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70s - Database access is hard and depends on the app
80s – Relational databases come on the scene
90s – Object oriented programming and DBs
00s – Interpreted languages, Agile

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Means an app that supports millions of users
Represents relationships
Variable usage (viral apps)
Data that is important aggregated, not by itself
Time-to-market vs. proper design
Uptime (availability) vs. correctness
Ease of management vs. customization

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Rejection of RDBMS as one-size-fits-all
Minimal functions and minimal admin
BASE
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 Basically available, Soft state, Eventually consistent
ACID
 Atomic, Consistent, Isolation, Durable

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Written in C
Open-source and free (no royalties)
New BSD license
By Salvatore Sanfilippo
Created for a Web analytics project
Sponsored by VMWare
Used by various projects: github, craigslist, stackoverflow, digg

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Key-value dictionary with sets, lists
Single-threaded
Delayed writes
Data needs to be kept in-memory
Simple protocol
Lack of table, schema, or database
Very basic security

 Session store
 One (or more) sessions per user
 Many reads, few writes
 Throw-away data
 Timeouts

 Logging
 Rapid, low latency writes
 Data you don’t care that much about
 Not that much data (must be in-memory)

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Low-latency write
Many reads throughout transaction
Short (less than a day)
Think a shopping cart or a file upload

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Data that you don’t mind losing
Records that can be accessed by a single primary key
Schema that that is either a single value or is a serialized object

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Java
 Jedis (github)
.NET
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 ServiceStack (Google Code)
Ruby
 redis-rb (github)

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SET k v
GET k
MSET k v [k2 v2]
MGET k [k2 …]
GETSET k v
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 Returns value before set, sets new value
SETNX k v (only sets if does not exist)
SETEX k n v (expires a key after n seconds)

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Set is unordered grouping of values
SADD k v
SCARD k – counts set
SISMEMBER k v – checks to see if v is in set
SUNION k [k2 …] – adds sets
SINTER k [k2 …] – intersects sets
SDIFF k [k2 …] – subtracts sets

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Ordered group
LPUSH k v – prepends
LPOP k v – removes 1 st element
LINSERT k BEFORE || AFTER n v – inserts v before or after the nth element
RPUSH kv – appends
RPOP k v – removes last element
LLEN k – number of elements
LRANGE k n m – gets range n to m inclusive

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SLAVEOF host port
Asynchronous
Can chain together pub -> slave -> slave
Cannot chain together pub <-> pub

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Sorted sets (indexed but with set operations, higher big-O complexity)
Hashes (many values for one key)
 HSET k field v – sets v for field for k
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 HGET k field
MULTI / EXEC / DISCARD / WATCH – xactions
Message queues (Pub/Sub)

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Startup info
Client logins
Databases and number of keys
Background saves and time
Replication

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Bottleneck on memory
Low CPU
Disk only on flush

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Amortized
SET, GET, etc – O(1)
KEYS – O(N)
ZADD, ZREM, etc – O(log(n))

 General Types of Databases
 Relational Databases – Oracle, Postgres, MySQL
 Object Stores – Objectivity, Cache, db4o
 Key Value Stores – Berkelely DB, Riak, Cassandra
 Document Stores – Mongo, Lotus, Couch
 Graph Databases – Neo4j, InfoGrid
Redis is a key value store

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Intentionally made for clustering
Replicas are not consistent
Written in Java
Much more robust organization
Often called a column-store, though this is a misnomer
Imitates Dynamo

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Written in Erlang
Robust organization
REST Api
Made for clustering, similar to Cassandra
Imitates Dynamo

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Document store – can represent objects natively – understands its data
Can access by values
Much more advanced architecture
Auto-sharding