Data Loss and Data Duplication in Kafka Jayesh Thakrar

Kafka is a distributed, partitioned, replicated, durable commit log service. It provides the functionality of a messaging system, but with a unique design. Exactly once - each message is delivered once and only once

Data Loss and Duplicate Prevention Monitoring AGENDA Kafka Overview Data Loss Data Duplication Data Loss and Duplicate Prevention Monitoring

Kafka Overview

Kafka As A Log Abstraction Client: Producer Kafka Server = Kafka Broker Topic: app_events Client: Consumer A Client: Consumer B Source: https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying

Topic Partitioning . . . Client: Producer or Consumer Kafka Broker Topic: app_events Source: https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying

Topic Partitioning – Scalability Kafka Broker 0 Leader Replica Kafka Broker 2 Replica Replica Replica Leader Kafka Broker 1 Replica Clients: Producer, Consumer Leader Replica

Topic Partitioning – redundancy Kafka Broker 0 Kafka Broker 2 Leader Replica Replica Replica Replica Leader Kafka Broker 1 Replica Client: Producer, Consumer Leader Replica

Topic Partitioning – Redundancy/durability Kafka Broker 0 Kafka Broker 2 Leader Replica Replica Replica Replica Leader Kafka Broker 1 Replica Pull-based inter-broker replication Leader Replica

Topic Partitioning – summary Log sharded into partitions Messages assigned to partitions by API or custom partitioner Partitions assigned to brokers (manual or automatic) Partitions replicated (as needed) Messages ordered within each partition Message offset = absolute position in partition Partitions stored on filesystem as ordered sequence of log segments (files)

Other Key Concepts Cluster = collection of brokers Broker-id = a unique id (integer) assigned to each broker Controller = functionality within each broker responsible for leader assignment and management, with one being the active controller Replica = partition copy, represented (identified) by the broker-id Assigned replicas = set of all replicas (broker-ids) for a partition ISR = In-Sync Replicas = subset of assigned replicas (brokers) that are “in-sync/caught-up”* with the leader (ISR always includes the leader)

Data Loss

Data Loss : Inevitable Upto 0.01% data loss For 700 billion messages / day, that's up to 7 million / day

Data loss at the producer Kafka Producer API API Call-tree kafkaProducer.send() …. accumulator.append() // buffer …. sender.send() // network I/O Messages accumulate in buffer in batches Batched by partition, retry at batch level Expired batches dropped after retries Error count and other metrics via JMX Data Loss at Producer Failure to close / flush producer on termination Dropped batches due to communication or other errors when acks = 0 or retry exhaustion Data produced faster than delivery, causing BufferExhaustedException (deprecated in 0.10+)

dATA LOSS AT The CLUSTER (BY BROKERS) 1 Was it a leader? 4 Other replicas in ISR? Y Y Broker Crashes Detected by Controller via zookeeper Elect another leader N N 2 Y 5 6 Relax, everything will be fine N Was it in ISR? Allow unclean election? Other replicas available? Y Y 3 N N ISR >= min.insync.replicas? N Y Partition unavailable !! 7

Non-leader broker crash 1 Was it a leader? 4 Other replicas in ISR? Y Y Broker Crashes Detected by Controller via zookeeper Elect another leader N N 2 Y 5 6 Relax, everything will be fine N Was it in ISR? Allow unclean election? Other replicas available? Y Y 3 N N ISR >= min.insync.replicas? N Y Partition unavailable !! 7

Leader broker crash: Scenario 1 Was it a leader? 4 Other replicas in ISR? Y Y Broker Crashes Detected by Controller via zookeeper Elect another leader N N 2 Y 5 6 Relax, everything will be fine N Was it in ISR? Allow unclean election? Other replicas available? Y Y 3 N N ISR >= min.insync.replicas? N Y Partition unavailable !! 7

Leader broker crash: Scenario 2 1 Was it a leader? 4 Other replicas in ISR? Y Y Broker Crashes Detected by Controller via zookeeper Elect another leader N N 2 Y 5 6 Relax, everything will be fine N Was it in ISR? Allow unclean election? Other replicas available? Y Y 3 N N ISR >= min.insync.replicas? N Y Partition unavailable !! 7

dATA LOSS AT The CLUSTER (BY BROKERS) 1 Was it a leader? 4 Other replicas in ISR? Y Y Broker Crashes Detected by Controller via zookeeper Elect another leader N N 2 Y 5 6 Relax, everything will be fine N Was it in ISR? Allow unclean election? Other replicas available? Y Y 3 N N ISR >= min.insync.replicas? N Y Potential data-loss depending upon acks config at producer. See KAFKA-3919 KAFKA-4215 Partition unavailable !! 7

FROM KAFKA-3919

FROM KAFKA-4215

Config for Data Durability and Consistency Producer config - acks = -1 (or all) - max.block.ms (blocking on buffer full, default = 60000) and retries - request.timeout.ms (default = 30000) – it triggers retries Topic config - min.insync.replicas = 2 (or higher) Broker config - unclean.leader.election.enable = false - timeout.ms (default = 30000) – inter-broker timeout for acks

Config for Availability and Throughput Producer config - acks = 0 (or 1) - buffer.memory, batch.size, linger.ms (default = 100) - request.timeout.ms, max.block.ms (default = 60000), retries - max.in.flight.requests.per.connection Topic config - min.insync.replicas = 1 (default) Broker config - unclean.leader.election.enable = true

Data Duplication

Data Duplication: How it occurs Client: Producer Producer (API) retries = messages resent after timeout when retries > 1 Kafka Broker Topic: app_events Consumer consumes messages more than once after restart from unclean shutdown / crash Client: Consumer A Client: Consumer B

Data Loss & Duplication Detection

How to Detect Data loss & Duplication - 1 1) Msg from producer to Kafka 2) Ack from Kafka with details 3) Producer inserts into store 4) Consumer reads msg 5) Consumer validates msg If exists not duplicate consume msg delete msg If missing duplicate msg Audit: Remaining msgs in store are "lost" or "unconsumed" msgs 1 Producer Kafka 4 Consumer 2 Memcache / HBase / Cassandra / Other Store 5 3 Topic, Partition, Offset | Msg Key or Hash KEY | VALUE

How to Detect Data loss & Duplication - 2 1 Producer Kafka 4 Consumer 1) Msg from producer to Kafka 2) Ack from Kafka with details 3) Producer maintains window stats 4) Consumer reads msg 5) Consumer validates window stats at end of interval 2 Memcache / HBase / Cassandra / Other Store 5 3 Source, time-window | Msg count or some other checksum (e.g. totals, etc) KEY | VALUE

Data Duplication: How to minimize at consumer Client: Producer Kafka Broker Topic: app_events If possible, lookup last processed offset in destination at startup Client: Consumer A Client: Consumer B

Monitoring

Monitoring and Operations: JMX Metrics Producer JMX Consumer JMX

Questions?

Jayesh Thakrar jthakrar@conversantmedia.com