Rapid Development of Data Generators Using Meta Generators in PDGFTilmann Rabl, Meikel Poess, Manuel Danisch, Hans-Arno Jacobsen

DBTest 2013, June 24, New York City

MIDDLEWARE SYSTEMSRESEARCH GROUP

MSRG.ORG

DBMS Benchmarking is Increasingly Complex• Data Volumes are sky rocketing

Enterprise data warehouses double every three years

Many enterprise data warehouses are in petabyte size

• Systems are becoming increasingly complex Large number of processor cores

Single systems (SMP) with high number of cores (80 on commodity hardware, 2048 on specialized hardware)

Multi node systems (sky is the limit) Large memory

Dell released a TPC-H benchmark with 15 TB of main memory on 64 systems

• How to challenge these systems?

Benchmarks are increasingly complex

• More tables, columns

• More relationships, dependencies, data types, …

• How to build these benchmarks?

• Parallel Data Generation Framework to the rescue!

TPC-A TPC-C TPC_E TPC-DS0

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4 9 33 2410

92

188

430

#Tables#Columns

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

Repeatable Data Generation• Data generation based on random numbers

• More specifically parallel random number generation

• Generation of numbers within range (e.g., age)

• What if we want NULL values?

• Repeat that logic in every generator?

PDGF Architecture

• Controller Initialization

• Meta Scheduler Inter node scheduling

• Scheduler Inter thread scheduling

• Worker Blockwise data generation

• Update Black Box Co-ordination of data updates

• Seeding System Random sequence adaption

• Generators Value generation

• Output system Data formating

• To generate data for a schema the user defines: Schema XML file

Defines relational schema

Generation XML file Defines output format (CSV, XML, merging tables)

Configuring PDGF• Schema configuration

Data model

• Relational model Tables, fields

• Properties Table size, characters, …

• Generators Base generators Meta generators

• Update definition Insert, update, delete Generated as change data capture

<table name="SUPPLIER"> <size>${S}</size> <field name="S_SUPPKEY" size="" type="NUMERIC“ primary="true" unique="true"> <gen_IdGenerator /> </field> <field name="S_NAME" size="25" type="VARCHAR"> <gen_PrePostfixGenerator> <gen_PaddingGenerator> <gen_OtherFieldValueGenerator> <reference field="S_SUPPKEY" /> </gen_OtherFieldValueGenerator > <character>0</character> <padToLeft>true</padToLeft> <size>9</size> </gen_PaddingGenerator > <prefix>Supplier </prefix> </gen_PrePostfixGenerator> </field>[..]

Base Generators in PDGF• DictList generator

Random line from file

• Long generator Random long in interval

• Others StaticValue Double Date String Text …

<table name="users"> <size>10000</size> <fields> <field name="name"> <type>java.sql.types.VARCHAR</type> <size>100</size> <gen_DictList> <file>dicts/names.dict</file> </gen_DictList> </field> <field name="age"> <type>java.sql.types.NUMERIC</type> <gen_LongGenerator> <min>0</min> <max>120</max> </gen_LongGenerator> </field> </fields></table>

Null Generator• Add NULL logic to every generator?

Could easily be implemented in higher class Adds to the configuration file Reduces performance (every time)

• Higher order generator NullGenerator Only used if added to the schema Can be added to any generator

<field name="age"> <type>java.sql.types.NUMERIC</type> <gen_NullGenerator> <probability>0.05</probability> <gen_LongGenerator> <min>0</min> <max>120</max> </gen_LongGenerator> </gen_NullGenerator></field>

Meta Generators• Control flow and post-processing generators

Null generator controls flow

• Post-processing FormattedNumberGenerator PaddingGenerator UpperLowerCaseGenerator PrePostfixGenerator FormulaGenerator

• Flow control ProbabilityGenerator SequentialGenerator IfGenerator SwitchGenerator ReferenceGenerator

Post-Processing Example• Phone number for users

10s of representations PhoneNumberGenerator was too inflexible

• Formatted long number Long numbers between 10010001 and 9999999999 Number formatting (%d%d%d) %d%d%d-%d%d%d%d

<field name="phonenumber"> <type>java.sql.types.VARCHAR</type> <size>30</size> <generator name="FormattedNumberGenerator"> <generator name="LongGenerator"> <min>10010001</min> <max>9999999999</max> </generator> <format>(%d%d%d) %d%d%d-%d%d%d%d</format> </generator></field>

Flow Control Example• More elaborate name field

Name male or female 50% chance

All upper case Padded to 100 characters

• Sequential generator Probability generator

DictList generator

UpperLowerCase generator Padding generator

<field name="name"> <type>java.sql.types.VARCHAR</type> <size>100</size> <generator name="SequentialGenerator"> <generator name="ProbabilityGenerator"> <probability value="0.5"> <generator name="DictList"> <file>dicts/female.dict</file> </generator> </probability> <probability value="0.5"> <generator name="DictList"> <file>dicts/male.dict</file> </generator> </probability> </generator> <generator name="UpperLowerCaseGenerator"> <mode>uppercase</mode> </generator> <generator name="PaddingGenerator"> <character> </character> <padToLeft>true</padToLeft> </generator> </generator></field>

Core Performance

• Test environment: single core laptop, no I/O• Base time for framework ~ 55 ns (Base Time)

Seeding, method invocation, setting a value

• Computation time for generator 50+ ns (Gen Time)• Cache update if referenced ~ 50 ns (Cache Update)• Cache lookup if intra row reference ~ 50 ns (Cache Lookup)• Sub-generator invocation ~ 50 ns

Static Value (no Cache)

Null Generator (100% NULL)

Null Generator (0% NULL)

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Base Time GeneratorBase Time Sub Sub Generator

Performance Basic Generators

• Basic generators without formatting 120ns – 510ns

DictList LongGeneratorDoubleGeneratorDateGenerator RandomString0

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Performance Formatted Values

• Basic Generators with formatting Usually > 1000ns

DictList SimpleFormat Number Generator

DateGenerator (formatted)

DoubleGenerator (4 places)

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Performance Meta Generators

• Meta generator overhead: Base overhead ~ 50 ns Generator overhead starts from 50 ns Sub generator invocation ~ 50ns

• Often negligible due to lazy formatting

Null G

ener

ator

(100

% N

ull)

Null G

ener

ator

(0%

Nul

l)Pr

ePos

tFix

Sequ

entia

l

(exe

c 2)

Sequ

entia

l

(con

cat 2

)

Sequ

entia

l

(2 fo

rmat

ed

+ lo

ng)

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Use Cases• TPC-H / SSB

8 tables, 61 columns (first non-trivial example) Without meta-FVGs: 26 custom FVGs 2h editing: 10 custom FVGs 1 day reimplementation: 0 custom FVGs, i.e. no coding SSB variations

skews on dimension attributes, fact measures, references

• TPC-DI (in process) 20 tables, 200 columns 19 custom FVGs (mainly for performance in corner

cases) 56x NullGenerator 32x ProbabilityGenerator 3000 lines of config (XML import for multiple files).

Conclusion & Future Work• Meta generators

Improve usability and expressiveness Speed up schema definition Remove necessity for coding Enlarged configuration files

• Used in TPC benchmark(s)

• Performance overhead is small, often negligible

• Future work GUI and SQL export SQL import and data extraction

Thanks

• Questions?

• Contact: tilmann.rabl@utoronto.ca

• Download and try PDGF:

• http://www.paralleldatageneration.org

• Some big data info in our BigBench presentation Tuesday, 4pm, Industry 3