Apache Kafka 0.8 basic training - Verisign

Transcript

• 1. Apache Kafka 0.8 basic training Michael G. Noll, Verisign mnoll@verisign.com / @miguno July 2014
• 2. Kafka? • Part 1: Introducing Kafka • “Why should I stay awake for the full duration of this workshop?” • Part 2: Kafka core concepts • Topics, partitions, replicas, producers, consumers, brokers • Part 3: Operating Kafka • Architecture, hardware specs, deploying, monitoring, P&S tuning • Part 4: Developing Kafka apps • Writing to Kafka, reading from Kafka, testing, serialization, compression, example apps • Part 5: Playing with Kafka using Wirbelsturm • Wrapping up Verisign Public 2
• 3. Part 1: Introducing Kafka Verisign Public 3
• 4. Overview of Part 1: Introducing Kafka • Kafka? • Kafka adoption and use cases in the wild Verisign Public • At LinkedIn • At other companies • How fast is Kafka, and why? • Kafka + X for processing • Storm, Samza, Spark Streaming, custom apps 4
• 5. Kafka? • http://kafka.apache.org/ • Originated at LinkedIn, open sourced in early 2011 • Implemented in Scala, some Java • 9 core committers, plus ~ 20 contributors Verisign Public 5 https://kafka.apache.org/committers.html https://github.com/apache/kafka/graphs/contributors
• 6. Kafka? • LinkedIn’s motivation for Kafka was: • “A unified platform for handling all the real-time data feeds a large company might have.” • Must haves • High throughput to support high volume event feeds. • Support real-time processing of these feeds to create new, derived feeds. • Support large data backlogs to handle periodic ingestion from offline systems. • Support low-latency delivery to handle more traditional messaging use cases. • Guarantee fault-tolerance in the presence of machine failures. Verisign Public 6 http://kafka.apache.org/documentation.html#majordesignelements
• 7. Kafka @ LinkedIn, 2014 Verisign Public 7 (Numbers have increased since.) https://twitter.com/SalesforceEng/status/466033231800713216/photo/1 http://www.hakkalabs.co/articles/site-reliability-engineering-linkedin-kafka-service
• 8. Data architecture @ LinkedIn, Feb 2013 Verisign Public 8 (Numbers are aggregated across all their clusters.) http://gigaom.com/2013/12/09/netflix-open-sources-its-data-traffic-cop-suro/
• 9. Kafka @ LinkedIn, 2014 • Multiple data centers, multiple clusters Verisign Public • Mirroring between clusters / data centers • What type of data is being transported through Kafka? • Metrics: operational telemetry data • Tracking: everything a LinkedIn.com user does • Queuing: between LinkedIn apps, e.g. for sending emails • To transport data from LinkedIn’s apps to Hadoop, and back • In total ~ 200 billion events/day via Kafka • Tens of thousands of data producers, thousands of consumers • 7 million events/sec (write), 35 million events/sec (read) <<< may include replicated events • But: LinkedIn is not even the largest Kafka user anymore as of 2014 9 http://www.hakkalabs.co/articles/site-reliability-engineering-linkedin-kafka-service http://www.slideshare.net/JayKreps1/i-32858698 http://search-hadoop.com/m/4TaT4qAFQW1
• 10. Kafka @ LinkedIn, 2014 “For reference, here are the stats on one of LinkedIn's busiest clusters (at peak): Verisign Public 10 15 brokers 15,500 partitions (replication factor 2) 400,000 msg/s inbound 70 MB/s inbound 400 MB/s outbound” https://kafka.apache.org/documentation.html#java
• 11. Staffing: Kafka team @ LinkedIn • Team of 8+ engineers Verisign Public • Site reliability engineers (Ops): at least 3 • Developers: at least 5 • SRE’s as well as DEV’s are on call 24x7 11 https://kafka.apache.org/committers.html http://www.hakkalabs.co/articles/site-reliability-engineering-linkedin-kafka-service
• 12. Kafka adoption and use cases • LinkedIn: activity streams, operational metrics, data bus Verisign Public • 400 nodes, 18k topics, 220B msg/day (peak 3.2M msg/s), May 2014 • Netflix: real-time monitoring and event processing • Twitter: as part of their Storm real-time data pipelines • Spotify: log delivery (from 4h down to 10s), Hadoop • Loggly: log collection and processing • Mozilla: telemetry data • Airbnb, Cisco, Gnip, InfoChimps, Ooyala, Square, Uber, … 12 https://cwiki.apache.org/confluence/display/KAFKA/Powered+By
• 13. Kafka @ Spotify https://www.jfokus.se/jfokus14/preso/Reliable-real-time-processing-with-Kafka-and-Storm.pdf (Feb 2014) Verisign Public 13
• 14. How fast is Kafka? • “Up to 2 million writes/sec on 3 cheap machines” Verisign Public • Using 3 producers on 3 different machines, 3x async replication • Only 1 producer/machine because NIC already saturated • Sustained throughput as stored data grows • Slightly different test config than 2M writes/sec above. • Test setup • Kafka trunk as of April 2013, but 0.8.1+ should be similar. • 3 machines: 6-core Intel Xeon 2.5 GHz, 32GB RAM, 6x 7200rpm SATA, 1GigE 14 http://engineering.linkedin.com/kafka/benchmarking-apache-kafka-2-million-writes-second-three-cheap-machines
• 15. Why is Kafka so fast? • Fast writes: Verisign Public • While Kafka persists all data to disk, essentially all writes go to the page cache of OS, i.e. RAM. • Cf. hardware specs and OS tuning (we cover this later) • Fast reads: • Very efficient to transfer data from page cache to a network socket • Linux: sendfile() system call • Combination of the two = fast Kafka! • Example (Operations): On a Kafka cluster where the consumers are mostly caught up you will see no read activity on the disks as they will be serving data entirely from cache. 15 http://kafka.apache.org/documentation.html#persistence
• 16. Why is Kafka so fast? • Example: Loggly.com, who run Kafka & Co. on Amazon AWS Verisign Public • “99.99999% of the time our data is coming from disk cache and RAM; only very rarely do we hit the disk.” • “One of our consumer groups (8 threads) which maps a log to a customer can process about 200,000 events per second draining from 192 partitions spread across 3 brokers.” • Brokers run on m2.xlarge Amazon EC2 instances backed by provisioned IOPS 16 http://www.developer-tech.com/news/2014/jun/10/why-loggly-loves-apache-kafka-how-unbreakable-infinitely-scalable-messaging-makes-log-management-better/
• 17. Kafka + X for processing the data? • Kafka + Storm often used in combination, e.g. Twitter • Kafka + custom Verisign Public • “Normal” Java multi-threaded setups • Akka actors with Scala or Java, e.g. Ooyala • Recent additions: • Samza (since Aug ’13) – also by LinkedIn • Spark Streaming, part of Spark (since Feb ’13) • Kafka + Camus for Kafka->Hadoop ingestion 17 https://cwiki.apache.org/confluence/display/KAFKA/Powered+By
• 18. Part 2: Kafka core concepts Verisign Public 18
• 19. Overview of Part 2: Kafka core concepts • A first look • Topics, partitions, replicas, offsets • Producers, brokers, consumers • Putting it all together Verisign Public 19
• 20. A first look • The who is who Verisign Public • Producers write data to brokers. • Consumers read data from brokers. • All this is distributed. • The data • Data is stored in topics. • Topics are split into partitions, which are replicated. 20
• 21. A first look Verisign Public 21 http://www.michael-noll.com/blog/2013/03/13/running-a-multi-broker-apache-kafka-cluster-on-a-single-node/
• 22. • Topic: feed name to which messages are published • Example: “zerg.hydra” Kafka prunes “head” based on age or max size or “key” Verisign Public Kafka topic Broker(s) Topics 22 ne w Producer A1 Producer A2 … Producer An Producers always append to “tail” (think: append to a file) … Older msgs Newer msgs
• 23. Consumer group C1 Consumers use an “offset pointer” to Consumer group C2 Verisign Public track/control their read progress (and decide the pace of consumption) Broker(s) Topics 23 ne w Producer A1 Producer A2 … Producer An Producers always append to “tail” (think: append to a file) … Older msgs Newer msgs
• 24. Topics • Creating a topic Verisign Public • CLI • API https://github.com/miguno/kafka-storm-starter/ blob/develop/src/main/scala/com/miguno/kafkastorm/storm/KafkaSt ormDemo.scala • Auto-create via auto.create.topics.enable = true • Modifying a topic • https://kafka.apache.org/documentation.html#basic_ops_modify_topic • Deleting a topic: DON’T in 0.8.1.x! 24 $ kafka-topics.sh --zookeeper zookeeper1:2181 --create --topic zerg.hydra --partitions 3 --replication-factor 2 --config x=y
• 25. Partitions Verisign Public 25 • A topic consists of partitions. • Partition: ordered + immutable sequence of messages that is continually appended to
• 26. Partitions Verisign Public 26 • #partitions of a topic is configurable • #partitions determines max consumer (group) parallelism • Cf. parallelism of Storm’s KafkaSpout via builder.setSpout(,,N) • Consumer group A, with 2 consumers, reads from a 4-partition topic • Consumer group B, with 4 consumers, reads from the same topic
• 27. Partition offsets Verisign Public 27 • Offset: messages in the partitions are each assigned a unique (per partition) and sequential id called the offset • Consumers track their pointers via (offset, partition, topic) tuples Consumer group C1
• 28. Replicas of a partition Verisign Public 28 • Replicas: “backups” of a partition • They exist solely to prevent data loss. • Replicas are never read from, never written to. • They do NOT help to increase producer or consumer parallelism! • Kafka tolerates (numReplicas - 1) dead brokers before losing data • LinkedIn: numReplicas == 2  1 broker can die
• 29. Topics vs. Partitions vs. Replicas Verisign Public 29 http://www.michael-noll.com/blog/2013/03/13/running-a-multi-broker-apache-kafka-cluster-on-a-single-node/
• 30. Inspecting the current state of a topic • --describe the topic Verisign Public • Leader: brokerID of the currently elected leader broker • Replica ID’s = broker ID’s • ISR = “in-sync replica”, replicas that are in sync with the leader • In this example: • Broker 0 is leader for partition 1. • Broker 1 is leader for partitions 0 and 2. • All replicas are in-sync with their respective leader partitions. 30 $ kafka-topics.sh --zookeeper zookeeper1:2181 --describe --topic zerg.hydra Topic:zerg2.hydra PartitionCount:3 ReplicationFactor:2 Configs: Topic: zerg2.hydra Partition: 0 Leader: 1 Replicas: 1,0 Isr: 1,0 Topic: zerg2.hydra Partition: 1 Leader: 0 Replicas: 0,1 Isr: 0,1 Topic: zerg2.hydra Partition: 2 Leader: 1 Replicas: 1,0 Isr: 1,0
• 31. Let’s recap • The who is who Verisign Public • Producers write data to brokers. • Consumers read data from brokers. • All this is distributed. • The data • Data is stored in topics. • Topics are split into partitions which are replicated. 31
• 32. Putting it all together Verisign Public 32 http://www.michael-noll.com/blog/2013/03/13/running-a-multi-broker-apache-kafka-cluster-on-a-single-node/
• 33. Side note (opinion) • Drawing a conceptual line from Kafka to Clojure's core.async • Cf. talk "Clojure core.async Channels", by Rich Hickey, at ~ 31m54 http://www.infoq.com/presentations/clojure-core-async Verisign Public 33
• 34. Part 3: Operating Kafka Verisign Public 34
• 35. Overview of Part 3: Operating Kafka • Kafka architecture • Kafka hardware specs • Deploying Kafka • Monitoring Kafka Verisign Public • Kafka apps • Kafka itself • ZooKeeper • "Auditing" Kafka (not: security audit) • P&S tuning • Ops-related Kafka references 35
• 36. Kafka architecture • Kafka brokers Verisign Public • You can run clusters with 1+ brokers. • Each broker in a cluster must have a unique broker.id. 36
• 37. Kafka architecture • Kafka requires ZooKeeper Verisign Public • LinkedIn runs (old) ZK 3.3.4, but latest 3.4.5 works, too. • ZooKeeper • v0.8: used by brokers and consumers, but not by producers. • Brokers: general state information, leader election, etc. • Consumers: primarily for tracking message offsets (cf. later) • v0.9: used by brokers only • Consumers will use special Kafka topics instead of ZooKeeper • Will substantially reduce the load on ZooKeeper for large deployments 37
• 38. Kafka broker hardware specs @ LinkedIn • Solely dedicated to running Kafka, run nothing else. Verisign Public • 1 Kafka broker instance per machine • 2x 4-core Intel Xeon (info outdated?) • 64 GB RAM (up from 24 GB) • Only 4 GB used for Kafka broker, remaining 60 GB for page cache • Page cache is what makes Kafka fast • RAID10 with 14 spindles • More spindles = higher disk throughput • Cache on RAID, with battery backup • Before H/W upgrade: 8x SATA drives (7200rpm), not sure about RAID • 1 GigE (?) NICs • EC2 example: m2.2xlarge @ $0.34/hour, with provisioned IOPS 38
• 39. ZooKeeper hardware specs @ LinkedIn • ZooKeeper servers Verisign Public • Solely dedicated to running ZooKeeper, run nothing else. • 1 ZooKeeper instance per machine • SSD’s dramatically improve performance • In v0.8.x, brokers and consumers must talk to ZK. In large-scale environments (many consumers, many topics and partitions) this means ZK can become a bottleneck because it processes requests serially. And this processing depends primarily on I/O performance. • 1 GigE (?) NICs • ZooKeeper in LinkedIn’s architecture • 5-node ZK ensembles = tolerates 2 dead nodes • 1 ZK ensemble for all Kafka clusters within a data center • LinkedIn runs multiple data centers, with multiple Kafka clusters 39
• 40. Deploying Kafka • Puppet module Verisign Public • https://github.com/miguno/puppet-kafka • Hiera-compatible, rspec tests, Travis CI setup (e.g. to test against multiple versions of Puppet and Ruby, Puppet style checker/lint, etc.) • RPM packaging script for RHEL 6 • https://github.com/miguno/wirbelsturm-rpm-kafka • Digitally signed by yum@michael-noll.com • RPM is built on a Wirbelsturm-managed build server • Public (Wirbelsturm) S3-backed yum repo • https://s3.amazonaws.com/yum.miguno.com/bigdata/ 40
• 41. Deploying Kafka • Hiera example Verisign Public 41
• 42. Operating Kafka • Typical operations tasks include: Verisign Public • Adding or removing brokers • Example: ensure a newly added broker actually receives data, which requires moving partitions from existing brokers to the new broker • Kafka provides helper scripts (cf. below) but still manual work involved • Balancing data/partitions to ensure best performance • Add new topics, re-configure topics • Example: Increasing #partitions of a topic to increase max parallelism • Apps management: new producers, new consumers • See Ops-related references at the end of this part 42
• 43. Lessons learned from operating Kafka at LinkedIn • Biggest challenge has been to manage hyper growth Verisign Public • Growth of Kafka adoption: more producers, more consumers, … • Growth of data: more LinkedIn.com users, more user activity, … • Typical tasks at LinkedIn • Educating and coaching Kafka users. • Expanding Kafka clusters, shrinking clusters. • Monitoring consumer apps – “Hey, my stuff stopped. Kafka’s fault!” 43 http://www.hakkalabs.co/articles/site-reliability-engineering-linkedin-kafka-service
• 44. Kafka security • Original design was not created with security in mind. • Discussion started in June 2014 to add security features. Verisign Public • Covers transport layer security, data encryption at rest, non-repudiation, A&A, … • See [DISCUSS] Kafka Security Specific Features • At the moment there's basically no security built-in. 44
• 45. Monitoring Kafka Verisign Public 45
• 46. Monitoring Kafka • Nothing fancy built into Kafka (e.g. no UI) but see: Verisign Public • https://cwiki.apache.org/confluence/display/KAFKA/System+Tools • https://cwiki.apache.org/confluence/display/KAFKA/Ecosystem 46 Kafka Web Console Kafka Offset Monitor
• 47. Monitoring Kafka • Use of standard monitoring tools recommended Verisign Public • Graphite • Puppet module: https://github.com/miguno/puppet-graphite • Java API, also used by Kafka: http://metrics.codahale.com/ • JMX • https://kafka.apache.org/documentation.html#monitoring • Collect logging files into a central place • Logstash/Kibana and friends • Helps with troubleshooting, debugging, etc. – notably if you can correlate logging data with numeric metrics 47
• 48. Monitoring Kafka apps • Almost all problems are due to: Verisign Public 1. Consumer lag 2. Rebalancing <<< we cover this later in part 4 48
• 49. Monitoring Kafka apps: consumer lag Lag = how far your consumer is behind the producers Consumer group C1 • Lag is a consumer problem Verisign Public • Too slow, too much GC, losing connection to ZK or Kafka, … • Bug or design flaw in consumer … • Operational mistakes: e.g. you brought up 6 servers in parallel, each one in turn triggering rebalancing, then hit Kafka's rebalance limit; cf. rebalance.max.retries (default: 4) & friends 49 Broker(s) ne w Producer A1 Producer A2 Producer An … Older msgs Newer msgs
• 50. Monitoring Kafka itself (1 of 3) • Under-replicated partitions Verisign Public • For example, because a broker is down. • Means cluster runs in degraded state. • FYI: LinkedIn runs with replication factor of 2 => 1 broker can die. • Offline partitions • Even worse than under-replicated partitions! • Serious problem (data loss) if anything but 0 offline partitions. 50
• 51. Monitoring Kafka itself (1 of 3) • Data size on disk Verisign Public • Should be balanced across disks/brokers • Data balance even more important than partition balance • FYI: New script in v0.8.1 to balance data/partitions across brokers • Broker partition balance • Count of partitions should be balanced evenly across brokers • See new script above. 51
• 52. Monitoring Kafka itself (1 of 3) • Leader partition count Verisign Public • Should be balanced across brokers so that each broker gets the same amount of load • Only 1 broker is ever the leader of a given partition, and only this broker is going to talk to producers + consumers for that partition • Non-leader replicas are used solely as safeguards against data loss • Feature in v0.8.1 to auto-rebalance the leaders and partitions in case a broker dies, but it does not work that well yet (SRE's still have to do this manually at this point). • Network utilization • Maxed network one reason for under-replicated partitions • LinkedIn don't run anything but Kafka on the brokers, so network max is due to Kafka. Hence, when they max the network, they need to add more capacity across the board. 52
• 53. Monitoring ZooKeeper • Ensemble (= cluster) availability Verisign Public • LinkedIn run 5-node ensembles = tolerates 2 dead • Twitter run 13-node ensembles = tolerates 6 dead • Latency of requests • Metric target is 0 ms when using SSD’s in ZooKeeper machines. • Why? Because SSD’s are so fast they typically bring down latency below ZK’s metric granularity (which is per-ms). • Outstanding requests • Metric target is 0. • Why? Because ZK processes all incoming requests serially. Non-zero values mean that requests are backing up. 53
• 54. "Auditing" Kafka LinkedIn's way to detect data loss etc. Verisign Public 54
• 55. “Auditing” Kafka • LinkedIn's way to detect data loss etc. in Kafka Verisign Public • Not part of open source stack yet. May come in the future. • In short: custom producer+consumer app that is hooked into monitoring. • Value proposition • Monitor whether you're losing messages/data. • Monitor whether your pipelines can handle the incoming data load. 55 http://www.hakkalabs.co/articles/site-reliability-engineering-linkedin-kafka-service
• 56. LinkedIn's Audit UI: a first look Verisign Public • Example 1: Count discrepancy • Caused by messages failing to reach a downstream Kafka cluster • Example 2: Load lag 56
• 57. “Auditing” Kafka • Every producer is also writing messages into a special topic about how many messages it produced, every 10mins. Verisign Public • Example: "Over the last 10mins, I sent N messages to topic X.” • This metadata gets mirrored like any other Kafka data. • Audit consumer • 1 audit consumer per Kafka cluster • Reads every single message out of “its” Kafka cluster. It then calculates counts for each topic, and writes those counts back into the same special topic, every 10mins. • Example: "I saw M messages in the last 10mins for topic X in THIS cluster” • And the next audit consumer in the next, downstream cluster does the same thing. 57
• 58. “Auditing” Kafka • Monitoring audit consumers Verisign Public • Completeness check • "#msgs according to producer == #msgs seen by audit consumer?" • Lag • "Can the audit consumers keep up with the incoming data rate?" • If audit consumers fall behind, then all your tracking data falls behind as well, and you don't know how many messages got produced. 58
• 59. “Auditing” Kafka • Audit UI Verisign Public • Only reads data from that special "metrics/monitoring" topic, but this data is reads from every Kafka cluster at LinkedIn. • What they producers said they wrote in. • What the audit consumers said they saw. • Shows correlation graphs (producers vs. audit consumers) • For each tier, it shows how many messages there were in each topic over any given period of time. • Percentage of how much data got through (from cluster to cluster). • If the percentage drops below 100%, then emails are sent to Kafka SRE+DEV as well as their Hadoop ETL team because that stops the Hadoop pipelines from functioning properly. 59
• 60. LinkedIn's Audit UI: a closing look Verisign Public • Example 1: Count discrepancy • Caused by messages failing to reach a downstream Kafka cluster • Example 2: Load lag 60
• 61. Kafka performance tuning Verisign Public 61
• 62. OS tuning • Kernel tuning Verisign Public • Don’t swap! vm.swappiness = 0 (RHEL 6.5 onwards: 1) • Allow more dirty pages but less dirty cache. • LinkedIn have lots of RAM in servers, most of it is for page cache (60 of 64 GB). They let dirty pages built up, but cache should be available as Kafka does lots of disk and network I/O. • See vm.dirty_*_ratio & friends • Disk throughput • Longer commit interval on mount points. (ext3 or ext4?) • Normal interval for ext3 mount point is 30s (?) between flushes; LinkedIn: 120s. They can tolerate losing 2mins worth of data (because of partition replicas) so they rather prefer higher throughput here. • More spindles (RAID10 w/ 14 disks) 62
• 63. Java/JVM tuning • Biggest issue: garbage collection Verisign Public • And, most of the time, the only issue • Goal is to minimize GC pause times • Aka “stop-the-world” events – apps are halted until GC finishes 63
• 64. Java garbage collection in Kafka @ Spotify Verisign Public 64 Before tuning After tuning https://www.jfokus.se/jfokus14/preso/Reliable-real-time-processing-with-Kafka-and-Storm.pdf
• 65. Java/JVM tuning • Good news: use JDK7u51 or later and have a quiet life! Verisign Public • LinkedIn: Oracle JDK, not OpenJDK • Silver bullet is new G1 “garbage-first” garbage collector • Available since JDK7u4. • Substantial improvement over all previous GC’s, at least for Kafka. 65 $ java -Xms4g -Xmx4g -XX:PermSize=48m -XX:MaxPermSize=48m -XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35
• 66. Kafka configuration tuning • Often not much to do beyond using the defaults, yay.  • Key candidates for tuning: Verisign Public 66 num.io.threads should be >= #disks (start testing with == #disks) num.network.threads adjust it based on (concurrent) #producers, #consumers, and replication factor
• 67. Kafka usage tuning – lessons learned from others • Don't break things up into separate topics unless the data in them is truly independent. Verisign Public • Consumer behavior can (and will) be extremely variable, don’t assume you will always be consuming as fast as you are producing. • Keep time related messages in the same partition. • Consumer behavior can extremely variable, don't assume the lag on all your partitions will be similar. • Design a partitioning scheme, so that the owner of one partition can stop consuming for a long period of time and your application will be minimally impacted (for example, partition by transaction id) 67 http://grokbase.com/t/kafka/users/145qtx4z1c/topic-partitioning-strategy-for-large-data
• 68. Ops-related references • Kafka FAQ Verisign Public • https://cwiki.apache.org/confluence/display/KAFKA/FAQ • Kafka operations • https://kafka.apache.org/documentation.html#operations • Kafka system tools • https://cwiki.apache.org/confluence/display/KAFKA/System+Tools • Consumer offset checker, get offsets for a topic, print metrics via JMX to console, read from topic A and write to topic B, verify consumer rebalance • Kafka replication tools • https://cwiki.apache.org/confluence/display/KAFKA/Replication+tools • Caveat: Some sections of this document are slightly outdated. • Controlled shutdown, preferred leader election tool, reassign partitions tool • Kafka tutorial • http://www.michael-noll.com/blog/2013/03/13/running-a-multi-broker-apache-kafka-cluster-on-a-single- node/ 68
• 69. Part 4: Developing Kafka apps Verisign Public 69
• 70. Overview of Part 4: Developing Kafka apps • Writing data to Kafka with producers Verisign Public • Example producer • Producer types (async, sync) • Message acking and batching of messages • Write operations behind the scenes – caveats ahead! • Reading data from Kafka with consumers • High-level consumer API and simple consumer API • Consumer groups • Rebalancing • Testing Kafka • Serialization in Kafka • Data compression in Kafka • Example Kafka applications • Dev-related Kafka references 70
• 71. Writing data to Kafka Verisign Public 71
• 72. Writing data to Kafka • You use Kafka “producers” to write data to Kafka brokers. Verisign Public • Available for JVM (Java, Scala), C/C++, Python, Ruby, etc. • The Kafka project only provides the JVM implementation. • Has risk that a new Kafka release will break non-JVM clients. • A simple example producer: • Full details at: • https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+Producer+Example 72
• 73. Producers • The Java producer API is very simple. Verisign Public • We’ll talk about the slightly confusing details next.  73
• 74. Producers • Two types of producers: “async” and “sync” Verisign Public • Same API and configuration, but slightly different semantics. • What applies to a sync producer almost always applies to async, too. • Async producer is preferred when you want higher throughput. • Important configuration settings for either producer type: 74 client.id identifies producer app, e.g. in system logs producer.type async or sync request.required.acks acking semantics, cf. next slides serializer.class configure encoder, cf. slides on Avro usage metadata.broker.list cf. slides on bootstrapping list of brokers
• 75. Sync producers • Straight-forward so I won’t cover sync producers here Verisign Public • Please go to https://kafka.apache.org/documentation.html • Most important thing to remember: producer.send() will block! 75
• 76. Async producer • Sends messages in background = no blocking in client. • Provides more powerful batching of messages (see later). • Wraps a sync producer, or rather a pool of them. Verisign Public • Communication from async->sync producer happens via a queue. • Which explains why you may see kafka.producer.async.QueueFullException • Each sync producer gets a copy of the original async producer config, including the request.required.acks setting (see later). • Implementation details: Producer, async.AsyncProducer, async.ProducerSendThread, ProducerPool, async.DefaultEventHandler#send() 76
• 77. Async producer • Caveats Verisign Public • Async producer may drop messages if its queue is full. • Solution 1: Don’t push data to producer faster than it is able to send to brokers. • Solution 2: Queue full == need more brokers, add them now! Use this solution in favor of solution 3 particularly if your producer cannot block (async producers). • Solution 3: Set queue.enqueue.timeout.ms to -1 (default). Now the producer will block indefinitely and will never willingly drop a message. • Solution 4: Increase queue.buffering.max.messages (default: 10,000). • In 0.8 an async producer does not have a callback for send() to register error handlers. Callbacks will be available in 0.9. 77
• 78. Producers • Two aspects worth mentioning because they significantly influence Kafka performance: Verisign Public 1. Message acking 2. Batching of messages 78
• 79. 1) Message acking • Background: Verisign Public • In Kafka, a message is considered committed when “any required” ISR (in-sync replicas) for that partition have applied it to their data log. • Message acking is about conveying this “Yes, committed!” information back from the brokers to the producer client. • Exact meaning of “any required” is defined by request.required.acks. • Only producers must configure acking • Exact behavior is configured via request.required.acks, which determines when a produce request is considered completed. • Allows you to trade latency (speed) <-> durability (data safety). • Consumers: Acking and how you configured it on the side of producers do not matter to consumers because only committed messages are ever given out to consumers. They don’t need to worry about potentially seeing a message that could be lost if the leader fails. 79
• 80. 1) Message acking • Typical values of request.required.acks Verisign Public • 0: producer never waits for an ack from the broker. • Gives the lowest latency but the weakest durability guarantees. • 1: producer gets an ack after the leader replica has received the data. • Gives better durability as the we wait until the lead broker acks the request. Only msgs that were written to the now-dead leader but not yet replicated will be lost. • -1: producer gets an ack after all ISR have received the data. • Gives the best durability as Kafka guarantees that no data will be lost as long as at least one ISR remains. • Beware of interplay with request.timeout.ms! • "The amount of time the broker will wait trying to meet the `request.required.acks` requirement before sending back an error to the client.” • Caveat: Message may be committed even when broker sends timeout error to client (e.g. because not all ISR ack’ed in time). One reason for this is that the producer acknowledgement is independent of the leader-follower replication, and ISR’s send their acks to the leader, the latter of which will reply to the client. 80 better latency better durability
• 81. 2) Batching of messages • Batching improves throughput Verisign Public • Tradeoff is data loss if client dies before pending messages have been sent. • You have two options to “batch” messages in 0.8: 1. Use send(listOfMessages). • Sync producer: will send this list (“batch”) of messages right now. Blocks! • Async producer: will send this list of messages in background “as usual”, i.e. according to batch-related configuration settings. Does not block! 2. Use send(singleMessage) with async producer. • For async the behavior is the same as send(listOfMessages). 81
• 82. 2) Batching of messages • Option 1: How send(listOfMessages) works behind the scenes Verisign Public • The original list of messages is partitioned (randomly if the default partitioner is used) based on their destination partitions/topics, i.e. split into smaller batches. • Each post-split batch is sent to the respective leader broker/ISR (the individual send()’s happen sequentially), and each is acked by its respective leader broker according to request.required.acks. 82 partitioner.class p6 p1 p4 p4 p6 p4 p4 p6 p6 p1 p4 p4 p6 p6 p1 Current send() leader ISR (broker) for partition 4 send() Current leader ISR (broker) for partition 6 …and so on…
• 83. 2) Batching of messages • Option 2: Async producer Verisign Public • Standard behavior is to batch messages • Semantics are controlled via producer configuration settings • batch.num.messages • queue.buffering.max.ms + queue.buffering.max.messages • queue.enqueue.timeout.ms • And more, see producer configuration docs. • Remember: Async producer simply wraps sync producer! • But the batch-related config settings above have no effect on “true” sync producers, i.e. when used without a wrapping async producer. 83
• 84. FYI: upcoming producer configuration changes Verisign Public 84 Kafka 0.8 Kafka 0.9 (unreleased) metadata.broker.list bootstrap.servers request.required.acks acks batch.num.messages batch.size message.send.max.retries retries (This list is not complete, see Kafka docs for details.)
• 85. Write operations behind the scenes • When writing to a topic in Kafka, producers write directly to the partition leaders (brokers) of that topic Verisign Public • Remember: Writes always go to the leader ISR of a partition! • This raises two questions: • How to know the “right” partition for a given topic? • How to know the current leader broker/replica of a partition? 85
• 86. 1) How to know the “right” partition when sending? • In Kafka, a producer – i.e. the client – decides to which target partition a message will be sent. Verisign Public • Can be random ~ load balancing across receiving brokers. • Can be semantic based on message “key”, e.g. by user ID or domain name. • Here, Kafka guarantees that all data for the same key will go to the same partition, so consumers can make locality assumptions. • But there’s one catch with line 2 (i.e. no key) in Kafka 0.8. 86
• 87. Keyed vs. non-keyed messages in Kafka 0.8 • If a key is not specified: Verisign Public • Producer will ignore any configured partitioner. • It will pick a random partition from the list of available partitions and stick to it for some time before switching to another one = NOT round robin or similar! • Why? To reduce number of open sockets in large Kafka deployments (KAFKA-1017). • Default: 10mins, cf. topic.metadata.refresh.interval.ms • See implementation in DefaultEventHandler#getPartition() • If there are fewer producers than partitions at a given point of time, some partitions may not receive any data. How to fix if needed? • Try to reduce the metadata refresh interval topic.metadata.refresh.interval.ms • Specify a message key and a customized random partitioner. • In practice it is not trivial to implement a correct “random” partitioner in Kafka 0.8. • Partitioner interface in Kafka 0.8 lacks sufficient information to let a partitioner select a random and available partition. Same issue with DefaultPartitioner. 87
• 88. Keyed vs. non-keyed messages in Kafka 0.8 • If a key is specified: Verisign Public • Key is retained as part of the msg, will be stored in the broker. • One can design a partition function to route the msg based on key. • The default partitioner assigns messages to a partition based on their key hashes, via key.hashCode % numPartitions. • Caveat: • If you specify a key for a message but do not explicitly wire in a custom partitioner via partitioner.class, your producer will use the default partitioner. • So without a custom partitioner, messages with the same key will still end up in the same partition! (cf. default partitioner’s behavior above) 88
• 89. 2) How to know the current leader of a partition? • Producers: broker discovery aka bootstrapping Verisign Public • Producers don’t talk to ZooKeeper, so it’s not through ZK. • Broker discovery is achieved by providing producers with a “bootstrapping” broker list, cf. metadata.broker.list • These brokers inform the producer about all alive brokers and where to find current partition leaders. The bootstrap brokers do use ZK for that. • Impacts on failure handling • In Kafka 0.8 the bootstrap list is static/immutable during producer run-time. This has limitations and problems as shown in next slide. • The current bootstrap approach will improve in Kafka 0.9. This change will make the life of Ops easier. 89
• 90. Bootstrapping in Kafka 0.8 • Scenario: N=5 brokers total, 2 of which are for bootstrap • Do’s: Verisign Public • Take down one bootstrap broker (e.g. broker2), repair it, and bring it back. • In terms of impacts on broker discovery, you can do whatever you want to brokers 3-5. • Don’ts: • Stop all bootstrap brokers 1+2. If you do, the producer stops working! • To improve operational flexibility, use VIP’s or similar for values in metadata.broker.list. 90 broker1 broker2 broker3 broker4 broker5
• 91. Reading data from Kafka Verisign Public 91
• 92. Reading data from Kafka • You use Kafka “consumers” to write data to Kafka brokers. Verisign Public • Available for JVM (Java, Scala), C/C++, Python, Ruby, etc. • The Kafka project only provides the JVM implementation. • Has risk that a new Kafka release will break non-JVM clients. • Examples will be shown later in the “Example Kafka apps” section. • Three API options for JVM users: 1. High-level consumer API <<< in most cases you want to use this one! 2. Simple consumer API 3. Hadoop consumer API • Most noteworthy: The “simple” API is anything but simple.  • Prefer to use the high-level consumer API if it meets your needs (it should). • Counter-example: Kafka spout in Storm 0.9.2 uses simple consumer API to integrate well with Storm’s model of guaranteed message processing. 92
• 93. Reading data from Kafka • Consumers pull from Kafka (there’s no push) Verisign Public • Allows consumers to control their pace of consumption. • Allows to design downstream apps for average load, not peak load (cf. Loggly talk) • Consumers are responsible to track their read positions aka “offsets” • High-level consumer API: takes care of this for you, stores offsets in ZooKeeper • Simple consumer API: nothing provided, it’s totally up to you • What does this offset management allow you to do? • Consumers can deliberately rewind “in time” (up to the point where Kafka prunes), e.g. to replay older messages. • Cf. Kafka spout in Storm 0.9.2. • Consumers can decide to only read a specific subset of partitions for a given topic. • Cf. Loggly’s setup of (down)sampling a production Kafka topic to a manageable volume for testing • Run offline, batch ingestion tools that write (say) from Kafka to Hadoop HDFS every hour. • Cf. LinkedIn Camus, Pinterest Secor 93
• 94. Reading data from Kafka • Important consumer configuration settings Verisign Public 94 group.id assigns an individual consumer to a “group” zookeeper.connect to discover brokers/topics/etc., and to store consumer state (e.g. when using the high-level consumer API) fetch.message.max.bytes number of message bytes to (attempt to) fetch for each partition; must be >= broker’s message.max.bytes
• 95. Reading data from Kafka • Consumer “groups” Verisign Public • Allows multi-threaded and/or multi-machine consumption from Kafka topics. • Consumers “join” a group by using the same group.id • Kafka guarantees a message is only ever read by a single consumer in a group. • Kafka assigns the partitions of a topic to the consumers in a group so that each partition is consumed by exactly one consumer in the group. • Maximum parallelism of a consumer group: #consumers (in the group) <= #partitions 95
• 96. Guarantees when reading data from Kafka • A message is only ever read by a single consumer in a group. • A consumer sees messages in the order they were stored in the log. • The order of messages is only guaranteed within a partition. Verisign Public • No order guarantee across partitions, which includes no order guarantee per-topic. • If total order (per topic) is required you can consider, for instance: • Use #partition = 1. Good: total order. Bad: Only 1 consumer process at a time. • “Add” total ordering in your consumer application, e.g. a Storm topology. • Some gotchas: • If you have multiple partitions per thread there is NO guarantee about the order you receive messages, other than that within the partition the offsets will be sequential. • Example: You may receive 5 messages from partition 10 and 6 from partition 11, then 5 more from partition 10 followed by 5 more from partition 10, even if partition 11 has data available. • Adding more processes/threads will cause Kafka to rebalance, possibly changing the assignment of a partition to a thread (whoops). 96
• 97. Rebalancing: how consumers meet brokers • Remember? • The assignment of brokers – via the partitions of a topic – to consumers is quite important, and it is dynamic at run-time. Verisign Public 97
• 98. Rebalancing: how consumers meet brokers • Why “dynamic at run-time”? Verisign Public • Machines can die, be added, … • Consumer apps may die, be re-configured, added, … • Whenever this happens a rebalancing occurs. • Rebalancing is a normal and expected lifecycle event in Kafka. • But it’s also a nice way to shoot yourself or Ops in the foot. • Why is this important? • Most Ops issues are due to 1) rebalancing and 2) consumer lag. • So Dev + Ops must understand what goes on. 98
• 99. Rebalancing: how consumers meet brokers • Rebalancing? Verisign Public • Consumers in a group come into consensus on which consumer is consuming which partitions  required for distributed consumption • Divides broker partitions evenly across consumers, tries to reduce the number of broker nodes each consumer has to connect to • When does it happen? Each time: • a consumer joins or leaves a consumer group, OR • a broker joins or leaves, OR • a topic “joins/leaves” via a filter, cf. createMessageStreamsByFilter() • Examples: • If a consumer or broker fails to heartbeat to ZK  rebalance! • createMessageStreams() registers consumers for a topic, which results in a rebalance of the consumer-broker assignment. 99
• 100. Testing Kafka apps Verisign Public 100
• 101. Testing Kafka apps • Won’t have the time to cover testing in this workshop. • Some hints: Verisign Public • Unit-test your individual classes like usual • When integration testing, use in-memory instances of Kafka and ZK • Test-drive your producers/consumers in virtual Kafka clusters via Wirbelsturm • Starting points: • Kafka’s own test suite • 0.8.1: https://github.com/apache/kafka/tree/0.8.1/core/src/test • trunk: https://github.com/apache/kafka/tree/trunk/core/src/test/ • Kafka tests in kafka-storm-starter • https://github.com/miguno/kafka-storm-starter/ 101
• 102. Serialization in Kafka Verisign Public 102
• 103. Serialization in Kafka • Kafka does not care about data format of msg payload • Up to developer (= you) to handle serialization/deserialization Verisign Public • Common choices in practice: Avro, JSON 103 (Code above is from the High Level Consumer API)
• 104. Serialization in Kafka: using Avro • One way to use Avro in Kafka is via Twitter Bijection. Verisign Public • https://github.com/twitter/bijection • Approach: Convert pojo to byte[], then send byte[] to Kafka. • Bijection in Scala: • Bijection in Java: https://github.com/twitter/bijection/wiki/Using-bijection-from-java • Full Kafka/Bijection example: • KafkaSpec in kafka-storm-starter • Alternatives to Bijection: • e.g. https://github.com/miguno/kafka-avro-codec 104
• 105. Data compression in Kafka Verisign Public 105
• 106. Data compression in Kafka • Again, no time to cover compression in this training. Verisign Public • But worth looking into! • Interplay with batching of messages, e.g. larger batches typically achieve better compression ratios. • Details about compression in Kafka: • https://cwiki.apache.org/confluence/display/KAFKA/Compression • Blog post by Neha Narkhede, Kafka committer @ LinkedIn: http://geekmantra.wordpress.com/2013/03/28/compression-in-kafka-gzip-or- snappy/ 106
• 107. Example Kafka applications Verisign Public 107
• 108. kafka-storm-starter • Written by yours truly • https://github.com/miguno/kafka-storm-starter Verisign Public 108 $ git clone https://github.com/miguno/kafka-storm-starter $ cd kafka-storm-starter # Now ready for mayhem! (Must have JDK 6 installed.)
• 109. kafka-storm-starter: run the test suite Verisign Public 109 $ ./sbt test • Will run unit tests plus end-to-end tests of Kafka, Storm, and Kafka- Storm integration.
• 110. kafka-storm-starter: run the KafkaStormDemo app Verisign Public 110 $ ./sbt run • Starts in-memory instances of ZooKeeper, Kafka, and Storm. Then runs a Storm topology that reads from Kafka.
• 111. Kafka related code in kafka-storm-starter • KafkaProducerApp Verisign Public • https://github.com/miguno/kafka-storm-starter/ blob/develop/src/main/scala/com/miguno/kafkastorm/kafka/KafkaPr oducerApp.scala • KafkaConsumerApp • https://github.com/miguno/kafka-storm-starter/ blob/develop/src/main/scala/com/miguno/kafkastorm/kafka/KafkaC onsumerApp.scala • KafkaSpec: test-drives producer and consumer above • https://github.com/miguno/kafka-storm-starter/ blob/develop/src/test/scala/com/miguno/kafkastorm/integration/Kaf kaSpec.scala 111
• 112. Dev-related references • Kafka documentation • Kafka FAQ • Kafka tutorials Verisign Public • http://www.michael-noll.com/blog/2013/03/13/running-a-multi-broker-apache- kafka-cluster-on-a-single-node/ • Code examples • https://github.com/miguno/kafka-storm-starter/ 112
• 113. Part 5: Playing with Kafka using Wirbelsturm 1-click Kafka deployments Verisign Public 113
• 114. Deploying Kafka via Wirbelsturm • Written by yours truly • https://github.com/miguno/wirbelsturm Verisign Public 114 $ git clone https://github.com/miguno/wirbelsturm.git $ cd wirbelsturm $ ./bootstrap $ vi wirbelsturm.yaml # uncomment Kafka section $ vagrant up zookeeper1 kafka1 (Must have Vagrant 1.6.1+ and VirtualBox 4.3+ installed.)
• 115. What can I do with Wirbelsturm? • Get a first impression of Kafka • Test-drive your producer apps and consumer apps • Test failure handling Verisign Public • Stop/kill brokers, check what happens to producers or consumers. • Stop/kill ZooKeeper instances, check what happens to brokers. • Use as sandbox environment to test/validate deployments • “What will actually happen when I run this reassign partition tool?” • "What will actually happen when I delete a topic?" • “Will my Hiera changes actually work?” • Reproduce production issues, share results with Dev • Also helpful when reporting back to Kafka project and mailing lists. • Any further cool ideas?  115
• 116. Wrapping up Verisign Public 116
• 117. Where to find help • No (good) Kafka book available yet. • Kafka documentation Verisign Public • http://kafka.apache.org/documentation.html • https://cwiki.apache.org/confluence/display/KAFKA/Index • Kafka ecosystem, e.g. Storm integration, Puppet • https://cwiki.apache.org/confluence/display/KAFKA/Ecosystem • Mailing lists • http://kafka.apache.org/contact.html • Code examples • examples/ directory in Kafka, https://github.com/apache/kafka/ • https://github.com/miguno/kafka-storm-starter/ 117
• 118. © 2014 VeriSign, Inc. All rights reserved. VERISIGN and other trademarks, service marks, and designs are registered or unregistered trademarks of VeriSign, Inc. and its subsidiaries in the United States and in foreign countries. All other trademarks are property of their respective owners.