kafka reliability guarantees atl kafka user group

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Kafka Reliability Guarantees


But First…What’s NEW???• Released 0.9.0 in late November• 87 Contributors, 523 JIRAs, Bunch o’ new Features.• Security!

• Kerberos/SASL Authentication• Authorization Plugin• SSL

• Kafka Connect• Quotas• New Consumer****


Kafka• High Throughput• Low Latency• Scalable• Centralized• Real-time


“If data is the lifeblood of high technology, Apache Kafka is the circulatory system”

--Todd PalinoKafka SRE @ LinkedIn


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?


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


Network

Broker MachineClient Machine

Data Flow

Kafka Client

Broker

O/S Socket Buffer

NIC

NIC

Page Cache

Disk

Application Thread

O/S Socket Buffer

async

callback

✗

✗

✗

✗

✗

✗✗✗ data

ack / exception


Client Machine

Kafka Client

O/S Socket Buffer

NIC

Application Thread

✗

✗✗Broker Machine

Broker

NIC

Page Cache

Disk

O/S Socket Buffer

miss

✗

✗

✗

✗Network

Data Flow

✗

data

offsets

ZK

Kafka

✗


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


Replication and ISRs

0

1

2

0

1

2

0

1

2

Producer

Broker 100

Broker 101

Broker 102

Topic:Partition

s:Replicas

:

my_topic33

Partition:

Leader:ISR:

1101

100,102

Partition:

Leader:ISR:

2102

101,100

Partition:

Leader:ISR:

0100

101,102


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 in 0.9

• Connection to Zookeeper


Terminology• Acked

• Producers will not retry sending. • Depends on producer setting

• Committed• Consumers can read. • Only when message got to all ISR.

• replica.lag.time.max.ms • how long can a dead replica prevent consumers from reading?


Replication• Acks = all

• only waits for in-sync replicas to reply.

Replica 3

100

Replica 2

100

Replica 1

100

Time


Replication

Replica 3

100

Replica 2

100101

Replica 1

100101

Time

• Replica 3 stopped replicating for some reason

Acked in acks = all“committed”

Acked in acks = 1but not

“committed”


Replication

Replica 3

100

Replica 2

100101

Replica 1

100101

Time

• One replica drops out of ISR, or goes offline• All messages are now acked and committed


Replication

Replica 3

100

Replica 2

100101

Replica 1

100101102103104Time

• 2nd Replica drops out, or is offline


Replication

Replica 3

100

Replica 2

100101

Replica 1

100101102103104Time

• Now we’re in trouble

✗


Replication• If Replica 2 or 3 come back online before the leader, you can will lose data.

Replica 3

100

Replica 2

100101

Replica 1

100101102103104Time

All those are “acked” and “committed”


So what to do• Disable Unclean Leader Election

• unclean.leader.election.enable = false• Set replication factor

• default.replication.factor = 3• Set minimum ISRs

• min.insync.replicas = 2


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)


Replication• Replication = 3• Min ISR = 2

Replica 3

100

Replica 2

100

Replica 1

100

Time


Replication

Replica 3

100

Replica 2

100101

Replica 1

100101

Time

• One replica drops out of ISR, or goes offline


Replication

Replica 3

100

Replica 2

100101

Replica 1

100101102

103104

Time

• 2nd Replica fails out, or is out of sync

Buffers in

Producer


Producer Internals• Producer sends batches of messages to a buffer

M3

Application Thread

Application Thread

Application Thread

send()M2 M1 M0

Batch 3Batch 2Batch 1

Fail? response

retry

Update Future

callback

drain

Metadata orException


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


“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 • message.send.max.retries (server config) defaults to 3• In flight requests could lead to message re-ordering


Consumer• Three choices for Consumer API

• Simple Consumer• High Level Consumer• “New Consumer”


New Consumer• Available in Kafka 0.9.0• Provides better control over offset management• Enhanced server-side group management


Consumer Offsets

P0 P2 P3 P4 P5 P6

Consumer Group

Consumer1

Consumer2

Consumer 3

Consumer 4


Consumer Offsets

P0 P2 P3 P4 P5 P6

Consumer Group

Consumer 1

Consumer 2

Consumer 3

Consumer 4

Commit?


Consumer Offsets

P0 P2 P3 P4 P5 P6

Consumer

Consumer 1

Consumer 2

Consumer 3

Consumer 4

Auto-commit

enabled

✗Commit


Consumer Offsets

P0 P2 P3 P4 P5 P6

Consumer

Thread 1 Thread 2 Thread 3 Thread 4

Auto-commit

enabled

✗


Consumer Offsets

P0 P2 P3 P4 P5 P6

Consumer


Auto-commit

enabled Consumer

Picks up here


Consumer Offsets

P0 P2 P3 P4 P5 P6

Consumer


Commit


Consumer Offsets

P0 P2 P3 P4 P5 P6

Consumer


Commit

Offset commits

for all threads


P0 P2 P3 P4 P5 P6

Consumer 1

Consumer 2

Consumer 3

Consumer 4

Consumer Offsets

Auto-commit

DISABLED

Commit


Consumer Recommendations• Set autocommit.enable = false• Manually commit offsets after the message data is processed / persisted consumer.commitOffsets();

• Run each consumer in it’s own thread


New Consumer!• No Zookeeper! At all!• Rebalance listener• Commit:

• Commit• Commit async• Commit( offset)

• Seek(offset)


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


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


Be Safe, Not Sorry• Acks = all• Block.on.buffer.full = true• Retries = MAX_INT• ( Max.inflight.requests.per.connect = 1 )• Producer.close()• Replication-factor >= 3• Min.insync.replicas = 2• Unclean.leader.election = false• Auto.offset.commit = false• Commit after processing• Monitor!


Thank you

kafka reliability guarantees atl kafka user group

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