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MySQL vs MonetDB Bencharmarks
- 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
Notes de l'éditeur
- Aikin’s original data had shown that MonetDB was benchmarking anywhere from 10,000 times faster to 141,000 times faster than MySQL Engines he tested on. Speculated on the possibility that the benchmarking software was just doing something weird, because it’s a benchmark. However, MySQL databases have been around for a while and they’re used in enterprise systems, so this couldn’t be a real benchmark.
- Aikin’s had forgotten the primary and foreign keys… Invalidating most of his project.
- Mention the original data of 100MB I did not think was sufficient to benchmark again.
- Note the huge difference from Aikin’s original 141,000 times and 32,000 times faster under MYISAM category.
- List the possible ways we can expand this study
- Concluding thoughts