Serverless Is Ruby Future

#rubyrussia 2019

Transcript

1. SERVERLESS IS UBY FUTURE NIKOLAY SVERCHKOV @ssnickolay
3. evilmartians.com New
4. ME Community Speech TWO OPTIONS
5. ME Community Speech VOTE 26 % 74 %
6. SERVERLESS AXIOMS Event-Driven Architecture
7. SERVERLESS AXIOMS Event-Driven Architecture Stateless
8. SERVERLESS AXIOMS Event-Driven Architecture Stateless ♾ Infinite Scaling
9. SERVERLESS AXIOMS Event-Driven Architecture Stateless ♾ Infinite Scaling Pay when the code is running
10. SERVERLESS AXIOMS Event-Driven Architecture Stateless ♾ Infinite Scaling Pay when the code is running
11. SERVERLESS UNDER THE MICROSCOPE
12. LOG $ serverless invoke -f index -l
13. LOG $ serverless invoke -f index -l START RequestId: <UUID> Version: $LATEST END RequestId: <UUID> REPORT RequestId: <UUID> Duration: 1.48 ms Billed Duration: 100 ms Memory Size: 1024 MB Max Memory Used: 31 MB
14. LOG $ serverless invoke -f index -l START RequestId: <UUID> Version: $LATEST END RequestId: <UUID> REPORT RequestId: <UUID> Duration: 1.48 ms Billed Duration: 100 ms Memory Size: 1024 MB Max Memory Used: 31 MB
15. SERVERLESS AXIOMS Event-Driven Architecture Stateless ♾ Infinite Scaling Pay when the code is running
16. LOG $ serverless invoke -f index -l START RequestId: <UUID> Version: $LATEST END RequestId: <UUID> REPORT RequestId: <UUID> Duration: 1.48 ms Billed Duration: 100 ms Memory Size: 1024 MB Max Memory Used: 31 MB
17. LOG $ serverless invoke -f index -l START RequestId: <UUID> Version: $LATEST END RequestId: <UUID> REPORT RequestId: <UUID> Duration: 1.48 ms Billed Duration: 100 ms Memory Size: 1024 MB Max Memory Used: 31 MB
18. WHAT ABOUT CPU?
19. AWS Lambda allocates CPU power proportional to the memory… (с) Amazon Docs
20. AWS Lambda allocates CPU power proportional to the memory… For example, if you allocate 256 MB memory, your Lambda function will receive twice the CPU share than if you allocated only 128 MB. (с) Amazon Docs
21. (CPU, CST) ""<=> MEMORY http://bit.ly/2UZ7xDL Completely Fair Scheduler (CFS)
22. https://aws.amazon.com/lambda/pricing/ 6$ "=> 28.8M/128mb!
23. COST SPEED ⏫ https://amzn.to/2Ix2t2O
24. COST ""<=> LANGUAGE
25. $ wrk -t10 -c400 -d20s -R40000 $ curl http://endpoint.url/[ruby/go] "=> {“success”: true} VS CHALLENGE:
26. 0 250 500 750 1000 0 1 2 4 5 MAX AVG AVG ~80ms 31712 requests in 20.01s Requests/sec: 1584.83 BATTLE (128 MB)
27. 0 250 500 750 1000 0 1 2 4 5 0 250 500 750 1000 0 1 2 4 5 MAX AVG AVG ~1.8ms AVG ~80ms 31712 requests in 20.01s Requests/sec: 1584.83 50347 requests in 20.00s Requests/sec: 2517.30 BATTLE (128 MB)
28. 0 250 500 750 1000 0 1 2 4 5 0 250 500 750 1000 0 1 2 4 5 MAX AVG AVG ~1.8ms AVG ~80ms 31712 requests in 20.01s Requests/sec: 1584.83 50347 requests in 20.00s Requests/sec: 2517.30 W IN BATTLE (128 MB)
29. 0 250 500 750 1000 0 1 2 4 5 0 250 500 750 1000 0 1 2 4 5 MAX AVG AVG ~1.8ms AVG ~80ms 31712 requests in 20.01s Requests/sec: 1584.83 50347 requests in 20.00s Requests/sec: 2517.30 W IN * BATTLE (128 MB)
30. BATTLE (128 MB) AVG ~1.8ms AVG ~80ms “Billed Duration: 100 ms” (c) Lambda Docs
31. WHEN YOU REWRITE EVERYTHING ON GO EXPECTATION REALITY
33. FREE! Free Enterprise
34. FREE! Free Enterprise LYING http://bit.ly/2lCt2dA
35. PREDICTABLY
36. PREDICTABLY
37. PREDICTABLY
38. http://serverlesscalc.com/ $2400 450M!! $918 1GB
39. BATTLE (128 MB) AVG ~1.8ms AVG ~80ms ❓❓❓❓❓❓❓❓❓ ❓❓❓❓❓❓❓❓❓❓ ❓ ❓ ❓ ❓ ❓
40. http://rubyonjets.com/
41. RAILS ON SERVERLESS Tung Nguyen
42. PURE RUBY LAMBDA # handler.rb require 'json' def heartbeat(event:, context:) { statusCode: 200, body: JSON.generate(success: true) } end
43. BATTLE (128 MB) AVG ~39ms AVG ~80ms
44. COST ""<=> DEPENDENCIES
45. MINIMIZE THE COMPLEXITY OF YOUR DEPENDENCIES
46. MINIMIZE THE COMPLEXITY OF YOUR DEPENDENCIES
47. MINIMIZE THE COMPLEXITY OF YOUR DEPENDENCIES
48. NO RAILS GOOD!
49. CHEAPLY NICE NO RAILS GOOD!
50. CHEAPLY NICE EVENTS AWESOME NO RAILS GOOD!
51. CHEAPLY NICE EVENTS AWESOME NO RAILS GOOD! SCALING AMAZING
52. TALK ABOUT THE PROBLEMS TELL THE TRUTH Sponsored by @stacey_om
53. IS INFINITE SCALING ALWAYS GOOD?
54. # before $ puma -t 8:32 -w 1 # after $ puma -t 8:32 -w 2 HOW RUBY DEV SCALES APP
56. SERVERLESS AXIOMS Event-Driven Architecture Stateless ♾ Infinite Scaling Pay when the code is running
57. When you write your Lambda function code, do not assume that AWS Lambda automatically reuses the execution context for subsequent function invocations…. (c) AWS Docs AWS LAMBDA EXECUTION CONTEXT
58. EXECUTION CONTEXT # 1M symbols work = proc { 123_456_789 "** 123_456 } def heartbeat(event:, context:) { statusCode: 200, body: … } end
59. EXECUTION CONTEXT # 1M symbols work = proc { 123_456_789 !** 123_456 } def heartbeat(event:, context:) value = work.() # not cached { statusCode: 200, body: JSON.generate(value: value) } end
60. EXECUTION CONTEXT # 1M symbols work = proc { 123_456_789 !** 123_456 } value = work.() # cached def heartbeat(event:, context:) { statusCode: 200, body: JSON.generate(value: value) } end
61. CONNECTION POOL pool = DB"::ConnectionPool.new() def heartbeat(event:, context:) connection = pool.get() # use connection connection.release() end
62. https://stackoverflow.com/q/54110697
63. SOLUTIONS* CLASSIC ($)
64. SOLUTIONS* CLASSIC ($) MODERN ($$) Serverless
65. SCALE ⬇ DOWSCALE ⬇ SCALE ⬇ …
66. BURSTY TRAFFIC BURSTY LOAD BURSTY ???
67. EXAMPLE ML
68. EXAMPLE: ETL
69. EXAMPLE: ETL
70. EXAMPLE: BLOG
71. GOTO 0; SERVERLESS IS RUBY FUTURE
72. GOTO 0; SERVERLESS IS RUBY FUTURE
73. DREAMS ABOUT SERVERLESS
74. DREAMS ABOUT SERVERLESS Incredible Processing Power
75. SERVERLESS Massive User Job
76. SERVERLESS Backends Massive User Job DIFFERENT BACKENDS
77. SERVERLESS Backends Massive User Job 10_000.times do Thread.new(job) end
78. SERVERLESS Backends Burst-Parallel Job
79. SERVERLESS Burst-Parallel Job Video Encoding: VP8
80. Burst-Parallel Job Video Encoding: Classic encode([, , …, ]) "-> keyframe + interframe(2:n)
81. Burst-Parallel Job Video Encoding: Existing encode([, , …, ]) "-> keyframe + interframe(2:n) 1MB +25kb * n
82. Burst-Parallel Job Video Encoding: Existing encode([, , …, ]) "-> keyframe + interframe(2:n) decode(keyframe + interframe[2:n]) "-> [, , …, ] 1MB +25kb * n
83. Burst-Parallel Job Video Encoding: Existing encode(i[1:10]) "-> keyframe1 + interframe(2:10) encode(i[11:20]) "-> keyframe2 + interframe(12:20) encode(i[21:30]) "-> keyframe3 + interframe(22:30) 1MB * N
84. VIDEO ENCODING: ALFALA http://bit.ly/2lZFrbO
85. Burst-Parallel Job preprocessor(compiler (assembler(linker (archiver(indexer (strip(source_file)…) Software Compilation: 7 Steps
86. Burst-Parallel Job Software Compilation
87. Burst-Parallel Job Software Compilation Video Encoding
88. DIRECTED ACYCLIC GRAPH BRO NOT A BRO
89. DIRECTED ACYCLIC GRAPH Problem: Different Nodes g f h m k l p r
90. DAG "=> FUNCTIONS preprocessor(compiler (assembler(linker (archiver(indexer (strip(source_file)…)
91. f(g(h(p(…)…)
92. f(g(i)) !== λ(f, λ(g, i)) THE NEW ABSTRACTION: λ
93. f(g(i)) !== λ(f, λ(g, i)) f = proc { |a| a + 1 } g = proc { |a| a + 2 } f.call(g.call(0)) # "=> 3 THE NEW ABSTRACTION: λ
94. f(g(i)) !== λ(f, λ(g, i)) f = proc { |a| a + 1 } g = proc { |a| a + 2 } f.call(g.call(0)) # !=> 3 λ = proc { |f, i| f.(i) } λ.call(f, λ.call(g, 0)) # "=> 3 THE NEW ABSTRACTION: λ
95. f(g(h(p(…)…) !== λ(f, λ(g, λ(h, λ(p(…)…)
96. f(g(h(p(…)…) !== λ(f, λ(g, λ(h, λ(p(…)…) λ(ƒ,arg) !=> arg WHERE
97. λ(ƒ,arg) !=> arg
98. λ(ƒ,arg) !=> arg λ(ƒ,arg) "=> Θ,arg
99. λ(ƒ,arg) !=> arg λ(ƒ,arg) !=> Θ,arg λ(T) "=> T
100. DIRECTED ACYCLIC GRAPH T2 T1 T3 T5 T4 T6 T7 T8 λ(T) "=> T, T{i} "<- T
101. SERVERLESS Backends Burst-Parallel Job
102. SERVERLESS Backends Burst-Parallel Job λ(T) "=> T
103. SERVERLESS Backends Burst-Parallel Job λ(T) "=> T λ
104. SERVERLESS Backends Burst-Parallel Job λ(T) "=> T λ IR T
105. SERVERLESS Backends Burst-Parallel Job IR λ(T) "=> T T λ STATIC
106. SERVERLESS Backends Burst-Parallel Job λ IR T Frontends T generator λ(T) "=> T
107. SERVERLESS Backends Burst-Parallel Job λ IR T Frontends T generator λ(T) "=> T
108. Burst-Parallel Job IR Frontends Coordinator HTTP λ
109. SERVERLESS Backend Burst-Parallel Job IR Frontends Coordinator STATELESS HTTP
110. Burst-Parallel Job IR Frontends Coordinator S3 HTTP λ
111. THE FUTURE IS HERE!
112. gg Backends Burst-Parallel Job gg IR Frontends gg Coordinator C++ HTTP ProtoBuf S3 Stanford Systems and Networking Research
113. Fib(4) fib(4) fib(3) fib(2) fib(2) fib(1) fib(1) fib(0) fib(1) fib(0) + + + +
114. Fib(4) fib(4) fib(3) fib(2) fib(2) fib(1) fib(1) fib(0) fib(1) fib(0) + + + + Hash#fib1 Hash#fib2
115. Нихуя не понял Но очень интересно! чего
116. gg FRAMEWORK https://stanford.io/2mtmiiH
117. DEMO
118. DEMO http://bit.ly/2m0G3Oo https://asciinema.org/a/270154
119. RESULTS gg real user sys 1m17.670s 1m49.040s 0m7.820s real user sys 0m43.354s 0m3.830s 0m5.490s real user sys 0m29.376s 0m0.950s 0m1.400s
120. RESULTS gg real user sys 1m17.670s 1m49.040s 0m7.820s real user sys 0m43.354s 0m3.830s 0m5.490s real user sys 0m29.376s 0m0.950s 0m1.400s
121. RESULTS gg Total: 1m56s Total: 0m38s ~X3 faster
122. COMPARISON OF COLD-CACHE BUILD TIMES https://cs.stanford.edu/~matei/papers/2019/usenix_atc_gg.pdf
123. DARK MAGIC!
124. https://github.com/StanfordSNR/gg
125. GOTO 0; SERVERLESS IS RUBY FUTURE
126. “As a computing substrate, we suspect cloud functions are in a similar position to Graphics Processing Units in the 2000s. At the time, GPUs were designed solely for 3D graphics, but the community gradually recognized that they had become programmable enough to execute some parallel algorithms unrelated to graphics.” (c)
127. GOTO 0; SERVERLESS IS RUBY FUTURE
128. SORBET?
129. IT saṃsāra Java Erlang C C++ "=> Scala "=> Elixir "=> Go "=> Rust
130. RUBY SELF-IDENTIFICATION
131. RUBY IS Fast Coding
132. RUBY IS Startups (Rails) Fast Coding
133. RUBY IS Startups (Rails) GIL Sensitive Fast Coding
134. RUBY IS Startups (Rails) オブジェクト指向スクリプト⾔言語 Ruby GIL Sensitive Fast Coding
135. RUBY IS Startups (Rails) GIL Sensitive The Object-oriented Scripting Language Ruby Fast Coding
136. RUBY IS Startups (Rails) GIL Sensitive The Object-oriented Scripting Language Ruby Fast Coding
137. RUBY IS Startups (Rails) GIL Sensitive The Object-oriented Scripting Language Ruby Fast Coding
138. RUBY IS Startups (Rails) GIL Sensitive The Object-oriented Scripting Language Ruby Fast Coding
139. RUBY IS Startups (Rails) GIL Sensitive The Object-oriented Scripting Language Ruby Fast Coding
140. 60 SECNDS OF SUMMARY
141. @ssnickolay Nikolay Sverchkov @ssnickolay THANK YOU! @evilmartians http://evl.ms/blog http://evl.ms/telegram