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January 24, 2000 This issue presents tips, techniques, and sample code for the following topics: This issue of the JDC Tech Tips is written by Stuart Halloway, a Java specialist at DevelopMentor. These tips were developed using Java 2 SDK, Standard Edition, v 1.2.2, and are not guaranteed to work with other versions.
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public class Finalize1 {
private static final int testIter = 100;
static public void main(String[] args) {
int n;
//Initialize a batch of objects
//that use Finalize to clean up
for (n=testIter; --n>=0;) {
UsesFinalize uf =
new UsesFinalize();
}
//Initialize a batch of objects
//that use an explicit close
//to clean up. Note that the
//code is more complex. This
//is a necessary evil.
for (n=testIter; --n>=0;) {
UsesClose uf = null;
try {
uf = new UsesClose();
}
finally {
if (uf != null)
uf.close();
}
}
System.out.println("This demo" +
"demonstrates the danger of relying" +
"on finally to expediently close" +
"resources.");
System.out.println("Testing" +
"with 100 resources:");
//Each of the classes tracking
//the maximum number of "open" resources
//at any given time.
System.out.println("Using Finalize" +
"to close resources required "
+ UsesFinalize.maxActive +
" open resources.");
System.out.println("Using
explicit close required "
+ UsesClose.maxActive +
" open resource.");
}
static public class UsesFinalize {
static int active;
static int maxActive;
UsesFinalize() {
active++;
maxActive =
Math.max(active,
maxActive);
}
public void finalize() {
active--;
}
}
static public class UsesClose {
static int active;
static int maxActive;
public UsesClose() {
active++;
maxActive =
Math.max(active,
maxActive);
}
public void close() {
active--;
}
}
}
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The Finalize1 program takes two alternative
approaches to cleaning
up resources. In the first approach it creates 100 objects,
incrementing a counter for each object. It then uses the
finalize
method to clean up each object. Each time it cleans up an
object,
it decrements the counter.
In the second approach, 100 objects are also created. However
here the finally statement and an explicit close
method are used
to clean up and decrement the counter.
If you run Finalize1, you'll see that the
Finalize approach does
not decrement the counter. None of the objects are closed.
However
the Finally-plus-close approach does the job it's intended to
do.
It decrements the counter for each object. It closes all the
objects.
The purpose of the Finalize method is often misunderstood by
programmers. The Javadoc comment for Finalize
states that it's
called by the garbage collector on an object when the garbage
collector determines that there are no more references to the
object. Presumably the garbage collector will, like its civil
servant namesake, visit the heap on a regular basis to clean
up
resources that are no longer in use.
As reasonable as it may seem, this interpretation of finalization relies on assumptions about garbage collection that are not supported by the Java language specification. The primary purpose of Java garbage collection is not to run finalizers. Garbage collection exists to prevent programmers from calling delete. This is a wonderful feature. For example, if you can't call delete, then you can't accidentally call delete twice on the same object. However, removing delete from the language is not the same thing as automatically cleaning up. The name "garbage collection" promises too much. Confusion might have been saved by using the name "delete prevention" instead. In fact, the Java garbage collection specification imposes only minimal requirements for the behavior of garbage collection, which include:
Either of these rules, taken alone, would be enough to make finalize a risky way to clean up resources. So why do developers leap to the wrong conclusion and rely on finalize? There are two reasons: they are swayed by the analogy to the C++ destructor, and they often get away with it in the short run. Combine a simple project with a better-than-average VM implementation, and finalize will appear almost as reliable as a C++ destructor. Don't be fooled by this temporary good luck. If you rely on finalize, your code will not scale to larger projects, and it will not run consistently on different virtual machines.
The correct approach to resource cleanup in Java language programs does not rely on finalize. Instead, you simply write explicit close methods for objects that wrap native resources. If you take this approach, you must document that the close method exists and when it should be called. Callers of the object must then remember to call close when they are finished with a resource.
This probably does not live up to your hopes for garbage collection, since you are back to the manual labor of freeing resources yourself. Moreover, this code still has a problem: if an exception is thrown from somewhere inside the method that calls close, the close method will never be reached. This calls for a way to force a code block to be executed, regardless of exceptions. Java's finally clause fits the bill perfectly. After any try block in Java, you can specify a finally block which will execute, no matter how the try block exits--either normally or exceptionally.
Click to view Source code for this tip, or right-click to download.
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How fast is the Java platform? For many applications, the answer is "fast enough"--if you make careful choices in your design and make good use of the language. But while design documents and coding standards might encourage efficient code, the only way to know for sure is by profiling, that is, obtaining method timing and other information pertinent to performance. Fortunately, the tools that you need to do profiling are part of the Java 2 SDK. This tip will get you started with HPROF, the Java Profiler Agent, and present an example where a simple code snippet is improved to run 100 times faster.
To see HPROF's options, enter the following at a command prompt:
java -Xrunhprof:help
One specification you can make for HPROF is
cpu=samples. This
setting enables you to profile by sampling. With sampling, the
Java virtual machine
1 regularly pauses execution
and checks to see which
method call is active. With enough samples (and a decent
sampling rate), you can pinpoint where your code spends its
time.
For example, consider the following example:
package com.develop.demos;
import java.io.IOException;
public class TestHprof {
public static String cat = null;
public final static int loop=5000;
public static void makeString() {
cat = new String();
for (int n=0; n<loop; n++) {
addToCat("more");
}
}
public static void addToCat(String more) {
cat = cat + more;
}
public static void makeStringInline() {
cat = new String();
for (int n=0; n<loop; n++) {
cat = cat + "more";
}
}
public static void makeStringWithLocal() {
String tmp = new String();
for (int n=0; n<loop; n++) {
tmp = tmp + "more";
}
cat = tmp;
}
public static void makeStringWithBuffer() {
StringBuffer sb = new StringBuffer();
for (int n=0; n<loop; n++) {
sb.append("more");
}
cat = sb.toString();
}
public static void main(String[] args) {
long begin = System.currentTimeMillis();
if (null !=
System.getProperty("WaitForProfiler")) {
System.out.println(
"Start your profiler, then
press any key to begin...");
try {
System.in.read();
}
catch (IOException ioe) {
}
}
makeString();
makeStringInline();
makeStringWithLocal();
makeStringWithBuffer();
long end =
System.currentTimeMillis();
System.out.println("Total run time of "
+ (end - begin) + " milliseconds");
}
}
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A call to makeString simply builds up a long
string by repeated
concatenation. This is definitely a slow way to build the
string,
but how can it be made faster? One possibility is to
eliminate the
overhead of a function call by putting the
addToCat method inline,
as in makeStringInLine.
Another possible optimization is illustrated in
makeStringWithLocal.
This method uses a temporary local variable; a local variable
might
be accessed more quickly than the static cat.
Still another possibility is to use the
StringBuffer class
instead of String, since the intermediate results
don't have
to be stored in Strings. This is demonstrated in
makeStringWithBuffer.
Which of these four implementations is fastest? Let's run HPROF against the program. Running HPROF will help determine which optimizations are worthwhile.
java -Xrunhprof:cpu=samples,depth=6 com.develop.demos.TestHprof
Notice the depth=6 specification. This indicates a stack trace
depth of 6. Note too that by default the profiler output goes
to java.hprof.txt. The interesting part of this
file is the table
at the bottom which lists the percentage of time spent in each
different stack trace:
CPU SAMPLES BEGIN (total = 7131) Wed Jan 12 13:12:40 2000 rank self accum count trace method 1 20.57% 20.57% 1467 47 demos/TestHprof.makeStringInline 2 20.40% 40.98% 1455 39 demos/TestHprof.addToCat 3 20.28% 61.25% 1446 53 demos/TestHprof.makeStringWithLocal 4 11.85% 73.10% 845 55 java/lang/String.getChars 5 11.75% 84.85% 838 42 java/lang/String.getChars 6 11.72% 96.58% 836 50 java/lang/String.getChars (remaining entries less than 1% each, omitted for brevity) |
The self column is an estimate of the percentage of time
a particular stack trace is active. In this case, you want to
time four methods (makeString, makeStringInline,
makeStringWithLocal, and makeStringWithBuffer); let's
call these
top-level methods. You cannot simply add the times for these
methods, because a sample that was not in a top-level method
might still have that top-level method somewhere in its call
stack.
So for each entry in the table, you need to crawl back up the stack to find the associated top-level method. The trace column is a pointer to the needed information, which is higher up in the HPROF output file. For example, the 4th ranked sample is trace 55:
TRACE 55:
java/lang/String.getChars(:Compiled method)
java/lang/StringBuffer.append(:Compiled method)
com/develop/demos/TestHprof.makeStringWithLocal \
(TestHprof.java:Compiled method)
com/develop/demos/TestHprof.main(TestHprof.java:57)
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Ahah! Trace 55 leads back to makeStringWithLocal,
so its time
should be added to the time for 3rd-ranked Trace 53, which is
a
direct invocation of makeStringWithLocal. If
necessary, you could
continue this process, and cross-reference all the call stacks
and the CPU samples by hand. Alternately, you could use a tool
that interprets the profiling output. In this simple example,
the top six samples are enough. Three of the top-level methods
(makeString, makeStringInline, and
makeStringWithLocal) each have
two entries in the top six. Adding up each method's entries
leads
to a tie. Each of the three contributed over 30% of the total
running time. On the other hand, makeStringBuffer's stack
traces
are way down the list, and total less than 0.3% of the runtime
of
the application. In other words, putting the function inline
and
using a local variable didn't help, but switching from String
to
StringBuffer caused the code to execute over 100
times faster.
Vive la HPROF!
This example only scratches the surface of Java profiling. Profiling data can often be used not only to time methods, but also to explain why one implementation is faster than another. With HPROF's cpu=timings flag, you can profile by explicitly timing methods. This gives more accurate results than sampling, but is slower and more intrusive. The heap options can be used to track memory problems.
Profiling is an important arrow in any Java developer's quiver, and HPROF is a free way to get started. Whatever tools you choose to use, make sure that you profile your code to find and prove performance gains.
Click to view Source code for this tip, or right-click to download.
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1 As used on this web site, the
terms "Java
virtual machine" or "JVM" mean a virtual
machine for the Java
platform.
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