Plasma In-Memory Object Store
08 Aug 2017
By Philipp Moritz and Robert Nishihara
Plasma: A High-Performance Shared-Memory Object Store
This blog post presents Plasma, an in-memory object store that is being developed as part of Apache Arrow. Plasma holds immutable objects in shared memory so that they can be accessed efficiently by many clients across process boundaries. In light of the trend toward larger and larger multicore machines, Plasma enables critical performance optimizations in the big data regime.
Plasma was initially developed as part of Ray, and has recently been moved to Apache Arrow in the hopes that it will be broadly useful.
One of the goals of Apache Arrow is to serve as a common data layer enabling zero-copy data exchange between multiple frameworks. A key component of this vision is the use of off-heap memory management (via Plasma) for storing and sharing Arrow-serialized objects between applications.
Expensive serialization and deserialization as well as data copying are a
common performance bottleneck in distributed computing. For example, a
Python-based execution framework that wishes to distribute computation across
multiple Python “worker” processes and then aggregate the results in a single
“driver” process may choose to serialize data using the built-in
library. Assuming one Python process per core, each worker process would have to
copy and deserialize the data, resulting in excessive memory usage. The driver
process would then have to deserialize results from each of the workers,
resulting in a bottleneck.
Using Plasma plus Arrow, the data being operated on would be placed in the Plasma store once, and all of the workers would read the data without copying or deserializing it (the workers would map the relevant region of memory into their own address spaces). The workers would then put the results of their computation back into the Plasma store, which the driver could then read and aggregate without copying or deserializing the data.
The Plasma API:
Object IDs: Each object is associated with a string of bytes.
Creating an object: Objects are stored in Plasma in two stages. First, the object store creates the object by allocating a buffer for it. At this point, the client can write to the buffer and construct the object within the allocated buffer. When the client is done, the client seals the buffer making the object immutable and making it available to other Plasma clients.
# Create an object.
object_id = pyarrow.plasma.ObjectID(20 * b'a')
object_size = 1000
buffer = memoryview(client.create(object_id, object_size))
# Write to the buffer.
for i in range(1000):
buffer[i] = 0
# Seal the object making it immutable and available to other clients.
Getting an object: After an object has been sealed, any client who knows the object ID can get the object.
# Get the object from the store. This blocks until the object has been sealed.
object_id = pyarrow.plasma.ObjectID(20 * b'a')
[buff] = client.get([object_id])
buffer = memoryview(buff)
If the object has not been sealed yet, then the call to
client.get will block
until the object has been sealed.
A sorting application
To illustrate the benefits of Plasma, we demonstrate an 11x speedup (on a machine with 20 physical cores) for sorting a large pandas DataFrame (one billion entries). The baseline is the built-in pandas sort function, which sorts the DataFrame in 477 seconds. To leverage multiple cores, we implement the following standard distributed sorting scheme.
- We assume that the data is partitioned across K pandas DataFrames and that each one already lives in the Plasma store.
- We subsample the data, sort the subsampled data, and use the result to define L non-overlapping buckets.
- For each of the K data partitions and each of the L buckets, we find the subset of the data partition that falls in the bucket, and we sort that subset.
- For each of the L buckets, we gather all of the K sorted subsets that fall in that bucket.
- For each of the L buckets, we merge the corresponding K sorted subsets.
- We turn each bucket into a pandas DataFrame and place it in the Plasma store.
Using this scheme, we can sort the DataFrame (the data starts and ends in the Plasma store), in 44 seconds, giving an 11x speedup over the baseline.
The Plasma store runs as a separate process. It is written in C++ and is designed as a single-threaded event loop based on the Redis event loop library. The plasma client library can be linked into applications. Clients communicate with the Plasma store via messages serialized using Google Flatbuffers.
Call for contributions
Plasma is a work in progress, and the API is currently unstable. Today Plasma is primarily used in Ray as an in-memory cache for Arrow serialized objects. We are looking for a broader set of use cases to help refine Plasma’s API. In addition, we are looking for contributions in a variety of areas including improving performance and building other language bindings. Please let us know if you are interested in getting involved with the project.