What Every Developer Should Know About Database Scalability

Jonathan Ellis (DataStax)
Databases
Location: Exhibit Hall 3
Average rating: ***..
(3.85, 13 ratings)

Introduction
-————-

  • CAP theorem
  • basic RDBMS architecture: random-write oriented (transaction log notwithstanding)
  • hardware limits
  • caching

Replication
-————

  • Good for read-mostly work loads
  • Relatively simple
  • Single-master + failover most common
  • Even multi-master is limited by the write throughput of a single node

Partitioning
-————-

  • Each node only has to handle a subset of the writes
  • More complex than replication
  • Vertical
  • Horizontal (“sharding”)

Bigtable/HBase/Hypertable
-———————————

  • Built on GFS/HDFS; relatively complex design
  • Multiple single points of failure (designed for consistency, not availability)
  • Relatively rich columnfamily-based data model
  • Largest OSS community

Dynamo/Dynomite/Voldemort
-———————————

  • P2P design with only one type of process; no single points of failure
  • Designed for availability; provides tools for client to make consistency decisions
  • key/value data model
  • Small OSS community

Cassandra
-———

  • Dynamo architecture + Bigtable data model, designed by one of the Dynamo authors
  • Small OSS community
Photo of Jonathan Ellis

Jonathan Ellis

DataStax

Jonathan is an expert in databases and distributed systems. At Mozy, he built a scalable multi-petabyte storage system based on Reed-Solomon encoding.

He currently works on cloud infrastructure for Rackspace.

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Comments

David James
07/23/2009 5:57pm PDT

Hmmm, this has a five star rating even though the talk hasn’t happened yet. :)

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