Analysis of Derby Performance

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1 Analysis of Derby Performance Staff Engineer Olav Sandstå Senior Engineer Dyre Tjeldvoll Sun Microsystems Database Technology Group This is a draft version that is subject to change. The authors can be contacted at Olav.Sandstaa@sun.com or Dyre.Tjeldvoll@sun.com 1

2 Overview Introduction What is a database (DBMS)? Derby Architecture What is performance? Performance Evaluation of Derby Performance Tips Comparison with MySQL and PostgreSQL 2

3 Introduction 3

4 Derby Architecture Embedded Network Server Appl. JDBC SQL Access Storage Appl. JDBC Network server JDBC SQL Access Storage 4

5 What Is Performance? How do you measure database performance? Throughput Response time Average? Max? Median? Out-of-the-box or carefully tuned? How to compare database systems with different tuning possibilities? Zero administration? 5

6 Performance Evaluation of Derby 6

7 Performance Evaluation of Derby Evaluation of disk and file system configurations Comparing Embedded and Network Server Throughput and response times Resource usage (CPU, network) The effects of adjusting the database buffer size (page cache size) The effect of keeping the log on a separate disk 7

8 Derby Performance Evaluation: Derby, OS and Hardware Configuration Derby configuration: Out of the box Main memory database Disk database Log device: Same as data device Separate log disk Disk write cache: Enabled Disabled 8

9 Disk and File System Configurations Hard disk: Write cache File system: Logging/journaling in UFS Direct I/O (partially evaluated) File meta-data update (not evaluated) 9

10 Disk Write Cache: Throughput and Response Time 10

11 Disk and File System Configurations: Conclusions Disk Write Cache: Do users want high throughput and low response times, or durability/recoverability? Who are we optimizing for? Without write cache on disks a higher number of clients are needed to get maximum throughput Things to evaluate: UFS logging Java NIO Direct I/O (not available from Java) Avoid update of file meta-data 11

12 Comparing Embedded with Network Server: TPC-B: Throughput and Response Time 12

13 Comparing Embedded with Network Server: Single-record SELECT 13

14 Comparing Embedded with Network Server: Resource Usage Derby Embedded: Derby Network Server: CPU? Network? Appl. JDBC SQL Access Storage Appl. JDBC CPU? Network? Network server JDBC SQL Access Storage 14

15 Comparing Embedded and Network Server: CPU Usage CPU usage per transaction [ms] User CPU System CPU TPC- B Emb TPC- B CS Insert Emb Insert CS Select Emb Select Cs Update Emb Update CS 15

16 Comparing Embedded and Network Server: CPU Usage, cont. Increase in CPU usage compared to Embedded: System CPU User CPU Total TPC-B % % 36% Insert % % 33% Select % % 65% Update % % 30% Message handling (TCP/IP) The Network Server Code 16

17 Comparing Embedded with Network Server: Conclusions Reduction in throughput of Network Server compared to Embedded: Update operations: 5% Select operations: 30-40% Response time: Small increase of about 0.5 ms per SQL operation Utilizing the write cache on the disks increases the difference for update operations What can be done? Profile to find CPU intensive parts in the Network Server code Optimize 17

18 Comparing Embedded with Network Server: Conclusions The Network Server adds 30%-60% to the CPU usage compared to the Embedded version The main causes for increased CPU usage: System CPU usage: Message sending and receiving User CPU usage: Message parsing (Strings) Character set conversions 18

19 Performance Tips 19

20 Performance Tips Programming: Prepared statements Database schema design: Indices Derby configuration: durability=test, if recovery is not important 20

21 Prepared Statements Compilation is expensive, especially in Derby which uses Java byte code generation Prepared statements virtually eliminates this cost Easy to fall into this trap for beginners (string concatenation is easy) <Example> 21

22 Indices Use indices to optimize much used access paths <Example> 22

23 Relaxed Durability Durability is expensive Log must be written to disk prior to commit This becomes a major bottleneck (cf. write cache, log on separate device) Durability can be disabled Setting durability=test disables log flushing at commit Dramatically increases throughput but database may be corrupt after a crash Would be better if Derby could recover to a consistent state (with some transaction loss) An alternative to a main memory database <Example> 23

24 Comparing the Performance of MySQL, PostgreSQL and Derby 24

25 Performance evaluation: MySQL, PostgreSQL and Derby Evaluated performance of: MySQL/InnoDB PostgreSQL Derby Embedded Derby Client/Server 25

26 What is a Database (DBMS)? Transactions ACID Atomicity Consistency Isolation Durability Database Atomicity Consistency Isolation Durability Derby Yes Yes Yes Yes PostgreSQL Yes Yes Yes Yes MySQL, Innodb Yes Yes Yes Yes MySQL, MyIsam No? Table lock Yes HSQLDB No?? No 26

27 Performance Experiments Two configurations: Small main-memory database: 10 MB data/50mb database buffer Big disk-based database: 50 MB data/10mb database buffer Tests: TPC-B Single-tuple operations: select Load: concurrent clients 27

28 Throughput: TPC-B Small db Big db Derby embedded Derby client/server MySQL (InnoDB) PostgreSQL Derby embedded Derby client/server MySQL (InnoDB) PostgreSQL Transactions per second Transactions per second Number of clients Number of clients 28

29 Throughput: Select ( small db) Derby embedded Derby client/server MySQL (Inno DB) PostgreSQL Transactions per second Number of clients 29

30 Conclusions Derby outperforms MySQL on large databases MySQL performs better on small main-memory databases No significant performance loss with client/server, except for SELECT operations Need to identify, and reduce the CPU overhead that limits throughput in CPU-bound configurations 30

31 Disk IO: Small Databases Db writes per transaction Log writes per transaction Derby embedded Derby client/server MySQL PostgreSQL 31

32 Disk IO: Big Databases Why does Derby out-perform MySQL for disk-based databases? TPC-B like load - 50 MB database - 10 MB buffer Db writes per transaction Log writes per transaction 0 Derby client/server MySQL 32

33 JDBC Driver: CPU Usage CPU usage per TPC-B transaction [ms]: Derby MySQL PostgreSQL User System 33

34 Conclusions: Resource Usage MySQL performs better than Derby when: The database is small and fits in the database buffer Throughput becomes CPU-bound Derby performs better than MySQL when: The database is large and does not fit in the database buffer Throughput becomes IO-bound Derby has focused on maintaining a low footprint 34

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