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FROM DELIVERING
PLUGINS TO
DELIVERING "AS A
SERVICE", SOME
ADVICES TO RUN IT EASILY
QUENTIN ADAM AT
ATLASSIAN CONNECT WEEK 2017
@WAXZCE
2013
Quentin ADAM from the Clever Cloud
@waxzce on twitter – github- soundcloud – instagram ….
WHO AM I ?
MY DAY TO DAY WORK :
CLEVER CLOUD,
THE IT AUTOMATION COMPANY
KEEP YOUR APPS ONLINE. MADE WITH
NODE.JS, SCALA, JAVA, RUBY, PHP,
PYTHON, GO…
Cloud & on premise ;-)
AND LEARN A LOT OF THINGS ABOUT
YOUR CODE, APPS, AND GOOD/BAD
DESIGN…
GIVE BACK TO THE COMMUNITY
NEVER
GONNA
LET YOU
DOWN
clever-cloud.com
FROM
SHIPPING
JAR TO
OPERATE A
SERVICE
WHEN YOU
NEED TO
SCALE
THERE ARE 2 WAYS
GROWING AND GROWING
UNTIL YOU EXPLODE OR
BECOME WEIRD
OR SPLIT THE WORK AND
MAKE YOUR SOFTWARE
WORK AS A TEAM
Build an army of fat app
YOU CAN DO BOTH
SO WE NEED TO BE ABLE
TO DISPATCH THE WORK
SCALE OUT
• Many workers
doing the same
thing
• No SPOF
• Growing is more
easy
• Introduce best
practice
SCALE UP
• 1 Fat instance
• 1 Fat application
• SPOF (single point
of failure)
• Hard to maintain
• Always has a limit
• Short term
meaning
IF YOU ONLY SCALE UP,
YOU GONNA HAVE A BAD TIME
SO, HOW TO
SCALE OUT
?
JUST SOME FACTS
SPLIT PROCESS AND
STORAGE
Storage
• Databases
• Files
• Sessions
• Events
• …
Code
• Can be replicated
• Stateless
• Process
Picking one instance or another doesn’t matter
STATELESSNESS IS THE KEY
CONSIDER MORE
THINGS AS DATA
• User account
• Users data
• Files
• Sessions
• Events
UNDERSTAND WHAT
IS PROVIDED
TRUST YOUR
MIDDLEWARE
USE AN EVENT BROKER
TO MODULARIZE YOUR
APP
USE AN EVENT BROKER
TO MODULARIZE YOUR
APP
• AMQP
• Celery
• 0MQ
• Redis
• JMS
• Even some http chunk or websocket
• Some case : hadoop, akka…
• …
My talk about rabbitMQ: https://www.youtube.com/watch?v=15mzY2MfDgM&t=3s
My talk about rabbitMQ: https://www.youtube.com/watch?v=15mzY2MfDgM&t=3s
CRON + FS IS NEITHER AN EVENT
QUEUE NOR A JOB SCHEDULER
BIG CHANGE IN
SOFTWARE INDUSTRY
One instance
One organization
One data repository
One instance
(distributed)
Multiple organization
+ a lot of users
One data repository
YESTEDAY NOW
Multi-tenant
INSTANCE FOR ONE
ORGANIZATION
ACID
• Atomicity
• Consistency
• Isolation
• Durability
Powerful
data
management
• Transaction
• User
management
• One above one
Take advantage of ACID
database
BUT IT DESIGNS A BOTTLENECK
EXAMPLE : E-SHOP ON
CLASSIC MODE
EXAMPLE : E-SHOP ON
CLASSIC MODE
User A buy a
hdd
Database Transaction :
• Stock management
• Order management
• Invoice generation
• Customer Account reward
• …
Transaction
user A is
processed
Stock &
Order are just
perfectly
synchronize
EXAMPLE : E-SHOP ON
CLASSIC MODE
User A buy a
hdd
Database Transaction :
• Stock management
• Order management
• Invoice generation
• Customer Account reward
• …
Transaction
user A is
processed
Stock &
Order are just
perfectly
synchronize
User B buy a
hdd Transaction
user B is
processed
then
EXAMPLE : E-SHOP ON
MULTI-TENANT MODE
i.e. : Multiple shop of various sellers on the same instance
EXAMPLE : E-SHOP ON
MULTI-TENANT MODE
User A buy a
hdd on seller A
Database Transaction :
• Stock management
• Order management
• Invoice generation
• Customer Account reward
• …
Transaction
user A is
processed
Stock &
Order are just
perfectly
synchronize
User B buy a
book on seller B Transaction
user B is
processed
then
WHO CARE THE SYNCHRONIZATION OF
VENDOR A & B ?
CHOOSE
YOUR
DATASTORE
WISELY
YOU CAN SHOULD USE MANY DATASTORES
DATASTORE CHOICES
ARE DRIVEN BY USAGE
Make
decisions
based on
needs
Do I need
atomicity of
requests ?
Do I need
concurrent
access ?
Do I mostly
read or
write ?
Do I need
relational ?
Do I need
big storage
capacity ?
Do I need
high
availability
?
USE ONLINE
DATABASE / BE
READY TO TEST
IN JUST A FEW
MINUTES
NO NEED TO TRASH YOUR COMPUTER
{P, DB, S} aaS
USE OPS FREE SOLUTION TO LEARN
AND START
COMMON MISTAKES
DO NOT USE THE FILE
SYSTEM AS A DATASTORE
File system are POSIX compliant
• POSIX is ACID
• POSIX is powerful but is a bottleneck
• File System is the nightmare of ops
• File System creates coupling (host provider/OS/language)
• SPOF-free multi tenant File System is a unicorn
STORE IN DATABASE, OR IN A DATASTORE LIKE
S3/RIAKCS DEDICATED TO FILE MANAGEMENT
DO NOT USE THE FILE
SYSTEM AS A DATASTORE
LOGS IN FILES
I HATE IT
USE STREAMING I/O TO
STREAM DATA DIRECTLY
TO DATABASE
HTTP Post data
Temporarily
store as file
or in memory
Store it into
your storage
backend
Say OK to
client
USE STREAMING I/O TO
STREAM DATA DIRECTLY
TO DATABASE
HTTP Post data
Directly stream
your data to
Storage
backend
Say OK to
client
DO NOT USE
MEMORY AS
DATABASE
LIKE : SHARED / GLOBAL VARIABLE,
CACHE “IN THE CODE”, INTENSIVE
SESSION USAGE…
DO NOT USE A VARIABLE
FOR MORE THAN ONE
REQUEST
F(X) = X * 2
F(2) = 4
^ WE ASSUME THAT
FOR SAME INPUT, SAME OUTPUT
IT’S LIKE MATH FUNCTION
Example : GET should not change data on server
RESPECT HTTP
data will be lost
CODE
WILL
FAIL
CAREFUL USE OF
DARK MAGIC
DON’T BE THAT GUY
DO NOT USE A
TECHNOLOGY BECAUSE
YOU <3 IT OR BECAUSE
IT’S HYPE : USE IT
BECAUSE IT FITS YOUR
NEEDS
BALANCE YOUR LEARNING CURVE WITH
THE TIME SAVED
DO NOT CREATE MONSTERS
Monitoring & Bug manager (Sentry)
TRACK BUG & GET METRICS
MAKE HARD
COMPUTATION ASYNC
SPLIT THE CODE :
MODULES
• Smallest code
base
• Deploy as
service for
each other
• Focus on best
technology for
a problem
SMALLEST CODE BASE POSSIBLE
FOR EACH PROGRAM
EACH MODULE
IS VIEWED AS
A SERVICE BY
OTHERS
HTTP OR AMQP COMUNICATION
OR AS A CLIENT
GET THE BEST TOOL TO
SOLVE YOUR PROBLEM
SCALE YOUR TEAM
MODULARIZE YOUR TEAM
THE POWER OF
REWRITE EVERYTHING
SMALL CODE BASE + MULTIPLE
TECHNOLOGIES =
LEGACY KILLER
REWRITE IS QUICK BECAUSE
YOU KNOW ALL THE PROBLEMS
BEFORE IT HAPPENS
HAPPY DEVELOPER WORKS BETTER :
ARE YOU HAPPY WHEN YOU START
YOUR IDE?
DO NOT BUILD “THE
SERVER” WITH NO DOC
USE PROCESS DEPLOYMENT
THE GOOD WAY OF DEPLOY AN
APP :
git push
AUTOMATE ALL THE
THINGS
ALWAYS USE A
REVERSE PROXY
Y U NO USE ONE ?
INNOVATE & WRITE KICK ASS APPS
I’m @waxzce on twitter
THX FOR LISTENING
& QUESTIONS TIME
There always be special deal
for Atlassian partners

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From delivering plugins to delivering "as a Service" - Atlassian connect 2017

Hinweis der Redaktion

  1. It’s a big impact on data management
  2. disposable