Fundamentals of Apache Kafka
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The document presents an overview of Apache Kafka, describing it as a high throughput distributed messaging system designed for data pipelines and real-time event processing. It covers Kafka's architecture, terminologies, benefits such as scalability and durability, and its applications in various industries. Additionally, it discusses Kafka's production implementation at Zapr, including configurations, challenges, and tools for management and monitoring.
Fundamentals of Apache Kafka
- 1. ZAPR MEDIA LABS Presented by: Chhavi Parasher and Ankit Timbadia
- 2. Agenda ● What is Apache Kafka? ● Why Apache Kafka? ● Fundamentals ○ Terminologies ○ Architecture ○ Protocol ● What does kafka offer? ● Where Kafka is used?
- 3. What is Apache Kafka?
- 4. What is kafka “A high throughput distributed pub-sub messaging system.
- 5. What is kafka “A pub-sub messaging system rethought as a distributed commit log storage.”
- 7. Why Kafka Client Source Data Pipelines Start like this.
- 8. Why Kafka Client Source Client Client Client Then we reuse them
- 9. Why Kafka Client Backend1 Client Client Client Then we add additional endpoints to the existing sources Backend2
- 10. Why Kafka Client Backend1 Client Client Client Then it starts to look like this Backend2 Backend3 Backend4
- 11. Birth of Kafka Kafka decouples Data Pipelines Source System Source System Source System Source System Hadoop Security Systems Real-time monitoring Data Warehouse Kafka Producers Brokers Consumers
- 13. Terminologies
- 14. Terminologies • Topic is a message stream (queue) • Partitions - Topic is divided into partitions • Ordered, immutable, a structured commit log • Segments - Partition has log segments on disk • Partitions broken down into physical files. • Offset - Segment has messages • Each message is assigned a sequential id
- 15. Anatomy of a topic 0 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 0 1 2 3 4 5 6 7 8 9 1 0 1 1 0 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 Partition 1 Partition 2 Partition 3 Writes Old New Segment1 Segment2
- 16. Kafka Message (Write once read many)
- 17. Partitions and Brokers • Each broker can be a leader or a replica for a partition • All writes & reads to a topic go through the leader
- 18. Producer writing to partition • Producers write to a single leader • Each write can be serviced by a separate broker
- 19. Producer writing to second partition.
- 20. Keyed Messages and Partitioning Producer
- 21. Partitioning Distributes clients across consumers Consumer Consumer Consumer Consumer Consumer Consumer Consumer Producer Producer Producer Producer Producer
- 22. Architecture
- 23. What does kafka offer?
- 24. What does kafka offer? • Fast • Scalable • Durable • Distributed by Design
- 25. What does kafka offer? • Fast • A single Kafka broker can handle 100Mbps of reads & writes from 1000 of clients. • Scalable • Durable • Distributed by Design
- 26. What does kafka offer? • Fast • Scalable • Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine • Durable • Distributed by Design
- 27. What does kafka offer? • Fast • Scalable • Durable • Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact. • Distributed by Design
- 28. What does kafka offer • Fast • Scalable • Durable • Distributed by Design • Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.
- 29. Efficiency - high throughput & low latency • Disks are fast when used sequentially • Append to end of log. • Fetch messages from a partition beginning from a particular message id. • Batching makes best use of network/IO • Batched send and receive • Batched compression (GZIP, Snappy and LZ4)
- 30. Efficiency - high throughput & low latency • No message caching in JVM • Zero-copy from file to socket (Java NIO)
- 31. ● ● ● ● ● ● Comparison to other messaging systems
- 32. Where is Kafka used?
- 33. Who uses Apache Kafka?
- 34. What do people use it for? • Activity tracking • page views or click tracking, profile updates • Real-time event processing • Provides low latency • Good integration with spark, samza. storm etc. • Collecting Operational Metrics • Monitoring & alerting • Commit log • database changes can be published to Kafka
- 35. Why Replication? • Broker can go down • controlled: rolling restart for code/config push • uncontrolled: isolated broker failure • If broker is down • some partitions are unavailable • could be permanent data loss • Replication ensures higher availability & durability
- 36. Caveats • Not designed for large payloads. • Decoding is done for a whole message (no streaming decoding). • Rebalancing can screw things up. • if you're doing any aggregation in the consumer by the partition key • Number of partitions cannot be easily changed (chose wisely). • Lots of topics can hurt I/O performance.
- 37. Guarantees • At least once delivery • In order delivery, per partition • For a topic with replication factor N, Kafka can tolerate up to N-1 server failures without “losing” any messages committed to the log
- 38. Summary • A high throughput distributed messaging system rethought as commit log • Originally developed by LinkedIn, Open Sourced in 2011 • Written in Scala, Clients for every popular language • Used by LinkedIn, Twitter, Netflix and many more companies. • When should we use Kafka ? • When should we not use kafka?
- 39. Kafka in Production @ Zapr
- 40. Agenda • Kafka Clusters at @Zapr • Kafka in AWS • Challenges Managing Kafka Brokers. • Monitoring of Kafka Clusters.
- 41. Kafka Version: 0.8.2 Total Kafka Clusters: 5 Number of Topics: 185 Brokers: 17 Partitions: 7827 Regions: 2 (us-east-1,ap-southeast-1) Kafka at Zapr
- 42. Kafka Clusters on Amazon Web Services. Quorum of 3 Zookeeper Nodes for High Availability Kafka Brokers
- 43. Resource Utilization Family Type vCPU Memory CPU Pricing Compute Optimized C4.2xlarge 8 16 ~75% 338$ General Purpose M4.2xlarge 8 32 ~25% 366$ After moving from C4 Class to M4 Class Machines 40% Improvement in CPU Usage
- 44. Production Kafka Broker Configurations
- 45. #Default Partitions and Replication Factor for Kafka Topics num.partitions=10 default.replication.factor=2 #Controlled shutdown for the proper shutdown of Kafka Broker for maintenance. num.recovery.threads.per.data.dir=4 controlled.shutdown.enable=true controlled.shutdown.max.retries=5 controlled.shutdown.retry.backoff.ms=60000 #Creation and Deletion of Kafka Topics delete.topic.enable=true auto.create.topics.enable=true Production Kafka Broker Configurations
- 46. Challenges
- 47. Dynamic Load • Kafka Scales Horizontally • For a topic, Kafka has N (Replicated Partitions) • Adding a Broker causes Partition to rebalanced
- 48. Current State 2 Brokers, 1 Topic and 6 Partitions & replication 2 Desired State after adding 3rd Broker
- 49. Kafka approach
- 50. Introducing Kafka Manager https://github.com/yahoo/kafka-manager It supports the following : ● Manage multiple clusters. ● Easy inspection of cluster state (topics, consumers, offsets, brokers) ● Generate partition assignments with option to select brokers to use ● Run reassignment of partition (based on generated assignments) ● Create a topic with optional topic configs (0.8.1.1 has different configs than 0.8.2+) ● Delete topic (only supported on 0.8.2+ and remember set delete.topic.enable=true in broker config) ● Add partitions to existing topic ● Update config for existing topic ● Optionally enable JMX polling for broker level and topic level metrics.
- 51. Clusters
- 52. Topic List
- 53. Topic View
- 55. Broker List
- 56. Broker View
- 58. Monitoring
- 59. Kafka Graphite Metric Plugin https://github.com/damienclaveau/kafka-graphite #Graphite Reporter kafka.metrics.reporters=com.criteo.kafka.KafkaGraphiteM etricsReporter kafka.graphite.metrics.reporter.enabled=true kafka.metrics.polling.interval.secs = 60 kafka.graphite.metrics.host= graphite.zapr.com kafka.graphite.metrics.port=2003 kafka.graphite.metrics.group=PROD.kafka-cluster-1.10-0-1- 90
- 61. Kafka Offset Monitoring https://github.com/quantifind/KafkaOffsetMonitor ● This is an app to monitor your kafka consumers and their position (offset) in the queue. ● You can see the current consumer groups, for each group the topics that they are consuming ● Offset are useful to understand how quick you are consuming from a queue and how fast the queue is growing
- 63. Kafka offset monitoring @Graphite
- 64. Future plans • Upgrading EC2 instances to next generations from M4 to M5 • Upgrading Kafka Version from 0.8.2 to latest version (1.2) • Using ST1 (throughput optimized HDD) EBS
- 65. Thank you!