Building a Distributed Message Log from Scratch

Apache Kafka has shown that the log is a powerful abstraction for data-intensive applications. It can play a key role in managing data and distributing it across the enterprise efficiently. Vital to any data plane is not just performance, but availability and scalability. In this session, we examine what a distributed log is, how it works, and how it can achieve these goals. Specifically, we'll discuss lessons learned while building NATS Streaming, a reliable messaging layer built on NATS that provides similar semantics. We'll cover core components like leader election, data replication, log persistence, and message delivery. Come learn about distributed systems!

Transcript

1. Building a Distributed Message Log from Scratch Tyler Treat · Iowa Code Camp · 11/04/17
2. - Messaging Nerd @ Apcera - Working on nats.io - Distributed systems - bravenewgeek.com Tyler Treat
4. - The Log
 -> What?
 -> Why? - Implementation
 -> Storage mechanics
 -> Data-replication techniques
 -> Scaling message delivery
 -> Trade-offs and lessons learned Outline
5. The Log
6. The Log A totally-ordered, append-only data structure.
7. The Log 0
8. 0 1 The Log
9. 0 1 2 The Log
10. 0 1 2 3 The Log
11. 0 1 2 3 4 The Log
12. 0 1 2 3 4 5 The Log
13. 0 1 2 3 4 5 newest record oldest record The Log
14. newest record oldest record The Log
15. Logs record what happened and when.
16. caches databases indexes writes
18. Examples in the wild: -> Apache Kafka
 -> Amazon Kinesis -> NATS Streaming
 -> Tank
19. Key Goals: -> Performance -> High Availability -> Scalability
20. The purpose of this talk is to learn…
 -> a bit about the internals of a log abstraction. -> how it can achieve these goals. -> some applied distributed systems theory.
21. You will probably never need to build something like this yourself, but it helps to know how it works.
22. Implemen- tation
23. Implemen- tation Don’t try this at home.
24. Some first principles… Storage Mechanics • The log is an ordered, immutable sequence of messages • Messages are atomic (meaning they can’t be broken up) • The log has a notion of message retention based on some policies (time, number of messages, bytes, etc.) • The log can be played back from any arbitrary position • The log is stored on disk • Sequential disk access is fast* • OS page cache means sequential access often avoids disk
25. http://queue.acm.org/detail.cfm?id=1563874
26. avg-cpu: %user %nice %system %iowait %steal %idle 13.53 0.00 11.28 0.00 0.00 75.19 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn xvda 0.00 0.00 0.00 0 0 iostat
27. Storage Mechanics log file 0
28. Storage Mechanics log file 0 1
29. Storage Mechanics log file 0 1 2
30. Storage Mechanics log file 0 1 2 3
31. Storage Mechanics log file 0 1 2 3 4
32. Storage Mechanics log file 0 1 2 3 4 5
33. Storage Mechanics log file … 0 1 2 3 4 5
34. Storage Mechanics log segment 3 file log segment 0 file 0 1 2 3 4 5
35. Storage Mechanics log segment 3 file log segment 0 file 0 1 2 3 4 5 0 1 2 0 1 2 index segment 0 file index segment 3 file
36. Zero-copy Reads user space kernel space page cache disk socket NIC application read send
37. Zero-copy Reads user space kernel space page cache disk NIC sendfile
38. Left as an exercise for the listener…
 -> Batching
 -> Compression
39. caches databases indexes writes
40. caches databases indexes writes
41. caches databases indexes writes
42. How do we achieve high availability and fault tolerance?
43. Questions:
 -> How do we ensure continuity of reads/writes? -> How do we replicate data? -> How do we ensure replicas are consistent? -> How do we keep things fast? -> How do we ensure data is durable?
44. Questions:
 -> How do we ensure continuity of reads/writes? -> How do we replicate data? -> How do we ensure replicas are consistent? -> How do we keep things fast? -> How do we ensure data is durable?
45. caches databases indexes writes
46. Questions:
 -> How do we ensure continuity of reads/writes? -> How do we replicate data? -> How do we ensure replicas are consistent? -> How do we keep things fast? -> How do we ensure data is durable?
47. Data-Replication Techniques 1. Gossip/multicast protocols Epidemic broadcast trees, bimodal multicast, SWIM, HyParView, NeEM
 2. Consensus protocols 2PC/3PC, Paxos, Raft, Zab, chain replication
48. Questions:
 -> How do we ensure continuity of reads/writes? -> How do we replicate data? -> How do we ensure replicas are consistent? -> How do we keep things fast? -> How do we ensure data is durable?
49. Data-Replication Techniques 1. Gossip/multicast protocols Epidemic broadcast trees, bimodal multicast, SWIM, HyParView, NeEM
 2. Consensus protocols 2PC/3PC, Paxos, Raft, Zab, chain replication
50. Consensus-Based Replication 1. Designate a leader 2. Replicate by either:
 a) waiting for all replicas
 —or— b) waiting for a quorum of replicas
51. Pros Cons All Replicas Tolerates f failures with f+1 replicas Latency pegged to slowest replica Quorum Hides delay from a slow replica Tolerates f failures with 2f+1 replicas Consensus-Based Replication
52. Replication in Kafka 1. Select a leader 2. Maintain in-sync replica set (ISR) (initially every replica) 3. Leader writes messages to write-ahead log (WAL) 4. Leader commits messages when all replicas in ISR ack 5. Leader maintains high-water mark (HW) of last committed message 6. Piggyback HW on replica fetch responses which replicas periodically checkpoint to disk
53. 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes Replication in Kafka
54. Failure Modes 1. Leader fails
55. 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes Leader fails
56. 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes Leader fails
57. 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes Leader fails
58. 0 1 2 3 HW: 3 0 1 2 3 HW: 3 b2 (leader) b3 (follower) ISR: {b2, b3} writes Leader fails
59. Failure Modes 1. Leader fails
 2. Follower fails
60. 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes Follower fails
61. 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes Follower fails
62. 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes Follower fails replica.lag.time.max.ms
63. 0 1 2 3 4 5 b1 (leader) HW: 3 0 1 2 3 HW: 3 b3 (follower) ISR: {b1, b3} writes Follower fails replica.lag.time.max.ms
64. Failure Modes 1. Leader fails
 2. Follower fails
 3. Follower temporarily partitioned
65. 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes Follower temporarily
 partitioned
66. Follower temporarily
 partitioned 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes
67. Follower temporarily
 partitioned 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes replica.lag.time.max.ms
68. Follower temporarily
 partitioned 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 3 0 1 2 3 HW: 3 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2} writes replica.lag.time.max.ms
69. Follower temporarily
 partitioned 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 5 0 1 2 3 HW: 5 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2} writes 5
70. Follower temporarily
 partitioned 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 5 0 1 2 3 HW: 5 HW: 3 b2 (follower) b3 (follower) ISR: {b1, b2} writes 5
71. Follower temporarily
 partitioned 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 5 0 1 2 3 HW: 5 HW: 4 b2 (follower) b3 (follower) ISR: {b1, b2} writes 5 4
72. Follower temporarily
 partitioned 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 5 0 1 2 3 HW: 5 HW: 5 b2 (follower) b3 (follower) ISR: {b1, b2} writes 5 4 5
73. Follower temporarily
 partitioned 0 1 2 3 4 5 b1 (leader) 0 1 2 3 4 HW: 5 0 1 2 3 HW: 5 HW: 5 b2 (follower) b3 (follower) ISR: {b1, b2, b3} writes 5 4 5
74. Replication in NATS Streaming 1. Metadata Raft group replicates client state
 2. Separate Raft group per topic replicates messages and subscriptions
 3. Conceptually, two logs: Raft log and message log
75. http://thesecretlivesofdata.com/raft
76. Challenges 1. Scaling Raft
77. Scaling Raft With a single topic, one node is elected leader and it heartbeats messages to followers
78. Scaling Raft As the number of topics increases unbounded, so do the number of Raft groups.
79. Scaling Raft Technique 1: run a fixed number of Raft groups and use a consistent hash to map a topic to a group.
80. Scaling Raft Technique 2: run an entire node’s worth of topics as a single group using a layer on top of Raft. https://www.cockroachlabs.com/blog/scaling-raft
81. Challenges 1. Scaling Raft 2. Dual writes
82. Dual Writes Raft Store committed
83. Dual Writes msg 1 Raft Store committed
84. Dual Writes msg 1 msg 2 Raft Store committed
85. Dual Writes msg 1 msg 2 Raft msg 1 msg 2 Store committed
86. Dual Writes msg 1 msg 2 sub Raft msg 1 msg 2 Store committed
87. Dual Writes msg 1 msg 2 sub msg 3 Raft msg 1 msg 2 Store committed
88. Dual Writes msg 1 msg 2 sub msg 3 add peer msg 4 Raft msg 1 msg 2 msg 3 Store committed
89. Dual Writes msg 1 msg 2 sub msg 3 add peer msg 4 Raft msg 1 msg 2 msg 3 Store committed
90. Dual Writes msg 1 msg 2 sub msg 3 add peer msg 4 Raft msg 1 msg 2 msg 3 msg 4 Store commit
91. Dual Writes msg 1 msg 2 sub msg 3 add peer msg 4 Raft msg 1 msg 2 msg 3 msg 4 Store 0 1 2 3 4 5 0 1 2 3 physical offset logical offset
92. Dual Writes msg 1 msg 2 sub msg 3 add peer msg 4 Raft msg 1 msg 2 Index 0 1 2 3 4 5 0 1 2 3 physical offset logical offset msg 3 msg 4
93. Treat the Raft log as our message write-ahead log.
94. Questions:
 -> How do we ensure continuity of reads/writes? -> How do we replicate data? -> How do we ensure replicas are consistent? -> How do we keep things fast? -> How do we ensure data is durable?
95. Performance 1. Publisher acks 
 -> broker acks on commit (slow but safe)
 -> broker acks on local log append (fast but unsafe)
 -> publisher doesn’t wait for ack (fast but unsafe) 
 2. Don’t fsync, rely on replication for durability
 3. Keep disk access sequential and maximize zero-copy reads
 4. Batch aggressively
96. Questions:
 -> How do we ensure continuity of reads/writes? -> How do we replicate data? -> How do we ensure replicas are consistent? -> How do we keep things fast? -> How do we ensure data is durable?
97. Durability 1. Quorum guarantees durability
 -> Comes for free with Raft
 -> In Kafka, need to configure min.insync.replicas and acks, e.g.
 topic with replication factor 3, min.insync.replicas=2, and
 acks=all
 2. Disable unclean leader elections
 3. At odds with availability,
 i.e. no quorum == no reads/writes
98. Scaling Message Delivery 1. Partitioning
99. Partitioning is how we scale linearly.
100. caches databases indexes writes
101. HELLA WRITES caches databases indexes
102. caches databases indexes HELLA WRITES
103. caches databases indexes writes writes writes writes Topic: purchases Topic: inventory
104. caches databases indexes writes writes writes writes Topic: purchases Topic: inventory Accounts A-M Accounts N-Z SKUs A-M SKUs N-Z
105. Scaling Message Delivery 1. Partitioning 2. High fan-out
106. High Fan-out 1. Observation: with an immutable log, there are no stale/phantom reads
 2. This should make it “easy” (in theory) to scale to a large number of consumers (e.g. hundreds of thousands of IoT/edge devices)
 3. With Raft, we can use “non-voters” to act as read replicas and load balance consumers
107. Scaling Message Delivery 1. Partitioning 2. High fan-out 3. Push vs. pull
108. Push vs. Pull • In Kafka, consumers pull data from brokers • In NATS Streaming, brokers push data to consumers • Pros/cons to both:
 -> With push we need flow control; implicit in pull
 -> Need to make decisions about optimizing for
 latency vs. throughput
 -> Thick vs. thin client and API ergonomics
109. Scaling Message Delivery 1. Partitioning 2. High fan-out 3. Push vs. pull 4. Bookkeeping
110. Bookkeeping • Two ways to track position in the log:
 -> Have the server track it for consumers
 -> Have consumers track it
 • Trade-off between API simplicity and performance/server complexity
 • Also, consumers might not have stable storage (e.g. IoT device, ephemeral container, etc.)
 • Can we split the difference?
111. Offset Storage • Can store offsets themselves in the log (in Kafka, originally had to store them in ZooKeeper)
 • Clients periodically checkpoint offset to log
 • Use log compaction to retain only latest offsets
 • On recovery, fetch latest offset from log
112. Offset Storage bob-foo-0
 11 alice-foo-0
 15 Offsets 0 1 2 3 bob-foo-1
 20 bob-foo-0
 18 4 bob-foo-0
 21
113. Offset Storage bob-foo-0
 11 alice-foo-0
 15 Offsets 0 1 2 3 bob-foo-1
 20 bob-foo-0
 18 4 bob-foo-0
 21
114. Offset Storage alice-foo-0
 15 bob-foo-1
 20 Offsets 1 2 4 bob-foo-0
 21
115. Offset Storage Advantages:
 -> Fault-tolerant
 -> Consistent reads
 -> High write throughput (unlike ZooKeeper)
 -> Reuses existing structures, so less server
 complexity
116. Trade-offs and Lessons Learned 1. Competing goals
117. Competing Goals 1. Performance
 -> Easy to make something fast that’s not fault-tolerant or scalable
 -> Simplicity of mechanism makes this easier
 -> Simplicity of “UX” makes this harder 2. Scalability (and fault-tolerance)
 -> Scalability and FT are at odds with simplicity
 -> Cannot be an afterthought—needs to be designed from day 1 3. Simplicity (“UX”)
 -> Simplicity of mechanism shifts complexity elsewhere (e.g. client)
 -> Easy to let server handle complexity; hard when that needs to be
 distributed and consistent while still being fast
118. Trade-offs and Lessons Learned 1. Competing goals 2. Availability vs. Consistency
119. Availability vs. Consistency • CAP theorem • Consistency requires quorum which hinders availability and performance • Minimize what you need to replicate
120. Trade-offs and Lessons Learned 1. Competing goals 2. Availability vs. Consistency 3. Aim for simplicity
121. Distributed systems are complex enough.
 Simple is usually better (and faster).
122. Trade-offs and Lessons Learned 1. Competing goals 2. Availability vs. Consistency 3. Aim for simplicity 4. Lean on existing work
123. Don’t roll your own coordination protocol,
 use Raft, ZooKeeper, etc.
124. Trade-offs and Lessons Learned 1. Competing goals 2. Availability vs. Consistency 3. Aim for simplicity 4. Lean on existing work 5. There are probably edge cases for which you haven’t written tests
125. There are many failure modes, and you can only write so many tests.
 
 Formal methods and property-based/ generative testing can help.
127. Trade-offs and Lessons Learned 1. Competing goals 2. Availability vs. Consistency 3. Aim for simplicity 4. Lean on existing work 5. There are probably edge cases for which you haven’t written tests 6. Be honest with your users
128. Don’t try to be everything to everyone. Be explicit about design decisions, trade- offs, guarantees, defaults, etc.
129. Thanks! @tyler_treat
 bravenewgeek.com