Ritta Narita interned at The University of Tokyo researching physics simulation. For two intern projects, she developed an original virtual machine for random forests in hivemall and implemented a socket manager with ServerEngine for fluentd's new multiprocess model. For hivemall, the virtual machine was 10 times faster than JavaScript and restricted code execution. For fluentd, the socket manager allows workers to share a listening socket and fully utilize multicore processors without additional configuration.

1. INTERNSHIP FINAL REPORT

2. Who are you?
Ritta Narita
(github:@naritta)
The University of Tokyo, Engineering M2
Researched about Physic simulation
I’ve worked in some companies.
2

3. Projects for Intern
hivemall: Original VM for Random Forests
!
fluentd: Socket Manager with ServerEngine
3

4. hivemall: Original VM for Random Forests
4

5. What’s Random Forest ?
make many decision trees,
accept a majority decision
decision tree(play golf or not)
to know the result of decision tree,
need calculation for bound features.
humidity
> 30 %?
whether
= sunny
wind speed
> 10 m/s ?
play golf
don’t play
don’t play
don’t play
yes
yes
yes
5

6. generate JS code
→execute using eval
!
At present, to calculate decision tree
if (x[0]==0){
if (x[1]>30){
return 1;}
・・・
}
else {
return 1;
}
x = [weather, humidity, wind]
0=play golf, 1=don’t play
humidity
> 30 %?
whether
= sunny
wind speed
> 10 m/s ?
play golf
don’t play
don’t play
don’t play
yes
yes
yes
6

7. !
!
due to using eval, can execute any code
!
For example
hostile JS code like infinite loop
→burden for TD
!
It’s difficult to restrict JS code
→need restricted environment to calculate decision tree
!
Problem for JS
7

8. Then
generate original op code from tree model
→execute on originalVM
PUT x[1]
PUT 0
IFEQ 10
!
・
・
・
x = [weather, humidity, wind]
0=play golf, 1=don’t play
if (x[0]==0){
if (x[1]>30){
return 1;}
・・・
}
else {
return 1;
}
8

9. What’s the merit?
・can find illegal code like infinite loop easily
・only for comparator, so very restricted
・less op code, very fast
9

10. My work
op code featured for comparator
only PUSH, POP, GOTO, IF~
!
can find infinite loop
In this code, supposed not to have loop
→don’t execute same code
10

11. hadoop version 2.6, Hive 1.2.0 (Tez 0.6.1)
!
hadoop cluster size: c3.2xlarge 8 nodes
!
!
randomforest
!
number of test examples in test_rf: 18083
!
number of trees: 500
!
!
!
compile num: 500
!
eval num: 500 * 18083
!
Javascript : 1062.04 s
(Nashorn)
!
VM: 106.84 s
comparison with JS
10 times faster
11

12. Why don’t you use Java bytecode and ASM ?
12

13. Because of the number of class loading
for example, if every clients make 500 models…
↓
too many class loading
If using one class and 500 method,
It is same.
13

14. summary
・very restricted, can find illegal code
!
・10 times faster
!
・future prospects:
can make it even faster by binary code
!
・merged in development branch
and will be released in v0.4
14

15. fluentd: Socket Manager with ServerEngine
15

16. In fluentd v0.14
produce
!
New multiprocess model
16

17. multiprocess at present
use in_multiprocess plugin
have to use multi sockets and assign each ports by user
super
visor
worker
worker
worker
port:
24224
port:
24226
port:
24225
17

18. multiprocess v0.14
super
visor
worker
worker
worker
port:
24224
using Socket Manager, share one listening socket
→can use multicore without any assignment
port:
24224
port:
24224
port:
24224
Socket
Manager
server
Socket
Manager
client
Socket
Manager
client
Socket
Manager
client
18

20. in Windows
20

21. can use multicore power fully without unconsciousness
setting file will get very simple 21
with SocketManagerwith in_multiprocess plugin
<source>
type multiprocess
<process>
cmdline -c /etc/td-agent/td-agent-child1.conf
</process>
<process>
cmdline -c /etc/td-agent/td-agent-child2.conf
</process>
</source>
!
#/etc/td-agent/td-agent-child1.conf
<source>
type forward
port 24224
</source>
!
#/etc/td-agent/td-agent-child2.conf
<source>
type forward
port 24225
</source>
<source>
type forward
port 24224
</source>
setting when using 2 core

22. To implement Socket Manager, I used ServerEngine
worker
worker
worker
super
visor
Server
Engine
live restart
Heartbeat via pipe
auto restart
22
ServerEngine is: a framework to implement
robust multiprocess servers like Unicorn.

24. Implementation (Unix)
②Unix Domain Socket (send_io file descriptor)
worker
worker
worker
Socket
Manager
client
Socket
Manager
client
Socket
Manager
client
FD
Spawn
Socket
Manager
server
super
visor
Server
Engine
①DRb (request listening socket)
24

25. Unix: very simple
Windows: a little complex
main difference
1. can’t share socket by FD
in Windows, socket descriptor ≠ file descriptor
It doesn’t make sense to share FD
(have to use Winsock2 API to share sockets)
!
2. have to lock accept
in unix, don’t need consider thundering herd
but do in windows.
25

26. Implementation (Windows)
DRb
create socket from port and bind
(WSASocket)
↓
duplicate exclusive socket by pid
(WSADuplicateSocket)
↓
get socket protocol (WSAProcolInfo)
worker
worker
worker
Socket
Manager
server
Socket
Manager
client
Socket
Manager
client
Socket
Manager
client
from WSAProcolInfo,
make WSASocket
↓
handle into FD
↓
IO.for_fd(FD)
send this IO to Cool.io
super
visor
Server
Engine
26

27. accept mutex
worker
worker
get
mutex
detach
release
mutex
attach
listening socket
to cool.io loop
accept
mutex
read and send data
to buffer/output
server socket
get
mutex
detach
release
mutex
attach
listening socket
to cool.io loop
accept
read and send data
to buffer/output
deal with post processing
in this process as it is
other process can listen
while this process is dealing with data
27

28. rotation in order
by accept mutex
①2376→②3456→③2696→④3388
→①2376→②3456→ 28

29. As a result of test,
Thundering herd doesn’t occur in windows.
Tentatively I implemented roughly with mutex,
but I want to use IOCP like livuv in the future.
!
Patches are welcome from Windows specialist!
29

30. benchmark result (unix)
AWS ubuntu 14.04 m4.xlarge
RPS IO
conventional
model
6798.69
/sec
1361.07
kb/s
new model
(4 workers)
13743.02
/sec
2751.29
kb/s
in_http → out_forward
30

31. benchmark result (windows)
AWS Microsoft Windows Server 2012 R2 m4.xlarge
RPS IO
conventional
model
1834.01
/sec
385.07
kb/s
new model
(4 workers)
3513.31
/sec
737.66
kb/s
in_http → out_forward
31

32. Future work
・Buffering in multiprocess
・accept mutex based IOCP…etc
summary
・Implemented fluentd Socket Manager with ServerEngine,
and will be faster without consciousness.
!
・There is details in ServerEngine Issue,
you can test my forked branch(fluentd and ServerEngine)
and I’ll send PR after this report.
32

33. That’s all,Thank you!
33

34. appendix

35. Why don’t you use Object serialization?
35

36. Because of memory problem
When Random forests model is big and many customers use it,
It is too much memory consumption
36

37. ServerEngine is:
To implement Socket Manager, I used ServerEngine
a framework to implement
robust multiprocess servers like Unicorn.
37

38. how to use Socket Manager in fluentd side
!
#get socket manager
socket_manager = ServerEngine::SocketManager.new_socket_manager
!
#get FD from socket manager
fd = socket_manager.get_tcp(bind, port)
!
#create listening socket from FD
lsock = TCPServer.for_fd(fd.to_i)
it doesn’t need consider about socket sharing in fluentd side,
ServerEngine deal with it inside.
38

40. Benchmark Result
I’ll add multiprocess buffering function,
After that I’ll do benchmark formally.
!
Tentatively Show the rough result
40