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Jon Meredith
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Front Range PHP NoSQL Databases
NoSQL Databases Jon
Meredith [email_address]
NOT a
product.
NOT a
single technology.
Mostly created in
response to scaling and reliability problems.
Huge differences between
'NoSQL' systems – but have elements in common.
Object databases
Graph databases
e-commerce
Social networking
All shambling under
the NoSQL banner.
My application needs
transactions
Data needs to
be nicely normalized
I have replication
for scalabilty/reliability
Sets
Arrays
Upgrade/rollback scripts have
to operate on the whole database – could be millions of rows.
Doing phased rollouts
is hard … the application needs to do work
Google's protocol buffers
Version objects
Every change on
the master happens on the slave.
Slaves are read-only.
Does not scale INSERT, UPDATE, DELETE queries.
Application responsible for
distributing queries to correct server.
Updates travel around
the ring
Add back in
to the ring
Replication takes time
– there is time lag between the first and last server to see an update.
You may not
read your writes – not getting aCid properties any more.
The application needs
to know how to resolve it
...Available...
...Maintainable...
with an RDBMs
requires large efforts by application developers and operational staff
App needs to
know data location
App needs to
handle failover
Network partitions common
Network routes are
flapping
Data centers are
being destroyed by tornadoes
Best seller lists
Fault tolerant: Keeps
N copies of the data
Designed for inconsistency
Totally decentralized –
nodes 'gossip' state
Self-healing
Availability
Amazon chose A-P
over C
Read operations (get)
require 'R' nodes to respond
Write operations (put)
require 'W' nodes to respond
If R+W >
N nodes will read their writes (if no failure)
NRW tunes the
cluster – typically (3,2,2)
Dynamo minimizes with
vector clocks
Vector Clocks
Partitioning
Shopping Cart -
Conflict Network Failure
Shopping Cart -
Merge
Project Voldemort
Google Earth
Table indexed by
{key,timestamp} and a variable number of sparse columns
Columns are grouped
into column families. Columns in a family are stored together.
Each table is
broken into tablets.
Tablets are stored
on a cluster file system (GFS).
BigTable – Column
Families Copyright Google
Programmers write two
functions map() and reduce().
Table is mapped,
then reduced.
Job control system
monitors and resubmits.
Map/Reduce Source: institutes.lanl.gov
CouchDB Map/Reduce
http://www.vineetgupta.com/ 2010/01/nosql-databases-part-1-landscape.html
So many projects!
Dynamo, BigTables, Redis Riak, Voldemort, CouchDb, Peanuts Hadoop/Hbase, Cassandra, Hypertable MongoDb, Terrastore, Scalaris, BerkleyDB MemcacheDB, Dynomite, Neo4J, TokyoCabinet … and more
Sparse Column Family
Decentralized
RESTful HTTP interface
Fully distributed
Clients for multiple
languages
Filesystem
Key/Value Store with
structured values
Written in C
Memcache-like protocol
Engine Yard
VideoWiki
Operations like increment,
decrement, intersection, push, pop
In-memory (can be
backed by disk)
Auto-sharding in client
libraries
No fault tolerance
(coming after 2.0)
Example: retwis –
Twitter clone in PHP
BigTable ColumnFamily data
model
Dynamo data distribution
Written in Java
Thrift based interface
Twitter
Used by Ubuntu
One
HTTP interface
Uses Javascript for
indexing/mapreduce
Incremental replication
Multi-process
Replicated
Neo4J – Graph
Database
Peanuts – Yahoo
Even range queries
are hard for distributed hash systems.
No transactions –
rules out some classes of applications.
Space is still
evolving
They force you
to think about distributed design issues like consistency.
Play with them!
Editor's Notes
Introduce Disclose work for Basho Working on Dynamo clone for the last couple of years
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