Databases and Information Systems II

Databases and

Information Systems II

Performance evaluation

Profiling

2/14 Databases and Information Systems II – SS 11 – Performance evaluation and Profiling

Performance evaluation

 New compression approach must be competible in terms of  Compression ratio

 Compression time  Decompression time  Query evaluation time  …

 Slow performance can be caused by  Slow algorithms

3/14 Databases and Information Systems II – SS 11 – Performance evaluation and Profiling

Performance evaluation - currentTimeMillis()

String ret= ""; int runs = 10; int dur = 0;

for (int rep = 0; rep < runs; rep++) { long start = System.currentTimeMillis(); for (int i = 0; i < 1000; i++) {

ret += i; }

dur += (System.currentTimeMillis() - start); }

System.out.println("String: " + (dur/runs) + " ms"); > String: 86 ms

> StringBuffer: 0 ms > StringBuilder: 0 ms

Performance evaluation – nanoTime()

String ret= ""; int runs = 10; int dur = 0;

for (int rep = 0; rep < runs; rep++) { long start = System.nanoTime(); for (int i = 0; i < 1000; i++) {

ret += i; }

dur += (System.nanoTime() - start); }

System.out.println("String: " + (dur/runs) + " ns"); > String: 89426463 ns

> StringBuffer: 335929 ns > StringBuilder: 169903 ns

5/14 Databases and Information Systems II – SS 11 – Performance evaluation and Profiling

Wall clock, User, System, and CPU time

 Wall clock time:

 Real-world time, while the user waits the system to be finished  System.nanoTime(), System.currentTimeMillis()

 User time:

 Time spent running the application’s code  System time:

 Time spent running OS code caused by your application (e.g. I/O)  CPU time:

 User time + System time; Total time spent using CPU for your application

6/14 Databases and Information Systems II – SS 11 – Performance evaluation and Profiling

Wall clock, User, System, and CPU time

 Wall clock time:

 Real-world time, while the user waits the system to be finished  System.nanoTime(), System.currentTimeMillis()

 User time:

 Time spent running the application’s code  System time:

 Time spent running OS code caused by your application (e.g. I/O)  CPU time:

 User time + System time; Total time spent using CPU for your application

import java.lang.management public long getCpuTime( ) {

ThreadMXBean bean = ManagementFactory.getThreadMXBean(); if(bean.isCurrentThreadCpuTimeSupported( ))

return bean.getCurrentThreadCpuTime( ); else return 0L;

}

public long getUserTime( ) {

ThreadMXBean bean = ManagementFactory.getThreadMXBean(); if(bean.isCurrentThreadCpuTimeSupported( ))

return bean.getCurrentThreadUserTime( ); else return 0L;

}

public long getSystemTime( ) {

ThreadMXBean bean = ManagementFactory.getThreadMXBean(); if(bean.isCurrentThreadCpuTimeSupported( ))

return (bean.getCurrentThreadCpuTime( )

-bean.getCurrentThreadUserTime( )); else return 0L;

7/14 Databases and Information Systems II – SS 11 – Performance evaluation and Profiling

Performance evaluation

 Performance time is influenced by  Operating system processes  Other processes

 Tasks of Virtual Machine (e.g. garbage collection)  Perform redundant measurements (> 10 runs)

 System.currentTimeMillis() and System.nanoTime(), CPU, User and System time

 Simple to use

 Good for comparing different approaches

 Not very convenient for finding performance bottlenecks  Use Profiler instead

Profiling

A profiler is a performance analysis tool that measures

the behavior of a program as it executes, particularly

the frequency and duration of function calls.

9/14 Databases and Information Systems II – SS 11 – Performance evaluation and Profiling

Profiling – JAMon

String ret = "";

Monitor monString = null;

for (int rep = 0; rep < 10; rep++) { MonitorFactory.start("String"); for (int i = 0; i < 1000; i++) {

ret += i; }

monString.stop(); }

String html = MonitorFactory.getReport();

Label Hits Avg Total StdDev Last Min Max … StringBuffer 10.0 0.4 4.0 0.516 0.0 0.0 1.0

StringBuilder10.0 0.1 1.0 0.316 0.0 0.0 1.0

String 10.0 11.4 114.0 5.059 9.0 7.0 23.0

10/14 Databases and Information Systems II – SS 11 – Performance evaluation and Profiling

Profiling – JAMon

 Advantages

 Convenient to use

 Independent of platform and IDE  Any granularity

 Especially easy to use for web and database applications  Disadvantages

11/14 Databases and Information Systems II – SS 11 – Performance evaluation and Profiling

Profiling – JIP (Java Interactive Profiler)

+---| Thread: 1

+---Time Percent --

---Count Total Net Total Net Location ===== ===== === ===== === ========= 1 457,6 5,5 100,0 1,2 +--Main:main () 1 446,1 446,1 97,5 97,5 | +--Main:testString () 1 5,2 5,2 1,1 1,1 | +--Main:testStringBuffer () 1 0,7 0,7 0,2 0,2 | +--Main:testStringBuilder ()

> java –javaagent:path/profiler.jar Main

Profiling – JIP (Java Interactive Profiler)

 Advantages  Easy to use  Efficient to use

 Independent of platform and IDE

 Disadvantages

 Not very convenient to analyze the results  Finest granularity: Methods

13/14 Databases and Information Systems II – SS 11 – Performance evaluation and Profiling

Profiling – TPTP

Eclipse Menu



Run



Profile

Configurations…

Profiling – TPTP

 Advantages

 Convenient to use

 No changes in program code  GUI allows detailed analysis  Disadvantages

 Eclipse plugin (dependent on Eclipse)  Finest granularity: Methods