Work distribution for small clusters.
Project description
MyCloud
Leverage small clusters of machines to increase your productivity.
MyCloud requires no prior setup; if you can SSH to your machines, then it will work out of the box. MyCloud currently exports a simple mapreduce API with several common input formats; adding support for your own is easy as well.
Usage
Starting your cluster:
import mycloud cluster = mycloud.Cluster(['machine1', 'machine2']) # or use defaults from ~/.config/mycloud # cluster = mycloud.Cluster()
Map over a list:
result = cluster.map(compute_factors, range(1000))
ClientFS makes accessing local files seamless!
def my_worker(filename): do_work(mycloud.fs.FS.open(filename, 'r')) cluster.map(['client:///my/local/file'], my_worker)
Use the MapReduce interface to easily handle processing of larger datasets:
from mycloud.mapreduce import MapReduce, group from mycloud.resource import CSV input_desc = [CSV('client:///path/to/my_input_%d.csv') % i for i in range(100)] output_desc = [CSV('client:///path/to/my_output_file.csv')] def map_identity(kv_iter, output): for k, v in kv_iter: output(k, int(v[0])) def reduce_sum(kv_iter, output): for k, values in group(kv_iter): output(k, sum(values)) mr = MapReduce(cluster, map_identity, reduce_sum, input_desc, output_desc) result = mr.run() for k, v in result[0].reader(): print k, v
Performance
It is, keep in mind, written entirely in Python.
Some simple operations I’ve used it for (6 machines, 96 cores):
Sorting a billion numbers: ~5m
Preprocessing 1.3 million images (resizing and SIFT feature extraction): ~1 hour
Input formats
Mycloud has builtin support for processing the following file types:
LevelDB
CSV
Text (lines)
Zip
Adding support for your own is simple - just write a resource class describing how to get a reader and writer. (see resource.py for details).
Why?!?
Sometimes you’re developing something in Python (because that’s what you do), and you decide you’d like it to be parallelized. Our current options are multiprocessing (limiting us to a single machine) and Hadoop streaming (limiting us to strings and Hadoop’s input formats).
Also, because I could.
Credits
MyCloud builds on the phenomonally useful cloud serialization, SSH/Paramiko, and LevelDB libraries.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file mycloud-0.49.tar.gz
.
File metadata
- Download URL: mycloud-0.49.tar.gz
- Upload date:
- Size: 11.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 11e76d45df445da82fd7d035e400765ce92f776cb6a43500f0384c86e75e93ce |
|
MD5 | a9a274c6662d4e9b46ebf11b423d3da2 |
|
BLAKE2b-256 | 0f31ee241621babf75d33414855b9ea469b43febe1da1c4996d1a96985b1463c |