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Web Scale with NoSQL
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Sergejus
April 09, 2011
Technology
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Web Scale with NoSQL
Sergejus
April 09, 2011
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Transcript
Web Scale with NoSQL Sergejus Barinovas (@sergejusb) http://sergejus.blogas.lt
None
Who Am I? Architect at Running NoSQL servers
in production Blogger (http://sergejus.blogas.lt, @sergejusb) Community member (http://dotnetgroup.lt) Contact me via
[email protected]
Powered by RDBMS Used everywhere… …even where it
shouldn’t Used for 30+ years!
Back to 1980’s…
Data boom
in numbers 600 000 000 users 30 000
servers 20+ TB raw data per day >20 PB stored data
You really think they use RDBMS?
RDBMS Scaling Example
Simple usage Customers Reads / Writes master
Scale reads Customers master slave slave
Scale writes Customers [A-M] master master Customers [N-Z]
Scale reads / writes Customers [A-M] master slave slave master
Customers [N-Z] slave slave
Pray your system won’t fail
None
Why NoSQL Limited SQL scalability Sharding and vertical
partitioning Limited SQL availability Master / slave configuration Limited SQL speed of read operations Multiple read replicas SQL limitations for huge amount of data Key / value / type columns
NoSQL history 2009, Eric Evans, no:sql(est) NoSQL –
open source distributed databases, not relational SQL databases NoSQL – not only SQL NoSQL → Big Data
NoSQL characteristics (1/2) Scalability The ability to horizontally
scale simple- operation throughput over many servers BASE A “weaker” concurrency model than the ACID transactions in most SQL systems
NoSQL characteristics (2/2) Distributed Efficient use of distributed
indexes and RAM for data storage Schema-less The ability to dynamically define new attributes or data schema
CAP theorem 2000, Eric Brewer It is impossible
for a distributed computer system to simultaneously provide all three of the following guarantees: Consistency Availability Partition tolerance
None
NoSQL Databases
NoSQL categories Key / value store Document database
Graph database Columnar database
Key / value store <key, value> or Tuple<key, v1,.
., vn> Simple operations Get Put Delete Byte[] Byte[] Key Value
Key / value store Key Value “current_date” 2013.02.01 “sergejusb” Binary
Object “sergejusb” JSON Object
Key / value stores Redis (+)messaging (-)no
shards Voldermort Membase (+)memcache interface Riak
Document database Document == complex object XML
YAML JSON / BSON Support for secondary indexes Schema can be defined at runtime Optional support for simple querying using Map / Reduce
Document databases MongoDB (+)shards CouchDB (+)master
/ master replication
Graph database Graph == network Basic constructs
Node Edge Properties sergejus sergejus.blogas.lt tdagys knows knows
Graph databases Neo4j (-)paid version required for scaling
FlockDB (+)fast (-)limited functionality
Columnar database For HUGE amount of data Columns
are added at a runtime Great scalability Horizontal Vertical
Columnar database Unusual data model Key Space →
Database Column Family → Table Columns and Super Columns Super Column → array of Columns Column → Tuple<Key, Value, Timestamp, TTL>
Columnar database Simple column
Columnar database Simple column
Columnar database Cassandra (+)easy scalable HBase
(+)consistent (+)part of Hadoop Hypertable
NoSQL is Cool! But…
None
NoSQL limitations ORDER BY ? Natural key order
GROUP BY ? Map / Reduce* JOIN ? Multiple Map / Reduce* SELECT * ? Multi-machine Map / Reduce* *if possible
NoSQL Limitations Maturity Tooling Specificity
SQL vs. NoSQL Choose the right tool for the
task You can use BOTH
Thank you! Sergejus Barinovas (@sergejusb)
[email protected]
http://sergejus.blogas.lt