* http://www.apache.org/licenses/LICENSE-2.0 *
* Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. """ from __future__ import absolute_import import copy import logging import uuid import kafka.streams.errors as Errors from .processor.stream_thread import StreamThread from .processor.partition_group import partition_grouper from .utils import AtomicInteger log = logging.getLogger(__name__) # container states CREATED = 0 RUNNING = 1 STOPPED = 2 class KafkaStreams(object): """ Kafka Streams allows for performing continuous computation on input coming from one or more input topics and sends output to zero or more output topics. The computational logic can be specified either by using the TopologyBuilder class to define the a DAG topology of Processors or by using the KStreamBuilder class which provides the high-level KStream DSL to define the transformation. The KafkaStreams class manages the lifecycle of a Kafka Streams instance. One stream instance can contain one or more threads specified in the configs for the processing work. A KafkaStreams instance can co-ordinate with any other instances with the same application ID (whether in this same process, on other processes on this machine, or on remote machines) as a single (possibly distributed) stream processing client. These instances will divide up the work based on the assignment of the input topic partitions so that all partitions are being consumed. If instances are added or failed, all instances will rebalance the partition assignment among themselves to balance processing load. Internally the KafkaStreams instance contains a normal KafkaProducer and KafkaConsumer instance that is used for reading input and writing output. A simple example might look like this: builder = (KStreamBuilder().stream('my-input-topic') .map_values(lambda value: str(len(value)) .to('my-output-topic')) streams = KafkaStreams(builder, application_id='my-stream-processing-application', bootstrap_servers=['localhost:9092'], key_serializer=json.dumps, key_deserializer=json.loads, value_serializer=json.dumps, value_deserializer=json.loads) streams.start() """ STREAM_CLIENT_ID_SEQUENCE = AtomicInteger(0) METRICS_PREFIX = 'kafka.streams' DEFAULT_CONFIG = { 'application_id': None, 'bootstrap_servers': None, 'client_id': 'kafka-python-streams', 'zookeeper_connect': '', 'state_dir': '/tmp/kafka-streams', 'replication_factor': 1, 'timestamp_extractor': lambda x: x.timestamp, 'partition_grouper': partition_grouper, 'key_serializer': None, 'key_deserializer': None, 'value_serializer': None, 'value_deserializer': None, 'commit_interval_ms': 30000, 'poll_ms': 100, 'num_stream_threads': 1, 'num_standby_replicas': 0, 'buffered_records_per_partition': 1000, 'state_cleanup_delay_ms': 60000, } def __init__(self, builder, **configs): """Construct the stream instance. Arguments: builder (...): The processor topology builder specifying the computational logic """ self.config = copy.copy(self.DEFAULT_CONFIG) for key in self.config: if key in configs: self.config[key] = configs.pop(key) # Only check for extra config keys in top-level class log.warning('Unrecognized configs: %s', configs.keys()) self._state = CREATED # processId is expected to be unique across JVMs and to be used # in userData of the subscription request to allow assignor be aware # of the co-location of stream thread's consumers. It is for internal # usage only and should not be exposed to users at all. self.config['process_id'] = uuid.uuid4().hex # The application ID is a required config and hence should always have value if 'application_id' not in self.config: raise Errors.StreamsError('application_id is a required parameter') builder.set_application_id(self.config['application_id']) if 'client_id' not in self.config: next_id = self.STREAM_CLIENT_ID_SEQUENCE.increment() self.config['client_id'] = self.config['application_id'] + "-" + str(next_id) # reporters = self.config['metric_reporters'] #MetricConfig metricConfig = new MetricConfig().samples(config.getInt(StreamsConfig.METRICS_NUM_SAMPLES_CONFIG)) # .timeWindow(config.getLong(StreamsConfig.METRICS_SAMPLE_WINDOW_MS_CONFIG), # TimeUnit.MILLISECONDS); #self._metrics = new Metrics(metricConfig, reporters, time); self._threads = [StreamThread(builder, **self.config) for _ in range(self.config['num_stream_threads'])] #synchronized def start(self): """Start the stream instance by starting all its threads. Raises: IllegalStateException if process was already started """ log.debug('Starting Kafka Stream process') if self._state == CREATED: for thread in self._threads: thread.start() self._state = RUNNING log.info('Started Kafka Stream process') elif self._state == RUNNING: raise Errors.IllegalStateError('This process was already started.') else: raise Errors.IllegalStateError('Cannot restart after closing.') #synchronized def close(self): """Shutdown this stream instance. Signals all the threads to stop, and then waits for them to join. Raises: IllegalStateException if process has not started yet """ log.debug('Stopping Kafka Stream process') if self._state == RUNNING: # signal the threads to stop and wait for thread in self._threads: thread.close() for thread in self._threads: thread.join() if self._state != STOPPED: #metrics.close() self._state = STOPPED log.info('Stopped Kafka Stream process') def set_uncaught_exception_handler(self, handler): """Sets the handler invoked when a stream thread abruptly terminates due to an uncaught exception. Arguments: handler: the object to use as this thread's uncaught exception handler. If None then this thread has no explicit handler. """ for thread in self._threads: thread.set_uncaught_exception_handler(handler)