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a68ca199a2
Commands able to use this parallelisation are `stop`, `kill` and `rm`. We're using a backported function from python 3, to allow us to make the most of a pool of threads without having to write the low level code for managing this ourselves. A default value for number of threads is a low enough number so it shouldn't cause performance problems but if someone knows the capability of their system and wants to increase it, they can via an environment variable DEFAULT_MAX_WORKERS Signed-off-by: Mazz Mosley <mazz@houseofmnowster.com>
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
import hashlib
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import json
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import logging
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import os
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import concurrent.futures
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from .const import DEFAULT_MAX_WORKERS
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log = logging.getLogger(__name__)
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def parallel_execute(command, containers, doing_msg, done_msg, **options):
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"""
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Execute a given command upon a list of containers in parallel.
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"""
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max_workers = os.environ.get('MAX_WORKERS', DEFAULT_MAX_WORKERS)
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def container_command_execute(container, command, **options):
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log.info("{} {}...".format(doing_msg, container.name))
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return getattr(container, command)(**options)
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with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
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future_container = {
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executor.submit(
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container_command_execute,
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container,
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command,
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**options
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): container for container in containers
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}
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for future in concurrent.futures.as_completed(future_container):
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container = future_container[future]
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log.info("{} {}".format(done_msg, container.name))
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def json_hash(obj):
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dump = json.dumps(obj, sort_keys=True, separators=(',', ':'))
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h = hashlib.sha256()
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h.update(dump)
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return h.hexdigest()
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