Overcoming the "Pickle Problem": Parallelising Protobuf Objects with Ray
Problem: You're trying to leverage the power of Ray, a popular library for distributed computing in Python, to speed up your data processing. However, you're using Protobuf (Protocol Buffers) objects, and Ray throws a pickle error, preventing you from distributing your workload effectively.
Rephrased: You want to divide your tasks and run them simultaneously on multiple machines or cores using Ray, but you hit a snag when you try to use your Protobuf data structures. Ray relies on pickling to send data between processes, and it seems your Protobuf objects are not compatible with this process.
Scenario and Code:
import ray
import ray.data as rd
from google.protobuf import text_format
from your_proto_file import YourProtoMessage
ray.init()
# Your protobuf data
data = [
YourProtoMessage(field1="value1", field2="value2"),
YourProtoMessage(field1="value3", field2="value4")
]
# Attempt to create a Ray dataset
ds = rd.from_items(data)
# Error: Cannot pickle protobuf objects
This code snippet shows a typical scenario where you're trying to create a Ray dataset using a list of Protobuf objects. You'll likely encounter a TypeError like:
TypeError: cannot pickle 'google.protobuf.message.Message' object
Analysis and Solutions:
The error stems from the fact that Protobuf objects are not inherently pickleable. Pickling is the process of serializing Python objects into byte streams that can be transmitted or stored and later restored to their original state. Protobuf objects, however, rely on their own serialization mechanisms that are not compatible with the standard pickle library.
To overcome this limitation, you have several options:
1. Convert Protobuf Objects to Dicts before Pickling:
-
Process: You can extract data from your Protobuf objects into dictionaries before creating the Ray dataset. This enables Ray to pickle the dictionaries, which are compatible with standard pickling.
-
Example:
import ray import ray.data as rd from google.protobuf import text_format from your_proto_file import YourProtoMessage ray.init() data = [ YourProtoMessage(field1="value1", field2="value2"), YourProtoMessage(field1="value3", field2="value4") ] # Convert Protobuf to dicts data_dicts = [ {field.name: getattr(obj, field.name) for field in obj.DESCRIPTOR.fields} for obj in data ] # Create Ray dataset ds = rd.from_items(data_dicts)
2. Use Ray's serialization_context:
-
Process: Ray provides the
serialization_contextfeature to customize serialization behavior. This allows you to register custom serializers for specific types. -
Example:
import ray import ray.data as rd from google.protobuf import text_format from your_proto_file import YourProtoMessage ray.init() # Register custom serializer for YourProtoMessage @ray.remote def serialize_protobuf(obj): return text_format.MessageToString(obj) @ray.remote def deserialize_protobuf(serialized_data): obj = YourProtoMessage() text_format.Parse(serialized_data, obj) return obj # Create Ray dataset ds = rd.from_items(data).map(serialize_protobuf).map(deserialize_protobuf)
3. Utilize Alternative Serialization Libraries:
- Process: If you need to handle complex data structures or prefer more flexible serialization, consider using libraries like
pickle5,dill, orcloudpicklewhich offer broader compatibility. However, these libraries might have their own limitations or overhead.
4. Protobuf Serialization and Deserialization with Ray's Dataset API:
-
Process: Ray's
DatasetAPI can directly handle protobuf serialization and deserialization. It provides specific methods likemap_batchesfor batch-wise processing. -
Example:
import ray import ray.data as rd from google.protobuf import text_format from your_proto_file import YourProtoMessage ray.init() data = [ YourProtoMessage(field1="value1", field2="value2"), YourProtoMessage(field1="value3", field2="value4") ] ds = rd.from_items(data).map_batches( lambda batch: [text_format.MessageToString(obj) for obj in batch], batch_size=1000, compute="actors" # use actors for faster processing ).map_batches( lambda batch: [text_format.Parse(obj, YourProtoMessage()) for obj in batch], batch_size=1000, compute="actors" )
Additional Value and Conclusion:
By understanding the limitations of pickling Protobuf objects and exploring the various solutions, you can effectively utilize Ray for parallel data processing with Protobuf data. Choose the method that best suits your specific needs and data structure complexity.
Remember to:
- Optimize your code for performance and resource utilization, especially when dealing with large datasets.
- Test your chosen solution thoroughly to ensure accuracy and compatibility.
References:
