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一个谷歌云数据库的简单介绍,英文版。我特地下来留着达~
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BigTable
A System for Distributed
Structured Storage
Jeff Dean
Joint work with :
Other twelve guys
Copyed from BigTable video talk
yftty
Motivation
•
Lots of (semi-)structured data at Google
–
URLs:
•
Contents, crawl metadata, links, anchors, pagerank,…
–
Per-user data:
•
User preference settings, recent queries/search results, …
–
Geographic locations:
•
Physical entities (shops, restaurants, etc.), roads, satellite
image data, user annotations, …
•
Scale is large
–
Billions of URLs, many versions/page (~20K/version)
–
Hundreds of millions of users, thousands of q/sec
–
100TB+ of satellite image data
Why not just use commercial DB ?
•
Scale is too large for most commercial databases
•
Even if it weren't, cost would be very high
–
Building internally means system can be applied across many
projects for low incremental cost
•
Low-level storage optimizations help performance
significantly
–
Much harder to do when running on top of a database layer
Also fun and challenging to build large-scale systems :)
Goals
•
Want asynchronous processes to be continuously updating
different pieces of data
–
Want access to most current data at any time
•
Need to support:
–
Very high read/write rates (millions of ops per second)
–
Efficient scans over all or interesting subsets of data
–
Efficient joins of large one-to-one and one-to-many datasets
•
Often want to examine data changes over time
–
E.g. Contents of a web page over multiple crawls
BigTable
•
Distributed multi-level map
–
With an interesting data model
•
Fault-tolerant, persistent
•
Scalable
–
Thousands of servers
–
Terabytes of in-memory data
–
Petabyte of disk-based data
–
Millions of reads/writes per second, efficient scans
•
Self-managing
–
Servers can be added/removed dynamically
–
Servers adjust to load imbalance
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