Bigtable

Google Bigtable Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber Google, Inc. UWCS OS Seminar Discussion Erik Paulson 2 October 2006 See also the (other)UW presentation by Jeff Dean in September of 2005 (See the link on the seminar page, or just google for “google bigtable”)

Before we begin…
Intersection of databases and distributed systems
Will try to explain (or at least warn) when we hit a patch of database
Remember this is a discussion!

Google Scale
Lots of data
Copies of the web, satellite data, user data, email and USENET, Subversion backing store
Many incoming requests
No commercial system big enough
Couldn’t afford it if there was one
Might not have made appropriate design choices
Firm believers in the End-to-End argument
450,000 machines (NYTimes estimate, June 14 th 2006

Building Blocks
Scheduler (Google WorkQueue)
Google Filesystem
Chubby Lock service
Two other pieces helpful but not required
Sawzall
MapReduce (despite what the Internet says)
BigTable: build a more application-friendly storage service using these parts

Google File System
Large-scale distributed “filesystem”
Master: responsible for metadata
Chunk servers: responsible for reading and writing large chunks of data
Chunks replicated on 3 machines, master responsible for ensuring replicas exist
OSDI ’04 Paper

Chubby
{lock/file/name} service
Coarse-grained locks, can store small amount of data in a lock
5 replicas, need a majority vote to be active
Also an OSDI ’06 Paper

Data model: a big map
<Row, Column, Timestamp> triple for key - lookup, insert, and delete API
Arbitrary “columns” on a row-by-row basis
Column family:qualifier. Family is heavyweight, qualifier lightweight
Column-oriented physical store- rows are sparse!
Does not support a relational model
No table-wide integrity constraints
No multirow transactions

SSTable
Immutable, sorted file of key-value pairs
Chunks of data plus an index
Index is of block ranges, not values
Index 64K block 64K block 64K block SSTable

Tablet
Contains some range of rows of the table
Built out of multiple SSTables
Index 64K block 64K block 64K block SSTable Index 64K block 64K block 64K block SSTable Tablet Start:aardvark End:apple

Table
Multiple tablets make up the table
SSTables can be shared
Tablets do not overlap, SSTables can overlap
SSTable SSTable SSTable SSTable Tablet aardvark apple Tablet apple_two_E boat

Finding a tablet

Servers
Tablet servers manage tablets, multiple tablets per server. Each tablet is 100-200 megs
Each tablet lives at only one server
Tablet server splits tablets that get too big
Master responsible for load balancing and fault tolerance
Use Chubby to monitor health of tablet servers, restart failed servers
GFS replicates data. Prefer to start tablet server on same machine that the data is already at

Editing a table
Mutations are logged, then applied to an in-memory version
Logfile stored in GFS
SSTable SSTable Tablet apple_two_E boat Insert Insert Delete Insert Delete Insert Memtable

Compactions
Minor compaction – convert the memtable into an SSTable
Reduce memory usage
Reduce log traffic on restart
Merging compaction
Reduce number of SSTables
Good place to apply policy “keep only N versions”
Major compaction
Merging compaction that results in only one SSTable
No deletion records, only live data

Locality Groups
Group column families together into an SSTable
Avoid mingling data, ie page contents and page metadata
Can keep some groups all in memory
Can compress locality groups
Bloom Filters on locality groups – avoid searching SSTable

Microbenchmarks

Application at Google

Lessons learned
Interesting point- only implement some of the requirements, since the last is probably not needed
Many types of failure possible
Big systems need proper systems-level monitoring
Value simple designs

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Bigtable

by nextlib on Jul 25, 2007

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Bigtable — Presentation Transcript