VLDB 2009 Tutorial on Column-Stores

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VLDB 2009 Tutorial on Column-Stores by Daniel Abadi, Peter Boncz, and Stavros Harizopoulos

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VLDB 2009 Tutorial on Column-Stores

  1. 1. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) VLDB Column-Oriented 2009 Tutorial Database Systems Part 1: Stavros Harizopoulos (HP Labs) Part 2: Daniel Abadi (Yale) Part 3: Peter Boncz (CWI) VLDB 2009 Tutorial 1 Column-Oriented Database Systems
  2. 2. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) What is a column-store? row-store column-store Date Store Product Customer Price Date Store Product Customer Price + easy to add/modify a record + only need to read in relevant data - might read in unnecessary data - tuple writes require multiple accesses => suitable for read-mostly, read-intensive, large data repositories VLDB 2009 Tutorial Column-Oriented Database Systems 2
  3. 3. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Are these two fundamentally different? l The only fundamental difference is the storage layout l However: we need to look at the big picture different storage layouts proposed row-stores row-stores++ row-stores++ converge? ‘70s ‘80s ‘90s ‘00s today column-stores new applications new bottleneck in hardware l How did we get here, and where we are heading Part 1 l What are the column-specific optimizations? Part 2 l How do we improve CPU efficiency when operating on Cs Part 3 VLDB 2009 Tutorial Column-Oriented Database Systems 3
  4. 4. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Outline l Part 1: Basic concepts — Stavros l Introduction to key features l From DSM to column-stores and performance tradeoffs l Column-store architecture overview l Will rows and columns ever converge? l Part 2: Column-oriented execution — Daniel l Part 3: MonetDB/X100 and CPU efficiency — Peter VLDB 2009 Tutorial Column-Oriented Database Systems 4
  5. 5. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Telco Data Warehousing example l Typical DW installation dimension tables account fact table or RAM l Real-world example usage source “One Size Fits All? - Part 2: Benchmarking toll Results” Stonebraker et al. CIDR 2007 star schema QUERY 2 SELECT account.account_number, sum (usage.toll_airtime), sum (usage.toll_price) Column-store Row-store FROM usage, toll, source, account WHERE usage.toll_id = toll.toll_id Query 1 2.06 300 AND usage.source_id = source.source_id Query 2 2.20 300 AND usage.account_id = account.account_id AND toll.type_ind in (‘AE’. ‘AA’) Query 3 0.09 300 AND usage.toll_price > 0 Query 4 5.24 300 AND source.type != ‘CIBER’ AND toll.rating_method = ‘IS’ Query 5 2.88 300 AND usage.invoice_date = 20051013 GROUP BY account.account_number Why? Three main factors (next slides) VLDB 2009 Tutorial Column-Oriented Database Systems 5
  6. 6. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Telco example explained (1/3): read efficiency row store column store read pages containing entire rows read only columns needed one row = 212 columns! in this example: 7 columns is this typical? (it depends) caveats: • “select * ” not any faster • clever disk prefetching What about vertical partitioning? • clever tuple reconstruction (it does not work with ad-hoc queries) VLDB 2009 Tutorial Column-Oriented Database Systems 6
  7. 7. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Telco example explained (2/3): compression efficiency l Columns compress better than rows l Typical row-store compression ratio 1 : 3 l Column-store 1 : 10 l Why? l Rows contain values from different domains => more entropy, difficult to dense-pack l Columns exhibit significantly less entropy l Examples: Male, Female, Female, Female, Male 1998, 1998, 1999, 1999, 1999, 2000 l Caveat: CPU cost (use lightweight compression) VLDB 2009 Tutorial Column-Oriented Database Systems 7
  8. 8. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Telco example explained (3/3): sorting & indexing efficiency l Compression and dense-packing free up space l Use multiple overlapping column collections l Sorted columns compress better l Range queries are faster l Use sparse clustered indexes What about heavily-indexed row-stores? (works well for single column access, cross-column joins become increasingly expensive) VLDB 2009 Tutorial Column-Oriented Database Systems 8
  9. 9. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Additional opportunities for column-stores l Block-tuple / vectorized processing l Easier to build block-tuple operators l Amortizes function-call cost, improves CPU cache performance l Easier to apply vectorized primitives l Software-based: bitwise operations l Hardware-based: SIMD Part 3 l Opportunities with compressed columns l Avoid decompression: operate directly on compressed l Delay decompression (and tuple reconstruction) more l Also known as: late materialization in Part 2 l Exploit columnar storage in other DBMS components l Physical design (both static and dynamic) See: Database Cracking, from CWI VLDB 2009 Tutorial Column-Oriented Database Systems 9
  10. 10. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “Column-Stores vs Row-Stores: How Different are They Effect on C-Store performance Really?” Abadi, Hachem, and Madden. SIGMOD 2008. Average for SSBM queries on C-store original Time (sec) C-store enable late column-oriented enable materialization join algorithm compression & operate on compressed VLDB 2009 Tutorial Column-Oriented Database Systems 10
  11. 11. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Summary of column-store key features columnar storage Part 1 header/ID elimination l Storage layout compression Part 2 Part 3 multiple sort orders column operators Part 1 Part 2 avoid decompression l Execution engine Part 2 late materialization vectorized operations Part 3 l Design tools, optimizer VLDB 2009 Tutorial Column-Oriented Database Systems 11
  12. 12. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Outline l Part 1: Basic concepts — Stavros l Introduction to key features l From DSM to column-stores and performance tradeoffs l Column-store architecture overview l Will rows and columns ever converge? l Part 2: Column-oriented execution — Daniel l Part 3: MonetDB/X100 and CPU efficiency — Peter VLDB 2009 Tutorial Column-Oriented Database Systems 12
  13. 13. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) From DSM to Column-stores TOD: Time Oriented Database – Wiederhold et al. 70s -1985: "A Modular, Self-Describing Clinical Databank System," Computers and Biomedical Research, 1975 More 1970s: Transposed files, Lorie, Batory, Svensson. “An overview of cantor: a new system for data analysis” Karasalo, Svensson, SSDBM 1983 1985: DSM paper “A decomposition storage model” Copeland and Khoshafian. SIGMOD 1985. 1990s: Commercialization through SybaseIQ Late 90s – 2000s: Focus on main-memory performance l DSM “on steroids” [1997 – now] CWI: MonetDB l Hybrid DSM/NSM [2001 – 2004] Wisconsin: PAX, Fractured Mirrors Michigan: Data Morphing CMU: Clotho 2005 – : Re-birth of read-optimized DSM as “column-store” MIT: C-Store CWI: MonetDB/X100 10+ startups VLDB 2009 Tutorial Column-Oriented Database Systems 13
  14. 14. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “A decomposition storage The original DSM paper model” Copeland and Khoshafian. SIGMOD 1985. l Proposed as an alternative to NSM l 2 indexes: clustered on ID, non-clustered on value l Speeds up queries projecting few columns l Requires more storage value ID 0100 0962 1000 .. 1 2 3 4 .. VLDB 2009 Tutorial Column-Oriented Database Systems 14
  15. 15. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Memory wall and PAX l 90s: Cache-conscious research “Cache Conscious Algorithms for from: Relational Query Processing.” Shatdal, Kant, Naughton. VLDB 1994. “DBMSs on a modern processor: “Database Architecture Optimized for and: Where does time go?” Ailamaki, to: the New Bottleneck: Memory Access.” DeWitt, Hill, Wood. VLDB 1999. Boncz, Manegold, Kersten. VLDB 1999. l PAX: Partition Attributes Across l Retains NSM I/O pattern l Optimizes cache-to-RAM communication “Weaving Relations for Cache Performance.” Ailamaki, DeWitt, Hill, Skounakis, VLDB 2001. VLDB 2009 Tutorial Column-Oriented Database Systems 15
  16. 16. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) More hybrid NSM/DSM schemes l Dynamic PAX: Data Morphing “Data morphing: an adaptive, cache-conscious storage technique.” Hankins, Patel, VLDB 2003. l Clotho: custom layout using scatter-gather I/O “Clotho: Decoupling Memory Page Layout from Storage Organization.” Shao, Schindler, Schlosser, Ailamaki, and Ganger. VLDB 2004. l Fractured mirrors l Smart mirroring with both NSM/DSM copies “A Case For Fractured Mirrors.” Ramamurthy, DeWitt, Su, VLDB 2002. VLDB 2009 Tutorial Column-Oriented Database Systems 16
  17. 17. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) MonetDB (more in Part 3) l Late 1990s, CWI: Boncz, Manegold, and Kersten l Motivation: l Main-memory l Improve computational efficiency by avoiding expression interpreter l DSM with virtual IDs natural choice l Developed new query execution algebra l Initial contributions: l Pointed out memory-wall in DBMSs l Cache-conscious projections and joins l … VLDB 2009 Tutorial Column-Oriented Database Systems 17
  18. 18. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) 2005: the (re)birth of column-stores l New hardware and application realities l Faster CPUs, larger memories, disk bandwidth limit l Multi-terabyte Data Warehouses l New approach: combine several techniques l Read-optimized, fast multi-column access, disk/CPU efficiency, light-weight compression l C-store paper: l First comprehensive design description of a column-store l MonetDB/X100 l “proper” disk-based column store l Explosion of new products VLDB 2009 Tutorial Column-Oriented Database Systems 18
  19. 19. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Performance tradeoffs: columns vs. rows DSM traditionally was not favored by technology trends How has this changed? l Optimized DSM in “Fractured Mirrors,” 2002 l “Apples-to-apples” comparison “Performance Tradeoffs in Read- Optimized Databases” Harizopoulos, Liang, Abadi, Madden, VLDB’06 l Follow-up study “Read-Optimized Databases, In- Depth” Holloway, DeWitt, VLDB’08 l Main-memory DSM vs. NSM “DSM vs. NSM: CPU performance tradeoffs in block-oriented query processing” Boncz, Zukowski, Nes, DaMoN’08 l Flash-disks: a come-back for PAX? “Query Processing Techniques “Fast Scans and Joins Using Flash for Solid State Drives” Drives” Shah, Harizopoulos, Tsirogiannis, Harizopoulos, Wiener, Graefe. DaMoN’08 VLDB 2009 Tutorial Shah, Wiener, Graefe, Column-Oriented Database Systems 19 SIGMOD’09
  20. 20. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Fractured mirrors: a closer look l Store DSM relations inside a B-tree “A Case For Fractured Mirrors” Ramamurthy, l Leaf nodes contain values DeWitt, Su, VLDB 2002. l Eliminate IDs, amortize header overhead l Custom implementation on Shore sparse Tuple TID Column Header Data B-tree on ID 1 a1 3 2 a2 3 a3 1 a1 2 a2 3 a3 4 a4 5 a5 4 a4 a1 a2 a3 a4 a5 1 4 5 a5 Similar: storage density “Efficient columnar comparable storage in B-trees” Graefe. to column stores Sigmod Record 03/2007. VLDB 2009 Tutorial Column-Oriented Database Systems 20
  21. 21. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Fractured mirrors: performance From PAX paper: column? time row regular DSM column? columns projected: 1 2 3 4 5 optimized l Chunk-based tuple merging DSM l Read in segments of M pages l Merge segments in memory l Becomes CPU-bound after 5 pages VLDB 2009 Tutorial Column-Oriented Database Systems 21
  22. 22. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “Performance Tradeoffs in Read- Column-scanner Optimized Databases” implementation Harizopoulos, Liang, Abadi, Madden, VLDB’06 row scanner column scanner Joe 45 … … Joe 45 … … SELECT name, age WHERE age > 40 apply S predicate(s) S #POS 45 Joe #POS … Direct I/O Sue … prefetch ~100ms apply 1 Joe 45 worth of data predicate #1 S 2 Sue 37 …… … 45 37 … VLDB 2009 Tutorial Column-Oriented Database Systems 22
  23. 23. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Scan performance l Large prefetch hides disk seeks in columns l Column-CPU efficiency with lower selectivity l Row-CPU suffers from memory stalls not shown, l Memory stalls disappear in narrow tuples details in the paper l Compression: similar to narrow VLDB 2009 Tutorial Column-Oriented Database Systems 23
  24. 24. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “Read-Optimized Databases, In- Even more results Depth” Holloway, DeWitt, VLDB’08 35 • Same engine as before narrow & compressed tuple: • Additional findings 30 CPU-bound! C-25% 25 C-10% R-50% 20 Time (s) 15 10 5 wide attributes: same as before 0 1 2 3 4 5 6 7 8 9 10 Columns Returned Non-selective queries, narrow tuples, favor well-compressed rows Materialized views are a win Column-joins are Scan times determine early materialized joins covered in part 2! VLDB 2009 Tutorial Column-Oriented Database Systems 24
  25. 25. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Speedup of columns over rows “Performance Tradeoffs in Read- Optimized Databases” Harizopoulos, Liang, Abadi, cycles per disk byte 144 Madden, VLDB’06 72 (cpdb) 36 +++ 18 _ = + ++ 9 8 12 16 20 24 28 32 36 tuple width l Rows favored by narrow tuples and low cpdb l Disk-bound workloads have higher cpdb VLDB 2009 Tutorial Column-Oriented Database Systems 25
  26. 26. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Varying prefetch size no competing disk traffic 40 Column 2 time (sec) 30 Column 8 20 Column 16 Column 48 (x 128KB) 10 Row (any prefetch size) 0 4 8 12 16 20 24 28 32 selected bytes per tuple l No prefetching hurts columns in single scans VLDB 2009 Tutorial Column-Oriented Database Systems 26
  27. 27. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Varying prefetch size with competing disk traffic 40 Column, 48 40 time (sec) 30 Row, 48 30 20 20 10 10 Column, 8 Row, 8 0 0 4 12 20 28 4 12 20 28 selected bytes per tuple l No prefetching hurts columns in single scans l Under competing traffic, columns outperform rows for any prefetch size VLDB 2009 Tutorial Column-Oriented Database Systems 27
  28. 28. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “DSM vs. NSM: CPU performance trade CPU Performance offs in block-oriented query processing” Boncz, Zukowski, Nes, DaMoN’08 l Benefit in on-the-fly conversion between NSM and DSM l DSM: sequential access (block fits in L2), random in L1 l NSM: random access, SIMD for grouped Aggregation VLDB 2009 Tutorial Column-Oriented Database Systems 28
  29. 29. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) New storage technology: Flash SSDs l Performance characteristics l very fast random reads, slow random writes l fast sequential reads and writes l Price per bit (capacity follows) l cheaper than RAM, order of magnitude more expensive than Disk l Flash Translation Layer introduces unpredictability l avoid random writes! l Form factors not ideal yet l SSD (Ł small reads still suffer from SATA overhead/OS limitations) l PCI card (Ł high price, limited expandability) l Boost Sequential I/O in a simple package l Flash RAID: very tight bandwidth/cm3 packing (4GB/sec inside the box) l Column Store Updates l useful for delta structures and logs l Random I/O on flash fixes unclustered index access l still suboptimal if I/O block size > record size l therefore column stores profit mush less than horizontal stores l Random I/O useful to exploit secondary, tertiary table orderings l the larger the data, the deeper clustering one can exploit VLDB 2009 Tutorial Column-Oriented Database Systems 29
  30. 30. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Even faster column scans on flash SSDs 30K Read IOps, 3K Write Iops l New-generation SSDs 250MB/s Read BW, 200MB/s Write l Very fast random reads, slower random writes l Fast sequential RW, comparable to HDD arrays l No expensive seeks across columns l FlashScan and Flashjoin: PAX on SSDs, inside Postgres “Query Processing Techniques for Solid State Drives” Tsirogiannis, Harizopoulos, Shah, Wiener, Graefe, SIGMOD’09 mini-pages with no qualified attributes are not accessed VLDB 2009 Tutorial Column-Oriented Database Systems 30
  31. 31. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Column-scan performance over time regular DSM (2001) column-store (2006) ..to 1.2x slower from 7x slower ..to 2x slower ..to same and 3x faster! optimized DSM (2002) SSD Postgres/PAX (2009) VLDB 2009 Tutorial Column-Oriented Database Systems 31
  32. 32. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Outline l Part 1: Basic concepts — Stavros l Introduction to key features l From DSM to column-stores and performance tradeoffs l Column-store architecture overview l Will rows and columns ever converge? l Part 2: Column-oriented execution — Daniel l Part 3: MonetDB/X100 and CPU efficiency — Peter VLDB 2009 Tutorial Column-Oriented Database Systems 32
  33. 33. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Architecture of a column-store storage layout l read-optimized: dense-packed, compressed l organize in extends, batch updates l multiple sort orders l sparse indexes engine l block-tuple operators l new access methods system-level l optimized relational operators l system-wide column support l loading / updates l scaling through multiple nodes l transactions / redundancy VLDB 2009 Tutorial Column-Oriented Database Systems 33
  34. 34. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “C-Store: A Column-Oriented DBMS.” Stonebraker et al. C-Store VLDB 2005. l Compress columns l No alignment l Big disk blocks l Only materialized views (perhaps many) l Focus on Sorting not indexing l Data ordered on anything, not just time l Automatic physical DBMS design l Optimize for grid computing l Innovative redundancy l Xacts – but no need for Mohan l Column optimizer and executor VLDB 2009 Tutorial Column-Oriented Database Systems 34
  35. 35. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) C-Store: only materialized views (MVs) l Projection (MV) is some number of columns from a fact table l Plus columns in a dimension table – with a 1-n join between Fact and Dimension table l Stored in order of a storage key(s) l Several may be stored! l With a permutation, if necessary, to map between them l Table (as the user specified it and sees it) is not stored! l No secondary indexes (they are a one column sorted MV plus a permutation, if you really want one) User view: Possible set of MVs: EMP (name, age, salary, dept) MV-1 (name, dept, floor) in floor order Dept (dname, floor) MV-2 (salary, age) in age order MV-3 (dname, salary, name) in salary order VLDB 2009 Tutorial Column-Oriented Database Systems 35
  36. 36. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Continuous Load and Query (Vertica) Hybrid Storage Architecture > Write Optimized > Read Optimized Store (WOS) Store (ROS) Trickle • On disk Load • Sorted / Compressed TUPLE MOVER Asynchronous • Segmented A B C Data Transfer • Large data loaded direct §Memory based A B C §Unsorted / Uncompressed §Segmented §Low latency / Small quick (A B C | A) inserts VLDB 2009 Tutorial Column-Oriented Database Systems 36
  37. 37. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Loading Data (Vertica) > INSERT, UPDATE, DELETE Write-Optimized Store (WOS) > Bulk and Trickle Loads In-memory §COPY Automatic §COPY DIRECT Tuple Mover > User loads data into logical Tables > Vertica loads atomically into storage Read-Optimized Store (ROS) On-disk VLDB 2009 Tutorial Column-Oriented Database Systems 37
  38. 38. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Applications for column-stores l Data Warehousing l High end (clustering) l Mid end/Mass Market l Personal Analytics l Data Mining l E.g. Proximity l Google BigTable l RDF l Semantic web data management l Information retrieval l Terabyte TREC l Scientific datasets l SciDB initiative l SLOAN Digital Sky Survey on MonetDB VLDB 2009 Tutorial Column-Oriented Database Systems 38
  39. 39. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) List of column-store systems l Cantor (history) l Sybase IQ l SenSage (former Addamark Technologies) l Kdb l 1010data l MonetDB l C-Store/Vertica l X100/VectorWise l KickFire l SAP Business Accelerator l Infobright l ParAccel l Exasol VLDB 2009 Tutorial Column-Oriented Database Systems 39
  40. 40. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Outline l Part 1: Basic concepts — Stavros l Introduction to key features l From DSM to column-stores and performance tradeoffs l Column-store architecture overview l Will rows and columns ever converge? l Part 2: Column-oriented execution — Daniel l Part 3: MonetDB/X100 and CPU efficiency — Peter VLDB 2009 Tutorial Column-Oriented Database Systems 40
  41. 41. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Simulate a Column-Store inside a Row-Store Date Store Product Customer Price 01/01 BOS Table Mesa $20 01/01 NYC Chair Lutz $13 Option B: Index Every Column 01/01 BOS Bed Mudd $79 Option A: Date Index Vertical Partitioning Date Store Product Customer Price TID Value TID Value TID Value TID Value TID Value 1 01/01 1 BOS 1 Table 1 Mesa 1 $20 Store Index 2 01/01 2 NYC 2 Chair 2 Lutz 2 $13 3 01/01 3 BOS 3 Bed 3 Mudd 3 $79 … VLDB 2009 Tutorial Column-Oriented Database Systems 41
  42. 42. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Simulate a Column-Store inside a Row-Store Date Store Product Customer Price 01/01 BOS Table Mesa $20 01/01 NYC Chair Lutz $13 Option B: Index Every Column 01/01 BOS Bed Mudd $79 Option A: Date Index Vertical Partitioning Date Store Product Customer Price Value StartPos Length TID Value TID Value TID Value TID Value 01/01 1 3 1 BOS 1 Table 1 Mesa 1 $20 Store Index 2 NYC 2 Chair 2 Lutz 2 $13 Can explicitly run- 3 BOS 3 Bed 3 Mudd 3 $79 length encode date “Teaching an Old Elephant New Tricks.” Bruno, CIDR 2009. … VLDB 2009 Tutorial Column-Oriented Database Systems 42
  43. 43. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Experiments l Star Schema Benchmark (SSBM) Adjoined Dimension Column Index (ADC Index) to Improve Star Schema Query Performance”. O’Neil et. al. ICDE 2008. l Implemented by professional DBA l Original row-store plus 2 column-store simulations on same row-store product 250.0 “Column-Stores vs Row-Stores: 200.0 How Different are They Really?” Abadi, Hachem, and Madden. Time (seconds) 150.0 SIGMOD 2008. 100.0 50.0 0.0 Vertically Partitioned Row-Store With All Normal Row-Store Row-Store Indexes Average 25.7 79.9 221.2 VLDB 2009 Tutorial Column-Oriented Database Systems 43
  44. 44. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) What’s Going On? Vertical Partitions l Vertical partitions in row-stores: l Work well when workload is known l ..and queries access disjoint sets of columns l See automated physical design Tuple TID Column Header Data 1 l Do not work well as full-columns 2 l TupleID overhead significant 3 l Excessive joins Queries touch 3-4 foreign keys in fact table, 1-2 numeric columns “Column-Stores vs. Row-Stores: Complete fact table takes up ~4 GB How Different Are They Really?” (compressed) Abadi, Madden, and Hachem. Vertically partitioned tables take up 0.7-1.1 SIGMOD 2008. GB (compressed) VLDB 2009 Tutorial Column-Oriented Database Systems 44
  45. 45. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) What’s Going On? All Indexes Case l Tuple construction l Common type of query: SELECT store_name, SUM(revenue) FROM Facts, Stores WHERE fact.store_id = stores.store_id AND stores.country = “Canada” GROUP BY store_name l Result of lower part of query plan is a set of TIDs that passed all predicates l Need to extract SELECT attributes at these TIDs l BUT: index maps value to TID l You really want to map TID to value (i.e., a vertical partition) Tuple construction is SLOW VLDB 2009 Tutorial Column-Oriented Database Systems 45
  46. 46. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) So…. l All indexes approach is a poor way to simulate a column-store l Problems with vertical partitioning are NOT fundamental l Store tuple header in a separate partition l Allow virtual TIDs l Combine clustered indexes, vertical partitioning l So can row-stores simulate column-stores? l Might be possible, BUT: l Need better support for vertical partitioning at the storage layer l Need support for column-specific optimizations at the executer level l Full integration: buffer pool, transaction manager, .. l When will this happen? See Part 2, Part 3 l Most promising features = soon for most promising features l ..unless new technology / new objectives change the game (SSDs, Massively Parallel Platforms, Energy-efficiency) VLDB 2009 Tutorial Column-Oriented Database Systems 46
  47. 47. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) End of Part 1 l Basic concepts — Stavros l Introduction to key features l From DSM to column-stores and performance tradeoffs l Column-store architecture overview l Will rows and columns ever converge? l Part 2: Column-oriented execution — Daniel l Part 3: MonetDB/X100 and CPU efficiency — Peter VLDB 2009 Tutorial Column-Oriented Database Systems 47
  48. 48. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Part 2 Outline l Compression l Tuple Materialization l Joins VLDB 2009 Tutorial Column-Oriented Database Systems 48
  49. 49. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) VLDB Column-Oriented 2009 Tutorial Database Systems Compression “Super-Scalar RAM-CPU Cache Compression” Zukowski, Heman, Nes, Boncz, ICDE’06 “Integrating Compression and Execution in Column- Oriented Database Systems” Abadi, Madden, and Ferreira, SIGMOD ’06 •Query optimization in compressed database systems” Chen, Gehrke, Korn, SIGMOD’01
  50. 50. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Compression l Trades I/O for CPU l Increased column-store opportunities: l Higher data value locality in column stores l Techniques such as run length encoding far more useful l Can use extra space to store multiple copies of data in different sort orders VLDB 2009 Tutorial Column-Oriented Database Systems 50
  51. 51. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Run-length Encoding Quarter Product ID Price Quarter Product ID Price (value, start_pos, run_length) (value, start_pos, run_length) Q1 1 5 (Q1, 1, 300) (1, 1, 5) 5 Q1 1 7 (2, 6, 2) 7 Q1 1 2 (Q2, 301, 350) 2 Q1 1 9 … (Q3, 651, 500) 9 Q1 1 6 (1, 301, 3) 6 Q1 2 8 (Q4, 1151, 600) (2, 304, 1) 8 Q1 2 5 … … … … 5 … Q2 1 3 3 Q2 1 8 8 Q2 1 1 1 Q2 2 4 … … … 4 … VLDB 2009 Tutorial Column-Oriented Database Systems 51
  52. 52. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “Integrating Compression and Execution in Column- Oriented Database Systems” Abadi et. al, SIGMOD ’06 Bit-vector Encoding l For each unique Product ID ID: 1 ID: 2 ID: 3 … value, v, in column c, create bit-vector 1 1 0 0 0 b 1 1 0 0 0 l b[i] = 1 if c[i] = v 1 1 0 0 0 1 1 0 0 0 l Good for columns 1 1 0 0 0 with few unique 2 0 1 0 0 values 2 0 1 0 0 l Each bit-vector … … … … … can be further 1 1 0 0 0 compressed if 1 1 0 0 0 sparse 2 0 1 0 0 3 0 0 1 0 … … … … … VLDB 2009 Tutorial Column-Oriented Database Systems 52
  53. 53. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “Integrating Compression and Execution in Column- Oriented Database Systems” Abadi et. al, SIGMOD ’06 Dictionary Encoding Quarter l For each Quarter unique value 0 Quarter 1 create Q1 3 dictionary entry 0 24 Q2 2 l Dictionary can 0 128 Q4 0 be per-block or 0 122 Q1 1 per-column 3 l Column-stores Q3 Q1 2 2 OR + have the Q1 + Dictionary advantage that Dictionary dictionary Q1 24: Q1, Q2, Q4, Q1 entries may Q2 0: Q1 … encode multiple Q4 1: Q2 122: Q2, Q4, Q3, Q3 values at once Q3 2: Q3 … Q3 3: Q4 128: Q3, Q1, Q1, Q1 … VLDB 2009 Tutorial Column-Oriented Database Systems 53
  54. 54. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Frame Of Reference Encoding Price Price l Encodes values as b bit Frame: 50 offset from chosen frame 45 -5 54 of reference 4 48 l Special escape code (e.g. -2 55 4 bits per all bits set to 1) indicates 5 value 51 1 a difference larger than 53 3 can be stored in b bits 40 ∞ l After escape code, 50 40 original (uncompressed) 49 Exceptions (see 0 part 3 for a better value is written 62 way to deal with -1 exceptions) 52 ∞ “Compressing Relations and Indexes 50 … 62 ” Goldstein, Ramakrishnan, Shaft, 2 ICDE’98 0 … VLDB 2009 Tutorial Column-Oriented Database Systems 54
  55. 55. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Differential Encoding Time Time l Encodes values as b bit offset from previous value l Special escape code (just like 5:00 5:00 frame of reference encoding) 5:02 2 indicates a difference larger than can be stored in b bits 5:03 1 2 bits per l After escape code, original 5:03 0 value (uncompressed) value is written 5:04 1 l Performs well on columns containing increasing/decreasing 5:06 2 sequences 5:07 1 l inverted lists 5:08 1 l timestamps l object IDs 5:10 2 Exception (see l sorted / clustered columns 5:15 ∞ part 3 for a better way to deal with 5:16 5:15 exceptions) “Improved Word-Aligned Binary 5:16 1 Compression for Text Indexing” … 0 Ahn, Moffat, TKDE’06 VLDB 2009 Tutorial Column-Oriented Database Systems 55
  56. 56. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) What Compression Scheme To Use? Does column appear in the sort key? yes no Is the average Are number of unique run-length > 2 values < ~50000 yes no yes Differential RLE Encoding no Does this column appear frequently in selection predicates? yes no Is the data numerical and exhibit good locality? yes no Bit-vector Dictionary Compression Compression Frame of Reference Leave Data Encoding Uncompressed OR Heavyweight Compression VLDB 2009 Tutorial Column-Oriented Database Systems 56
  57. 57. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “Super-Scalar RAM-CPU Cache Compression” Zukowski, Heman, Nes, Boncz, ICDE’06 Heavy-Weight Compression Schemes l Modern disk arrays can achieve > 1GB/s l 1/3 CPU for decompression Ł 3GB/s needed Ł Lightweight compression schemes are better Ł Even better: operate directly on compressed data VLDB 2009 Tutorial Column-Oriented Database Systems 57
  58. 58. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “Integrating Compression and Execution in Column- Oriented Database Systems” Abadi et. al, SIGMOD ’06 Operating Directly on Compressed Data l I/O - CPU tradeoff is no longer a tradeoff l Reduces memory–CPU bandwidth requirements l Opens up possibility of operating on multiple records at once VLDB 2009 Tutorial Column-Oriented Database Systems 58
  59. 59. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “Integrating Compression and Execution in Column- Oriented Database Systems” Abadi et. al, SIGMOD ’06 Operating Directly on Compressed Data Quarter Product ID 1 2 3 … … … ProductID, COUNT(*)) (Q1, 1, 300) 1 0 0 301-306 0 0 1 (1, 3) (Q2, 301, 6) 0 1 0 (2, 1) (Q3, 307, 500) 1 0 0 1 0 0 (3, 2) (Q4, 807, 600) 0 0 1 0 1 0 SELECT ProductID, Count(*) 1 0 0 FROM table 0 0 1 WHERE (Quarter = Q2) 0 1 0 GROUP BY ProductID Index Lookup + Offset jump 0 0 1 … … … VLDB 2009 Tutorial Column-Oriented Database Systems 59
  60. 60. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “Integrating Compression and Execution in Column- Oriented Database Systems” Abadi et. al, SIGMOD ’06 Operating Directly on Compressed Data SELECT ProductID, Count(*) Block API FROM table WHERE (Quarter = Q2) Data GROUP BY ProductID Aggregation isOneValue() Operator isValueSorted() isPosContiguous() isSparse() Selection getNext() Operator decompressIntoArray() (Q1, 1, 300) getValueAtPosition(pos) (Q2, 301, 6) getMin() Compression- getMax() (Q3, 307, 500) Aware Scan getSize() (Q4, 807, 600) Operator VLDB 2009 Tutorial Column-Oriented Database Systems 60
  61. 61. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) VLDB Column-Oriented 2009 Tutorial Database Systems Tuple Materialization and Column-Oriented Join Algorithms “Materialization Strategies in a Column- “Query Processing Techniques for Oriented DBMS” Abadi, Myers, DeWitt, Solid State Drives” Tsirogiannis, and Madden. ICDE 2007. Harizopoulos Shah, Wiener, and Graefe. SIGMOD 2009. “Self-organizing tuple reconstruction in column-stores“, Idreos, Manegold, “Cache-Conscious Radix-Decluster Kersten, SIGMOD’09 Projections”, Manegold, Boncz, Nes, VLDB’04 “Column-Stores vs Row-Stores: How Different are They Really?” Abadi, Madden, and Hachem. SIGMOD 2008.
  62. 62. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) When should columns be projected? l Where should column projection operators be placed in a query plan? l Row-store: l Column projection involves removing unneeded columns from tuples l Generally done as early as possible l Column-store: l Operation is almost completely opposite from a row-store l Column projection involves reading needed columns from storage and extracting values for a listed set of tuples § This process is called “materialization” l Early materialization: project columns at beginning of query plan § Straightforward since there is a one-to-one mapping across columns l Late materialization: wait as long as possible for projecting columns § More complicated since selection and join operators on one column obfuscates mapping to other columns from same table l Most column-stores construct tuples and column projection time § Many database interfaces expect output in regular tuples (rows) § Rest of discussion will focus on this case VLDB 2009 Tutorial Column-Oriented Database Systems 62
  63. 63. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) When should tuples be constructed? Select + Aggregate QUERY: 4 2 2 7 SELECT custID,SUM(price) FROM table 4 1 3 13 WHERE (prodID = 4) AND 4 3 3 42 (storeID = 1) AND GROUP BY custID 4 1 3 80 Construct l Solution 1: Create rows first (EM). But: l Need to construct ALL tuples (4,1,4) 2 2 7 1 3 13 l Need to decompress data 3 3 42 l Poor memory bandwidth 1 3 80 utilization prodID storeID custID price VLDB 2009 Tutorial Column-Oriented Database Systems 63
  64. 64. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Solution 2: Operate on columns QUERY: 1 0 SELECT custID,SUM(price) 1 1 AGG FROM table 1 0 WHERE (prodID = 4) AND 1 1 (storeID = 1) AND Data Data GROUP BY custID Source Source custID price Data Data Source Source AND 4 2 2 7 4 2 4 1 3 13 Data Data 4 1 4 3 3 42 Source Source 4 3 4 1 3 80 prodID storeID 4 1 prodID storeID custID price prodID storeID VLDB 2009 Tutorial Column-Oriented Database Systems 64
  65. 65. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Solution 2: Operate on columns QUERY: 0 SELECT custID,SUM(price) AGG FROM table 1 WHERE (prodID = 4) AND 0 (storeID = 1) AND Data Data 1 GROUP BY custID Source Source custID price AND AND 4 2 2 7 1 0 4 1 3 13 Data Data 1 1 4 3 3 42 Source Source 1 0 4 1 3 80 prodID storeID 1 1 prodID storeID custID price VLDB 2009 Tutorial Column-Oriented Database Systems 65
  66. 66. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Solution 2: Operate on columns QUERY: 3 13 1 SELECT custID,SUM(price) AGG 3 80 1 FROM table WHERE (prodID = 4) AND (storeID = 1) AND 2 0 7 0 GROUP BY custID Data Data Source Source 3 1 Data Data 13 1 custID price Source Source 3 0 42 0 3 1 80 1 AND custID price 4 2 2 7 0 4 1 3 13 Data Data 1 4 3 3 42 Source Source 0 4 1 3 80 prodID storeID 1 prodID storeID custID price VLDB 2009 Tutorial Column-Oriented Database Systems 66
  67. 67. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) Solution 2: Operate on columns QUERY: SELECT custID,SUM(price) AGG FROM table WHERE (prodID = 4) AND 3 1 1 93 (storeID = 1) AND Data Data GROUP BY custID Source Source custID price AGG AND 4 2 2 7 4 1 3 13 Data Data 3 1 13 1 4 3 3 42 Source Source 3 1 80 1 4 1 3 80 prodID storeID prodID storeID custID price VLDB 2009 Tutorial Column-Oriented Database Systems 67
  68. 68. Re-use permitted when acknowledging the original © Stavros Harizopoulos, Daniel Abadi, Peter Boncz (2009) “Materialization Strategies in a Column-Oriented DBMS” Abadi, Myers, DeWitt, and Madden. ICDE 2007. For plans without joins, late materialization is a win 10 QUERY: 9 8 SELECT C1, SUM(C2) Time (seconds) 7 FROM table 6 WHERE (C1 < CONST) AND 5 4 (C2 < CONST) 3 GROUP BY C1 2 1 l Ran on 2 compressed 0 Low selectivity Medium High selectivity columns from TPC-H selectivity scale 10 data Early Materialization Late Materialization VLDB 2009 Tutorial Column-Oriented Database Systems 68

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