Hypercubes In Hbase
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This document discusses using HBase to store and query multi-dimensional data cubes. It describes how data cubes can be modeled in HBase by storing projections instead of distinct dimensions, and pre-computing all aggregations. An example row key and column family structure is given to store pageview counts by country, user agent, domain, and time. The document also introduces Zohmg, a distributed, multi-dimensional, time-series data store built on HBase that can be configured and mapped using YAML and Python scripts.
![User’s mapper.
def map(key, value):
from lfm.data.parse import web
log = web.parse(value)
dimensions = {'country' : geoip(log.host),
'domain' : log.domain,
'useragent' : classify(log.useragent),
'usertype' : ("user", "anon")[log.userid == None]
}
values = {'pageviews' : 1}
yield log.timestamp, dimensions, values](https://image.slidesharecdn.com/fredrik-hypercubes-in-hbase-090918104800-phpapp01/85/Hypercubes-In-Hbase-20-320.jpg)
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Hypercubes In Hbase
- 1. Hypercubes in HBase Fredrik Möllerstrand <[email protected]> Hadoop User Group UK, April 14 2009
- 2. Hello. • Per Andersson, Fredrik Möllerstrand • Chalmers University of Technology, Sweden • Master thesis at last.fm
- 3. stats.last.fm • Web statistics for in-house use. • Served out of mysql.
- 4. stats.last.fm • y-axis: pageviews. • x-axis: time. • also: countries.
- 5. stats.last.fm SELECT pageviews, country FROM webstatistics GROUP BY country;
- 6. SQL: Star schema • Facts table & dimension tables. • Joins!
- 7. Data Cube Foundations • n-dimensional cube • attribute => dimension • attribute value => measurement
- 8. Data Cube Foundations • dimensionality reductions • projections
- 9. Data Cube Foundations • Aggregation: sum, count, average, &c. • Data cubes modeled as: in RDBMSs, modeled as star schemas. in HBase, modeled with column-families.
- 10. Data Cubes in HBase • Store projections, not distinct dimensions. • Pre-compute *everything*.
- 11. Data Cubes in HBase • Rowkey: unit + time i.e. ‘pageviews-20090414’ • One column-family for every projection. i.e. ‘country-useragent’ • One qualifier per point in n-space. i.e. ‘US-safari’, ‘NO-opera’, &c.
- 12. The SQL-DB Problem • Too much data to keep in memory. • Plenty of joins makes queries complex. • Can’t serve at mouse click rate.
- 13. The Solution; A data store that is: • Distributed • Multi-dimensional • Magnetic(!) • Just general enough
- 14. Enter: Zohmg.
- 15. Zohmg; A data store that is: • Distributed • Multi-dimensional • Time-series-based • Magnetic(!) • Just general enough
- 16. Tech • Rides on the back of Dumbo. • Stores aggregates in HBase. • Serves JSON.
- 17. Zohmg • $> setup.py # create hbase database. • $> import.py --mapper weblogs.py # run dumbo job. • $> serve.py # start web server.
- 18. Developers, developers. • Configuration - yaml. • Mapper - python.
- 19. User’s configuration. project_name: webmetrics dimensions: - country - domain - useragent - usertype units: - pageviews projections: country: - country domain-usertype: - domain - usertype country-domain-useragent-usertype: - country - domain - useragent - usertype
- 20. User’s mapper. def map(key, value): from lfm.data.parse import web log = web.parse(value) dimensions = {'country' : geoip(log.host), 'domain' : log.domain, 'useragent' : classify(log.useragent), 'usertype' : ("user", "anon")[log.userid == None] } values = {'pageviews' : 1} yield log.timestamp, dimensions, values
- 21. Example.
- 22. Dimensions in HBase • Column-family: country-useragent-domain • Qualifier: US-firefox-last.fm
- 23. Questions?