def main():
# Start by loading all the modules
action_map = get_module_map(kafka.tools.assigner.actions, kafka.tools.assigner.actions.ActionModule)
sizer_map = get_module_map(kafka.tools.assigner.sizers, kafka.tools.assigner.sizers.SizerModule)
plugins = get_all_plugins()
# Set up and parse all CLI arguments
args = set_up_arguments(action_map, sizer_map, plugins)
run_plugins_at_step(plugins, 'set_arguments', args)
tools_path = get_tools_path(args.tools_path)
check_java_home()
cluster = Cluster.create_from_zookeeper(args.zookeeper, getattr(args, 'default_retention', 1))
run_plugins_at_step(plugins, 'set_cluster', cluster)
# If the module needs the partition sizes, call a size module to get the information
check_and_get_sizes(action_map[args.action], args, cluster, sizer_map)
run_plugins_at_step(plugins, 'after_sizes')
print_leadership("before", cluster, args.leadership)
# Clone the cluster, and run the action to generate a new cluster state
newcluster = cluster.clone()
action_to_run = action_map[args.action](args, newcluster)
action_to_run.process_cluster()
run_plugins_at_step(plugins, 'set_new_cluster', action_to_run.cluster)
print_leadership("after", newcluster, args.leadership)
move_partitions = cluster.changed_partitions(action_to_run.cluster)
batches = split_partitions_into_batches(move_partitions, batch_size=args.moves, use_class=Reassignment)
run_plugins_at_step(plugins, 'set_batches', batches)
log.info("Partition moves required: {0}".format(len(move_partitions)))
log.info("Number of batches: {0}".format(len(batches)))
dry_run = is_dry_run(args)
for i, batch in enumerate(batches):
log.info("Executing partition reassignment {0}/{1}: {2}".format(i + 1, len(batches), repr(batch)))
batch.execute(i + 1, len(batches), args.zookeeper, tools_path, plugins, dry_run)
run_plugins_at_step(plugins, 'before_ple')
if not args.skip_ple:
all_cluster_partitions = [p for p in action_to_run.cluster.partitions(args.exclude_topics)]
batches = split_partitions_into_batches(all_cluster_partitions, batch_size=args.ple_size, use_class=ReplicaElection)
log.info("Number of replica elections: {0}".format(len(batches)))
run_preferred_replica_elections(batches, args, tools_path, plugins, dry_run)
run_plugins_at_step(plugins, 'finished')
if args.output_json:
data = {
'before': cluster.to_dict(),
'after': action_to_run.cluster.to_dict()
}
sys.stdout.write(json.dumps(data, indent=4, sort_keys=True))
return os.EX_OK
def test_split_batches_proper_class(self):
batches = split_partitions_into_batches(self.topic.partitions,
batch_size=100,
use_class=Reassignment)
assert isinstance(batches[0], Reassignment)
batches = split_partitions_into_batches(self.topic.partitions,
batch_size=100,
use_class=ReplicaElection)
assert isinstance(batches[0], ReplicaElection)
def main():
# Start by loading all the modules
action_map = get_module_map(kafka.tools.assigner.actions, kafka.tools.assigner.actions.ActionModule)
sizer_map = get_module_map(kafka.tools.assigner.sizers, kafka.tools.assigner.sizers.SizerModule)
plugins = get_all_plugins()
# Set up and parse all CLI arguments
args = set_up_arguments(action_map, sizer_map, plugins)
run_plugins_at_step(plugins, 'set_arguments', args)
tools_path = get_tools_path(args.tools_path)
check_java_home()
cluster = Cluster.create_from_zookeeper(args.zookeeper)
run_plugins_at_step(plugins, 'set_cluster', cluster)
# If the module needs the partition sizes, call a size module to get the information
check_and_get_sizes(action_map[args.action], args, cluster, sizer_map)
run_plugins_at_step(plugins, 'after_sizes')
print_leadership("before", cluster, args.leadership)
# Clone the cluster, and run the action to generate a new cluster state
newcluster = cluster.clone()
action_to_run = action_map[args.action](args, newcluster)
action_to_run.process_cluster()
run_plugins_at_step(plugins, 'set_new_cluster', action_to_run.cluster)
print_leadership("after", newcluster, args.leadership)
move_partitions = cluster.changed_partitions(action_to_run.cluster)
batches = split_partitions_into_batches(move_partitions, batch_size=args.moves, use_class=Reassignment)
run_plugins_at_step(plugins, 'set_batches', batches)
log.info("Partition moves required: {0}".format(len(move_partitions)))
log.info("Number of batches: {0}".format(len(batches)))
dry_run = is_dry_run(args)
for i, batch in enumerate(batches):
log.info("Executing partition reassignment {0}/{1}: {2}".format(i + 1, len(batches), repr(batch)))
batch.execute(i + 1, len(batches), args.zookeeper, tools_path, plugins, dry_run)
run_plugins_at_step(plugins, 'before_ple')
if not args.skip_ple:
all_cluster_partitions = [p for p in action_to_run.cluster.partitions()]
batches = split_partitions_into_batches(all_cluster_partitions, batch_size=args.ple_size, use_class=ReplicaElection)
log.info("Number of replica elections: {0}".format(len(batches)))
run_preferred_replica_elections(batches, args, tools_path, plugins, dry_run)
run_plugins_at_step(plugins, 'finished')
return os.EX_OK
def test_split_batches_notenough(self):
batches = split_partitions_into_batches(self.topic.partitions,
batch_size=20,
use_class=Reassignment)
partition_count = sum([len(batch.partitions) for batch in batches])
assert len(batches) == 1
assert partition_count == 10
def test_split_batches_notenough(self):
batches = split_partitions_into_batches(self.topic.partitions, batch_size=20, use_class=Reassignment)
partition_count = sum([len(batch.partitions) for batch in batches])
assert len(batches) == 1
assert partition_count == 10
def test_split_batches_proper_class(self):
batches = split_partitions_into_batches(self.topic.partitions, batch_size=100, use_class=Reassignment)
assert isinstance(batches[0], Reassignment)
batches = split_partitions_into_batches(self.topic.partitions, batch_size=100, use_class=ReplicaElection)
assert isinstance(batches[0], ReplicaElection)
def test_split_batches_empty(self):
partitions = []
batches = split_partitions_into_batches(partitions, batch_size=1, use_class=Reassignment)
assert len(batches) == 0
def main():
# Start by loading all the modules
action_map = get_action_map()
sizer_map = get_sizer_map()
plugins_list = get_plugins_list()
# Instantiate all plugins
plugins = [plugin() for plugin in plugins_list]
# Set up and parse all CLI arguments
args = set_up_arguments(action_map, sizer_map, plugins)
for plugin in plugins:
plugin.set_arguments(args)
tools_path = get_tools_path(args.tools_path)
check_java_home()
cluster = Cluster.create_from_zookeeper(args.zookeeper)
for plugin in plugins:
plugin.set_cluster(cluster)
# If the module needs the partition sizes, call a size module to get the information
check_and_get_sizes(action_map[args.action], args, cluster, sizer_map)
for plugin in plugins:
plugin.after_sizes()
if args.leadership:
log.info("Cluster Leadership Balance (before):")
cluster.log_broker_summary()
# Clone the cluster, and run the action to generate a new cluster state
newcluster = cluster.clone()
action_to_run = action_map[args.action](args, newcluster)
action_to_run.process_cluster()
for plugin in plugins:
plugin.set_new_cluster(action_to_run.cluster)
if args.leadership:
log.info("Cluster Leadership Balance (after):")
newcluster.log_broker_summary()
move_partitions = cluster.changed_partitions(action_to_run.cluster)
batches = split_partitions_into_batches(move_partitions, batch_size=args.moves, use_class=Reassignment)
for plugin in plugins:
plugin.set_batches(batches)
log.info("Partition moves required: {0}".format(len(move_partitions)))
log.info("Number of batches: {0}".format(len(batches)))
dry_run = args.generate or not args.execute
if dry_run:
log.info("--execute flag NOT specified. DRY RUN ONLY")
for i, batch in enumerate(batches):
log.info("Executing partition reassignment {0}/{1}: {2}".format(i + 1, len(batches), repr(batch)))
batch.execute(i + 1, len(batches), args.zookeeper, tools_path, plugins, dry_run)
for plugin in plugins:
plugin.before_ple()
if not args.skip_ple:
batches = split_partitions_into_batches(move_partitions, batch_size=args.moves, use_class=ReplicaElection)
log.info("Number of replica elections: {0}".format(len(batches)))
run_preferred_replica_elections(batches, args, tools_path, plugins, dry_run)
for plugin in plugins:
plugin.finished()
return 0
def test_split_batches_empty(self):
partitions = []
batches = split_partitions_into_batches(partitions,
batch_size=1,
use_class=Reassignment)
assert len(batches) == 0