1. Variations of the Star Schema Benchmark to Test the Effects of Data Skew on Query Performance TILMANN RABL, MEIKEL POESS, HANS- ARNO JACOBSEN, PATRICK AND ELIZABETH O’NEIL MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG ICPE 2013, PRAGUE, 24/04/2013

2. Real Life Data is Distributed Uniformly… ◦Customers zip codes typically clustered around metropolitan areas ◦Seasonal items (lawn mowers, snow shovels, …) sold mostly during specific periods ◦US retail sales: ◦peak during Holiday Season ◦December sales are 2x of January sales RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 2 Source: US Census Data Well, Not Really

3. Student Seminar Signup Distribution RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 3

4. How Can Skew Effect Database Systems? Data placement ◦Partitioning ◦Indexing Data structures ◦Tree balance ◦Bucket fill ratio ◦Histograms Optimizer  finding the optimal query plan ◦Index vs. non-index driven plans ◦Hash join vs. merge join ◦Hash group by vs. sort group by 4 RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS

5. Agenda Data Skew in Current Benchmarks Star Schema Benchmark (SSB) Parallel Data Generation Framework (PDGF) Introducing Skew in SSB 5 RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS

6. Data Skew in Benchmarks TPC-D (1994-1999): only uniform data ◦SIGMOD 1997 - “Successor of TPC-D should include data skew” ◦No effect until … TPC-DS (released 2012) ◦Contains comparability zones ◦Not fully utilized TPC-D/H variations ◦Chaudhuri and Narayasa: Zipfian distribution on all columns ◦Crolotte and Ghazal: comparability zones Still lots of open potential 6 RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS

7. Star Schema Benchmark I Star schema version of TPC-H ◦Merged Order and Lineitem ◦Date dimension ◦Dropped Partsupp ◦Selectivity hierarchies ◦C_City  C_Nation  C_Region ◦… RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 7

8. Star Schema Benchmark II Completely new set of queries 4 flights of 3-4 queries ◦Designed for functional coverage and selectivity coverage ◦Drill down in dimension hierarchies ◦Predefined selectivity RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 8 select sum(lo_extendedprice*lo_discount) as revenue from lineorder, date where lo_orderdate = d_datekey and d_year = 1993 and lo_discount between 1 and 3 and lo_quantity < 25; select sum(lo_extendedprice*lo_discount) as revenue from lineorder, date where lo_orderdate = d_datekey and d_yearmonthnum = 199301 and lo_discount between 1 and 3 and lo_quantity between 26 and 35; Drilldown Q1.1 Q1.2

9. Parallel Data Generation Framework Generic data generation framework Relational model ◦Schema specified in configuration file ◦Post-processing stage for alternative representations Repeatable computation ◦Based on XORSHIFT random number generators ◦Hierarchical seeding strategy RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 9 Frank, Poess, and Rabl: Efficient Update Data Generation for DBMS Benchmarks. ICPE '12. Rabl and Poess: Parallel Data Generation for Performance Analysis of Large, Complex RDBMS. DBTest '11. Poess, Rabl, Frank, and Danisch: A PDGF Implementation for TPC-H. TPCTC '11. Rabl, Frank, Sergieh, and Kosch: A Data Generator for Cloud-Scale Benchmarking. TPCTC '10.

10. Configuring PDGF Schema configuration Relational model ◦Tables, fields Properties ◦Table size, characters, … Generators ◦Simple generators ◦Metagenerators Update definition ◦Insert, update, delete ◦Generated as change data capture RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 10 ${S} <field name="S_SUPPKEY" size="" type="NUMERIC“ primary="true" unique="true"> 0 true 9 Supplier [..] PDGF XML DB

11. Opportunities to Inject Data Skew in Foreign key relations ◦E.g., L_PARTKEY One fact table measures ◦E.g., L_Quantity Single dimension hierarchy ◦E.g., P_Brand → P_Category → P_Mfgr Multiple dimension hierarchies ◦E.g., City → Nation in Supplier and Customer Experimental methodology ◦One experiment series for each of the above ◦Comparison to original SSB ◦Comparison of index-forced, non-index, and automatic optimizer mode ◦SSB scale factor 100 (100 GB), x86 server 11 RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS

12. Skew in Foreign Key Relations Very realistic Easy to implement in PDGF ◦Just add a distribution to the reference But! Dimension attributes uniformly distributed Dimension keys uncorrelated to dimension attributes  Very limited effect on selectivity Focus on attributes in selectivity predicates 12 RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS

13. Lo_Quantity distribution ◦Values range between 0 and 50 ◦Originally uniform distribution with: ◦P(X=x)=0.02 ◦Coefficient of variation of 0.00000557 ◦Proposed skewed distribution with: ◦ Query 1.1 ◦lo_quantity < x, x ∈ [2, 51] Results ◦Switches too early to non-index plan ◦Switches too late to non-index plan ◦Optimizer agnostic to distribution Skew in Fact Table Measure – Lo_Quantity 13 RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS

14. Skew in Single Dimension Hierarchy - Part P_Category distribution ◦Uniform P(X=x)=0.04 ◦Skewed P(X=x)= 0.01 - 48.36 ◦Probabilities explicitly defined Query 2.1 ◦Restrictions on two dimensions Results uniform case ◦Index driven superior ◦Optimizer chooses non-index driven Results skewed case ◦Switches too early to non-index plan RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 14

15. Skewed S_City & C_City ◦Probabilites exponentially distributed Query 3.3 ◦Restrictions on 3 dimensions ◦Variation on Supplier and Customer city Results uniform and skewed cases ◦Automatic plan performs best ◦Cross over between automatic uniform and skewed too late Skew in Multiple Dimension Hierarchies – S_City & C_City RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 15 Join CardinalityElapsed Time

16. Conclusion & Future Work PDGF implementation of SSB Introduction of skew in SSB Extensive performance analysis ◦Several interesting optimizer effects ◦Performance impact of skew Future Work Further analysis on impact of skew Skew in query generation Complete suite for testing skew effects 16 RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS

17. Thanks Questions? Download and try PDGF: http://www.paralleldatageneration.org (scripts used in the study available on website above) RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 17

18. Back-up Slides RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 18

19. Configuring PDGF Generation Generation configuration Defines the output ◦Scheduling ◦Data format ◦Sorting ◦File name and location Post processing ◦Filtering of values ◦Merging of tables ◦Splitting of tables ◦Templates (e.g. XML / queries) RABL, POESS, JACOBSEN, O'NEIL, O'NEIL - SSB SKEW VARIATIONS 19 [..] <!-- int y = (fields [0]. getPlainValue ()).intValue (); int d = (fields [1]. getPlainValue ()).intValue (); int q = (fields [2]. getPlainValue ()).intValue (); String n = pdgf.util.Constants.DEFAULT_LINESEPARATOR; buffer.append("-- Q1.1" + n); buffer.append("select sum(lo_extendedprice *"); buffer.append(" lo_discount) as revenue" + n); buffer.append(“ from lineorder, date" + n); buffer.append(“ where lo_orderdate = d_datekey" + n); buffer.append(“ and d_year = " + y + n); buffer.append(“ and lo_disc between " + (d - 1)); buffer.append(“ and " + (d + 1) + n); buffer.append(“ and lo_quantity < " + q + ";" + n); -->