mysql conference 2011 -- the secret sauce of sharding -- ryan thiessen

The Secret Sauce of Sharding

Ryan Thiessen Database Operations April 2011

1 Sharding 101

2 Bad Sharding

3 Facebook’s Universal Database

4 Re-Sharding

5 Operational Implications

Agenda

Sharding 101

Bad news: there is no single way to shard

▪  What is the secret sauce of anything?

▪  Some basic building blocks

▪  More about what NOT to do rather than a specific recipe

▪  Wide variation in implementation

Why not to shard your data

▪  Can’t do JOINs inside the RDBMS across shards

▪  Data denormalization has drawbacks

▪  Redundant storage

▪  Chore to keep everything in sync

▪  Ops & Maintenance is harder

▪  Schema changes, are more difficult

▪  Monitoring challenges

▪  You don’t do this because it’s cool, but because you have to

Why to shard your data

▪  Because you have to

▪  Doing joins outside of the RDBMS isn’t that bad

▪  Less contention on hot tables

▪  Continue using commodity hardware

▪  Single instance failure affects only a small proportion of users

Basic building blocks of good sharding

▪  Shard uniformity

▪  SKU, schema, queries

▪  Organize shards according to data access patterns

▪  Picking the right key to shard on

▪  Ability to grow, re-shard and shed load quickly

▪  Achieve operational efficiencies of scale

Bad Sharding

“Sharding” by application Bad sharding

▪  Example: each application gets its own database

▪  Result:

▪  Data distribution is non-uniform, massive hot spots

▪  Every data access pattern is unique

▪  Very little efficiency of scale

Commerce Database

User Database

Logging Database

Customer Database

Sales Database

Config Database

Fixed hashing Bad Sharding

▪  Example: you have X instances

▪  Hashing algorithm splits data evenly across each

▪  Result:

▪  Unbalanced load, hot spots

▪  What to do about data growth?

▪  How do you re-shard and/or shed load?

Hyper-sharding Bad Sharding

▪  Example: hash keys randomly across all instances, without any grouping

▪  Result:

▪  every fetch has to touch many shard to fulfills request

▪  Request latency becomes the max() of all shard latencies

▪  A single shard’s availability issue affects every request

How to choose a good shard key?

▪  Understand how your applications will access your data

▪  Be careful of data distribution

▪  Example: user ID

▪  Example: time grouping

▪  Example: random sharding

▪  TL;DR: use the same methodology as picking a partition key

Facebook’s Universal Database

Multiple shards per physical host Facebook UDB

▪  Multiple database shards per MySQL instance

▪  Multiple MySQL instances per host on different ports

▪  Each shard has identical schemas

▪  This enables web scale

Hashing Facebook UDB

▪  Group related objects together

▪  Collocate most user data on a single shard

▪  If an application has related objects, group them together

▪  When referring to objects in a remote shard, store a reference to the object in both shards

▪  Multiple logical hashing schemes can co-exist over the same set physical hosts

Shard management service Facebook UDB

▪  Methods:

▪  Map object IDs to logical (shard) IDs – procedural (simple hash)

▪  Map shard IDs to physical instances – manual

▪  Use Thrift to access these methods from any language

▪  Distribute shard metadata close to apps to reduce request latency

▪  Extremely read heavy

▪  Updated relatively infrequently

Example: fetching data from a shard Facebook UDB

▪  Example: application request to get data for object ID 12345678901

▪  Call a function: 12345678901 % 40000 => maps to shard 38901

▪  Resolve shard ID 38901 to physical instances

▪  Application is in region B and only needs read, so prefer to return a connection to shard 38901 on instance db983:3307

Instance Repl Type Region Enabled

db243:3306 master A enabled

db533:3308 replica A enabled

db874:3306 replica B disabled

db983:3307 replica B enabled

Adding nodes Facebook UDB

▪  New user pools

▪  List(s) of shard IDs where new objects go

▪  Reverse the hashing function, generate object ID which maps to one of the new ID pool shards

▪  Usually new instances to add more overall capacity to the tier

▪  Can be existing instances to get more utilization

App requests storage on new

node

Get list of available

shards, pick one

Generate ID which maps to

that shard

Connect to the selected shard,

save object

Re-Sharding

The Easy Way: shedding load Re-Sharding

▪  Split off logical dbs from a single MySQL instance

Host1:3306

ShardA

ShardB

ShardC

ShardD

Host2:3306

ShardA

ShardB

ShardC

ShardD

Host1:3306

ShardA

ShardC

Host2:3306

ShardB

ShardD

Split

1.  Block writes 2.  Break replication from

Host1->Host2 3.  Drop databases 4.  Reconfigure Shard Manager

to point to new instances 5.  Re-enable writes

•  Splitting off instances running on different ports is easier

The Hard Way: double-write data Re-Sharding

1.  Create new layout on all new instances

2.  On each new write, store in both places

3.  Separate process to backfill from the legacy storage

4.  Switch over reads to the new storage

5.  Monitor the old storage for reads

6.  Stop double-writes, drop old tables

▪  This is I/O intensive and painful, but very possible

Operational Implications

Everything is harder Operational Implications of sharding

▪  Monitoring is harder

▪  Schema changes are harder

▪  Upgrades are harder

▪  Backups and restores are harder

▪  Etc. Seriously.

▪  “This will probably never happen” will probably happen

▪  90% of your time can be spent on 10% of the shards (or less)

Top-N monitoring Operational Implications

▪  Problems with individual shards can get lost in the aggregate or mean

▪  Look at the worst “offenders”, identify outliers

▪  pmysql is an excellent tool for doing this this quickly

$ cat hosts.txt | pmysql ‘show status like “threads_running”’ | sort –k3 –n | tail –n20!!OHAI!!

Uniformity of shards Operational Implications

▪  Every shard should have the same schema

▪  Keeps the SKUs, configurations, etc, as consistent as possible

▪  Don’t scale shards by migrating the worst to better hardware

▪  Ops will have to keep track of this in the future

Application gating Operational Implications

▪  Very easy for a bad application to consume all shard resources

▪  Limit per-shard concurrency for each application

▪  User limits are OK

▪  Admission control is better

▪  Log failures at both client and server levels

The Good News: efficiencies of scale Operational Implications

▪  The problems are hard, but there are solutions

▪  Fixing the problems of the worst shards usually also have benefit the median shards

▪  Loss of a single shard is not the end of your website

▪  Easy to safely test changes on a small subset

▪  Automation and tooling mean the team can debug and fix problems with high parallelism

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