MySQL vs. MonetDB

MySQL vs. MonetDBA benchmark comparison between in-memory and out-of-memory databases

Derek AikinsAdvisor: Dr. Feng Yu

Overview• History of MySql.• What is a relational database.• History of Monetdb.• What is a column-store database.• Relational vs. Column-Store databases operations• Goal of Project• TPC-H• Installing and compiling TPC-H• Generating data using TPC-H• Generating queries using TPC-H• Queries• Results of Tests• Problems Encountered• Conclusion

History of MySQL• The world’s most popular open source relational database.• Leading database choice for web-based applications and used by high

profile web properties including Facebook, Twitter, and Youtube.• Created by a Swedish company, MySQL AB, originally developed by

David Axmark and Micheal Widenius in 1994.• First Version release on May 23, 1995.• MySQL AB was acquired by Sun Microsystems in 2008• Oracle acquired Sun Microsystems on January 27, 2010

What is a Relational Database?• MySql is a relational database• A relational database is a digital database that organizes data into one

or more tables of columns and rows.• Tables are known as relations• Each table represents one “entity type” such as customer or product• Rows (records) represent instances of that type of entity such as

“Lee” or “chair”• Columns represent values to that instance such as address or price.

Examples of a databaseRegion Table

R_REGIONKEY R_NAME R_COMMENT

0 AFRICAlar deposits. blithely final packages cajole. regular waters are final requests. regular accounts are

1 AMERICA hs use ironic, even requests. s

2 ASIA ges. thinly even pinto beans ca

3 EUROPE ly final courts cajole furiously final excuse

4 MIDDLE EASTuickly special accounts cajole carefully blithely close requests. carefully final asymptotes

Nation Table

N_NATIONKEY

N_NAME N_REGIONKEY

N_COMMENT

0 ALGERIA 0 haggle. carefully final

1 ARGINTINA 1 al foxes promise slyly

2 BRAZIL 1 y alongside of the pending

3 CANADA 1 eas hang ironic,

4 EGYPT 4 y above the carefully

History of Monetdb• An open source column-store database • Developed in the Netherlands at the Centrum Wiskunde & Informatica (CWI)• Data mining project in the 1990s required improved database support which resulted in

a CWI spin-off called Data Distilleries, which used early implementations in its analytical suite • Data Distilleries became a subsidiary of SPSS in 2003, which was later acquired by IBM

in 2009• MonetDB in its current form was first created by Peter A. Boncz and Martin L. Kerstein

at the University of Amsterdam• Please refer to Dr. Boncz’s thesis for more details

Monet: a next-Generation DBMS Kernel For Query-Intensive Applications

• The first version was released on September 30, 2004

What is a Column-Store Database• Column-store databases store data as columns rather than rows• By storing data in columns rather than rows, the database can access

the data it needs to answer a query rather than scanning and discarding unwanted data in rows• Query performance is often increased as a result, particularly in very

large data sets• In most cases Column-Store databases store data in-memory (RAM)

unlike most row based databases that store their data on the harddrive

Relational vs. Column-Store database operation

Cust_ID Name Address City State Zip code Area Code

Phone # Rent/Own Annual Income

1 Jack 12 A St. Howland OH 44481 330 369-3597 Rent 74,000

2 Brian 13 B St. Howland OH 44481 330 856-1534 Rent 58,000

3 Mike 8 K St Warren OH 44483 330 373-1215 Own 92,000

4 Anna 62 Main St. Sharon PA 16101 724 654-0893 Own 110,000

5 Tasha 546 1st St. Stow OH 44752 216 849-5775 Rent 52,000

6 Sidney 84 Third St. Gilbert AZ 76534 480 758-6549 Own 90,000

7 Tyler 846 Wick Rd. Las Vegas NV 65487 231 654-5473 Own 60,000

8 Aaron 213 Maple St. Daytona FL 32547 519 159-3425 Rent 66,000

9 Beth 8749 Trump St. Detriot MI 87945 375 325-1849 Own 50,000

Goal of this Project• Take a standard dataset and a standard set of queries and run the test

on two different databases, MySQL and Monetdb. • By doing so, I intend to demonstrate the efficiency and speed that a

column-store database has over a traditional relational database. • To do this I will be using a data generator, TPC-H, for benchmarking

databases which can also generate the queries for the data. • Then push all data generated by TPC-H into both databases and run

each queries multiple times to get average times on both databases.• After all runs are complete, I will gather all results generated and

compare how the two databases performed.

TPC-H• A decision support benchmark• Consists of a suite of business oriented ad-hoc queries and concurrent

data modifications• The queries and the data populating the database have been chosen

to have broad industry-wide relevance• This benchmark illustrates decision support systems that examine

large volumes of data, execute queries with a high degree of complexity, and give answers to critical business questions

Installing and Compiling TPC-H• The program used to generate the data from TPC-H is called dbgen.• To install dbgen first I need to download the file from the TPC-H site using the

following command cd Downloads/tpch_2_16_0/tpch_2_15_0/dbgen/ • Then I had to create a makefile and go in and change the lines to CC = gcc

DATABASE=SQLSERVER Machine=LINUXWorkload=TPCH• Next in the dbgen folder I had to find the tpcd.h file and edit the lines define START_TRAN "BEGIN WORK;“ define END_TRAN "COMMIT WORK;“• Then I ran the make command.

Generating the data- 100 Mb• After installing and setup TPC-H, I generated the data using dbgen• Using the command ./dbgen -s 0.1 to generate the data where the 0.1 in the

command dictates the amount of data to be generated in this case I use 100 Mb• Once the data was generated I created the database in MySQL using the CREATE

DATABASE tpch; command and then chose the database to load the data in the tables.• I then created each table with the CREATE TABLE command and set all the

descriptions for each column• Once the tables were created it was time to load the data into each table using

the LOAD DATA LOCAL INFILE 'customer.tbl' INTO TABLE CUSTOMER FIELDS TERMINATED BY '|‘ and changing the table name to each table

Query 1mysql> select s_acctbal, s_name, n_name, p_partkey, where p_mfgr, p_partkey = ps_partkey s_address, and s_suppkey = ps_suppkey s_phone, and s_nationkey = n_nationkey s_comment and n_regionkey = r_regionkey from and r_name = 'ASIA' part, order by supplier, s_acctbal desc, partsupp, n_name, nation, s_name region p_partkey: where p_partkey = ps_partkey and s_suppkey = ps_suppkey and p_size = 19 and p_type like 'PROMO ANODIZED BRASS' and s_nationkey = n_nationkey and n_regionkey = r_regionkey and r_name = 'ASIA' and ps_supplycost = ( select min(ps_supplycost) from partsupp, supplier, nation, region

)

Query 2 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 '1995-08-15' -> and l_shipdate > date '1995-08-27' -> group by -> l_orderkey, -> o_orderdate, -> o_shippriority -> order by -> revenue desc, -> o_orderdate;

Query 3select o_orderpriority, count(*) as order_count from orders where o_orderdate >= date '1996-10-29' and o_orderdate < date '1996-10-29' + interval '3' month and exists ( select * from lineitem where where l_orderkey = o_orderkey and l_commitdate < l_receiptdate ) ) group by o_orderpriority order by o_orderpriority;

Query 4select sum(l_extendedprice * l_discount) as revenuefrom lineitemwhere l_shipdate >= date '1993-03-05' and l_shipdate < date '1993-03-06' + interval '1' year and l_discount between .03 and .06 and l_quantity < 3;

Query 5

select sum(l_extendedprice) / 7.0 as avg_yearlyfrom lineitem, partwhere p_partkey = l_partkeyt and p_brand = 'Brand#11' and p_container = 'MED JAR' and l_quantity < ( select 0.2 * avg(l_quantity) from lineitem where l_partkey = p_partkey );

Results for Queries 1 & 2

Query 1 Results

Query 10

5

10

15

20

25

3026.156667 25.97666667

0.0120647

Chart Title

MySQL (InnoDB) MySQL (MyISAM) Monetdb

TIM

E IN

SEC

ON

DS

Query 2 Results

Query 20

100

200

300

400

500

600

700 645.31667 652.2966667

0.0201565

Chart Title


TIM

E IN

SEC

ON

DS

Results for Query 3 & 4

Query 30

500

1000

1500

2000

2500

853.25

2086.01

0.014732667

QUERY 3


TIM

E IN

SEC

ON

DS

Query 40

0.1

0.2

0.3

0.4

0.5

0.6

0.483333333

0.29333333

0.011313667

QUERY 4


TIM

E IN

SEC

ON

DS

Result for Query 5

Query 50

50

100

150

200

250

300

350

400

137.1533333

337.2766667

0.018765667

QUERY 5


TIM

E IN

SEC

ON

DS

Total Numerical ResultsMySQL (InnoDB) MySQL (MyISAM) Monetdb

QUERY TIME TIME (in seconds) QUERY TIME TIME (in seconds) Query TIME TIME (in seconds)

Query 1 Query 1 Query 1

run 1 26.14 sec 26.14 run 1 25.83 sec 25.83 run 1 12.854 ms 0.012854



Average Time 26.15666667 Average Time 25.977 Average Time 0.012064667


Query 2 TIME TIME (in seconds) Query 2 TIME TIME (in seconds) Query 2 TIME TIME (in seconds)

run 1 10 min 42.03 sec 642.03 run 1 10 min 52.36 sec 652.36 run 1 23.548 ms 0.023548



Average time 645.3166667 Average time 652.2966667 Average time 0.0201565







QUERY TIME TIME (in seconds) QUERY TIME TIME (in seconds) Query TIME TIME (in seconds)


run 1 .48 sec 0.48 run 1 0.29 sec 0.29 run 1 12.992 ms 0.012992









Comparison ResultsQueries MySQL

(InnoDB)MySQL

(MyISAM)Monetdb InnoDB/

MyISAMInnoDB/ Monetdb

MyISAM/ Monetdb

Average time Average time Average time

Query 1 26.157 25.977 0.0121 0.0069 times faster than InnoDB

2,168 times faster than InnoDB

2,153 times faster than MyISAM

Query 2 645.317 652.297 0.0201 0.0108 times slower than InnoDB






Query 4 0.0483 0.293 0.0113 1.648 times faster than InnoDB

43 times faster than InnoDB

26 times faster than MyISAM


7309 times faster than InnoDB


Challenges Encountered• Throughout this project I encountered several challenges:• The first difficulty encountered was the installation of the several programs

used for this project• Once all programs were installed the next challenge was the uploading of the

data to the databases• After all data was loaded into the database tables, one of the largest

challenges was to examine each query and fill in areas that needed exact information from the tables for the query to even run• The largest challenge I faced through this entire project was learning to use

the command line to do everything as I have not had much experience with this.

Summary• A relational database is a digital database that organizes data into one

or more tables of columns and rows.• Column-store databases store data as columns rather than rows• TPC-H is a decision support benchmark that examine large volumes of

data, execute queries with a high degree of complexity, and give answers to critical business questions• As the data shows from the tests conducted on the two different

databases, column-store databases such as Monetdb are considerably faster in run time compared to traditional relational databases such as MySQL.

ALL Results and Test Queries can be found at:

https://github.com/Djaikins/MySQL-vs-Monetdbor

By searching djaikins on Github

https://github.com/Djaikins/MySQL-vs-Monetdb

https://github.com/Djaikins/MySQL-vs-Monetdb

Date post:	20-Mar-2017
Category:	Technology
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MySQL vs. MonetDB

Technology