carrotquest/django-clickhouse
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MultiModelConverter now processes sub_models in parallel threads

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This commit is contained in:
M1ha 2019-01-21 14:32:12 +05:00
parent 480a857fe8
commit 69c2c5504b
2 changed files with 22 additions and 17 deletions
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17
src/django_clickhouse/clickhouse_models.py
View File

@ -19,7 +19,7 @@ from .exceptions import RedisLockTimeoutError
from .models import ClickHouseSyncModel
from .query import QuerySet
from .serializers import Django2ClickHouseModelSerializer
from .utils import lazy_class_import, exec_multi_db_func
from .utils import lazy_class_import, exec_multi_arg_func, exec_in_parallel
class ClickHouseModelMeta(InfiModelBase):
@ -160,7 +160,7 @@ class ClickHouseModel(with_metaclass(ClickHouseModelMeta, InfiModel)):
pk_by_db[using].add(pk)
# Selecting data from multiple databases should work faster in parallel, if connections are independent.
objs = exec_multi_db_func(
objs = exec_multi_arg_func(
lambda db_alias: cls.get_sync_query_set(db_alias, pk_by_db[db_alias]),
pk_by_db.keys()
)
@ -281,16 +281,21 @@ class ClickHouseMultiModel(ClickHouseModel):
if import_objects:
batches = {}
with statsd.timer(statsd_key.format('steps.get_insert_batch')):
for model_cls in cls.sub_models:
def _sub_model_func(model_cls):
model_statsd_key = "%s.sync.%s.{0}" % (config.STATSD_PREFIX, model_cls.__name__)
with statsd.timer(model_statsd_key.format('steps.get_insert_batch')):
batches[model_cls] = model_cls.get_insert_batch(import_objects)
return model_cls, model_cls.get_insert_batch(import_objects)
res = exec_multi_arg_func(_sub_model_func, cls.sub_models, threads_count=len(cls.sub_models))
batches = dict(res)
with statsd.timer(statsd_key.format('steps.insert')):
for model_cls, batch in batches.items():
def _sub_model_func(model_cls):
model_statsd_key = "%s.sync.%s.{0}" % (config.STATSD_PREFIX, model_cls.__name__)
with statsd.timer(model_statsd_key.format('steps.insert')):
model_cls.insert_batch(batch)
model_cls.insert_batch(batches[model_cls])
exec_multi_arg_func(_sub_model_func, cls.sub_models, threads_count=len(cls.sub_models))
with statsd.timer(statsd_key.format('steps.post_sync')):
storage.post_sync(import_key)

20
src/django_clickhouse/utils.py
View File

@ -238,25 +238,25 @@ def exec_in_parallel(func: Callable, args_queue: Queue, threads_count: Optional[
return results
def exec_multi_db_func(func: Callable, using: Iterable[str], *args, threads_count: Optional[int] = None,
def exec_multi_arg_func(func: Callable, split_args: Iterable[Any], *args, threads_count: Optional[int] = None,
**kwargs) -> List[Any]:
"""
Executes multiple databases function in parallel threads. Thread functions (func) receive db alias as first argument
Executes function in parallel threads. Thread functions (func) receive one of split_args as first argument
Another arguments passed to functions - args and kwargs
If function uses single shard, separate threads are not run, main thread is used.
:param func: Function to execute on single database
:param using: A list of database aliases to use.
If len(split_args) <= 0, separate threads are not run, main thread is used.
:param func: Function to execute. Must accept split_arg as first parameter
:param split_args: A list of arguments to split threads by
:param threads_count: Maximum number of threads to run in parallel
:return: A list of execution results. Order of execution is not guaranteed.
"""
using = list(using)
if len(using) == 0:
split_args = list(split_args)
if len(split_args) == 0:
return []
elif len(using) == 1:
return [func(using[0], *args, **kwargs)]
elif len(split_args) == 1:
return [func(split_args[0], *args, **kwargs)]
else:
q = Queue()
for s in using:
for s in split_args:
q.put(([s] + list(args), kwargs))
return exec_in_parallel(func, q, threads_count=threads_count)