Thinking in Java - 4th Edition
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Thinking in Java - 4th Edition

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the object that it points to and then follow all the references in that object, tracing into
the objects they point to, etc., until you’ve moved through the entire Web that originated with
the reference on the stack or in static storage. Each object that you move through must still
be alive. Note that there is no problem with detached self-referential groups—these are
simply not found, and are therefore automatically garbage.

In the approach described here, the JVM uses an adaptive garbage-collection scheme, and
what it does with the live objects that it locates depends on the variant currently being used.
One of these variants is stop-and-copy. This means that—for reasons that will become
apparent—the program is first stopped (this is not a background collection scheme). Then,
each live object is copied from one heap to another, leaving behind all the garbage. In
addition, as the objects are copied into the new heap, they are packed end-to-end, thus
compacting the new heap (and allowing new storage to simply be reeled off the end as
previously described).

Of course, when an object is moved from one place to another, all references that point at the
object must be changed. The reference that goes from the heap or the static storage area to
the object can be changed right away, but there can be other references pointing to this object

Initialization & Cleanup 123 

that will be encountered later during the “walk.” These are fixed up as they are found (you
could imagine a table that maps old addresses to new ones).

There are two issues that make these so-called “copy collectors” inefficient. The first is the
idea that you have two heaps and you slosh all the memory back and forth between these two
separate heaps, maintaining twice as much memory as you actually need. Some JVMs deal
with this by allocating the heap in chunks as needed and simply copying from one chunk to

The second issue is the copying process itself. Once your program becomes stable, it might be
generating little or no garbage. Despite that, a copy collector will still copy all the memory
from one place to another, which is wasteful. To prevent this, some JVMs detect that no new
garbage is being generated and switch to a different scheme (this is the “adaptive” part). This
other scheme is called mark-and-sweep, and it’s what earlier versions of Sun’s JVM used all
the time. For general use, mark-and-sweep is fairly slow, but when you know you’re
generating little or no garbage, it’s fast.

Mark-and-sweep follows the same logic of starting from the stack and static storage, and
tracing through all the references to find live objects. However, each time it finds a live
object, that object is marked by setting a flag in it, but the object isn’t collected yet. Only
when the marking process is finished does the sweep occur. During the sweep, the dead
objects are released. However, no copying happens, so if the collector chooses to compact a
fragmented heap, it does so by shuffling objects around.

“Stop-and-copy” refers to the idea that this type of garbage collection is not done in the
background; instead, the program is stopped while the garbage collection occurs. In the Sun
literature you’ll find many references to garbage collection as a low-priority background
process, but it turns out that the garbage collection was not implemented that way in earlier
versions of the Sun JVM. Instead, the Sun garbage collector stopped the program when
memory got low. Mark-and-sweep also requires that the program be stopped.

As previously mentioned, in the JVM described here memory is allocated in big blocks. If you
allocate a large object, it gets its own block. Strict stop-and-copy requires copying every live
object from the source heap to a new heap before you can free the old one, which translates
to lots of memory. With blocks, the garbage collection can typically copy objects to dead
blocks as it collects. Each block has a generation count to keep track of whether it’s alive. In
the normal case, only the blocks created since the last garbage collection are compacted; all
other blocks get their generation count bumped if they have been referenced from
somewhere. This handles the normal case of lots of short-lived temporary objects.
Periodically, a full sweep is made—large objects are still not copied (they just get their
generation count bumped), and blocks containing small objects are copied and compacted.
The JVM monitors the efficiency of garbage collection and if it becomes a waste of time
because all objects are long-lived, then it switches to mark-andsweep. Similarly, the JVM
keeps track of how successful mark-and-sweep is, and if the heap starts to become
fragmented, it switches back to stop-and-copy. This is where the “adaptive” part comes in, so
you end up with a mouthful: “Adaptive generational stop-and-copy mark-andsweep.”

There are a number of additional speedups possible in a JVM. An especially important one
involves the operation of the loader and what is called a just-in-time (JIT) compiler. A JIT
compiler partially or fully converts a program into native machine code so that it doesn’t
need to be interpreted by the JVM and thus runs much faster. When a class must be loaded
(typically, the first time you want to create an object of that class), the .class file is located,
and the bytecodes for that class are brought into memory. At this point, one approach is to
simply JIT compile all the code, but this has two drawbacks: It takes a little more time,
which, compounded throughout the life of the program, can add up; and it increases the size
of the executable (bytecodes are significantly more compact than expanded JIT code), and
this might cause paging, which definitely slows down a program. An alternative approach is
lazy evaluation, which means that the code is not JIT compiled until necessary. Thus, code

124 Thinking in Java Bruce Eckel

that never gets executed might never be JIT compiled. The Java HotSpot technologies in
recent JDKs take a similar approach by increasingly optimizing a piece of code each time it is
executed, so the more the code is executed, the faster it gets.

Member initialization
Java goes out of its way to guarantee that variables are properly initialized before they are
used. In the case of a method’s local variables, this guarantee comes in the form of a compile-
time error. So if you say:

void f() {
 int i;
 i++; // Error -- i not initialized

you’ll get an error message that says that i might not have been initialized. Of course, the
compiler could have given i a default value, but an uninitialized local variable is probably a
programmer error, and a default value would have covered that up. Forcing the programmer
to provide an initialization value is more likely to catch a bug.

If a primitive is a field in a class, however, things are a bit different. As you saw in the
Everything Is an Object chapter, each primitive field of a class is guaranteed to get an initial
value. Here’s a program that verifies this, and shows the values:

//: initialization/
// Shows default initial values.
import static net.mindview.util.Print.*;
public class InitialValues {
 boolean t;
 char c;
 byte b;
 short s;
 int i;
 long l;
 float f;
 double d;
 InitialValues reference;
 void printInitialValues() {
 print("Data type Initial value");
 print("boolean " + t);
 print("char [" + c + "]");
 print("byte " + b);
 print("short " + s);
 print("int " + i);
 print("long " + l);
 print("float " + f);
 print("double " + d);
 print("reference " + reference);
 public static void main(String[] args) {
 InitialValues iv = new InitialValues();
 /* You could also say:
 new InitialValues().printInitialValues();