Always persist metadata alongside data and implement proper checking mechanisms to prevent duplicates and ensure consistency in distributed storage scenarios. When writing data to storage backends, metadata should be written atomically with the data itself. Additionally, implement separate checking logic for existence vs. active operations to prevent race...
Always persist metadata alongside data and implement proper checking mechanisms to prevent duplicates and ensure consistency in distributed storage scenarios. When writing data to storage backends, metadata should be written atomically with the data itself. Additionally, implement separate checking logic for existence vs. active operations to prevent race conditions and duplicate storage.
For distributed systems, implement fallback checks when local caches miss, as other nodes may have added data not present in the local hot cache. Use separate read/write clients when possible to optimize for different access patterns and enable role-based separation.
Example implementation pattern:
# Always write metadata with data
async def store_data(self, key, data):
metadata = pack_metadata(data.shape, data.dtype, data.nbytes)
# Write both atomically
await self._write_data_and_metadata(key, data, metadata)
# Update local cache
with self.hot_lock:
self.hot_cache[key] = metadata
# Separate existence check from active operations check
def contains(self, key: CacheEngineKey) -> bool:
# Check local cache first
with self.hot_lock:
if key in self.hot_cache:
return True
# Fallback to filesystem check for distributed scenarios
return self._try_to_read_metadata(key)
def store(self, key: CacheEngineKey, data):
# Check active operations to prevent duplicates
with self.put_lock:
if key in self.put_tasks:
return # Already being stored
self.put_tasks.add(key)
# Proceed with storage...
This pattern ensures data integrity, prevents duplicate operations, and maintains consistency across distributed storage backends.
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