xos/synchronizer/ceilometer/ceilometer_pub_sub/kafka_broker.py

# Copyright 2017-present Open Networking Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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.


#
# Copyright 2015 Cisco Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
#      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.

import json

import kafka
from kafka import TopicPartition
from oslo_config import cfg
from oslo_utils import netutils
from six.moves.urllib import parse as urlparse
import logging as LOG


class KafkaBrokerPublisher():
    def __init__(self, parsed_url):
        self.kafka_client = None
        self.kafka_server = None

        self.host, self.port = netutils.parse_host_port(
            parsed_url.netloc, default_port=9092)

        self.local_queue = []

        params = urlparse.parse_qs(parsed_url.query)
        self.topic = params.get('topic', ['ceilometer'])[-1]
        self.policy = params.get('policy', ['default'])[-1]
        self.max_queue_length = int(params.get(
            'max_queue_length', [1024])[-1])
        self.max_retry = int(params.get('max_retry', [100])[-1])

        if self.policy in ['default', 'drop', 'queue']:
            LOG.info(('Publishing policy set to %s') % self.policy)
        else:
            LOG.warn(('Publishing policy is unknown (%s) force to default')
                     % self.policy)
            self.policy = 'default'

        try:
            self._get_client()
            self._get_server()
        except Exception as e:
            LOG.exception("Failed to connect to Kafka service: %s", e)

    def publish_samples(self, context, samples):
        """Send a metering message for kafka broker.

        :param context: Execution context from the service or RPC call
        :param samples: Samples from pipeline after transformation
        """
        samples_list = [
            utils.meter_message_from_counter(
                sample, cfg.CONF.publisher.telemetry_secret)
            for sample in samples
        ]

        self.local_queue.append(samples_list)

        try:
            self._check_kafka_connection()
        except Exception as e:
            raise e

        self.flush()

    def flush(self):
        queue = self.local_queue
        self.local_queue = self._process_queue(queue)
        if self.policy == 'queue':
            self._check_queue_length()

    def publish_events(self, context, events):
        """Send an event message for kafka broker.

        :param context: Execution context from the service or RPC call
        :param events: events from pipeline after transformation
        """
        events_list = [utils.message_from_event(
            event, cfg.CONF.publisher.telemetry_secret) for event in events]

        self.local_queue.append(events_list)

        try:
            self._check_kafka_connection()
        except Exception as e:
            raise e

        self.flush()

    def _process_queue(self, queue):
        current_retry = 0
        while queue:
            data = queue[0]
            try:
                self._send(data)
            except Exception:
                LOG.warn(("Failed to publish %d datum"),
                         sum([len(d) for d in queue]))
                if self.policy == 'queue':
                    return queue
                elif self.policy == 'drop':
                    return []
                current_retry += 1
                if current_retry >= self.max_retry:
                    self.local_queue = []
                    LOG.exception(("Failed to retry to send sample data "
                                      "with max_retry times"))
                    raise
            else:
                queue.pop(0)
        return []

    def _check_queue_length(self):
        queue_length = len(self.local_queue)
        if queue_length > self.max_queue_length > 0:
            diff = queue_length - self.max_queue_length
            self.local_queue = self.local_queue[diff:]
            LOG.warn(("Kafka Publisher max local queue length is exceeded, "
                     "dropping %d oldest data") % diff)

    def _check_kafka_connection(self):
        try:
            self._get_client()
        except Exception as e:
            LOG.exception(_LE("Failed to connect to Kafka service: %s"), e)

            if self.policy == 'queue':
                self._check_queue_length()
            else:
                self.local_queue = []
            raise Exception('Kafka Client is not available, '
                            'please restart Kafka client')

    def _get_client(self):
        if not self.kafka_client:
            self.kafka_client = kafka.KafkaClient(
                "%s:%s" % (self.host, self.port))
            self.kafka_producer = kafka.SimpleProducer(self.kafka_client)
    
    def _get_server(self):
        if not self.kafka_server:
           self.kafka_server = kafka.KafkaClient(
                "%s:%s" % (self.host, self.port))
           #self.kafka_consumer = kafka.KafkaConsumer(self.topic,bootstrap_servers = ["%s:%s" % (self.host, self.port)])
           self.kafka_consumer=kafka.KafkaConsumer(bootstrap_servers=["%s:%s" % (self.host,self.port)])
           self.kafka_consumer.assign([TopicPartition(self.topic,0)])
           self.kafka_consumer.seek_to_end()

    def _send(self, data):
        #for d in data:
            try:
                self.kafka_producer.send_messages(
                    self.topic, json.dumps(data))
            except Exception as e:
                LOG.exception(("Failed to send sample data: %s"), e)
                raise