1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
|
"""
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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
* <p>
* http://www.apache.org/licenses/LICENSE-2.0
* <p>
* 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)
|
