Source code for qdrant_client.parallel_processor
import logging
import os
from enum import Enum
from multiprocessing import Queue, get_context
from multiprocessing.context import BaseContext
from multiprocessing.process import BaseProcess
from multiprocessing.sharedctypes import Synchronized as BaseValue
from queue import Empty
from typing import Any, Dict, Iterable, List, Optional, Type
# Single item should be processed in less than:
processing_timeout = 10 * 60 # seconds
max_internal_batch_size = 200
[docs]class QueueSignals(str, Enum):
stop = "stop"
confirm = "confirm"
error = "error"
[docs]class Worker:
[docs] @classmethod
def start(cls, **kwargs: Any) -> "Worker":
raise NotImplementedError()
[docs] def process(self, items: Iterable[Any]) -> Iterable[Any]:
raise NotImplementedError()
def _worker(
worker_class: Type[Worker],
input_queue: Queue,
output_queue: Queue,
num_active_workers: BaseValue,
worker_id: int,
kwargs: Optional[Dict[str, Any]] = None,
) -> None:
"""
A worker that pulls data pints off the input queue, and places the execution result on the output queue.
When there are no data pints left on the input queue, it decrements
num_active_workers to signal completion.
"""
if kwargs is None:
kwargs = {}
logging.info(f"Reader worker: {worker_id} PID: {os.getpid()}")
try:
worker = worker_class.start(**kwargs)
# Keep going until you get an item that's None.
def input_queue_iterable() -> Iterable[Any]:
while True:
item = input_queue.get()
if item == QueueSignals.stop:
break
yield item
for processed_item in worker.process(input_queue_iterable()):
output_queue.put(processed_item)
except Exception as e: # pylint: disable=broad-except
logging.exception(e)
output_queue.put(QueueSignals.error)
finally:
# It's important that we close and join the queue here before
# decrementing num_active_workers. Otherwise our parent may join us
# before the queue's feeder thread has passed all buffered items to
# the underlying pipe resulting in a deadlock.
#
# See:
# https://docs.python.org/3.6/library/multiprocessing.html?highlight=process#pipes-and-queues
# https://docs.python.org/3.6/library/multiprocessing.html?highlight=process#programming-guidelines
output_queue.close()
output_queue.join_thread()
with num_active_workers.get_lock():
num_active_workers.value -= 1
logging.info(f"Reader worker {worker_id} finished")
[docs]class ParallelWorkerPool:
def __init__(self, num_workers: int, worker: Type[Worker], start_method: Optional[str] = None):
self.worker_class = worker
self.num_workers = num_workers
self.input_queue: Optional[Queue] = None
self.output_queue: Optional[Queue] = None
self.ctx: BaseContext = get_context(start_method)
self.processes: List[BaseProcess] = []
self.queue_size = self.num_workers * max_internal_batch_size
self.num_active_workers: Optional[BaseValue] = None
[docs] def start(self, **kwargs: Any) -> None:
self.input_queue = self.ctx.Queue(self.queue_size)
self.output_queue = self.ctx.Queue(self.queue_size)
ctx_value = self.ctx.Value("i", self.num_workers)
assert isinstance(ctx_value, BaseValue)
self.num_active_workers = ctx_value
for worker_id in range(0, self.num_workers):
assert hasattr(self.ctx, "Process")
process = self.ctx.Process(
target=_worker,
args=(
self.worker_class,
self.input_queue,
self.output_queue,
self.num_active_workers,
worker_id,
kwargs.copy(),
),
)
process.start()
self.processes.append(process)
[docs] def unordered_map(self, stream: Iterable[Any], *args: Any, **kwargs: Any) -> Iterable[Any]:
try:
self.start(**kwargs)
assert self.input_queue is not None, "Input queue was not initialized"
assert self.output_queue is not None, "Output queue was not initialized"
pushed = 0
read = 0
for item in stream:
if pushed - read < self.queue_size:
try:
out_item = self.output_queue.get_nowait()
except Empty:
out_item = None
else:
try:
out_item = self.output_queue.get(timeout=processing_timeout)
except Empty as e:
self.join_or_terminate()
raise e
if out_item is not None:
if out_item == QueueSignals.error:
self.join_or_terminate()
raise RuntimeError("Thread unexpectedly terminated")
yield out_item
read += 1
self.input_queue.put(item)
pushed += 1
for _ in range(self.num_workers):
self.input_queue.put(QueueSignals.stop)
while read < pushed:
out_item = self.output_queue.get(timeout=processing_timeout)
if out_item == QueueSignals.error:
self.join_or_terminate()
raise RuntimeError("Thread unexpectedly terminated")
yield out_item
read += 1
finally:
assert self.input_queue is not None, "Input queue is None"
assert self.output_queue is not None, "Output queue is None"
self.input_queue.close()
self.output_queue.close()
[docs] def join_or_terminate(self, timeout: Optional[int] = 1) -> None:
"""
Emergency shutdown
@param timeout:
@return:
"""
for process in self.processes:
process.join(timeout=timeout)
if process.is_alive():
process.terminate()
self.processes.clear()
[docs] def join(self) -> None:
for process in self.processes:
process.join()
self.processes.clear()
def __del__(self) -> None:
"""
Terminate processes if the user hasn't joined. This is necessary as
leaving stray processes running can corrupt shared state. In brief,
we've observed shared memory counters being reused (when the memory was
free from the perspective of the parent process) while the stray
workers still held a reference to them.
For a discussion of using destructors in Python in this manner, see
https://eli.thegreenplace.net/2009/06/12/safely-using-destructors-in-python/.
"""
for process in self.processes:
process.terminate()
