Kafka reliability velocity 17
•Als PPTX, PDF herunterladen•
3 gefällt mir•1,238 views
How to tune Apache Kafka for high reliability
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie Kafka reliability velocity 17
Ähnlich wie Kafka reliability velocity 17 (20)
Mehr von Gwen (Chen) Shapira
Mehr von Gwen (Chen) Shapira (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Kafka reliability velocity 17
- 1. 1 When it absolutely, positively, has to be there Reliability Guarantees in Apache Kafka @jeffholoman @gwenshap
- 2. 4 Apache Kafka High Throughput Low Latency Scalable Centralized Real-time
- 3. 5 Streaming Platform Producer Consumer Streaming Applications Connectors Connectors Apache Kafka
- 4. 6 Versions of Apache Kafka • 0.7.0 <- Please don’t • 0.8.0 <- Replication exists, it will continue evolving with every release • 0.8.2 <- New producer, offset commits to Kafka • 0.9.0 <- New consumer, Connect APIs • 0.10.0 <- New consumer improvements, Streams APIs • 0.11.0 <- Idempotent producer, transactional semantics, Exactly once.
- 5. 7 Kafka Components • Broker • Java clients: • Producer • Consumers • Kafka Streams • Kafka Connect • Non-Java: • Librdkafka • Librdkafka based – Python, Go, NodeJS, C#... • Others
- 6. 8 If Kafka is a critical piece of our pipeline Can we be 100% sure that our data will get there? Can we lose messages? How do we verify? Who’s fault is it?
- 7. 10 Distributed Systems Things Fail Systems are designed to tolerate failure We must expect failures and design our code and configure our systems to handle them
- 8. 11 Network Broker MachineClient Machine Data Flow Kafka Client Broker O/S Socket Buffer NIC NIC Page Cache Disk Application Thread O/S Socket Buffercallbac k ✗ ✗ ✗ ✗ ✗ ✗ ✗✗ data ack / exception Replication
- 9. 12 Client Machine Kafka Client O/S Socket Buffer NIC Application Thread ✗ ✗ ✗Broker Machine Broker NIC Page Cache Disk O/S Socket Buffer ✗ ✗ ✗ ✗ Network Data Flow ✗ data offsets ZK Kafka✗
- 10. 13 Kafka is super reliable. Stores data, on disk. Replicated. … if you know how to configure it that way.
- 11. 14 Replication is your friend Kafka protects against failures by replicating data The unit of replication is the partition One replica is designated as the Leader Follower replicas fetch data from the leader The leader holds the list of “in-sync” replicas
- 13. 16 ISR 2 things make a replica in-sync • Lag behind leader • replica.lag.time.max.ms – replica that didn’t fetch or is behind • replica.lag.max.messages – will go away has gone away in 0.9 • Connection to Zookeeper
- 14. 17 Terminology Acked • Producers will not retry sending. • Depends on producer setting Committed • Only when message got to all ISR (future leaders have it). • Consumers can read. • replica.lag.time.max.ms controls: how long can a dead replica prevent consumers from reading? Committed Offsets • Consumer told Kafka the latest offsets it read. By default the consumer will not see these events again.
- 15. 18 Replication Acks = all • Waits for all in-sync replicas to reply. Replica 3 100 Replica 2 100 Replica 1 100 Time
- 16. 19 Replica 3 stopped replicating for some reason Replication Replica 3 100 Replica 2 100 101 Replica 1 100 101 Time Acked in acks = all “committed” Acked in acks = 1 but not “committed”
- 17. 20 Replication Replica 3 100 Replica 2 100 101 Replica 1 100 101 Time One replica drops out of ISR, or goes offline All messages are now acked and committed
- 18. 21 2nd Replica drops out, or is offline Replication Replica 3 100 Replica 2 100 101 Replica 1 100 101 102 103 104Time
- 19. 22 Replication Replica 3 100 Replica 2 100 101 Replica 1 100 101 102 103 104Time Now we’re in trouble ✗
- 20. 23 Replication If Replica 2 or 3 come back online before the leader, you can will lose data. Replica 3 100 Replica 2 100 101 Replica 1 100 101 102 103 104Time All those are “acked” and “committed”
- 21. 24 So what to do Disable Unclean Leader Election • unclean.leader.election.enable = false • Default from 0.11.0 Set replication factor • default.replication.factor = 3 Set minimum ISRs • min.insync.replicas = 2
- 22. 25 Warning min.insync.replicas is applied at the topic-level. Must alter the topic configuration manually if created before the server level change Must manually alter the topic < 0.9.0 (KAFKA-2114)
- 23. 26 Replication Replication = 3 Min ISR = 2 Replica 3 100 Replica 2 100 Replica 1 100 Time
- 24. 27 Replication Replica 3 100 Replica 2 100 101 Replica 1 100 101 Time One replica drops out of ISR, or goes offline
- 25. 28 Replication Replica 3 100 Replica 2 100 101 Replica 1 100 101102 103 104 Time 2nd Replica fails out, or is out of sync Buffers in Produc er
- 26. 29
- 27. 30 Producer Internals Producer sends batches of messages to a buffer M3 Application Thread Application Thread Application Thread send() M2 M1 M0 Batch 3 Batch 2 Batch 1 Fail ? response retry Update Future callback drain Metadata or Exception
- 28. 31 Basics Durability can be configured with the producer configuration request.required.acks • 0 The message is written to the network (buffer) • 1 The message is written to the leader • all The producer gets an ack after all ISRs receive the data; the message is committed Make sure producer doesn’t just throws messages away! • block.on.buffer.full = true < 0.9.0 • max.block.ms = Long.MAX_VALUE • Or handle the BufferExhaustedException / TimeoutException yourself
- 29. 32 “New” Producer All calls are non-blocking async 2 Options for checking for failures: • Immediately block for response: send().get() • Do followup work in Callback, close producer after error threshold • Be careful about buffering these failures. Future work? KAFKA-1955 • Don’t forget to close the producer! producer.close() will block until in-flight txns complete retries (producer config) defaults to 0 In flight requests could lead to message re-ordering
- 30. 33
- 31. 34 Consumer Three choices for Consumer API • Simple Consumer • High Level Consumer (ZookeeperConsumer) • New KafkaConsumer
- 32. 35 New Consumer – auto commit props.put("enable.auto.commit", "true"); props.put("auto.commit.interval.ms", "10000"); KafkaConsumer<String, String> consumer = new KafkaConsumer<String, String>(props); consumer.subscribe(Arrays.asList("foo", "bar")); while (true) { ConsumerRecords<String, String> records = consumer.poll(100); for (ConsumerRecord<String, String> record : records) { processAndUpdateDB(record); } } What if we crash after 8 seconds? Commit automatically every 10 seconds
- 33. 36 New Consumer – manual commit props.put("enable.auto.commit", "false"); KafkaConsumer<String, String> consumer = new KafkaConsumer<String, String>(props); consumer.subscribe(Arrays.asList("foo", "bar")); while (true) { ConsumerRecords<String, String> records = consumer.poll(100); for (ConsumerRecord<String, String> record : records) processAndUpdateDB(record); consumer.commitSync(); } Commit entire batch outside the loop!
- 34. 43 Minimize Duplicates for At Least Once Consuming 1. Commit your own offsets - Set autocommit.enable = false 2. Use Rebalance Listener to limit duplicates 3. Make sure you commit only what you are done processing 4. Note: New consumer is single threaded – one consumer per thread.
- 35. 44 Exactly Once Semantics At most once is easy At least once is not bad either – commit after 100% sure data is safe Exactly once is tricky • Commit data and offsets in one transaction • Idempotent producer Kafka Connect – many connectors (especially Confluent’s) are exactly once by using an external database to write events and store offsets in one transaction Kafka Streams – starting at 0.11.0 have easy to configure exactly once (exactly.once=true). Other stream processing systems – have their own thing.
- 36. 47 How we test Kafka? """Replication tests. These tests verify that replication provides simple durability guarantees by checking that data acked by brokers is still available for consumption in the face of various failure scenarios. Setup: 1 zk, 3 kafka nodes, 1 topic with partitions=3, replication-factor=3, and min.insync.replicas=2 - Produce messages in the background - Consume messages in the background - Drive broker failures (shutdown, or bounce repeatedly with kill -15 or kill -9) - When done driving failures, stop producing, and finish consuming - Validate that every acked message was consumed """
- 37. 48 Monitoring for Data Loss • Monitor for producer errors – watch the retry numbers • Monitor consumer lag – MaxLag or via offsets • Standard schema: • Each message should contain timestamp and originating service and host • Each producer can report message counts and offsets to a special topic • “Monitoring consumer” reports message counts to another special topic • “Important consumers” also report message counts • Reconcile the results
- 38. 49 Be Safe, Not Sorry Acks = all Max.block.ms = Long.MAX_VALUE Retries = MAX_INT ( Max.inflight.requests.per.connection = 1 ) Producer.close() Replication-factor >= 3 Min.insync.replicas = 2 Unclean.leader.election = false Auto.offset.commit = false Commit after processing Monitor!
- 39. 50 Thank You!
Hinweis der Redaktion
- Apache Kafka is no longer just pub-sub messaging. Because of its persistence and reliability, it makes a great place to manage general streams of events and to drive streaming applications.
- We are going to start by discussing reliability guarantees as implemented by the broker’s replication protocol. We then discuss how to configure the clients for better reliability. We’ll use Java clients as an example. For non-Java clients: The C client (librdkafka) works pretty much the same way – same configurations and guarantees will work. Same for clients in other languages based on Librdkafka. For other clients… its hard to make generalizations. Some are very different and the advice in this talk will not work for them.
- Low Level Diagram: Not talking about producer / consumer design yet…maybe this is too low-level though Show diagram of network send -> os socket -> NIC -> ---- NIC -> Os socket buffer -> socket -> internal message flow / socket server -> response back to client -> how writes get persisted to disk including os buffers, async write etc Then overlay places where things can fail.
- Low Level Diagram: Not talking about producer / consumer design yet…maybe this is too low-level though Show diagram of network send -> os socket -> NIC -> ---- NIC -> Os socket buffer -> socket -> internal message flow / socket server -> response back to client -> how writes get persisted to disk including os buffers, async write etc Then overlay places where things can fail.
- Highlight boxes with different color
- When Replica 3 is back, it will catch up
- Kafka exposes it’s binary TCP protocol via a Java api which is what we’ll be discussing here. So everything in the box is what’s happening inside the producer. Generally speaking, you have an application thread, or threads that take individual messages and “send” them to Kafka. What happens under the covers is that these messages are batched up where possible in order to amortize the overhead of the send, stored in a buffer and communicated over to kafka. After Kafka has completed it’s work, a response is returned back for each message. This happens asynchronously, using Java’s concurrent API. This response is comprised of either an exception or a metadata record. If the metadata is returned, which contains the offset, partition and topic, then things are good and we continue processing. However, if an error has returned the producer will automatically retry the failed message, up to a configurable # or amount of time. When this exception occurs and we have retries enabled, these retries actually just go right back to the start of the batches being prepared to send back to Kafka.
- Commit every 10 seconds, but we don’t really have any control over what’s processed, and this can lead to duplicates
- If you are doing too much work commits don’t count as heartbeat ->
- So lets so we have auto-commit enabled, and we are chugging along, and counting on the consumer to commit our offsets for us. This is great because we don’t have to code anything, and don’t have think about the frequency of commits and the impact that might have on our throughput. Life is good. But now we’ve lost a thread or a process. And we don’t really know where we are in the processing, Because the last auto-commit committed stuff that we hadn’t actually written to disk.
- So now we’re in a situation where we think we’ve read all of our data but we will have gaps in data. Note the same risk applies if we lose a partition or broker and get a new leader. OR