Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

3,909 views

Published on

Published in:
Technology

License: CC Attribution License

No Downloads

Total views

3,909

On SlideShare

0

From Embeds

0

Number of Embeds

54

Shares

0

Downloads

117

Comments

0

Likes

4

No embeds

No notes for slide

- 1. Faster Column-Oriented Indexes Daniel Lemire http://www.professeurs.uqam.ca/pages/lemire.daniel.htm blog: http://www.daniel-lemire.com/ Joint work with Owen Kaser (UNB) and Kamel Aouiche (post-doc). February 10, 2010 Daniel Lemire Faster Column-Oriented Indexes
- 2. Some trends in business intelligence (BI) Low-latency BI, Complex Event Processing [Hyde, 2010] Commotization, open source software: Pentaho, LucidDB (http://www.luciddb.org/) Column-oriented databases ← source: gooddata.com Daniel Lemire Faster Column-Oriented Indexes
- 3. Row Stores name, date, age, sex, salary name, date, age, sex, salary name, date, age, sex, salary Dominant paradigm name, date, age, sex, salary Transactional: Quick append and delete name, date, age, sex, salary Daniel Lemire Faster Column-Oriented Indexes
- 4. Column Stores Goes back to StatCan in the seventies [Turner et al., 1979] Made fashionable again in Data name date age sex salary Warehousing by Stonebraker [Stonebraker et al., 2005] New: Oracle Exadata hybrid columnar compression Daniel Lemire Faster Column-Oriented Indexes
- 5. Vectorization Modern superscalar CPUs support const i n t N = 2048; vectorization (SSE) i n t a [N] , b [N ] ; This code is four times faster with i n t i =0; -ftree-vectorize (GNU GCC) f o r ( ; i <N ; i ++) Need long streams, same data type, and a [ i ] += b [ i ] ; no branching. Columns are good candidates! Daniel Lemire Faster Column-Oriented Indexes
- 6. Main column-oriented indexes (1) Bitmap indexes [O’Neil, 1989] (2) Projection indexes [O’Neil and Quass, 1997] Both are compressible. Daniel Lemire Faster Column-Oriented Indexes
- 7. Bitmap indexes SELECT * FROM T WHERE x=a Vectors of booleans AND y=b; Above, compute {r | r is the row id of a row where x = a} ∩ {r | r is the row id of a row where y = b} Daniel Lemire Faster Column-Oriented Indexes
- 8. Other applications of the bitmaps/bitsets The Java language has had a bitmap class since the beginning: java.util.BitSet. (Sun’s implementation is based on 64-bit words.) Search engines use bitmaps to ﬁlter queries, e.g. Apache Lucene: org.apache.lucene.util.OpenBitSet.java. Daniel Lemire Faster Column-Oriented Indexes
- 9. Bitmaps and fast AND/OR operations Computing the union of two sets of integers between 1 and 64 (eg row ids, trivial table). . . E.g., {1, 5, 8} ∪ {1, 3, 5}? Can be done in one operation by a CPU: BitwiseOR( 10001001, 10101000) Extend to sets from 1..N using N/64 operations. To compute [a0 , . . . , aN−1 ] ∨ [b0 , b1 , . . . , bN−1 ] : a0 , . . . , a63 BitwiseOR b0 , . . . , b63 ; a64 , . . . , a127 BitwiseOR b64 , . . . , b127 ; a128 , . . . , a192 BitwiseOR b128 , . . . , b192 ; ... It is a form of vectorization. Daniel Lemire Faster Column-Oriented Indexes
- 10. What are bitmap indexes for? Myth: bitmap indexes are for low cardinality columns (e.g., SEX). the Bitmap index is the conclusive choice for data warehouse design for columns with high or low cardinality [Zaker et al., 2008]. Daniel Lemire Faster Column-Oriented Indexes
- 11. Projection indexes name date city Write out the (normalized) column values sequentially. It is a projection of the table on a single column. name Best for low selectivity queries on few columns: date SELECT sum(number*price) city FROM T;. Daniel Lemire Faster Column-Oriented Indexes
- 12. How to compress column indexes? Must handle long streams of identical values eﬃciently ⇒ Run-length encoding? (RLE) Bitmap: a run of 0s, a run of 1s, a run of 0s, a run of 1s, . . . So just encode the run lengths, e.g., 0001111100010111 → 3, 5, 3, 1,1,3 It is a bit more complicated (more another day) Daniel Lemire Faster Column-Oriented Indexes
- 13. What about other compression types? With RLE, we can often process the data in compressed form Hence, with RLE, compression saves both storage and CPU cycles!!!! Not always true with other techniques such as Huﬀman, LZ77, Arithmetic Coding, . . . Daniel Lemire Faster Column-Oriented Indexes
- 14. How do we improve performance? Smaller indexes are faster. In data warehousing: data is often updated in batches. So spend time at construction time optimizing the index. Daniel Lemire Faster Column-Oriented Indexes
- 15. Modelling the size of an index Any formal result? Tricky: There are many variations on RLE. Use: number of runs of identical value in a column AAABBBCCAA has 4 runs Daniel Lemire Faster Column-Oriented Indexes
- 16. Improving compression by reordering the rows RLE is order-sensitive: they compress sorted tables better; But ﬁnding the best row ordering is NP-hard [Lemire et al., 2010]. Actually an instance of the Traveling Salesman Problem (TSP) So we use heuristics: lexicographically Gray codes Hilbert, . . . Daniel Lemire Faster Column-Oriented Indexes
- 17. How many ways to sort? (1) Lexicographic row sorting is a a fast, even for very large a b tables. a c easy: sort is a Unix staple. b a Substantial index-size reductions b b (often 2.5 times, beneﬁts grow b c with table size) Daniel Lemire Faster Column-Oriented Indexes
- 18. How many ways to sort? (2) Gray Codes are list of tuples a a with successive (Hamming) a b distance of 1 [Knuth, 2005]. a c b c Reﬂected Gray Code order is b b sometimes slightly better than lexicographical. . . b a Daniel Lemire Faster Column-Oriented Indexes
- 19. How many ways to sort? (3) a a Reﬂected Gray Code order is not a b the only Gray code. a c b c Knuth also presents Modular b a Gray-code. b b Daniel Lemire Faster Column-Oriented Indexes
- 20. How many ways to sort? (4) Hilbert Index [Hamilton and Rau-Chaplin, 2007]. Also a Gray code (conditionnally) Gives very bad results for column-oriented indexes. Daniel Lemire Faster Column-Oriented Indexes
- 21. Recursive orders Lexicographical, reﬂected Gray code and modular Gray code belong to a larger class: recursive orders. They sort on the ﬁrst column, then the second and so on. Not all Gray codes are recursive orders: Hilbert is not. Daniel Lemire Faster Column-Oriented Indexes
- 22. Best column order? Column order is important for recursive orders. We almost have this result [Lemire and Kaser, 2009]: any recursive order order the columns by increasing cardinality (small to LARGE) Proposition The expected number of runs is minimized (among all possible column orders). Daniel Lemire Faster Column-Oriented Indexes
- 23. How do you know when the lexicographical order is good enough? Even though row reordering is NP-hard, we ﬁnd it hard to improve over recursive orders. Sometimes, fancier alternatives (to be discussed another day) work better, but not always. Daniel Lemire Faster Column-Oriented Indexes
- 24. Thankfully, we can detect cases where recursive orders are good enough We can bound the suboptimality of all recursive orders. Proposition Consider a table with n distinct rows and column cardinalities Ni for i = 1, . . . , c. Recursive ordering is µ-optimal for the problem of minimizing the runs where min(N1 , n) + min(N1 N2 , n) + · · · + min(N1 N2 · · · Nc , n) µ = . n Daniel Lemire Faster Column-Oriented Indexes
- 25. Bounding the optimality of sorting: the computation How do you compute µ very fast so you know lexicographical sort is good enough? Trick is to determine n, the number of distinct rows without sorting the table. Thankfully: n can be estimated quickly with probabilistic methods [Aouiche and Lemire, 2007]. Daniel Lemire Faster Column-Oriented Indexes
- 26. Bounding the optimality of sorting: actual numbers columns µ Census-Income 4-D 4 2.63 DBGEN 4-D 4 1.02 Netﬂix 4 2.00 Census1881 7 5.09 Daniel Lemire Faster Column-Oriented Indexes
- 27. Take away message Column stores are good because of vectorization and RLE/sorting Sorting is sometimes nearly optimal, but not always but we can sometimes tell when sorting is optimal Daniel Lemire Faster Column-Oriented Indexes
- 28. Future direction? Minimizing the number of runs it the wrong problem! We want to maximize long runs! Must study fancier row-reordering heuristics. Daniel Lemire Faster Column-Oriented Indexes
- 29. Questions? ? Daniel Lemire Faster Column-Oriented Indexes
- 30. Aouiche, K. and Lemire, D. (2007). A comparison of ﬁve probabilistic view-size estimation techniques in OLAP. In DOLAP’07, pages 17–24. Hamilton, C. H. and Rau-Chaplin, A. (2007). Compact Hilbert indices: Space-ﬁlling curves for domains with unequal side lengths. Information Processing Letters, 105(5):155–163. Hyde, J. (2010). Data in ﬂight. Commun. ACM, 53(1):48–52. Knuth, D. E. (2005). The Art of Computer Programming, volume 4, chapter fascicle 2. Addison Wesley. Lemire, D. and Kaser, O. (2009). Daniel Lemire Faster Column-Oriented Indexes
- 31. Reordering columns for smaller indexes. in preparation, available from http://arxiv.org/abs/0909.1346. Lemire, D., Kaser, O., and Aouiche, K. (2010). Sorting improves word-aligned bitmap indexes. Data & Knowledge Engineering, 69(1):3–28. O’Neil, P. and Quass, D. (1997). Improved query performance with variant indexes. In SIGMOD ’97, pages 38–49. O’Neil, P. E. (1989). Model 204 architecture and performance. In 2nd International Workshop on High Performance Transaction Systems, pages 40–59. Stonebraker, M., Abadi, D. J., Batkin, A., Chen, X., Cherniack, M., Ferreira, M., Lau, E., Lin, A., Madden, S., O’Neil, E., O’Neil, P., Rasin, A., Tran, N., and Zdonik, S. (2005). Daniel Lemire Faster Column-Oriented Indexes
- 32. C-store: a column-oriented DBMS. In VLDB’05, pages 553–564. Turner, M. J., Hammond, R., and Cotton, P. (1979). A DBMS for large statistical databases. In VLDB’79, pages 319–327. Zaker, M., Phon-Amnuaisuk, S., and Haw, S. (2008). An adequate design for large data warehouse systems: Bitmap index versus B-Tree index. IJCC, 2(2). Daniel Lemire Faster Column-Oriented Indexes

No public clipboards found for this slide

Be the first to comment