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TPC-H in
MongoDB
Aung Thu Rha Hein(g5536871)
Agenda
•   Introduction to MongoDB
•   TPC-H Data Setup
•   Schema
•   Advantages and Disadvantages of New Schema
•   Queries
    o   Pricing Summary Record
    o   National Market Share Query
    o   Total Supplier Query
    o   Potential Part Promotion Query
    o   Suppliers who kept orders waiting query
    o   Global Sales Opportunity Query
• Benchmark result
• Discussion
• Demonstration
Introduction to MongoDB
• Open source, document-oriented and schema-free
• Store data in BSON format
• Easy to understand
• Flexible, Scalable & lightweight
• Ease of use
• No ‘join’ operation

• SQL to MongoDB Sample Query
•   Select * from users where status = “A” ORDER BY USER_ID DESC


•   db.users.find( { status: "A" } ).sort( { user_id: -1 } )
TPC-H Data Setup
• Import data into MongoDB
   o Use MongoVue to import from MySQL
   o Time consuming and difficult

• To achieve flexibility:
   o Embedded all tables into single collection
   o Replace all foreign keys with objects from lineitem table
   o Choose lineitem table because of
      • No primary keys
Schema
  • Final Schema of TPC-H in MongoDB




lineitemOrder   CustomerNation Region   Partsupp Part supplier N R
Advantages and Disadvantages
      of New Schema
• Advantages
  o Easier to understand than SQL schema
  o One document: one record
  o No need to join tables

• Disadvantages
  o   Higher memory usage
  o   Update operation becomes more demanding
  o   Converting to BSON takes time
  o   Require lot of computational power
  o   Only around 300,000(5%) count of lineitem able to convert
Queries
• Select 6 queries to run on MongoDB with Map-
  Reduce & Aggregation Framework
• Compare the result with MySQL

PROBLEMS
• Outputs are not the same because of failure during
  converting data
• Aggregation framework is still in development
Q1: Pricing Summary
    Record Query
Q8:National Market Share
Query
Q15:Top Supplier Query
Q20:Potential part Promotion
           Query
Q21:Supplier who kept order
waiting
Q22:Global Sales Opportunity
Benchmark result
• All benchmarks run on Intel Core i7-3610QM 2.30GHz 6MB
  cache,4GB DDR3,750GB 7200 RPM,Win64 system
• Query1
        MongoDB                              6.1 sec

        MySQL     0.2 sec


• Query 8
        MongoDB                   1.6 sec

        MySQL     0.1 sec
• Query15
        MongoDB                0.7 sec

        MySQL        0.4 sec
Benchmark result(cont.)
• Query 20
   MongoDB             1.1 sec

   MySQL                                               174.4 sec

• Query 21
   MongoDB                         6.2 sec

   MySQL                 5.5 sec

• Query 22
   MongoDB                                   7.6 sec

   MySQL     0.8 sec
Discussion & Conclusion
• MongoDB left behind in all queries
   o   Design problem
   o   Aggregation framework problem
   o   No standard Query Language
   o   Server side query processing is not the nature of NoSQL
   o   Complex SQL cannot convert easily

• Only suitable for Applications:
   o Business card database
   o Web Blog
   o Applications without complex transactions
Demonstration

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TPC-H in MongoDB

  • 1. TPC-H in MongoDB Aung Thu Rha Hein(g5536871)
  • 2. Agenda • Introduction to MongoDB • TPC-H Data Setup • Schema • Advantages and Disadvantages of New Schema • Queries o Pricing Summary Record o National Market Share Query o Total Supplier Query o Potential Part Promotion Query o Suppliers who kept orders waiting query o Global Sales Opportunity Query • Benchmark result • Discussion • Demonstration
  • 3. Introduction to MongoDB • Open source, document-oriented and schema-free • Store data in BSON format • Easy to understand • Flexible, Scalable & lightweight • Ease of use • No ‘join’ operation • SQL to MongoDB Sample Query • Select * from users where status = “A” ORDER BY USER_ID DESC • db.users.find( { status: "A" } ).sort( { user_id: -1 } )
  • 4. TPC-H Data Setup • Import data into MongoDB o Use MongoVue to import from MySQL o Time consuming and difficult • To achieve flexibility: o Embedded all tables into single collection o Replace all foreign keys with objects from lineitem table o Choose lineitem table because of • No primary keys
  • 5. Schema • Final Schema of TPC-H in MongoDB lineitemOrder CustomerNation Region Partsupp Part supplier N R
  • 6. Advantages and Disadvantages of New Schema • Advantages o Easier to understand than SQL schema o One document: one record o No need to join tables • Disadvantages o Higher memory usage o Update operation becomes more demanding o Converting to BSON takes time o Require lot of computational power o Only around 300,000(5%) count of lineitem able to convert
  • 7. Queries • Select 6 queries to run on MongoDB with Map- Reduce & Aggregation Framework • Compare the result with MySQL PROBLEMS • Outputs are not the same because of failure during converting data • Aggregation framework is still in development
  • 8. Q1: Pricing Summary Record Query
  • 12. Q21:Supplier who kept order waiting
  • 14. Benchmark result • All benchmarks run on Intel Core i7-3610QM 2.30GHz 6MB cache,4GB DDR3,750GB 7200 RPM,Win64 system • Query1 MongoDB 6.1 sec MySQL 0.2 sec • Query 8 MongoDB 1.6 sec MySQL 0.1 sec • Query15 MongoDB 0.7 sec MySQL 0.4 sec
  • 15. Benchmark result(cont.) • Query 20 MongoDB 1.1 sec MySQL 174.4 sec • Query 21 MongoDB 6.2 sec MySQL 5.5 sec • Query 22 MongoDB 7.6 sec MySQL 0.8 sec
  • 16. Discussion & Conclusion • MongoDB left behind in all queries o Design problem o Aggregation framework problem o No standard Query Language o Server side query processing is not the nature of NoSQL o Complex SQL cannot convert easily • Only suitable for Applications: o Business card database o Web Blog o Applications without complex transactions