Fixing ArcPy Out of Memory Issues: A Complete Guide
ArcPy, Python’s gateway to ArcGIS functionality, is a powerful tool for automating GIS workflows. However, when working with large datasets or complex operations, you may encounter the dreaded “out of memory” error. This comprehensive guide will help you understand, diagnose, and resolve memory issues in your ArcPy scripts.
Understanding Memory Issues in ArcPy
Memory problems in ArcPy typically manifest as:
RuntimeError: Out of memoryMemoryErrorexceptions- Slow performance or hanging processes
- ArcGIS crashing during script execution
These issues often occur because ArcPy operations can be memory-intensive, especially when processing large datasets, creating temporary files, or performing complex geoprocessing tasks.
Common Causes of Memory Issues
1. Large Dataset Processing
Processing extensive shapefiles, rasters, or geodatabases can quickly consume available RAM.
2. Memory Leaks
Improper object management can cause memory to accumulate without being released.
3. Inefficient Cursors
Not properly closing cursors or using them inefficiently can lead to memory buildup.
4. Temporary File Accumulation
ArcPy creates temporary files during operations that may not be cleaned up automatically.
Diagnostic Techniques
Before implementing fixes, identify where memory issues occur:
import psutil
import os
def monitor_memory():
process = psutil.Process(os.getpid())
memory_usage = process.memory_info().rss / 1024 / 1024 # MB
print(f"Current memory usage: {memory_usage:.2f} MB")
return memory_usage
# Call this function at different points in your script
monitor_memory()
Memory Optimization Strategies
1. Proper Cursor Management
Wrong Way:
import arcpy
cursor = arcpy.SearchCursor("feature_class")
for row in cursor:
# Process data
pass
# Cursor not explicitly deleted
Right Way:
import arcpy
cursor = arcpy.SearchCursor("feature_class")
try:
for row in cursor:
# Process data
pass
finally:
del cursor # Explicitly delete cursor
# Or better yet, use with statement (ArcGIS 10.1+)
with arcpy.da.SearchCursor("feature_class", ["FIELD1", "FIELD2"]) as cursor:
for row in cursor:
# Process data
pass
2. Process Data in Chunks
Instead of loading entire datasets into memory:
import arcpy
def process_in_chunks(feature_class, chunk_size=1000):
total_count = int(arcpy.GetCount_management(feature_class)[0])
for start in range(0, total_count, chunk_size):
# Create a feature layer with a subset of features
where_clause = f"OBJECTID >= {start} AND OBJECTID < {start + chunk_size}"
arcpy.MakeFeatureLayer_management(feature_class, "temp_layer", where_clause)
# Process the chunk
with arcpy.da.SearchCursor("temp_layer", ["FIELD1", "FIELD2"]) as cursor:
for row in cursor:
# Process individual records
pass
# Clean up
arcpy.Delete_management("temp_layer")
3. Use Appropriate Cursor Types
Use arcpy.da cursors instead of legacy cursors for better performance and memory management:
# Use arcpy.da cursors (recommended)
with arcpy.da.SearchCursor(feature_class, ["FIELD1", "FIELD2"]) as cursor:
for row in cursor:
field1_value = row[0]
field2_value = row[1]
# Instead of legacy cursors
# cursor = arcpy.SearchCursor(feature_class) # Avoid this
4. Manage Temporary Files
Explicitly clean up temporary datasets:
import arcpy
import tempfile
import os
# Create temporary workspace
temp_dir = tempfile.mkdtemp()
arcpy.env.scratchWorkspace = temp_dir
try:
# Your processing code here
temp_fc = arcpy.CreateFeatureclass_management(temp_dir, "temp_features", "POLYGON")
# Process data
# ...
finally:
# Clean up temporary files
if arcpy.Exists(temp_fc):
arcpy.Delete_management(temp_fc)
# Clean up scratch workspace
arcpy.env.scratchWorkspace = None
if os.path.exists(temp_dir):
import shutil
shutil.rmtree(temp_dir)
5. Optimize Geoprocessing Environment
Configure ArcPy environment settings for better memory management:
import arcpy
# Set processing extent to limit data processing
arcpy.env.extent = "study_area_boundary"
# Use in-memory workspace for temporary data
arcpy.env.workspace = "in_memory"
# Set output coordinate system to avoid unnecessary transformations
arcpy.env.outputCoordinateSystem = input_coordinate_system
# Control pyramid and statistics creation
arcpy.env.pyramid = "NONE"
arcpy.env.rasterStatistics = "NONE"
6. Use Generator Functions for Large Datasets
Process data incrementally using generators:
def feature_generator(feature_class, fields):
"""Generator that yields features one at a time"""
with arcpy.da.SearchCursor(feature_class, fields) as cursor:
for row in cursor:
yield row
# Use the generator
for feature in feature_generator("large_dataset", ["FIELD1", "FIELD2"]):
# Process one feature at a time
process_feature(feature)
System-Level Solutions
1. Increase Virtual Memory
- Increase page file size on Windows
- Ensure adequate swap space on Linux/Mac
2. Close Unnecessary Applications
Free up system memory by closing other applications during processing.
3. Use 64-bit Python and ArcGIS
Ensure you’re using 64-bit versions for access to more memory.
4. Monitor System Resources
import psutil
def check_system_memory():
memory = psutil.virtual_memory()
print(f"Total memory: {memory.total / 1024**3:.2f} GB")
print(f"Available memory: {memory.available / 1024**3:.2f} GB")
print(f"Memory usage: {memory.percent}%")
check_system_memory()
Advanced Memory Management Techniques
1. Implement Progress Tracking with Memory Monitoring
import arcpy
import psutil
import os
def process_with_memory_monitoring(feature_class):
process = psutil.Process(os.getpid())
with arcpy.da.SearchCursor(feature_class, ["OID@", "SHAPE@"]) as cursor:
for i, row in enumerate(cursor):
# Process feature
# Monitor memory every 100 features
if i % 100 == 0:
memory_mb = process.memory_info().rss / 1024 / 1024
print(f"Processed {i} features. Memory usage: {memory_mb:.2f} MB")
# Optional: Force garbage collection
import gc
gc.collect()
2. Use Multiprocessing Carefully
import multiprocessing as mp
import arcpy
def process_subset(feature_subset):
# Process a subset of features
# Each process gets its own memory space
pass
def parallel_processing(feature_class, num_processes=4):
# Split data into chunks
chunks = create_data_chunks(feature_class, num_processes)
# Process in parallel
with mp.Pool(processes=num_processes) as pool:
results = pool.map(process_subset, chunks)
return results
Best Practices Summary
- Always close cursors explicitly or use
withstatements - Process data in chunks rather than loading everything into memory
- Clean up temporary files and datasets
- Use appropriate cursor types (
arcpy.dacursors) - Monitor memory usage during development
- Configure environment settings appropriately
- Use generators for large dataset processing
- Implement error handling with proper cleanup
- Test with representative data sizes during development
- Consider system limitations and upgrade hardware if necessary
Troubleshooting Checklist
When facing memory issues, work through this checklist:
- Are all cursors properly closed?
- Are temporary datasets being deleted?
- Is the dataset size appropriate for available memory?
- Are you using the most efficient cursor type?
- Is the processing environment optimally configured?
- Are there memory leaks in loops?
- Is system memory adequate?
- Are other applications consuming memory?
Memory management in ArcPy requires attention to detail and proactive coding practices. By implementing proper cursor management, processing data in chunks, cleaning up temporary files, and monitoring memory usage, you can significantly reduce the likelihood of encountering out-of-memory errors. Remember that prevention is better than cure – designing memory-efficient code from the start will save time and frustration later.
The key is to be mindful of memory usage throughout your script development process and implement these strategies as standard practice rather than as afterthoughts when problems occur.
