A corrected comparison between the databases by Tyler Weatherby 2017 Spring. A benchmark is done between MySQL MyISAM engine, MySQL Memory engine, and MonetDB engine on TPC-H data. In this project, we added the index/key to important tables.

1. MySQL and MonetDB
Benchmarks
A corrected comparison between the databases
Author: Tyler Weatherby
Advisor: Dr. Feng Yu

2. Overview
• History
• The Difference Between Tables
• Engine Background
• Goals of This Project
• TPC-H Background
• Installing TPC-H
• Main Project Issue
• Issue Resolved
• Expansion of the Original Data
• Creating Tables and Loading Data
• Query Scripts
• Graphical Interpretation of Results
• Numeric Interpretation of Results
• Breakdown and Comparison
• Challenges Encountered
• Interpretation
• Possibilities to Further Expand
• Conclusion

3. History
• MySQL: Developed 1994
• MySQL acquired in 2008 by Sun Microsystems then by Oracle in 2010
• MySQL has a proprietary license
• MySQL is a row store database
• MonetDB: Developed around 1996
• MonetDB is open source and cross-platform
• R and Python support (2014-Present)
• MonetDB is a column store database

4. The Difference Between Tables
• Row Store
• Stores data by rows like a typical table
• Uses Primary and Foreign Keys
• Primary Key: Unique identifier
• Foreign Key: Targets a Primary Key to another table
• Column Store
• Stores data within the columns instead of rows
• Only affected columns need to be read when queried

5. Engine Background
• MySQL: MYISAM: Stored on disk in three files
• Row store and the default engine
• MySQL: Memory: Contents loaded into memory
• Row store
• Vulnerable to crashes, hardware issues, and power loss
• MonetDB: Uses main memory for processing
• Column store
• Does not require all data be active in physical memory at once

6. Goals of This Project
• This project was intended to provide benchmark comparisons against
MySQL engines and MonetDB
• Expand upon current benchmarked data
• Provide fairness for an accurate interpretation
• Use TPH-C to achieve this goal and benchmark 1GB of data

7. TPC-H Background
• Decision support benchmark
• Useful tools to quickly generate data
• Can handle large volumes of data
• Can produce queries with great complexity
• Generates 8 tables
• Some tables have over millions of records

8. Installing TPC-H: Step 1
• Recommended that you make a dir to store tpc-h files
• mkdir tpch
• Download tpch files with the following command
• wget http://www.tpc.org/TPC_Documents_Current_Versions/download_programs/tools-
download-request.asp?bm_type=TPC-H&bm_vers=2.17.2&mode=CURRENT-ONLY
• Extract downloaded files from compressed format and install
• unzip TPCH_FileName.zip –tpch

9. Installing TPC-H: Step 2
• Create makefile before installing, this will set some parameters we
need
• CC = gcc DATABASE = ORACLE
• MACHINE = LINUX WORKLOAD = TPCH
• After we have set the proper parameters for the machine, we can
then make TPC-H by simply running the following command
• Make
• TPC-H should now be installed

10. Main Project Issue: Running Time Analysis
• Claim: MonetDB was as much as 141,000 times faster than MySQL
engines (InnoDB & MYISAM)
• MySQL MYISAM engine queried previous data with times ranging
from ten to thirty minutes
• Original theory was to contribute this speed to memory hierarchy

11. Issue Resolved: Not Memory Hierarchy
• Examination of the original data showed the neglect to follow the
benchmarks proper table schema
• Turns out that keys are useful in a database
• Old benchmarks are therefore invalid because of the failure to
provide fairness

12. Expansion of the Original Data
• Generated 1 GB of data using TPH-C benchmarking tools
• -s is scaled as gigabytes, so -s 0.1 would be 100 MB and -s 1 would be 1 GB
• ./dbgen -s 1
• Generated queries using TPH-C benchmarking tools
• ./qgen (random seed)
• After you’ve generated the data and queries, you can begin to focus
on the database side of things

13. Creating Tables and Loading Data
• Tables are defined in the TPC-H Documentation, there are 8 of them
• Loading the data into a MySQL table: MySQL must be running from the same
directory as *.tbl files (wherever the user started the program)
• LOAD DATA LOCAL INFILE ‘TableName.tbl' INTO TABLE supplier FIELDS TERMINATED BY '|';
• Loading the data into MonetDB tables were a bit trickier
• copy into customer from '/home/teweatherby/tpch_2_17_0/dbgen/1g/customer.tbl';
• In MonetDB you have to know your full directory name to load data to the table!

14. Query 1: 2.sql
select s_acctbal, s_name, n_name, p_partkey, p_mfgr, s_address, s_phone, s_comment
from part, supplier, partsupp, nation, region where p_partkey = ps_partkey and s_suppkey = ps_suppkey
and p_size = 4 and p_type like '%STEEL' and s_nationkey = n_nationkey
and n_regionkey = r_regionkey and r_name = 'MIDDLE EAST‘
and ps_supplycost = (select min(ps_supplycost)
from partsupp, supplier, nation, region where p_partkey = ps_partkey and s_suppkey = ps_suppkey
and s_nationkey = n_nationkey and n_regionkey = r_regionkey
and r_name = 'MIDDLE EAST') order by s_acctbal desc, n_name, s_name, p_partkey;
Note: Spacing reduced to preserve readability

15. Query 2: 3.sql
select l_orderkey, sum(l_extendedprice * (1 - l_discount)) as revenue,
o_orderdate, o_shippriority from customer, orders, lineitem
where c_mktsegment = 'AUTOMOBILE' and c_custkey = o_custkey
and l_orderkey = o_orderkey and o_orderdate < date '1995-03-27' and
l_shipdate > date '1995-03-27‘ group by l_orderkey, o_orderdate,
o_shippriority order by revenue desc, o_orderdate;
Note: Spacing reduced to preserve readability

16. Query 3: 18.sql
select c_name, c_custkey, o_orderkey, o_orderdate, o_totalprice,
sum(l_quantity) from customer, orders, lineitem
where o_orderkey in (select l_orderkey from lineitem
group by l_orderkey having sum(l_quantity) > 315)
and c_custkey = o_custkey and o_orderkey = l_orderkey
group by c_name, c_custkey, o_orderkey, o_orderdate,
o_totalprice order by o_totalprice desc, o_orderdate;
Note: Spacing reduced to preserve readability

17. Results: Query 1: Three Trials Each
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
MYISAM (MySQL) Memory (MySQL) MonetDB
Time(ms)
Query 1 Results
Trial 1 Trial 2 Trial 3
Time is listed in milliseconds

18. Results: Query 2: Three Trials Each
0
1000
2000
3000
4000
5000
6000
7000
MYISAM (MySQL) Memory (MySQL) MonetDB
Time(ms)
Query 2 Results
Trial 1 Trial 2 Trial 3
Time is listed in milliseconds

19. Results: Query 3: Three Trials Each
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
MYISAM (MySQL) Memory (MySQL) MonetDB
Time(ms)
Query 3 Results
Trial 1 Trial 2 Trial 3
Time is listed in milliseconds

20. Total Results: Query 1
Time in milliseconds MySQL (MYISAM) MySQL (Memory) MonetDB
Trial 1 4930 1060 48
Trial 2 4950 1090 51
Trial 3 4970 1080 62
Average Running Time: 4950 1077 54

21. Total Results: Query 2
Time in milliseconds MySQL (MYISAM) MySQL (Memory) MonetDB
Trial 1 6040 1810 138
Trial 2 6070 1820 146
Trial 3 6020 1800 136
Average Running Time: 6043 1810 140

22. Total Results: Query 3
Time in milliseconds MySQL (MYISAM) MySQL (Memory) MonetDB
Trial 1 8440 5600 231
Trial 2 8410 5530 209
Trial 3 8420 5540 205
Average Running Time: 8423 5557 215

23. Breakdown and Comparison
Query MySQL
(MYISAM)
MySQL
(Memory)
MonetDB MYISAM/
Memory
MYISAM/
MonetDB
Memory/
MonetDB
Query 1 4950 1077 54 4.60 times
faster than
MYISAM
91.7 times
faster than
MYISAM
19.94 times
faster than
Memory
Query 2 6043 1810 140 3.34 times
faster than
MYISAM
43.16 times
faster than
MYISAM
12.93 times
faster than
Memory
Query 3 8423 5557 215 1.52 times
faster than
MYISAM
39.18 times
faster than
MYISAM
25.85 times
faster than
Memory
Note: When we say “… times faster than Memory” we are referring to a MySQL Engine
Time is listed in milliseconds

24. Challenges Encountered
• Learning Ubuntu command lines and proficiently manipulating the
environment
• Had to increase MySQL’s maximum memory storage to store 1GB of
data in memory. Otherwise table full error.
• SET GLOBAL tmp_table_size = 1024 * 1024 * 1024 * 2; SET GLOBAL
max_heap_table_size = 1024 * 1024 * 1024 * 2
• MonetDB administrative structure

25. Interpretation
• Certainly, MySQL Memory is faster than MySQL MYISAM
• MonetDB does have a faster time over MySQL MYISAM engines
• MonetDB seems to be faster than MySQL Memory Engines
• Keys are useful for databases!!!
• Is MonetDB better?

26. Possibilities to Further Expand
• Only compared for querying, how would they perform for
modification?
• Is MonetDB simpler? Easier to understand?
• System resource limitation (memory)
• Other databases (Cassandra)

27. Conclusion
• Keys in a database matter
• MonetDB seems to have and edge on MySQL’s Memory Engine
• MonetDB certainly has an advantage on MySQL’s MYISAM Engine
• There are opportunities to further expand on this examination