SortaSQL

SortaSQLIan Pye <ian@cloudﬂare.com>

Motivation

Everyone likes SQL• Tables• Joins• Online Transaction Processing• Transactions!• Arbitrary Queries

Scaling?• What happens to joins when your data doesn’t ﬁt in memory?• I only need get and set for my data• Sharding is too hard/unreliable• A “monopolistically competitive market”?

ScalingSeamless Horizontal Scalability from 1 to N

Proposal: Let the Filesystem do the hard work• RDBMS presents a full SQL interface to applications, automatically accessing files to get data as needed• RDBMS stores metadata allowing it to find the right data files• Embedded key/value store handles the record level storage, locking, caching, etc.• FS (local or distributed) stores data and is responsible for replication, performance, locking, etc.

Major Wins• Scales continuously from 1-100 servers (FS permitting)• Hot/cold storage hierarchy• Allows ad-hoc queries via mature SQL• Everyone already has built in bindings

Architecture• Application Talks SQL to PostgreSQL• PostgreSQL stores metadata• Performs post processing on rows retrieved from KC files• KC files live on a POSIX filesystem

Architecture Application SQL PostgreSQL SortaSQL Plugin libKC libKC libKC libKC Kyoto Kyoto Kyoto KyotoCabinet Cabinet Cabinet Cabinet Filesystem Filesystem

Big Table“A Bigtable is a sparse, distributed, persistent multidimensional sorted map.”

Multi-Dimensional • Storing values as protocol buffers allow for arbitrarily complex maps • Logic so that when maps get too big, they are promoted to top level KC stores

Persistent and Sorted• Any Key/Value store which allows for binary values accessed via a B+Tree of keys will do• We use Kyoto Cabinet (successor to Tokyo Cabinet)

Sparse• Values can be arbitrarily different.• NULLs are free (or cheap)• Protocol Buffers again to the rescue.

Distributed• All about the ﬁlesystem here

Sharding Made Easy• Fine grained metadata allowed for efﬁcient storage hierarchy

SortaSQL: Summery• BigTable like structure• Accessed via SQL (PHP bindings come for free!)• Ofﬂoad the hard part to the Filesystem Folks

Case Study: CloudFlare• 400 GB data/day (Medium Data?) • Facebook = 25 TB data/day • USPS = 25.6 GB text data delivered/day• Mix of Flash and Magnetic storage• Mirrored• Fixed user queries• Random BizDev queries

Data Scheme

MetadataPartitioned by owner, period and data type

Disk Layout• 2 80GB SSDs (small and blazing)• 2.5T RAID5 (big and slow)

Disk Layout

First Steps(access some records)

Silly SQL Tricks

Window Functions are Your Friends

NoSQL to MySQL with Memcached • Replace Language not Storage Engine • Speak Memcached not SQL

In Context• https://github.com/cloudﬂare/SortaSQL• http://dev.mysql.com/tech-resources/articles/ nosql-to-mysql-with-memcached.html• http://queue.acm.org/detail.cfm?id=1961297

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SortaSQL

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