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57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
import gc
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from datetime import datetime
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import psutil
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from openhands.io.json import dumps
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def get_memory_usage():
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"""Get current memory usage of the process"""
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process = psutil.Process()
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return process.memory_info().rss
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def test_json_encoder_memory_leak():
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# Force garbage collection before test
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gc.collect()
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initial_memory = get_memory_usage()
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# Create a large dataset that will need encoding
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large_data = {
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'datetime': datetime.now(),
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'nested': [{'timestamp': datetime.now()} for _ in range(1000)],
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}
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# Track memory usage over multiple iterations
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memory_samples = []
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for i in range(10):
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# Perform multiple serializations in each iteration
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for _ in range(100):
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dumps(large_data)
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dumps(large_data, indent=2) # Test with kwargs too
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# Force garbage collection
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gc.collect()
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memory_samples.append(get_memory_usage())
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# Check if memory usage is stable (not continuously growing)
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# We expect some fluctuation but not a steady increase
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max_memory = max(memory_samples)
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min_memory = min(memory_samples)
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memory_variation = max_memory - min_memory
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# Allow for some memory variation (2MB) due to Python's memory management
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assert (
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memory_variation < 2 * 1024 * 1024
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), f'Memory usage unstable: {memory_variation} bytes variation'
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# Also check total memory increase from start
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final_memory = memory_samples[-1]
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memory_increase = final_memory - initial_memory
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# Allow for some memory increase (2MB) as some objects may be cached
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assert (
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memory_increase < 2 * 1024 * 1024
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), f'Memory leak detected: {memory_increase} bytes increase'
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