Slides from Surge 2012

1. Data Models and Consumer
Idioms Using Apache Kafka for
Continuous Data Stream
Processing
Surge’12
September 27, 2012
Erik Onnen
@eonnen

2. About Me
• Director of Architecture and Development at Urban
Airship
• Formerly Jive Software, Liberty Mutual, Opsware,
Progress
• Java, C++, Python
• Background in messaging systems
• Contributor to ActiveMQ
• Global Tibco deployments
• ESB Commercial Products

3. About Urban Airship
• Engagement platform using location and push
notifications
• Analytics for delivery, conversion and influence
• High precision targeting capabilities

4. This Talk
• How UA uses Kafka
• Kafka architecture digest
• Data structures and stream processing w/ Kafka
• Operational considerations

5. Kafka at Urban Airship

6. Kafka at Urban Airship
“The use for activity stream processing makes Kafka comparable to Facebook's
Scribe or Apache Flume... though the architecture and primitives are very different
for these systems and make Kafka more comparable to a traditional messaging
system.”
- http://incubator.apache.org/kafka/ Sep 27, 2012

7. Kafka at Urban Airship
“The use for activity stream processing makes Kafka comparable to Facebook's
Scribe or Apache Flume... though the architecture and primitives are very different
for these systems and make Kafka more comparable to a traditional messaging
system.”
- http://incubator.apache.org/kafka/ Sep 27, 2012
“Let’s use it for all the things”
- me, 2010

8. Kafka at Urban Airship

9. Kafka at Urban Airship
• On the critical path for many of our core capabilities

10. Kafka at Urban Airship
• On the critical path for many of our core capabilities
• Device metadata

11. Kafka at Urban Airship
• On the critical path for many of our core capabilities
• Device metadata
• Message delivery analytics

12. Kafka at Urban Airship
• On the critical path for many of our core capabilities
• Device metadata
• Message delivery analytics
• Device connectivity state

13. Kafka at Urban Airship
• On the critical path for many of our core capabilities
• Device metadata
• Message delivery analytics
• Device connectivity state
• Feeds our operational data warehouse

14. Kafka at Urban Airship
• On the critical path for many of our core capabilities
• Device metadata
• Message delivery analytics
• Device connectivity state
• Feeds our operational data warehouse
• Three Kafka clusters doing in aggregate > 7B msg/day

15. Kafka at Urban Airship
• On the critical path for many of our core capabilities
• Device metadata
• Message delivery analytics
• Device connectivity state
• Feeds our operational data warehouse
• Three Kafka clusters doing in aggregate > 7B msg/day
• Peak capacity observed single consumer 750K msg/sec

16. Kafka at Urban Airship
• On the critical path for many of our core capabilities
• Device metadata
• Message delivery analytics
• Device connectivity state
• Feeds our operational data warehouse
• Three Kafka clusters doing in aggregate > 7B msg/day
• Peak capacity observed single consumer 750K msg/sec
• All bare metal hardware hosted with an MSP

17. Kafka at Urban Airship
• On the critical path for many of our core capabilities
• Device metadata
• Message delivery analytics
• Device connectivity state
• Feeds our operational data warehouse
• Three Kafka clusters doing in aggregate > 7B msg/day
• Peak capacity observed single consumer 750K msg/sec
• All bare metal hardware hosted with an MSP
• Factoring prominently in our multi-facility architecture

18. Kafka Core Concepts - The Big Picture

19. Kafka Core Concepts - The Big Picture

20. Kafka Core Concepts

21. Kafka Core Concepts
• Publish subscribe system (not a queue)
• One producer, zero or more consumers
• Consumers aren’t contending with each other for
messages
• Messages retained for a configured window of time
• Messages grouped by topics
• Consumers partition a topic as a group:
•1 consumer thread - all topic messages
•2 consumers threads - each .5 total messages
•3 consumers threads - each .3 total messages

22. Kafka Core Concepts - Producers

23. Kafka Core Concepts - Producers
• Producers have no idea who will consume a message or
when
• Deliver messages to one and only one topic
• Deliver messages to one and only one broker*
• Deliver a message to one and only one partition on a
broker
• Messages are not ack’d in any way (not when received,
not when on disk, not on a boat, not in a plane...)
• Messages largely opaque to producers
• Send messages at or below a configured size†

24. Kafka Core Concepts - Brokers

25. Kafka Core Concepts - Brokers
• Dumb by design
• No shared state
• Publish small bits of metadata to ZooKeeper
• Messages are pulled by consumers (no push state
management)
• Manage sets of segment files, one per topic + partition
combination
• All delivery done through sendfile calls on mmap’d files
- very fast, avoids system -> user -> system copy for
every send

26. Kafka Core Concepts - Brokers
• Nearly invisible in the grand scheme of operations if
they have enough disk and RAM

27. Kafka Core Concepts - Brokers
• Don’t fear the JVM (just put it in a corner)
• Most of the heavy lifting is done in system calls
• Minimal on-heap buffering keeps most garbage in
ParNew
• 20 minute sample has approximately 100 ParNew
collections for a total of .42 seconds in GC
(0.0003247526)

28. Kafka Core Concepts - Consumers

29. Kafka Core Concepts - Consumers
• Consumer configured for one and only one group
• Messages are consumed in KafkaMessageStream
iterators that never stop but may block
• Message message stream is a combination of:
• Topic (SPORTS)
• Group (SPORTS EVENT LOGGER | SCORE UPDATER)
• Broker(s) - 1 or more brokers feed a logical stream
• Partition(s) - 1 or more partitions from a broker + topic

30. Kafka Is Excellent for...

31. Kafka Is Excellent for...
• Small, expressive messages - BYOD

32. Kafka Is Excellent for...
• Small, expressive messages - BYOD
• Throughput

33. Kafka Is Excellent for...
• Small, expressive messages - BYOD
• Throughput
• Decimates any JMS or AMQP servers for PubSub
throughput

34. Kafka Is Excellent for...
• Small, expressive messages - BYOD
• Throughput
• Decimates any JMS or AMQP servers for PubSub
throughput
• >70x better throughput than beanstalkd

35. Kafka Is Excellent for...
• Small, expressive messages - BYOD
• Throughput
• Decimates any JMS or AMQP servers for PubSub
throughput
• >70x better throughput than beanstalkd
• Scales well with number of consumers, topics

36. Kafka Is Excellent for...
• Small, expressive messages - BYOD
• Throughput
• Decimates any JMS or AMQP servers for PubSub
throughput
• >70x better throughput than beanstalkd
• Scales well with number of consumers, topics
• Re-balance after consumer failures

37. Kafka Is Excellent for...
• Small, expressive messages - BYOD
• Throughput
• Decimates any JMS or AMQP servers for PubSub
throughput
• >70x better throughput than beanstalkd
• Scales well with number of consumers, topics
• Re-balance after consumer failures
• Rewind in time scenarios

38. Kafka Is Excellent for...
• Small, expressive messages - BYOD
• Throughput
• Decimates any JMS or AMQP servers for PubSub
throughput
• >70x better throughput than beanstalkd
• Scales well with number of consumers, topics
• Re-balance after consumer failures
• Rewind in time scenarios
• Allowing transient “taps” into streams of data for roughly
the cost of transport

39. But, Kafka Makes Critical Concessions - Brokers

40. But, Kafka Makes Critical Concessions - Brokers
• Data not redundant - if a broker dies, you have to
restore it to recover that data

41. But, Kafka Makes Critical Concessions - Brokers
• Data not redundant - if a broker dies, you have to
restore it to recover that data
• Shore up hardware

42. But, Kafka Makes Critical Concessions - Brokers
• Data not redundant - if a broker dies, you have to
restore it to recover that data
• Shore up hardware
• Consume as fast as possible

43. But, Kafka Makes Critical Concessions - Brokers
• Data not redundant - if a broker dies, you have to
restore it to recover that data
• Shore up hardware
• Consume as fast as possible
• Persist to shared storage or use BRDB

44. But, Kafka Makes Critical Concessions - Brokers
• Data not redundant - if a broker dies, you have to
restore it to recover that data
• Shore up hardware
• Consume as fast as possible
• Persist to shared storage or use BRDB
• Upcoming replication

45. But, Kafka Makes Critical Concessions - Brokers
• Data not redundant - if a broker dies, you have to
restore it to recover that data
• Shore up hardware
• Consume as fast as possible
• Persist to shared storage or use BRDB
• Upcoming replication
• Segment corruption can be fatal for that topic + partition

46. Kafka Critical Concessions - Consumers

47. Kafka Critical Concessions - Consumers
• Messages can be delivered out of order

48. Kafka Critical Concessions - Consumers
• Messages can be delivered out of order

49. Kafka Critical Concessions - Consumers

50. Kafka Critical Concessions - Consumers
• No once and only once semantics

51. Kafka Critical Concessions - Consumers
• No once and only once semantics
• Consumers must correctly handle the same message
multiple times

52. Kafka Critical Concessions - Consumers
• No once and only once semantics
• Consumers must correctly handle the same message
multiple times
• Rebalance after fail can result in redelivery

53. Kafka Critical Concessions - Consumers
• No once and only once semantics
• Consumers must correctly handle the same message
multiple times
• Rebalance after fail can result in redelivery
• Consumer failure or unclean shutdown can result in
redelivery

54. Kafka Critical Concessions - Consumers
• No once and only once semantics
• Consumers must correctly handle the same message
multiple times
• Rebalance after fail can result in redelivery
• Consumer failure or unclean shutdown can result in
redelivery
• Possibility of out of order delivery and redelivery require
idempotent, commutative consumers when dealing with
systems of record

55. Storage Patterns and Data Structures

56. Storage Patterns and Data Structures
• Urban Airship uses Kafka for

57. Storage Patterns and Data Structures
• Urban Airship uses Kafka for
• Analytics

58. Storage Patterns and Data Structures
• Urban Airship uses Kafka for
• Analytics
• Producers write device data to Kafka

59. Storage Patterns and Data Structures
• Urban Airship uses Kafka for
• Analytics
• Producers write device data to Kafka
• Consumers create dimensional indexes in HBase

60. Storage Patterns and Data Structures
• Urban Airship uses Kafka for
• Analytics
• Producers write device data to Kafka
• Consumers create dimensional indexes in HBase
• Operational Data

61. Storage Patterns and Data Structures
• Urban Airship uses Kafka for
• Analytics
• Producers write device data to Kafka
• Consumers create dimensional indexes in HBase
• Operational Data
• Producers are services writing to Kafka

62. Storage Patterns and Data Structures
• Urban Airship uses Kafka for
• Analytics
• Producers write device data to Kafka
• Consumers create dimensional indexes in HBase
• Operational Data
• Producers are services writing to Kafka
• Consumers write to ODW (HBase as JSON)

63. Storage Patterns and Data Structures
• Urban Airship uses Kafka for
• Analytics
• Producers write device data to Kafka
• Consumers create dimensional indexes in HBase
• Operational Data
• Producers are services writing to Kafka
• Consumers write to ODW (HBase as JSON)
• Presence Data

64. Storage Patterns and Data Structures
• Urban Airship uses Kafka for
• Analytics
• Producers write device data to Kafka
• Consumers create dimensional indexes in HBase
• Operational Data
• Producers are services writing to Kafka
• Consumers write to ODW (HBase as JSON)
• Presence Data
• Producers are connectivity nodes writing to Kafka

65. Storage Patterns and Data Structures
• Urban Airship uses Kafka for
• Analytics
• Producers write device data to Kafka
• Consumers create dimensional indexes in HBase
• Operational Data
• Producers are services writing to Kafka
• Consumers write to ODW (HBase as JSON)
• Presence Data
• Producers are connectivity nodes writing to Kafka
• Consumers write to LevelDB

66. Storage Patterns - Device Metadata

67. Storage Patterns - Device Metadata
{ deviceId:”PONIES”, tags:[”BEYONCE”], timestamp:1}

68. Storage Patterns - Device Metadata
{ deviceId:”PONIES”, tags:[”BEYONCE”], timestamp:1}
{ deviceId:”PONIES”, tags:[”BEYONCE”, “JAY-Z”, “NICKLEBACK”],
timestamp:2}

69. Storage Patterns - Device Metadata
{ deviceId:”PONIES”, tags:[”BEYONCE”], timestamp:1}
{ deviceId:”PONIES”, tags:[”BEYONCE”, “JAY-Z”, “NICKLEBACK”],
timestamp:2}
{ deviceId:”PONIES”, tags:[”BEYONCE”, “JAY-Z”, “NICKLEBACK”],
timestamp:3}

70. Storage Patterns - Device Metadata

71. Storage Patterns - Device Metadata
• Primitive incarnation - blast an update into a row, keyed
on deviceID

72. Storage Patterns - Device Metadata
• Primitive incarnation - blast an update into a row, keyed
on deviceID
• RDBMS

73. Storage Patterns - Device Metadata
• Primitive incarnation - blast an update into a row, keyed
on deviceID
• RDBMS
• INSERT OR UPDATE DEVICE_METADATA (ID, VALUE)
VALUES (DEVICE_ID, BLOB) WHERE ID = deviceID;

74. Storage Patterns - Device Metadata
• Primitive incarnation - blast an update into a row, keyed
on deviceID
• RDBMS
• INSERT OR UPDATE DEVICE_METADATA (ID, VALUE)
VALUES (DEVICE_ID, BLOB) WHERE ID = deviceID;
• Denormalize - forget joining to read tags, way too
expensive

75. Storage Patterns - Device Metadata

76. Storage Patterns - Device Metadata
• Column Store

77. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=NULL -> v= BLOB

78. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=NULL -> v= BLOB
• Both

79. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=NULL -> v= BLOB
• Both
• Idempotent

80. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=NULL -> v= BLOB
• Both
• Idempotent
• FAIL - mutations can arrive out of order, can be
replayed

81. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=NULL -> v= BLOB
• Both
• Idempotent
• FAIL - mutations can arrive out of order, can be
replayed
• Commutative

82. Storage Patterns - Device Metadata

83. Storage Patterns - Device Metadata
• Improved approach - leverage the timestamp of the
mutation

84. Storage Patterns - Device Metadata
• Improved approach - leverage the timestamp of the
mutation
• RDBMS

85. Storage Patterns - Device Metadata
• Improved approach - leverage the timestamp of the
mutation
• RDBMS
• INSERT OR UPDATE DEVICE_METADATA (KEY, VALUE,
TS) VALUES (DEVICE_ID, BLOB, TS) WHERE ID =
deviceID AND TS = TS;

86. Storage Patterns - Device Metadata
• Improved approach - leverage the timestamp of the
mutation
• RDBMS
• INSERT OR UPDATE DEVICE_METADATA (KEY, VALUE,
TS) VALUES (DEVICE_ID, BLOB, TS) WHERE ID =
deviceID AND TS = TS;
• Heavy-handed approach

87. Storage Patterns - Device Metadata
• Improved approach - leverage the timestamp of the
mutation
• RDBMS
• INSERT OR UPDATE DEVICE_METADATA (KEY, VALUE,
TS) VALUES (DEVICE_ID, BLOB, TS) WHERE ID =
deviceID AND TS = TS;
• Heavy-handed approach
• Massive I/O on TS index or risk reading an entire
block per version with no adjacent blocks

88. Storage Patterns - Device Metadata

89. Storage Patterns - Device Metadata
• Column Store

90. Storage Patterns - Device Metadata
• Column Store

91. Storage Patterns - Device Metadata
• Column Store

92. Storage Patterns - Device Metadata
• Column Store

93. Storage Patterns - Device Metadata

94. Storage Patterns - Device Metadata
• Column Store

95. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=INV(ts) -> v=BLOB

96. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=INV(ts) -> v=BLOB
• Reads are simple slices of one column, easy for LSM
(pop the top column in the row)

97. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=INV(ts) -> v=BLOB
• Reads are simple slices of one column, easy for LSM
(pop the top column in the row)
• No transactions required, much smaller lock footprint

98. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=INV(ts) -> v=BLOB
• Reads are simple slices of one column, easy for LSM
(pop the top column in the row)
• No transactions required, much smaller lock footprint
• Both

99. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=INV(ts) -> v=BLOB
• Reads are simple slices of one column, easy for LSM
(pop the top column in the row)
• No transactions required, much smaller lock footprint
• Both
• Idempotent

100. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=INV(ts) -> v=BLOB
• Reads are simple slices of one column, easy for LSM
(pop the top column in the row)
• No transactions required, much smaller lock footprint
• Both
• Idempotent
• Commutative

101. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=INV(ts) -> v=BLOB
• Reads are simple slices of one column, easy for LSM
(pop the top column in the row)
• No transactions required, much smaller lock footprint
• Both
• Idempotent
• Commutative
• Old versions not removed automatically

102. Storage Patterns - Device Metadata
• Column Store
• Write k=deviceId -> c=INV(ts) -> v=BLOB
• Reads are simple slices of one column, easy for LSM
(pop the top column in the row)
• No transactions required, much smaller lock footprint
• Both
• Idempotent
• Commutative
• Old versions not removed automatically
• Secondary indexes very difficult

103. Storage Patterns - Device Metadata

104. Storage Patterns - Device Metadata
• Gangam Style - tag per column, deletions tombstoned

105. Storage Patterns - Device Metadata
• Gangam Style - tag per column, deletions tombstoned
• RDBMS - select for update and/or big txns?

106. Storage Patterns - Device Metadata
• Gangam Style - tag per column, deletions tombstoned
• RDBMS - select for update and/or big txns?
• Column Store

107. Storage Patterns - Device Metadata
• Gangam Style - tag per column, deletions tombstoned
• RDBMS - select for update and/or big txns?
• Column Store
• Addition k=deviceId -> c=TAG -> v=TS

108. Storage Patterns - Device Metadata
• Gangam Style - tag per column, deletions tombstoned
• RDBMS - select for update and/or big txns?
• Column Store
• Addition k=deviceId -> c=TAG -> v=TS
• Deletion k=deviceId -> c=TAG -> v=-(TS)

109. Storage Patterns - Device Metadata
• Gangam Style - tag per column, deletions tombstoned
• RDBMS - select for update and/or big txns?
• Column Store
• Addition k=deviceId -> c=TAG -> v=TS
• Deletion k=deviceId -> c=TAG -> v=-(TS)
• Cell timestamp set to event timestamp in both cases
(old updates ignored)

110. Storage Patterns - Device Metadata
• Gangam Style - tag per column, deletions tombstoned
• RDBMS - select for update and/or big txns?
• Column Store
• Addition k=deviceId -> c=TAG -> v=TS
• Deletion k=deviceId -> c=TAG -> v=-(TS)
• Cell timestamp set to event timestamp in both cases
(old updates ignored)
• Easy to (re)build secondary indexes, tag counts

111. Storage Patterns - Device Metadata
• Gangam Style - tag per column, deletions tombstoned
• RDBMS - select for update and/or big txns?
• Column Store
• Addition k=deviceId -> c=TAG -> v=TS
• Deletion k=deviceId -> c=TAG -> v=-(TS)
• Cell timestamp set to event timestamp in both cases
(old updates ignored)
• Easy to (re)build secondary indexes, tag counts
• Commutative, Idempotent and Fast

112. Storage Patterns - Device Metadata

113. Storage Patterns - Device Metadata

114. Storage Patterns - Device Metadata

115. Storage Patterns - Device Metadata

116. Operational Considerations - Buffering

117. Operational Considerations - Buffering
•A message in a broker is not immediately visible to a
consumer

118. Operational Considerations - Buffering
•A message in a broker is not immediately visible to a
consumer
• Kafka buffers data until one of two conditions is true

119. Operational Considerations - Buffering
•A message in a broker is not immediately visible to a
consumer
• Kafka buffers data until one of two conditions is true
• log.flush.interval reached

120. Operational Considerations - Buffering
•A message in a broker is not immediately visible to a
consumer
• Kafka buffers data until one of two conditions is true
• log.flush.interval reached
• log.default.flush.interval.ms elapsed

121. Operational Considerations - Buffering
•A message in a broker is not immediately visible to a
consumer
• Kafka buffers data until one of two conditions is true
• log.flush.interval reached
• log.default.flush.interval.ms elapsed
• False latency for low throughput workloads

122. Operational Considerations - Buffering
•A message in a broker is not immediately visible to a
consumer
• Kafka buffers data until one of two conditions is true
• log.flush.interval reached
• log.default.flush.interval.ms elapsed
• False latency for low throughput workloads
• The smaller of the two represents loss message potential

123. Operational Considerations - The FetcherRunnable

124. Operational Considerations - The FetcherRunnable
• Consumer spawns a number of FetcherRunnable threads
to read from brokers

125. Operational Considerations - The FetcherRunnable
• Consumer spawns a number of FetcherRunnable threads
to read from brokers
• FetcherRunnable feeds messages into queues that back
the KafkaMessageStream API

126. Operational Considerations - The FetcherRunnable
• Consumer spawns a number of FetcherRunnable threads
to read from brokers
• FetcherRunnable feeds messages into queues that back
the KafkaMessageStream API
• FetchRunnable must remain healthy for consumers to see
messages

127. Operational Considerations - The FetcherRunnable
• Consumer spawns a number of FetcherRunnable threads
to read from brokers
• FetcherRunnable feeds messages into queues that back
the KafkaMessageStream API
• FetchRunnable must remain healthy for consumers to see
messages
// consume the messages in the threads
for(final KafkaStream<Message> stream: streams) {
executor.submit(new Runnable() {
public void run() {
for(MessageAndMetadata msgAndMetadata: stream) {
// process message (msgAndMetadata.message())
}}};}

128. Operational Considerations - The FetcherRunnable

129. Operational Considerations - The FetcherRunnable

130. Operational Considerations - The FetcherRunnable
•A given FetcherRunnable is the lone source of data for
its streams

131. Operational Considerations - The FetcherRunnable
•A given FetcherRunnable is the lone source of data for
its streams
• When a FetcherRunnable dies, the streams block
indefinitely

132. Operational Considerations - The FetcherRunnable
•A given FetcherRunnable is the lone source of data for
its streams
• When a FetcherRunnable dies, the streams block
indefinitely
2012-06-15 00:31:39,422 - ERROR [FetchRunnable-0:kafka.consumer.FetcherRunnable] - error in
FetcherRunnable
java.io.IOException: Connection reset by peer
at sun.nio.ch.FileDispatcher.read0(Native Method)
at sun.nio.ch.SocketDispatcher.read(SocketDispatcher.java:21)
at sun.nio.ch.IOUtil.readIntoNativeBuffer(IOUtil.java:202)
at sun.nio.ch.IOUtil.read(IOUtil.java:175)
at sun.nio.ch.SocketChannelImpl.read(SocketChannelImpl.java:243)
at kafka.utils.Utils$.read(Utils.scala:483)
at kafka.network.BoundedByteBufferReceive.readFrom(BoundedByteBufferReceive.scala:53)
at kafka.network.Receive$class.readCompletely(Transmission.scala:56)
at kafka.network.BoundedByteBufferReceive.readCompletely(BoundedByteBufferReceive.scala:28)
at kafka.consumer.SimpleConsumer.getResponse(SimpleConsumer.scala:181)
at kafka.consumer.SimpleConsumer.liftedTree2$1(SimpleConsumer.scala:129)
at kafka.consumer.SimpleConsumer.multifetch(SimpleConsumer.scala:119)
at kafka.consumer.FetcherRunnable.run(FetcherRunnable.scala:63)

133. Operational Considerations - Rate is King

134. Operational Considerations - Rate is King
• MONITOR YOUR CONSUMPTION RATES

135. Operational Considerations - Rate is King
• MONITOR YOUR CONSUMPTION RATES
• Kafka JMX Beans

136. Operational Considerations - Rate is King
• MONITOR YOUR CONSUMPTION RATES
• Kafka JMX Beans
• Application metrics for specific consumption behaviors
(use Yammer Timer metrics)

137. Operational Considerations - Rate is King
• MONITOR YOUR CONSUMPTION RATES
• Kafka JMX Beans
• Application metrics for specific consumption behaviors
(use Yammer Timer metrics)
• Understand what “normal” is, alert when you are out of
that band by some tolerance

138. Operational Considerations - Rate is King
• MONITOR YOUR CONSUMPTION RATES
• Kafka JMX Beans
• Application metrics for specific consumption behaviors
(use Yammer Timer metrics)
• Understand what “normal” is, alert when you are out of
that band by some tolerance
• Not overcommitting consumers helps - nobody is idle

139. Operational Considerations - The Retention Window

140. Operational Considerations - The Retention Window
• Data written to a segment file on a broker (topic +
partition)

141. Operational Considerations - The Retention Window
• Data written to a segment file on a broker (topic +
partition)
• Every consumer group has a relative offset within a
segment

142. Operational Considerations - The Retention Window
• Data written to a segment file on a broker (topic +
partition)
• Every consumer group has a relative offset within a
segment
• Individual consumers move the offset and store to
ZooKeeper on a regular interval

143. Operational Considerations - The Retention Window
• Data written to a segment file on a broker (topic +
partition)
• Every consumer group has a relative offset within a
segment
• Individual consumers move the offset and store to
ZooKeeper on a regular interval
• Segments are retained for log.retention.hours

144. Operational Considerations - The Retention Window
• Data written to a segment file on a broker (topic +
partition)
• Every consumer group has a relative offset within a
segment
• Individual consumers move the offset and store to
ZooKeeper on a regular interval
• Segments are retained for log.retention.hours
• Segments deleted when outside retention window

145. Operational Considerations - The Retention Window

146. Operational Considerations - The Retention Window

147. Operational Considerations - The Retention Window

148. Operational Considerations - The Retention Window

149. Operational Considerations - The Retention Window

150. Operational Considerations - The Retention Window
• Consumers update offsets in ZooKeeper

151. Operational Considerations - The Retention Window
• Consumers update offsets in ZooKeeper
• Monitor them and make sure they’re progressing

152. Operational Considerations - The Retention Window
• Consumers update offsets in ZooKeeper
• Monitor them and make sure they’re progressing
• Look for skew in rate of change between partition offsets

153. Operational Considerations - The Retention Window
• Consumers update offsets in ZooKeeper
• Monitor them and make sure they’re progressing
• Look for skew in rate of change between partition offsets
• Monitoring consumption rate can also help

154. Operational Considerations - Scala
“Reading that Scala stack trace
sure was easy”
- Nobody Ever

155. Operational Considerations - Scala
2012-07-04 11:49:08,469 - WARN [ZkClient-EventThread-132-
zookeeper-0:2181,zookeeper-1:2181,zookeeper-2:2181:org.I0Itec.zkclient.ZkEventThread] - Error handling event ZkEvent[Children of /
brokers/topics/SEND_EVENTS changed sent to kafka.consumer.ZookeeperConsumerConnector$ZKRebalancerListener@43d248b4]
java.lang.NullPointerException
at scala.util.parsing.combinator.Parsers$NoSuccess.<init>(Parsers.scala:131)
at scala.util.parsing.combinator.Parsers$Failure.<init>(Parsers.scala:158)
at scala.util.parsing.combinator.Parsers$$anonfun$acceptIf$1.apply(Parsers.scala:489)
at scala.util.parsing.combinator.Parsers$$anonfun$acceptIf$1.apply(Parsers.scala:487)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:182)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:203)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:203)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:182)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:182)
... (~50 lines elided)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:203)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:203)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:182)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:182)
at scala.util.parsing.combinator.Parsers$Success.flatMapWithNext(Parsers.scala:113)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$flatMap$1.apply(Parsers.scala:200)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$flatMap$1.apply(Parsers.scala:200)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:182)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$flatMap$1.apply(Parsers.scala:200)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$flatMap$1.apply(Parsers.scala:200)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:182)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:203)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:203)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:182)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$append$1.apply(Parsers.scala:208)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$append$1.apply(Parsers.scala:208)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:182)
at scala.util.parsing.combinator.Parsers$$anon$2.apply(Parsers.scala:742)
at scala.util.parsing.json.JSON$.parseRaw(JSON.scala:71)
at scala.util.parsing.json.JSON$.parseFull(JSON.scala:85)

156. Operational Considerations - Brokers

157. Operational Considerations - Brokers
• Monitor IOPS and IOUtil

158. Operational Considerations - Brokers
• Monitor IOPS and IOUtil
• Under no circumstances allow a broker to run out of disk
space (don’t even get close)

159. Operational Considerations - Brokers
• Monitor IOPS and IOUtil
• Under no circumstances allow a broker to run out of disk
space (don’t even get close)
• fetch.size - amount of data a consumer will pull

160. Operational Considerations - Brokers
• Monitor IOPS and IOUtil
• Under no circumstances allow a broker to run out of disk
space (don’t even get close)
• fetch.size - amount of data a consumer will pull
• max.message.size - largest message a producer can
submit to a broker

161. Operational Considerations - Brokers
• Monitor IOPS and IOUtil
• Under no circumstances allow a broker to run out of disk
space (don’t even get close)
• fetch.size - amount of data a consumer will pull
• max.message.size - largest message a producer can
submit to a broker
• Broker enforces neither of these prior to v0.8 :(

162. Operational Considerations - Brokers
• Monitor IOPS and IOUtil
• Under no circumstances allow a broker to run out of disk
space (don’t even get close)
• fetch.size - amount of data a consumer will pull
• max.message.size - largest message a producer can
submit to a broker
• Broker enforces neither of these prior to v0.8 :(
• KAFKA-490

163. Operational Considerations - Brokers
• Monitor IOPS and IOUtil
• Under no circumstances allow a broker to run out of disk
space (don’t even get close)
• fetch.size - amount of data a consumer will pull
• max.message.size - largest message a producer can
submit to a broker
• Broker enforces neither of these prior to v0.8 :(
• KAFKA-490
• KAFKA-247

164. Operational Considerations - Brokers

165. Operational Considerations - Brokers
2012-06-15 04:47:35,632 - ERROR [FetchRunnable-2:kafka.consumer.FetcherRunnable] - error in
FetcherRunnable for RN-OL:3-22
kafka.common.InvalidMessageSizeException: invalid message size:152173251 only received bytes:307196
at 0 possible causes (1) a single message larger than the fetch size; (2) log corruption
at kafka.message.ByteBufferMessageSet$$anon$1.makeNext(ByteBufferMessageSet.scala:75)
at kafka.message.ByteBufferMessageSet$$anon$1.makeNext(ByteBufferMessageSet.scala:61)
at kafka.utils.IteratorTemplate.maybeComputeNext(IteratorTemplate.scala:58)
at kafka.utils.IteratorTemplate.hasNext(IteratorTemplate.scala:50)
at kafka.message.ByteBufferMessageSet.validBytes(ByteBufferMessageSet.scala:49)
at kafka.consumer.PartitionTopicInfo.enqueue(PartitionTopicInfo.scala:70)
at kafka.consumer.FetcherRunnable$$anonfun$run$3.apply(FetcherRunnable.scala:80)
at kafka.consumer.FetcherRunnable$$anonfun$run$3.apply(FetcherRunnable.scala:66)
at scala.collection.LinearSeqOptimized$class.foreach(LinearSeqOptimized.scala:61)
at scala.collection.immutable.List.foreach(List.scala:45)
at kafka.consumer.FetcherRunnable.run(FetcherRunnable.scala:66)

166. Operational Considerations - Consumers

167. Operational Considerations - Consumers
• Consumer tuning is an art

168. Operational Considerations - Consumers
• Consumer tuning is an art
• Overcommit - more threads than partitions

169. Operational Considerations - Consumers
• Consumer tuning is an art
• Overcommit - more threads than partitions
• Idling (often entire consumer processes)

170. Operational Considerations - Consumers
• Consumer tuning is an art
• Overcommit - more threads than partitions
• Idling (often entire consumer processes)
• Excessive rebalancing

171. Operational Considerations - Consumers
• Consumer tuning is an art
• Overcommit - more threads than partitions
• Idling (often entire consumer processes)
• Excessive rebalancing
• Under commit - less threads than partitions

172. Operational Considerations - Consumers
• Consumer tuning is an art
• Overcommit - more threads than partitions
• Idling (often entire consumer processes)
• Excessive rebalancing
• Under commit - less threads than partitions
• Serial fetchers won’t keep up depending on workload

173. Operational Considerations - Consumers
• Consumer tuning is an art
• Overcommit - more threads than partitions
• Idling (often entire consumer processes)
• Excessive rebalancing
• Under commit - less threads than partitions
• Serial fetchers won’t keep up depending on workload
• Big GCs can cause rebalancing

174. Operational Considerations - Consumers
• Consumer tuning is an art
• Overcommit - more threads than partitions
• Idling (often entire consumer processes)
• Excessive rebalancing
• Under commit - less threads than partitions
• Serial fetchers won’t keep up depending on workload
• Big GCs can cause rebalancing
• Just right - 2 partitions / consumer thread ratio

175. Operational Considerations - Consumers
• Consumer tuning is an art
• Overcommit - more threads than partitions
• Idling (often entire consumer processes)
• Excessive rebalancing
• Under commit - less threads than partitions
• Serial fetchers won’t keep up depending on workload
• Big GCs can cause rebalancing
• Just right - 2 partitions / consumer thread ratio
• Mostly pivots on consumer workload (i.e. latency)

176. Operational Considerations - Incubators Gonna
Incubate

177. Operational Considerations - Incubators Gonna
Incubate
• Deployed in some large installations

178. Operational Considerations - Incubators Gonna
Incubate
• Deployed in some large installations
• Largely learning in production

179. Operational Considerations - Incubators Gonna
Incubate
• Deployed in some large installations
• Largely learning in production
• Hasn’t lived through a long lineage of people being
mean to it or using in anger

180. Operational Considerations - Incubators Gonna
Incubate
• Deployed in some large installations
• Largely learning in production
• Hasn’t lived through a long lineage of people being
mean to it or using in anger
2012-06-15 04:25:00,774 - ERROR [kafka-processor-3:Processor@215] - java.lang.RuntimeException:
OOME with size 1195725856
java.lang.RuntimeException: OOME with size 1195725856
at kafka.network.BoundedByteBufferReceive.byteBufferAllocate(BoundedByteBufferReceive.scala:81)
at kafka.network.BoundedByteBufferReceive.readFrom(BoundedByteBufferReceive.scala:60)
at kafka.network.Processor.read(SocketServer.scala:283)
at kafka.network.Processor.run(SocketServer.scala:202)
at java.lang.Thread.run(Thread.java:662)
Caused by: java.lang.OutOfMemoryError: Java heap space
at java.nio.HeapByteBuffer.<init>(HeapByteBuffer.java:39)
at java.nio.ByteBuffer.allocate(ByteBuffer.java:312)
at kafka.network.BoundedByteBufferReceive.byteBufferAllocate(BoundedByteBufferReceive.scala:77)

181. Operational Considerations - Incubators Gonna
Incubate

182. Operational Considerations - Incubators Gonna
Incubate

183. Operational Considerations - Incubators Gonna
Incubate

184. Operational Considerations - Incubators Gonna
Incubate
• With any incubator project, assume it will be rough
around the edges

185. Operational Considerations - Incubators Gonna
Incubate
• With any incubator project, assume it will be rough
around the edges
• Assume that if you point your monitoring agent at the
service port, things will break

186. Operational Considerations - Incubators Gonna
Incubate
• With any incubator project, assume it will be rough
around the edges
• Assume that if you point your monitoring agent at the
service port, things will break
• As a general practice, measure the intended outcome of
production changes

187. Acknowledgements
The storage models proposed were inspired and adapted
by:
http://engineering.twitter.com/2010/05/introducing-
flockdb.html
https://github.com/mochi/statebox

188. Q&A
We’re hiring!
• Infrastructure
• Django
• Operations
Contact:
erik@urbanairship.com (that I put my email in slides is not
an invitation to sell me software so don’t do that)
@eonnen - twitter