path: root/kafka/producer/record_accumulator.py

from __future__ import absolute_import

import collections
import copy
import logging
import threading
import time

import six

from ..common import TopicPartition
from ..protocol.message import Message, MessageSet
from .buffer import MessageSetBuffer, SimpleBufferPool
from .future import FutureRecordMetadata, FutureProduceResult

import kafka.common as Errors


log = logging.getLogger(__name__)


class AtomicInteger(object):
    def __init__(self, val=0):
        self._lock = threading.Lock()
        self._val = val

    def increment(self):
        with self._lock:
            self._val += 1
            return self._val

    def decrement(self):
        with self._lock:
            self._val -= 1
            return self._val

    def get(self):
        return self._val


class RecordBatch(object):
    def __init__(self, tp, records):
        self.record_count = 0
        #self.max_record_size = 0 # for metrics only
        now = time.time()
        #self.created = now # for metrics only
        self.drained = None
        self.attempts = 0
        self.last_attempt = now
        self.last_append = now
        self.records = records
        self.topic_partition = tp
        self.produce_future = FutureProduceResult(tp)
        self._retry = False

    def try_append(self, key, value):
        if not self.records.has_room_for(key, value):
            return None

        self.records.append(self.record_count, Message(value, key=key))
        # self.max_record_size = max(self.max_record_size, Record.record_size(key, value)) # for metrics only
        self.last_append = time.time()
        future = FutureRecordMetadata(self.produce_future, self.record_count)
        self.record_count += 1
        return future

    def done(self, base_offset=None, exception=None):
        log.debug("Produced messages to topic-partition %s with base offset"
                  " %s and error %s.", self.topic_partition, base_offset,
                  exception) # trace
        if exception is None:
            self.produce_future.success(base_offset)
        else:
            self.produce_future.failure(exception)

    def maybe_expire(self, request_timeout_ms, linger_ms):
        since_append_ms = 1000 * (time.time() - self.last_append)
        if ((self.records.is_full() and request_timeout_ms < since_append_ms)
            or (request_timeout_ms < (since_append_ms + linger_ms))):
            self.records.close()
            self.done(-1, Errors.KafkaTimeoutError('Batch Expired'))
            return True
        return False

    def in_retry(self):
        return self._retry

    def set_retry(self):
        self._retry = True

    def __str__(self):
        return 'RecordBatch(topic_partition=%s, record_count=%d)' % (
            self.topic_partition, self.record_count)


class RecordAccumulator(object):
    """
    This class maintains a dequeue per TopicPartition that accumulates messages
    into MessageSets to be sent to the server.

    The accumulator attempts to bound memory use, and append calls will block
    when that memory is exhausted.

    Keyword Arguments:
        batch_size (int): Requests sent to brokers will contain multiple
            batches, one for each partition with data available to be sent.
            A small batch size will make batching less common and may reduce
            throughput (a batch size of zero will disable batching entirely).
            Default: 16384
        buffer_memory (int): The total bytes of memory the producer should use
            to buffer records waiting to be sent to the server. If records are
            sent faster than they can be delivered to the server the producer
            will block up to max_block_ms, raising an exception on timeout.
            In the current implementation, this setting is an approximation.
            Default: 33554432 (32MB)
        compression_type (str): The compression type for all data generated by
            the producer. Valid values are 'gzip', 'snappy', 'lz4', or None.
            Compression is of full batches of data, so the efficacy of batching
            will also impact the compression ratio (more batching means better
            compression). Default: None.
        linger_ms (int): An artificial delay time to add before declaring a
            messageset (that isn't full) ready for sending. This allows
            time for more records to arrive. Setting a non-zero linger_ms
            will trade off some latency for potentially better throughput
            due to more batching (and hence fewer, larger requests).
            Default: 0
        retry_backoff_ms (int): An artificial delay time to retry the
            produce request upon receiving an error. This avoids exhausting
            all retries in a short period of time. Default: 100
    """
    _DEFAULT_CONFIG = {
        'buffer_memory': 33554432,
        'batch_size': 16384,
        'compression_type': None,
        'linger_ms': 0,
        'retry_backoff_ms': 100,
    }

    def __init__(self, **configs):
        self.config = copy.copy(self._DEFAULT_CONFIG)
        for key in self.config:
            if key in configs:
                self.config[key] = configs.pop(key)

        self._closed = False
        self._drain_index = 0
        self._flushes_in_progress = AtomicInteger()
        self._appends_in_progress = AtomicInteger()
        self._batches = collections.defaultdict(collections.deque) # TopicPartition: [RecordBatch]
        self._tp_locks = {None: threading.Lock()} # TopicPartition: Lock, plus a lock to add entries
        self._free = SimpleBufferPool(self.config['buffer_memory'],
                                      self.config['batch_size'])
        self._incomplete = IncompleteRecordBatches()

    def append(self, tp, key, value, max_time_to_block_ms):
        """Add a record to the accumulator, return the append result.

        The append result will contain the future metadata, and flag for
        whether the appended batch is full or a new batch is created

        Arguments:
            tp (TopicPartition): The topic/partition to which this record is
                being sent
            key (bytes): The key for the record
            value (bytes): The value for the record
            max_time_to_block_ms (int): The maximum time in milliseconds to
                block for buffer memory to be available

        Returns:
            tuple: (future, batch_is_full, new_batch_created)
        """
        assert isinstance(tp, TopicPartition), 'not TopicPartition'
        assert not self._closed, 'RecordAccumulator is closed'
        # We keep track of the number of appending thread to make sure we do not miss batches in
        # abortIncompleteBatches().
        self._appends_in_progress.increment()
        try:
            if tp not in self._tp_locks:
                with self._tp_locks[None]:
                    if tp not in self._tp_locks:
                        self._tp_locks[tp] = threading.Lock()

            with self._tp_locks[tp]:
                # check if we have an in-progress batch
                dq = self._batches[tp]
                if dq:
                    last = dq[-1]
                    future = last.try_append(key, value)
                    if future is not None:
                        batch_is_full = len(dq) > 1 or last.records.is_full()
                        return future, batch_is_full, False

            # we don't have an in-progress record batch try to allocate a new batch
            message_size = MessageSet.HEADER_SIZE + Message.HEADER_SIZE
            if key is not None:
                message_size += len(key)
            if value is not None:
                message_size += len(value)
            assert message_size <= self.config['buffer_memory'], 'message too big'

            size = max(self.config['batch_size'], message_size)
            log.debug("Allocating a new %d byte message buffer for %s", size, tp) # trace
            buf = self._free.allocate(max_time_to_block_ms)
            with self._tp_locks[tp]:
                # Need to check if producer is closed again after grabbing the
                # dequeue lock.
                assert not self._closed, 'RecordAccumulator is closed'

                if dq:
                    last = dq[-1]
                    future = last.try_append(key, value)
                    if future is not None:
                        # Somebody else found us a batch, return the one we
                        # waited for! Hopefully this doesn't happen often...
                        self._free.deallocate(buf)
                        batch_is_full = len(dq) > 1 or last.records.is_full()
                        return future, batch_is_full, False

                records = MessageSetBuffer(buf, self.config['batch_size'],
                                           self.config['compression_type'])
                batch = RecordBatch(tp, records)
                future = batch.try_append(key, value)
                if not future:
                    raise Exception()

                dq.append(batch)
                self._incomplete.add(batch)
                batch_is_full = len(dq) > 1 or batch.records.is_full()
                return future, batch_is_full, True
        finally:
            self._appends_in_progress.decrement()

    def abort_expired_batches(self, request_timeout_ms, cluster):
        """Abort the batches that have been sitting in RecordAccumulator for
        more than the configured request_timeout due to metadata being
        unavailable.

        Arguments:
            request_timeout_ms (int): milliseconds to timeout
            cluster (ClusterMetadata): current metadata for kafka cluster

        Returns:
            list of RecordBatch that were expired
        """
        expired_batches = []
        to_remove = []
        count = 0
        for tp, dq in six.iteritems(self._batches):
            assert tp in self._tp_locks, 'TopicPartition not in locks dict'
            with self._tp_locks[tp]:
                # iterate over the batches and expire them if they have stayed
                # in accumulator for more than request_timeout_ms
                for batch in dq:
                    # check if the batch is expired
                    if batch.maybe_expire(request_timeout_ms,
                                          self.config['linger_ms']):
                        expired_batches.append(batch)
                        to_remove.append(batch)
                        count += 1
                        self.deallocate(batch)
                    elif not batch.in_retry():
                        break

                # Python does not allow us to mutate the dq during iteration
                # Assuming expired batches are infrequent, this is better than
                # creating a new copy of the deque for iteration on every loop
                if to_remove:
                    for batch in to_remove:
                        dq.remove(batch)
                    to_remove = []

        if expired_batches:
            log.debug("Expired %d batches in accumulator", count) # trace

        return expired_batches

    def reenqueue(self, batch):
        """Re-enqueue the given record batch in the accumulator to retry."""
        now = time.time()
        batch.attempts += 1
        batch.last_attempt = now
        batch.last_append = now
        batch.set_retry()
        assert batch.topic_partition in self._tp_locks, 'TopicPartition not in locks dict'
        assert batch.topic_partition in self._batches, 'TopicPartition not in batches'
        dq = self._batches[batch.topic_partition]
        with self._tp_locks[batch.topic_partition]:
            dq.appendleft(batch)

    def ready(self, cluster):
        """
        Get a list of nodes whose partitions are ready to be sent, and the
        earliest time at which any non-sendable partition will be ready;
        Also return the flag for whether there are any unknown leaders for the
        accumulated partition batches.

        A destination node is ready to send data if ANY one of its partition is
        not backing off the send and ANY of the following are true:

         * The record set is full
         * The record set has sat in the accumulator for at least linger_ms
           milliseconds
         * The accumulator is out of memory and threads are blocking waiting
           for data (in this case all partitions are immediately considered
           ready).
         * The accumulator has been closed

        Arguments:
            cluster (ClusterMetadata):

        Returns:
            tuple:
                ready_nodes (set): node_ids that have ready batches
                next_ready_check (float): secs until next ready after backoff
                unknown_leaders_exist (bool): True if metadata refresh needed
        """
        ready_nodes = set()
        next_ready_check = 9999999.99
        unknown_leaders_exist = False
        now = time.time()

        exhausted = bool(self._free.queued() > 0)
        for tp, dq in six.iteritems(self._batches):

            leader = cluster.leader_for_partition(tp)
            if leader is None or leader == -1:
                unknown_leaders_exist = True
                continue
            elif leader in ready_nodes:
                continue

            with self._tp_locks[tp]:
                if not dq:
                    continue
                batch = dq[0]
                retry_backoff = self.config['retry_backoff_ms'] / 1000.0
                linger = self.config['linger_ms'] / 1000.0
                backing_off = bool(batch.attempts > 0 and
                                   batch.last_attempt + retry_backoff > now)
                waited_time = now - batch.last_attempt
                time_to_wait = retry_backoff if backing_off else linger
                time_left = max(time_to_wait - waited_time, 0)
                full = bool(len(dq) > 1 or batch.records.is_full())
                expired = bool(waited_time >= time_to_wait)

                sendable = (full or expired or exhausted or self._closed or
                            self._flush_in_progress())

                if sendable and not backing_off:
                    ready_nodes.add(leader)
                else:
                    # Note that this results in a conservative estimate since
                    # an un-sendable partition may have a leader that will
                    # later be found to have sendable data. However, this is
                    # good enough since we'll just wake up and then sleep again
                    # for the remaining time.
                    next_ready_check = min(time_left, next_ready_check)

        return ready_nodes, next_ready_check, unknown_leaders_exist

    def has_unsent(self):
        """Return whether there is any unsent record in the accumulator."""
        for tp, dq in six.iteritems(self._batches):
            with self._tp_locks[tp]:
                if len(dq):
                    return True
        return False

    def drain(self, cluster, nodes, max_size):
        """
        Drain all the data for the given nodes and collate them into a list of
        batches that will fit within the specified size on a per-node basis.
        This method attempts to avoid choosing the same topic-node repeatedly.

        Arguments:
            cluster (ClusterMetadata): The current cluster metadata
            nodes (list): list of node_ids to drain
            max_size (int): maximum number of bytes to drain

        Returns:
            dict: {node_id: list of RecordBatch} with total size less than the
                requested max_size.
        """
        if not nodes:
            return {}

        now = time.time()
        batches = {}
        for node_id in nodes:
            size = 0
            partitions = list(cluster.partitions_for_broker(node_id))
            ready = []
            # to make starvation less likely this loop doesn't start at 0
            self._drain_index %= len(partitions)
            start = self._drain_index
            while True:
                tp = partitions[self._drain_index]
                if tp in self._batches:
                    with self._tp_locks[tp]:
                        dq = self._batches[tp]
                        if dq:
                            first = dq[0]
                            backoff = (
                                bool(first.attempts > 0) and
                                bool(first.last_attempt +
                                     self.config['retry_backoff_ms'] / 1000.0
                                     > now)
                            )
                            # Only drain the batch if it is not during backoff
                            if not backoff:
                                if (size + first.records.size_in_bytes() > max_size
                                    and len(ready) > 0):
                                    # there is a rare case that a single batch
                                    # size is larger than the request size due
                                    # to compression; in this case we will
                                    # still eventually send this batch in a
                                    # single request
                                    break
                                else:
                                    batch = dq.popleft()
                                    batch.records.close()
                                    size += batch.records.size_in_bytes()
                                    ready.append(batch)
                                    batch.drained = now

                self._drain_index += 1
                self._drain_index %= len(partitions)
                if start == self._drain_index:
                    break

            batches[node_id] = ready
        return batches

    def deallocate(self, batch):
        """Deallocate the record batch."""
        self._incomplete.remove(batch)
        self._free.deallocate(batch.records.buffer())

    def _flush_in_progress(self):
        """Are there any threads currently waiting on a flush?"""
        return self._flushes_in_progress.get() > 0

    def begin_flush(self):
        """
        Initiate the flushing of data from the accumulator...this makes all
        requests immediately ready
        """
        self._flushes_in_progress.increment()

    def await_flush_completion(self):
        """
        Mark all partitions as ready to send and block until the send is complete
        """
        for batch in self._incomplete.all():
            batch.produce_future.await()
        self._flushes_in_progress.decrement()

    def abort_incomplete_batches(self):
        """
        This function is only called when sender is closed forcefully. It will fail all the
        incomplete batches and return.
        """
        # We need to keep aborting the incomplete batch until no thread is trying to append to
        # 1. Avoid losing batches.
        # 2. Free up memory in case appending threads are blocked on buffer full.
        # This is a tight loop but should be able to get through very quickly.
        while True:
            self._abort_batches()
            if not self._appends_in_progress.get():
                break
        # After this point, no thread will append any messages because they will see the close
        # flag set. We need to do the last abort after no thread was appending in case the there was a new
        # batch appended by the last appending thread.
        self._abort_batches()
        self._batches.clear()

    def _abort_batches(self):
        """Go through incomplete batches and abort them."""
        error = Errors.IllegalStateError("Producer is closed forcefully.")
        for batch in self._incomplete.all():
            tp = batch.topic_partition
            # Close the batch before aborting
            with self._tp_locks[tp]:
                batch.records.close()
            batch.done(exception=error)
            self.deallocate(batch)

    def close(self):
        """Close this accumulator and force all the record buffers to be drained."""
        self._closed = True


class IncompleteRecordBatches(object):
    """A threadsafe helper class to hold RecordBatches that haven't been ack'd yet"""

    def __init__(self):
        self._incomplete = set()
        self._lock = threading.Lock()

    def add(self, batch):
        with self._lock:
            return self._incomplete.add(batch)

    def remove(self, batch):
        with self._lock:
            return self._incomplete.remove(batch)

    def all(self):
        with self._lock:
            return list(self._incomplete)