1 / 19

Google 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.

valerie
Download Presentation

Google Bigtable

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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”)

  2. 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!

  3. 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 14th 2006

  4. 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

  5. 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

  6. 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

  7. 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

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

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

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

  11. Finding a tablet

  12. 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

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

  14. 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

  15. 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

  16. Microbenchmarks

  17. Application at Google

  18. 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

More Related