12-step program for scaling web application on postgresql

7/21/2019 12-Step Program for Scaling Web Application on PostgreSQL

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Proprietary andConfidential

Konstantin GredeskoulCTO, Wanelo.com

12-Step Program for Scaling Web

Applications on PostgreSQL

@kig

@kigster


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What does it mean, to scale on top o

PostgreSQL?


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And why should you care?


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Scalingmeans supporting morework load concurrently, where work

is ofen interchanged with users

But why on PostgreSQL?

Because NoNoSQL is hawt! (again)


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Relational databasesare great atsupporting constant change insofware

They are not as great in auto

scaling, like RIAK or Cassandra

So the choice critically dependsonwhat you are trying to build


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Huge majority o applications arerepresented well by the relational

model

So i I need to build a new productor a service, my deault choicewould be PostgreSQL or criticaldata, + whatever else as needed


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This presentation is a walk-throughfilled with practical solutions

Its based on a story o scalingwanelo.comto sustain 10s o thousand

concurrent users, 3k req/sec

But lets explore the applicationto learn a bit about waneloor our scalability journey


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Founded in 2010, Wanelo (wah-nee-loh, romWant, Need, Love) is a community and a social

network for all of the world's shopping.

Wanelo is a home to 12Mproducts, millionsousers, 200K+stores, and products on Wanelo

have been savedinto collectionsover 2B times


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move ast with product development scale as needed, stay ahead o the curve keep overall costs low but spend where it matters automate everything avoid reinventing the wheel learn as we go remain in control o our inrastructure

Early on we wanted to:


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Heroku or Not?

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Assuming we want ull control o our

application layer, places like Heroku arent agreat fit

But Heroku can be a great place to start.Itall depends on the size and complexity o theapp we are building.

Ours would have been cost prohibitive.


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Foundations of web apps

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app server (we use unicorn) scalable web server in ront (we use nginx) database (we use postgresql)

hosting environment (we useJoyentCloud)

deployment tools (capistrano)

server configuration tools (we use chef)

programming language + ramework (RoR)

many others, such as monitoring, alerting


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Lets review Basic Web App

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/var/pgsql/data

incominghttp

PostgreSQLServer

/home/user/app/current/public

nginx Unicorn / PassengerRuby VM

N x UnicornsRuby VM

no redundancy, no caching (yet)

can only process N concurrent requests nginx will serve static assets, deal with slow clients

web sessions probably in the DB or cookie


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First optimizations:cheap early on, well worth it

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Personalization via AJAX, so controller actionscan be cached entirely using caches_action

Page returned unpersonalized, additional AJAX

request loads personalization


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A few more basic performancetweaks that go a long way

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Install 2+ memcachedservers or caching anduse Dalli gem to connect to it or redundancy

Switch to memcached-based web sessions. Usesessions sparingly, assume transient nature

Setup CDNor asset_host and any usergenerated content. We use astly.com

Redis is also an option, but I preer memcachedor redundancy


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browser PostgreSQL

Server

/home/user/app/current/public

nginx Unicorn / Passenger

Ruby VM

N x Unicorns

Ruby VM

memcachedCDN

cache images, JS

Caching goes a long way

geo distribute and cache your UGC and CSS/JS assets

cache html and serialize objects in memcached can increase TTL to alleviate load, i traffic spikes


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Adding basic redundancy

Multiple app serversrequire haproxy

between nginx and unicorn

Multiple long-running tasks (such as

posting to Facebook or Twitter) requirebackground job processing ramework

Multiple load balancersrequire DNS

round robin and short TTL (dyn.com)


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PostgreSQL

Unicorn / Passenger

Ruby VM (times N)haproxy

incoming http

DNS round robin

or failover / HA solution

nginx

memcached

redis

CDN

cache images, JS

Load Balancers

App Servers

single DBObject Store

User Generated

Content

Sidekiq / Resque

Background WorkersData storesTransient to

Permanent

this architecture can horizontally scale up as

ar the databaseat its center

every other component can be scaled byadding more of it, to handle more traffic


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As long as we can scale the data

store on the backend, we can scalethe app!

Mostly :)At some point we may hit a limit on TCP/IPnetwork throughput, # o connections, butthis is at a whole another scale level


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The traffic keeps climbing


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Performance limits are near

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First signs o perormance problems start creeping up

Symptoms o readscalability problems

Pages load slowly or timeout

Users are getting 503 Service Unavailable

Database is slammed (very high CPU or read IO)

Symptoms o writescalability problems

Database write IO is maxed out, CPU is not

Update operations are waiting on each other, piling up

Application locks up, timeouts Replicas are not catching up

Some pages load (cached?), some dont


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Both situations may easily result indowntime


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Even though we

achieved 99.99% uptimein 2013, in 2014 we hada couple shortdowntimes caused by

overloaded replica thatlasted around 5 minutes.

But users quicklynotice


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Perhaps not :)


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Common patterns or scaling high traffic webapplications, based on wanelo.com

12-Step Programor curing your dependency on slow application latency


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For small / ast HTTP services, 10-12ms or lower

I your app is high traffic (100K+ RPM) Irecommend 80ms or lower

Whats a good latency?


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RubyVM (30ms) + Garbage collection (6ms) is CPUburn, easy to scale by adding more app servers

Web services + Solr (25ms), memcached (15ms),database (6ms) are all waiting on IO

CPU burn vs Waiting on IO?


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Step 1:

Add More Cache!


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Moar Cache!!!

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Anything that can be cached, should be

Cache hit = many database hits avoided

Hit rate o 17% still saves DB hits

We can cache many types o things

Cache is cheap and ast (memcached)


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Cache many types of things

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caches_actionin controllers is very effective

fragmentcaches o reusable widgets

we use gem Compositoror JSON API. We cacheserialized object ragments, grab them rom

memcached using multi_getand merge them

Shopiy open sourced IdentityCache, whichcaches AR models, so you can Product.etch(id)

https://github.com/wanelo/compositorhttps://github.com/Shopiy/identity_cache


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But Caching has its issues

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Expiring cache is not easy

CacheSweepersin Rails help

We ound ourselves doing 4000 memcacheddeletes in a single request!

Could deer expiring caches to background jobs,or use TTL i possible

But we can cache even outside o our app:we cache JSON API responses usingCDN(astly.com)


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Step 2:

Optimize SQL


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SQL Optimization Find slow SQL(>100ms) and either remove it, cache

the hell out o it, or fix/rewrite the query

Enable slow query login postgresql.con:

pg_stat_statementsis an invaluable contrib module:


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Fixing Slow Query

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Run explain plan to understand how DB runs the query

Are there adequate indexes or the query? Is the database usingappropriate index? Has the table been recently analyzed?

Can a complex join be simplified into a subselect?

Can this query use an index-only scan? Can order by column be added to the index?

pg_stat_user_indexesand pg_stat_user_tables or seq scans,

unused indexes, cache ino


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SQL Optimization, ctd

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Instrumentation sofware such as NewRelic shows slow queries, withexplain plans, and time consuming transactions


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SQL Optimization: Example

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One day, I noticed lots of temp filescreated in the postgres.log


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Lets run this query

This join takes a whole second to return :(


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Follows table


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Stories table


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So our index is partial, only on state = active

So this query is a ull table scan

But there stateisnt used in the query, a bug?

Lets add state = active

It was meant to be there anyway


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Step 3:Upgrade Hardware and RAM


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Hardware + RAM

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Sounds obvious, but better or aster hardware is anobvious choice when scaling out

Large RAM will be used as file system cache

On Joyents SmartOS ARC FS cache is very effective

should be set to 25% o RAM or 12GB,

whichever is smaller

Using ast SSD disk array can make a huge difference

Joyents native 16-disk RAID managed by ZFS insteado controller provides excellent perormance


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Hardware in the cloud

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SSD offerings rom Joyent and AWS

Joyents max SSD node $12.9/hr

AWS max SSD node $6.8/hr


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So whos better?

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JOYENT

16 SSD drives: RAID10 + 2

SSD Make: DCS3700

CPU: E5-26902.9GHz

AWS

8 SSD drives

SSD Make: ?

CPU: E5-26702.6Ghz

Perhaps you get what you pay or afer all.


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Step 4:Scale Reads by Replication


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Scale Reads by Replication

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postgresql.con (both master & replica)

These settings have been tuned or SmartOS and ourapplication requirements (thanks PGExperts!)


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How to distribute reads?

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Some people have success using this setup or reads:app haproxy pgBouncer replica

pgBouncer replica

Id like to try this method eventually, but we choose to

deal with distributing read traffic at the application level

We tried many ruby-based solutions that claimed to dothis well, but many werent production ready


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Makarais a ruby gem rom

TaskRabbit that we portedrom MySQL to PostgreSQLor sending reads to replicas

Was the simplest library tounderstand, and port to PG

Worked in the multi-threadedenvironment o SidekiqBackground Workers

automatically retries i replicagoes down

load balances with weights

Was running in production


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Special considerations

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Application must be tuned to support eventualconsistency. Data may not yet be on replica!

Must explicitly force fetchrom the master DB whenits critical (i.e. afer a user accounts creation)

We ofen use below pattern o first trying the etch, i

nothing ound retry on master db


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Replicas can specialize

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Background Workers can use dedicated replica notshared with the app servers, to optimize hit rate orfile system cache (ARC) on both replicas

PostgreSQL

Master

Unicorn / Passenger

Ruby VM (times N)

App ServersSidekiq / Resque

Background Workers

PostgreSQL

Replica 1

PostgreSQL

Replica 2

PostgreSQL

Replica 3

ARC cache warm with

queries from web traffic

ARC cache warm with

background job queries


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Big heavy reads go there

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Long heavy queries should run by the background jobsagainst a dedicated replica, to isolate their effect on

web traffic

PostgreSQL

Master

Sidekiq / Resque

Background Workers

PostgreSQL

Replica 1

Each type o load will produce a unique set o datacached by the file system


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Step 5:Use more appropriate tools


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Leveraging other tools

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Not every type o data is well suited or storing in a relationalDB, even though initially it may be convenient

Redisis a great data store or transient or semi-

persistent data with list, hash or set semantics We use it or ActivityFeed by precomputing each feed at write

time. But we can regenerate it i the data is lost rom Redis

We use twemproxyin ront o Redis which provides automatic

horizontal sharding and connection pooling.

We run clusters of 256 redis shardsacross many virtual zones;sharded redis instances use many cores, instead o one

Solris great or ull text search, and deep paginatedsorted lists, such as trending, or related products


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True story: applying WAL logs onreplicas creates significant disk write load

But we still have single master DB taking

all the writes

Replicas are unable to both serve live traffic andcatch up on replication. They fall behind.

Back to PostgreSQL


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When replicas fall behind, application generateserrors, unable to find data it expects


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Step 6:Move write-heavy tables out:Replace with non-DB solutions

M l


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Move event log out

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We were appending all user events into this table

We were generating millions of rows per day!

We solved it by replacing user event recording system touse rsyslog, appending to ASCII files

We discovered rom pg_stat_user_tables top table bywrite volume was user_events

Its cheap, reliable and scalable

We now useJoyents Manta to analyze this data inparallel. Manta is an object store + native compute on


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For more inormation about how we migrateduser events to a file-based append-only log, and

analyze it with Manta, please read

http://wanelo.ly/event-collection


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Step 7:Tune PostgreSQL and yourFilesystem


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Tuning ZFS

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Problem: zones (virtual hosts) with writeproblems appeared to be writing 16 times

more data to disk, compared to what virtual filesystem reports

vfsstatsays 8Mb/sec write volume

So whats going on?

iostatsays 128Mb/sec is actually written to disk

T i Fil t


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Turns out deault ZFS block size is 128Kb,

and PostgreSQL page size is 8Kb.

Every small write that touched a page, had towrite 128Kb o a ZFS block to the disk

Tuning Filesystem

This may be good or huge sequential writes,but not or random access, lots o tiny writes

T ning ZFS & PgSQL


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Solution: Joyent changed ZFS block size or our zone,iostatwrite volume dropped to 8Mb/sec

We also added commit_delay

Tuning ZFS & PgSQL

Installing and Configuring PG


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Many such settings are pre-defined in our open-sourceChef cookbookor installing PostgreSQL rom sources

Installing and Configuring PG

https://github.com/wanelo-chef/postgres

It installs PG in eg /opt/local/postgresql-9.3.2

It configures its data in /var/pgsql/data93

It allows seamlessand safeupgrades o minor or majorversions o PostgreSQL, never overwriting binaries

Additional resources online


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Additional resources online

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Josh Berkuss 5 steps to PostgreSQLPerormance on SlideShare is antastic

PostgreSQL wiki pages on perormance tuning is

excellent

Run pgBench to determine and compareperormance o systems

http://www.slideshare.net/PGExperts/five-steps-perorm2013

http://wiki.postgresql.org/wiki/Perormance_Optimization

http://wiki.postgresql.org/wiki/Tuning_Your_PostgreSQL_Server


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Step 8:Buffer and serialize frequent

updates

Counters counters


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Counters, counters

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Problem: products.saves_countisincremented every time someone saves a

product (by 1)

At 200 inserts/sec, thats a lot o updates

How can we reduce number of writesandlock contention?

Worse: 100s o concurrent requests trying toobtain a row level lockon the same popularproduct

Buffering and serializing


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Buffering and serializing

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Sidekiq background job ramework has twointer-related eatures:

scheduling in the future(say 10 minutes ahead)

UniqueJobextension

Once every 10 minutes popular products are updated byadding a value stored in Redis to the database value, andresetting Redis value to 0

We increment a counter in redis, and enqueue a jobthat says update product in 10 minutes

Buffering explained


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Buffering explained

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Save Product

Save Product

Save Product

1. enqueue update

request for product

with a delay

PostgreSQL

Update Request already

on the queue

3. Process Job

Redis Cache

2. increment

counter

4. Read & Reset to 0

5. Update Product

Buffering conclusions


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Buffering conclusions

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I not, to achieve read consistency, we candisplay the count as database value + redisvalue at read time

I we show objects rom the database, theymight be sometimes behind on the counter. Itmight be ok


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Step 9:Optimize DB schema

MVCC does copy on write


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MVCC does copy on write

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Problem: PostgreSQL rewrites the row or most updates (someexceptions exist, ie non-indexed column, a counter, timestamp)

But we ofen index these so we can sort by them

Rails and Hibernates partial updatesare not helping

Are we updating User on each request?

So updates can become expensiveon wide tables

Schema tricks


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Schema tricks

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Solution: split wide tables into several 1-1tables to reduce update impact

Much less vacuuming required when smallertables are requently updated

id

email

d d

Users

id

email

Users

refactor


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Dont update anything on each request :)

encrypted_password

reset_password_tokenreset_password_sent_at

remember_created_at

sign_in_count

current_sign_in_at

last_sign_in_at

current_sign_in_ip

last_sign_in_ip

confirmation_token

confirmed_atconfirmation_sent_at

unconfirmed_email

failed_attempts

unlock_token

locked_at

authentication_token

created_at

updated_at

username

avatar

state

followers_count

saves_count

collections_count

stores_count

following_count

stories_count

created_at

usernameavatar

state

user_id

encrypted_passwordreset_password_token

reset_password_sent_at

remember_created_at

sign_in_count

current_sign_in_at

last_sign_in_at

current_sign_in_ip

last_sign_in_ip

confirmation_token

confirmed_at

confirmation_sent_at

unconfirmed_email

failed_attempts

unlock_token

locked_at

authentication_token

updated_at

UserLogins

user_id

followers_countsaves_count

collections_count

stores_count

following_count

stories_count

UserCounts


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Step 10:Shard Busy Tables Vertically

Vertical sharding


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Heavy tables with too many writes, can bemoved into their own separate database

For us it was saves: now @ 2B+ rows

At hundreds o inserts per second, and 4 indexes,we were eeling the pain

It turns out moving a single table (in Rails) out isa not a huge effort: it took our team 3 days

Vertical sharding - how to


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Update code to point to the new database

Implement any dynamic Rails associationmethods as real methods with 2 etches

ie. save.productsbecomes a method on Save

model, lookup up Products by IDs

Update development and test setup with two

primary databases and fix all the tests

Here the application


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Web App

PostgreSQL

Master (Main Schema)

PostgreSQL

Replica (Main Schema)

Vertically Sharded Database

PostgreSQL

Master (Split Table)

connects to main masterDB + replicas, and a singlededicated DB or the busy

table we moved

Vertical sharding, deploying


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g, p y g

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Drop in write IO on the main DB afer splitting offthe high IOtable into a dedicated compute node


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For a complete and more detailed account oour vertical sharding effort, please read our

blog post:

http://wanelo.ly/vertical-sharding


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Step 11:Wrap busy tables with services

Splitting off services


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Vertical Sharding is a great precursorto amicro-services architecture

New service: Sinatra, client and server libs,updated tests & development, CI, deployment

without changing db schema 2-3 weeks a pair of engineerslevel o effort

We already have Saves in another database,lets migrate it to a light-weight HTTP service

Adapter pattern to the rescue


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Main AppUnicorn w/ Rails

PostgreSQLHTTP

Client Adapter

Service App

Unicorn w/Sinatra

Native

Client Adaptor

We used Adapter pattern to write two clientadapters: native and HTTP, so we can use the lib,but not yet switch to HTTP

Services conclusions


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Now we can independently scale service

backend, in particular reads by using replicas

This prepares us or the next inevitable step:horizontal sharding

At a cost o added request latency, lots o extracode, extra runtime inrastructure, and 2

weeks of work

Do this only i you absolutely have to


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Step 12:Shard Services Backend

Horizontally

Horizontal sharding in ruby


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We wanted to stick with PostgreSQL or criticaldata such as saves

Really liked Instagrams approach with schemas

Built our own schema-based shardingin ruby,

on top o Sequel gem, and open sourced it

It supports mapping o physical to logical shards,and connection pooling

https://github.com/wanelo/sequel-schema-sharding

Schema design for sharding


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user_id

product_idcollection_idcreated_at

index__on_user_id_and_collection_id

UserSaves Sharded by user_id

product_id

user_idupdated_at

index__on_product_id_and_user_idindex__on_product_id_and_updated_at

ProductSaves Sharded by product_idWe needed two lookups, by user_idand by product_id hence we neededtwo tables, independently sharded

Since saves is a join table betweenuser, product, collection, we did not

need unique ID generated

Composite base62 encoded ID:fpua-1BrV-1kKEt

Spreading your shards


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We split saves into 8192 logical shards,distributed across 8 PostgreSQL databases

Running on 8 virtual zonesspanning 2 physical SSDservers, 4 per compute node

Each database has 1024schemas (twice, because we

sharded saves into two tables)


2 x 32-core 256GB RAM

16-drive SSD RAID10+2

PostgreSQL 9.3

1

3 4

2


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Sampleconfiguration oshard mapping tophysical nodeswith readreplicas,supported by thelibrary


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How can we migrate the data rom old non-sharded backend to the new sharded backend

without a long downtime?


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New records go to bothProprietary and

HTTP Service

Old Non-Sharded Backend

New Sharded Backend

1

3 4

2

Read/Write

Background

Worker

Enqueue

Sidekiq Queue

Create Save

We migrated several times beore we got this right


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HTTP Service


New Sharded Backend

1

3 4

2

Read/Write

Background

Worker

Enqueue

Sidekiq Queue

Create Save

Migration Script

Migrate old rows



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Swap old and new backends

HTTP Service

New Sharded Backend

1

3 4

2Read/Write

Background

Worker

Enqueue

Sidekiq Queue

Create Save

Horizontal sharding conclusions


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This is the final destination o any scalablearchitecture: just add more boxes

Pretty sure we can now scale to 1,000, or 10,000inserts/second by scaling out

Took 2 months of 2 engineers, including migration,but zero downtime. Its an advanced level effort andour engineers really nailed this.


Putting it all together


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This inrastructure complexity is not free

It requires new automation, monitoring,

graphing, maintenance and upgrades, and bringswith it a new source of bugs

In addition, micro-services can be ownedby small teamsin the uture, achievingorganizational autonomy

But the advantages are clear when scalingisone o the requirements

4-core 16GB zones

memcached

iPhone Android Desktop clients

MemCached Cluster

Cluster of MemCached Servers

is accessed via Dali fault tolerant library

one or more can go down


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Systems Diagram

incoming http

requests

8-core 8GB zones

haproxy

nginx

Fastly CDNcache images, JS

Load Balancers

Amazon S3

Product Images

User Profile Pictures

32-core 256GB 16-driv e SSD RAID10+2

Supermicro "Richmond"

SSD Make: Intel DCS3700,

CPU: Intel E5-2690, 2.9GHz

PostgreSQL 9.2

Master

Primary Database Schema

User and Product Saves, Horizontally Sharded, Replicated

32-core 256GB RAM

16-drive SSD RAID10+2

PostgreSQL 9.3

1

3 4

2

Read Replicas (non SSD)

2

4 2

1

Read Replica (SSD)

PostgreSQL

Async Replicas

32-core 32GB high-CPU instances

Unicorn

Main Web/API App,

Ruby 2.0

Unicorn

Saves Service

haproxy

pgbouncer

iPhone, Android, Desktop clientsMakaradistributes DB

load across 3 replicas

and 1 master

Redis Clusters for various custom

user feeds, such as product feed

1-core 1GB zones

twemproxy

Redis Proxy Cluster

16GB high-mem 4-core zones

32 redis instances per server

redis-001

redis-256

8GB High CPU zones

Solr Replica

8GB High CPU zone

Solr Master

App Servers + Admin Servers

Apache Solr Clusters

32-core 32GB high-CPU instances

Sidekiq Background

Worker

Unicorn

Saves Service

haproxy

pgbouncer

to DBs

Solr Reads

Solr Updates

Background Worker Nodes

redis

Redis Sidekiq

Jobs Queue / Bus

Systems Status: Dashboard Monitoring & Graphing with Circonus, NewRelic, statsd, nagios


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Backend Stack & Key Vendors

MRI R b jR b Si R b R il


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MRI Ruby, jRuby, Sinatra, Ruby on Rails

PostgreSQL, Solr, redis, twemproxymemcached, nginx, haproxy, pgbouncer

Joyent Cloud, SmartOS, Manta Object StoreZFS, ARC Cache, superb IO, SMF, Zones, dTrace, humans

DynDNS, SendGrid, Che, SifScience

LeanPlum, MixPanel, Graphite analytics, A/B Testing

AWS S3 + Fastly CDN or user / product images

Circonus, NewRelic, statsd, Boundary,PagerDuty, nagios:trending / monitoring / alerting


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We are hiring!

DevOps, FullStack, Scaling Experts, iOS & Android

Talk to me afer the presentation i you are interested in workingon real scalability problems, and on a product used and loved by millions :)

http://wanelo.com/about/play

Or email [email protected]

Thanks!


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Thanks!

github.com/wanelo

github.com/wanelo-che

wanelo technical blog (srsly awsm)

building.wanelo.com

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@kig

@kig

@kigster

12-step program for scaling web application on postgresql

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