Python redis.WatchError方法代碼示例

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def get_current_task(self):
        with self.local_redis.pipeline() as pipe:
            while True:
                try:
                    pipe.watch(TASK_ID_KEY)
                    task_id = int(retry_get(pipe, TASK_ID_KEY))
                    if task_id == self.cached_task_id:
                        logger.debug('[worker] Returning cached task {}'.format(task_id))
                        break
                    pipe.multi()
                    pipe.get(TASK_DATA_KEY)
                    logger.info('[worker] Getting new task {}. Cached task was {}'.format(task_id, self.cached_task_id))
                    self.cached_task_id, self.cached_task_data = task_id, deserialize(pipe.execute()[0])
                    break
                except redis.WatchError:
                    continue
        return self.cached_task_id, self.cached_task_data 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def do_renew(self, new_timeout):
        pipe = self.redis.pipeline()
        pipe.watch(self.name)
        lock_value = pipe.get(self.name)
        if lock_value != self.local.token:
            raise LockError("Cannot extend a lock that's no longer owned")
        pipe.multi()
        pipe.pexpire(self.name, int(new_timeout * 1000))

        try:
            response = pipe.execute()
        except WatchError:
            # someone else acquired the lock
            raise LockError("Cannot extend a lock that's no longer owned")
        if not response[0]:
            # pexpire returns False if the key doesn't exist
            raise LockError("Cannot extend a lock that's no longer owned")
        return True 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def _redis_set_atomically(batch_id, subtask, key, val):
    """
    Atomically sets a field in the Redis batch object to a value.
    """
    with Redis.pipeline() as pipe:
        while 1:
            try:
                pipe.watch(batch_id)
                batch_struct = json.loads(pipe.get(batch_id))
                pipe.multi()
                batch_struct[subtask][key] = val
                pipe.set(batch_id, json.dumps(batch_struct))
                pipe.execute()
                break
            except WatchError:
                continue 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def try_lock(self):
        interval_delta = datetime.timedelta(seconds=self.interval)
        with self.conn.pipeline() as pipe:
            try:
                now = datetime.datetime.utcnow()
                pipe.watch(self.last_run_key)
                last_run = pipe.get(self.last_run_key)
                if last_run:
                    last_run_date = datetime.datetime.strptime(last_run, DATETIME_FORMAT)
                    if now - last_run_date < interval_delta:
                        pipe.unwatch()
                        return False
                pipe.multi()
                pipe.set(self.last_run_key, now.strftime(DATETIME_FORMAT))
                pipe.execute()
                return True
            except redis.WatchError:
                return False 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def begin_rate_limit(page, interval_seconds=None):
    if page.endpoint_id == publisher_equivalent_endpoint_id:
        return True

    interval_seconds = interval_seconds or scrape_interval_seconds(page)

    r = redis.from_url(os.environ.get("REDIS_URL"))
    started_key = redis_key(page, 'started')
    finished_key = redis_key(page, 'finished')

    with r.pipeline() as pipe:
        try:
            pipe.watch(started_key)
            pipe.watch(finished_key)

            scrape_started = unpickle(r.get(started_key))
            scrape_finished = unpickle(r.get(finished_key))

            if (scrape_started and scrape_started >= datetime.utcnow() - timedelta(hours=1)) or (
                scrape_finished and scrape_finished >= datetime.utcnow() - timedelta(seconds=interval_seconds)
            ):
                return False

            pipe.multi()
            pipe.set(started_key, pickle.dumps(datetime.utcnow()))
            pipe.set(finished_key, pickle.dumps(None))
            pipe.execute()
            return True
        except WatchError:
            return False 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def save_enqueued(self, pipe):
        """
        Preparing job to enqueue. Works via pipeline.
        Nothing done if WatchError happens while next `pipeline.execute()`.
        """
        job = self.create_job(status=JobStatus.QUEUED)
        self.set_job_params(pipeline=pipe)
        job.origin = self.origin
        job.enqueued_at = utcnow()
        if job.timeout is None:
            job.timeout = self.timeout
        job.save(pipeline=pipe)
        self.job = job 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def save_deferred(self, depends_on, pipe):
        """
        Preparing job to defer (add as dependent). Works via pipeline.
        Nothing done if WatchError happens while next `pipeline.execute()`.
        """
        job = self.create_job(depends_on=depends_on, status=JobStatus.DEFERRED)
        self.set_job_params(pipeline=pipe)
        job.register_dependency(pipeline=pipe)
        job.save(pipeline=pipe)

        return job 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def add_document(self, doc):
        """Add a document to the batch.

        Adds a document tuple to the batch and checks if it exists.

        Args:
            doc (tuple): A standard document tuple.

        Raises:
            NidabaInputException: The document tuple does not refer to a file.
        """
        if self.lock:
            raise NidabaInputException('Executed batch may not be modified')

        if not self.storage.is_file(*doc):
            raise NidabaInputException('Input document is not a file.')

        with self.redis.pipeline() as pipe:
            while(1):
                try:
                    pipe.watch(self.id)
                    self._restore_and_create_scratchpad(pipe)
                    self.docs.append(doc)
                    self.scratchpad['scratchpad']['docs'] = self.docs
                    pipe.set(self.id, json.dumps(self.scratchpad))
                    pipe.execute()
                    break
                except WatchError:
                    continue 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def rm_document(self, doc):
        """Removes a document from the (unexecuted) batch.

        Removes a document tuple from the batch.

        Args:
            doc (tuple): A standard document tuple.

        Raises:
            NidabaInputException: The document tuple does not refer to a file.
        """
        if self.lock:
            raise NidabaInputException('Executed batch may not be modified')

        with self.redis.pipeline() as pipe:
            while(1):
                try:
                    pipe.watch(self.id)
                    self._restore_and_create_scratchpad(pipe)
                    self.docs.remove(list(doc))
                    self.scratchpad['scratchpad']['docs'] = self.docs
                    pipe.set(self.id, json.dumps(self.scratchpad))
                    pipe.execute()
                    break
                except WatchError:
                    continue
                except ValueError:
                    raise NidabaInputException('Document not part of the batch') 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def incr_ratelimit(cls, entity_key: str, max_api_calls: int, max_api_window: int) -> None:
        """Increases the rate-limit for the specified entity"""
        list_key, set_key, _ = cls.get_keys(entity_key)
        now = time.time()

        # Start redis transaction
        with client.pipeline() as pipe:
            count = 0
            while True:
                try:
                    # To avoid a race condition between getting the element we might trim from our list
                    # and removing it from our associated set, we abort this whole transaction if
                    # another agent manages to change our list out from under us
                    # When watching a value, the pipeline is set to Immediate mode
                    pipe.watch(list_key)

                    # Get the last elem that we'll trim (so we can remove it from our sorted set)
                    last_val = pipe.lindex(list_key, max_api_calls - 1)

                    # Restart buffered execution
                    pipe.multi()

                    # Add this timestamp to our list
                    pipe.lpush(list_key, now)

                    # Trim our list to the oldest rule we have
                    pipe.ltrim(list_key, 0, max_api_calls - 1)

                    # Add our new value to the sorted set that we keep
                    # We need to put the score and val both as timestamp,
                    # as we sort by score but remove by value
                    pipe.zadd(set_key, {str(now): now})

                    # Remove the trimmed value from our sorted set, if there was one
                    if last_val is not None:
                        pipe.zrem(set_key, last_val)

                    # Set the TTL for our keys as well
                    api_window = max_api_window
                    pipe.expire(list_key, api_window)
                    pipe.expire(set_key, api_window)

                    pipe.execute()

                    # If no exception was raised in the execution, there were no transaction conflicts
                    break
                except redis.WatchError:  # nocoverage # Ideally we'd have a test for this.
                    if count > 10:
                        raise RateLimiterLockingException()
                    count += 1

                    continue 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def __init__(self, id=None):
        # stuff depending on a valid configuration
        from nidaba import storage
        from nidaba import config
        self.storage = storage

        # slowly importing stuff
        from nidaba import tasks
        from nidaba import plugins
        from nidaba import celery
        self.task_reg = tasks
        self.celery = celery

        self.id = id
        if self.id is None:
            self.id = uuid.uuid4().get_hex()
            self.storage.prepare_filestore(self.id)
        if not self.storage.is_valid_job(self.id):
            raise NidabaInputException('Storage not prepared for task')

        self.docs = []
        self.scratchpad = {}
        self.redis = config.Redis

        self.tasks = OrderedDict([('img', []),
                                  ('binarize', []),
                                  ('segmentation', []),
                                  ('ocr', []),
                                  ('stats', []),
                                  ('postprocessing', []),
                                  ('output', []),
                                  ('archive', [])])

        # defines if tasks in a group are run in parallel or in sequence and their merge mode
        self.order = {'img': ('sequence', False),
                      'binarize': ('parallel', False),
                      'segmentation': ('parallel', False),
                      'ocr': ('parallel', False),
                      'stats': ('parallel', False),
                      'postprocessing': ('sequence', 'doc'),
                      'output': ('sequence', False),
                      'archive': ('parallel', True)}

        self.lock = False
        with self.redis.pipeline() as pipe:
            while(1):
                try:
                    pipe.watch(self.id)
                    self._restore_and_create_scratchpad(pipe)
                    if 'scratchpad' not in self.scratchpad:
                        self.lock = True
                    pipe.execute()
                    break
                except WatchError:
                    continue 

# 需要導入模塊: import redis [as 別名]
# 或者: from redis import WatchError [as 別名]
def add_task(self, group, method, **kwargs):
        """Add a task.

        Adds a ``task``, a single executable task gathering one or more input
        documents and returning a single output document, to the current tick.
        Multiple jobs are run in parallel.

        Args:
            group (unicode): A task group identifier
            method (unicode): A task identifier
            **kwargs: Arguments to the task

        Raises:
            NidabaInputException: Trying to modify executed task.
            NidabaNoSuchAlgorithmException: Invalid method given.
        """
        if self.lock:
            raise NidabaInputException('Executed batch may not be modified')
        # validate that the task exists
        if group not in self.tasks:
            raise NidabaNoSuchAlgorithmException('Unknown task group {}'.format(group))
        if u'nidaba.{}.{}'.format(group, method) not in self.celery.app.tasks:
            raise NidabaNoSuchAlgorithmException('Unknown task {} {}'.format(group, method))
        task = self.celery.app.tasks[u'nidaba.{}.{}'.format(group, method)]
        # validate arguments first against getcallargs
        try:
            getcallargs(task.run, ('', ''), **kwargs)
        except TypeError as e:
            raise NidabaInputException(str(e))
        # validate against arg_values field of the task
        task_arg_validator(task.get_valid_args(), **kwargs)
        with self.redis.pipeline() as pipe:
            while(1):
                try:
                    pipe.watch(self.id)
                    self._restore_and_create_scratchpad(pipe)
                    self.tasks[group].append((method, kwargs))
                    self.scratchpad['scratchpad']['simple_tasks'] = self.tasks
                    pipe.set(self.id, json.dumps(self.scratchpad))
                    pipe.execute()
                    break
                except WatchError:
                    continue