir": "/local_disk0/tmp"
}
✅ Increase shuffle memory fraction to avoid excessive disk spills:
{
"spark.shuffle.memoryFraction": "0.6"
}
✅ Use spark.sql.adaptive.enabled = true
to dynamically adjust shuffle partitions:
{
"spark.sql.adaptive.enabled": "true"
}
Step-by-Step Troubleshooting Guide
Step 1: Check Available Disk Space on Worker Nodes
df -h
- If disk space is low, consider increasing instance size.
Step 2: Identify Large Shuffle Operations in Spark UI
- Open Databricks Spark UI → Storage Tab → Shuffle Read/Write
- Look for excessive shuffle file sizes.
Step 3: Reduce Shuffle Size Using Broadcast Joins
from pyspark.sql.functions import broadcast
df_large = df_large.join(broadcast(df_small), "id")
Step 4: Optimize Partitioning to Reduce Shuffle Overhead
df = df.repartition(200) # Adjust based on data size
Step 5: Adjust Spark Shuffle Configuration
{
"spark.sql.adaptive.enabled": "true",
"spark.sql.shuffle.partitions": "200",
"spark.local.dir": "/local_disk0/tmp"
}
Best Practices to Prevent Shuffle Read Failures
✅ Use Broadcast Joins for Small Tables
- Prevents large shuffle operations during joins.
✅ Optimize Partitioning to Balance Shuffle Load
df = df.repartition(100)
- Prevents skewed partitions that cause shuffle failures.
✅ Increase Cluster Disk Space for Shuffle Operations
- Use larger instance types with more disk space (AWS, Azure, GCP).
✅ Enable Adaptive Query Execution (AQE)
{
"spark.sql.adaptive.enabled": "true"
}
- Dynamically adjusts shuffle partitions to avoid excessive disk writes.
✅ Monitor Disk Usage and Optimize Shuffle Memory
{
"spark.shuffle.memoryFraction": "0.6"
}
- Ensures shuffle data is handled efficiently before spilling to disk.
Real-World Example: Fixing a Shuffle Read Failure in Databricks
Scenario:
A large ETL pipeline failed in Databricks with the SPARK004 Shuffle Read Failure error.
Root Cause:
- The shuffle operation exceeded available disk space.
- Excessive small partitions led to high shuffle overhead.
- Broadcast joins were not used, causing unnecessary shuffles.
Solution:
- Increased disk space by upgrading worker nodes.
- Enabled broadcast joins to reduce shuffle size:
df_large = df_large.join(broadcast(df_small), "id")
- Repartitioned data to optimize shuffle operations:
df = df.repartition(200)
- Enabled Adaptive Query Execution (AQE):
{ "spark.sql.adaptive.enabled": "true" }
✅ Result: Job execution time reduced by 50%, and shuffle failures were eliminated.
Conclusion
The SPARK004 Shuffle Read Failure (Insufficient Disk Space) error in Databricks occurs due to large shuffle operations exceeding disk capacity. By optimizing partitions, using broadcast joins, increasing cluster disk space, and adjusting shuffle settings, teams can prevent shuffle failures and improve Spark job performance.