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How to scale large database
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duongkai
May 23, 2013
Programming
3
200
How to scale large database
Bài nói về các kĩ thuật để mở rộng một database lớn.
duongkai
May 23, 2013
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Transcript
How To Scale Large Database Phạm Tùng Dương – CIO03
Course: Advanced Database
Overview • First glance about Large Database • Typical techniques
to scale • Database sharding • Database sharding in MySQL
First glance about Large Database
When You Talk about Large Database
Example Tumblr @2012
Example • 400 million active users • 5 billion pieces
of content per week • 3 billion photos uploaded per month Facebook@2010
Example • 1 billion tweets per week • 140 million
tweets sent per day • 456 tweets per second @MJ death • 6939 tweets per second on NY day Twitter@2011
What is The Large Database • Large working data sets
• I/O write intensive
Typical approaches
What is The Bottleneck? I/O, I/O and I/O
We have a job which is called Performance Tuning
Scale up • Adding more RAM, more CPU • High
I/O HDD
Scale topo Replication (Master – Slave) Master Slave Client Read/Write
Read Only Master Master Storage Client Cluster (shared storage)
Caching • Memcached • Redis
Finally, Everything in RAM is a Dream!
But, No Silver Bullet!
Database Sharding
What is Database Sharding • Horizontal Partitioning • Data is
stored in small chunks and distributed across many computers • Often use with Replication
Database sharding topo Primary DB Shard1 Shard2
Shard3 Slave1 Slave2 Slave3
3 types • Range sharding • List sharding (Lookup table)
• Hash sharding
Range sharding • Distributed by the range of Primary Key
• Example – Primary Key: user_id (1..1000) user_shard1 (1..500) user_shard2 (501..1000)
List sharding • Distributed data by the attribute of the
data • Example: database of people in VN – Sharded by the city_name (Ha_Noi, Hai_Phong, Da_Nang,…)
Hash sharding (modulus) • Distributed data by using a hash
function on primary key. • Example: primary_key mod N
Pros of Database Sharding • Easy to scale (data, write
I/O) • Using commodity hardware • Minimum effect when system failed
Cons of Database sharding • You MUST implement by yourselves
• Operation is harder • Handle join operation is very difficult • Data denormalization – > Don’t do it because it’s COOL!
Database Sharding in MySQL
Sharding Solutions • Application layer • Storage layer • Heavy
middleware • Lightweight middleware
Application layer • Hibernate Shards • HiveDB
Storage layer • MySQL Spider – Requires to change storage engine
of MySQL
Heavy Middleware • Twitter Gizzard • dbShards – Each db
has an agent
Lightweight Middleware • Acts like a proxy • Route the
request • Spock, CUBRID
You Will Do It Because You Have To … not
because it’s Cool!
Q&A