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+# Distributed databases and alternative database models
+
+The gains from generic structures in a relational database work against the losses from having generic structures in an unreliable database.
+
+## Distributing data
+The user should not be aware the database is distributed
+
+## Why distribute?
+* Parallelize operations
+* No single point of failure
+* Divide large dataset
+* Data already partitioned (different departments in the company)
+
+## Approaches to distributing RDBMS
+### Local autonomy
+* Sites operate independently
+* One site cannot interfere with another
+* No site should relay on another for its operation
+
+### No centralization
+* No single site conrols transactions
+* No single site failure breaks the system
+
+### Continuous operation
+* The system is available most of the time
+* The system is reliable - it fails rearely and recovers quickly (and gracefully)
+
+### Location independence
+* User need not know where any data might be located
+* Data may be moved without changing functionality
+
+### Particion independence
+* User need not know how the data is partitioned
+* Data may be repartitioned without changing functionality
+
+Note: Optimization can still benefit from Partition structure
+
+### Distributed queries
+* Execute queries close to the data
+
+### X independence
+* Distribution making no difference to the user means:
+  * Hardware independence
+  * OS independence
+  * Network independence
+
+### DBMS independence
+* Distribution over different DBMS implementations
+
+## Concepts
+### Partitioning
+* How will you divide your data?
+* We divide the data as well as the processing
+* Also called fragmentation
+* Vertical partitioning
+  * Divide table by columns
+  * Normalization is a type of partitioning
+* Horizontal partitioning
+  * Divide the table by rows
+
+### Catalog Management
+Relations are not just the column but the definition of the table.
+* How is information about the data distributed?
+* It should be distributed in a manner that maintains integrity and consistency of the DB
+
+### Recovery control
+* Transaction usually use 'two-phase protocol'
+* This required one site to act as a co-ordinator in any give transaction
+
+### Brewer's Conjecture
+Three goals (CAP) in tension:
+* Consistency: All parts of the database should converge on a consistent state
+* Availability: Every request should result in a response (eventually)
+* Partition tolerance: If a network flaw breaks the network into separate subnets, the database should run and recover
+
+## Key/Value databases and MapReduce
+> If distributing databases is complex, simplify your data structures.
+
+### Key-value
+* Associative array/dictionary
+* No explicit connection between entries
+* Processing either
+  * Retrieval by key
+  * Exhaustive exploration
+* Easy parallel processing
+* Easy partition
+* Partition is always horizontal
+* Processing should happen near the data where possible
+
+### MapReduce
+'MapReduce' is an example, of key-value processing.
+
+This is based on the observation that the query has two stages:
+* Gathering and processing information form base tables
+* Processing and summarizing the result
+
+```SQL
+SELECT Actor1, COUNT(Title) --- Map phase
+FROM    MovieLocations -- Reduce phase
+        INNER JOIN Actors
+        ON Actor1=Name
+WHERE   DataOfBirth > "1950-01-01"
+GROUP BY Actor1; --- Map phase
+```
+
+### Map phase
+* Carried out with direct access to the data
+* Peration usually loops over all data
+* Output is a new key-value set
+
+### Reduce phase
+It doesn't need to happen close to the data
+* Carried out by reducer workers
+* Summarize data based on key
+* Because we know that the data is based on the key, we can assign the data to workers based on key
+* Output is collated and setn to the users
+
+### Why use MapReduce?
+* Simple
+* Easy for distributed data
+* Easy for distributed processing
+* Move the data you need, not the data  you have
+* Can recover form failure of all but co-ordinating node easily
+* 
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