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Omitting an instance field at run time in Java

Java's assert mechanism allows disabling putting in assertions which have essentially no run time cost (aside from a bigger class file) if assertions are disabled. But this may cover all situations.
For instance, many of Java's collections feature "fail-fast" iterators that attempt to detect when you're using them in a thread-unsafe way. But this requires both the collection and the iterator itself to maintain extra state that would not be needed if these checks weren't there.
Suppose someone wanted to do something similar, but allow the checks to be disabled and if they are disabled, it saves a few bytes in the iterator and likewise a few more bytes in the ArrayList, or whatever.
Alternatively, suppose we're doing some sort of object pooling that we want to be able to turn on and off at runtime; when it's off, it should just use Java's garbage collection and take no room for reference counts, like this (note that the code as written is very broken):
class MyClass {
static final boolean useRefCounts = my.global.Utils.useRefCounts();
static {
if(useRefCounts)
int refCount; // want instance field, not local variable
}
void incrementRefCount(){
if(useRefCounts) refCount++; // only use field if it exists;
}
/**return true if ready to be collected and reused*/
boolean decrementAndTestRefCount(){
// rely on Java's garbage collector if ref counting is disabled.
return useRefCounts && --refCount == 0;
}
}
The trouble with the above code is that the static bock makes no sense. But is there some trick using low-powered magic to make something along these lines work? (If high powered magic is allowed, the nuclear option is generate two versions of MyClass and arrange to put the correct one on the class path at start time.)

NOTE: You might not need to do this at all. The JIT is very good at inlining constants known at runtime especially boolean and optimising away the code which isn't used.
The int field is not ideal, however, if you are using a 64 bit JVM, the object size might not change.
On the OpenJDK/Oracle JVM (64-bit), the header is 12 bytes by default. The object alignment is 8 byte so the object will use 16 bytes. The field, adds 4 bytes, which after alignment is also 16 bytes.
To answer the question, you need two classes (unless you use generated code or hacks)
class MyClass {
static final boolean useRefCounts = my.global.Utils.useRefCounts();
public static MyClass create() {
return useRefCounts ? new MyClassPlus() : new MyClass();
}
void incrementRefCount() {
}
boolean decrementAndTestRefCount() {
return false;
}
}
class MyClassPlus extends MyClass {
int refCount; // want instance field, not local variable
void incrementRefCount() {
refCount++; // only use field if it exists;
}
boolean decrementAndTestRefCount() {
return --refCount == 0;
}
}

If you accept a slightly higher overhead in the case you’re using your ref count, you may resort to external storage, i.e.
class MyClass {
static final WeakHashMap<MyClass,Integer> REF_COUNTS
= my.global.Utils.useRefCounts()? new WeakHashMap<>(): null;
void incrementRefCount() {
if(REF_COUNTS != null) REF_COUNTS.merge(this, 1, Integer::sum);
}
/**return true if ready to be collected and reused*/
boolean decrementAndTestRefCount() {
return REF_COUNTS != null
&& REF_COUNTS.compute(this, (me, i) -> --i == 0? null: i) == null;
}
}
There is a behavioral difference for the case that someone invokes decrementAndTestRefCount() more often than incrementRefCount(). While your original code silently runs into a negative ref count, this code will throw a NullPointerException. I prefer failing with an exception in this case…
The code above will leave you with the overhead of a single static field in case you’re not using the feature. Most JVMs should have no problems eliminating the conditionals regarding the state of a static final variable.
Note further that the code allows MyClass instances to get garbage collected while having a non-zero ref count, just like when it was an instance field, but also actively removes the mapping when the count reaches the initial state of zero again, to minimize the work needed for cleanup.

why does this Java method leak—and why does inlining it fix the leak?

I wrote a minimal somewhat-lazy (int) sequence class, GarbageTest.java, as an experiment, to see if I could process very long, lazy sequences in Java, the way I can in Clojure.
Given a naturals() method that returns the lazy, infinite, sequence of natural numbers; a drop(n,sequence) method that drops the first n elements of sequence and returns the rest of the sequence; and an nth(n,sequence) method that returns simply: drop(n, lazySeq).head(), I wrote two tests:
static int N = (int)1e6;
// succeeds # N = (int)1e8 with java -Xmx10m
#Test
public void dropTest() {
assertThat( drop(N, naturals()).head(), is(N+1));
}
// fails with OutOfMemoryError # N = (int)1e6 with java -Xmx10m
#Test
public void nthTest() {
assertThat( nth(N, naturals()), is(N+1));
}
Note that the body of dropTest() was generated by copying the body of nthTest() and then invoking IntelliJ's "inline" refactoring on the nth(N, naturals()) call. So it seems to me that the behavior of dropTest() should be identical to the behavior of nthTest().
But it isn't identical! dropTest() runs to completion with N up to 1e8 whereas nthTest() fails with OutOfMemoryError for N as small as 1e6.
I've avoided inner classes. And I've experimented with a variant of my code, ClearingArgsGarbageTest.java, that nulls method parameters before calling other methods. I've applied the YourKit profiler. I've looked at the byte code. I just cannot find the leak that causes nthTest() to fail.
Where's the "leak"? And why does nthTest() have the leak while dropTest() does not?
Here's the rest of the code from GarbageTest.java in case you don't want to click through to the Github project:
/**
* a not-perfectly-lazy lazy sequence of ints. see LazierGarbageTest for a lazier one
*/
static class LazyishSeq {
final int head;
volatile Supplier<LazyishSeq> tailThunk;
LazyishSeq tailValue;
LazyishSeq(final int head, final Supplier<LazyishSeq> tailThunk) {
this.head = head;
this.tailThunk = tailThunk;
tailValue = null;
}
int head() {
return head;
}
LazyishSeq tail() {
if (null != tailThunk)
synchronized(this) {
if (null != tailThunk) {
tailValue = tailThunk.get();
tailThunk = null;
}
}
return tailValue;
}
}
static class Incrementing implements Supplier<LazyishSeq> {
final int seed;
private Incrementing(final int seed) { this.seed = seed;}
public static LazyishSeq createSequence(final int n) {
return new LazyishSeq( n, new Incrementing(n+1));
}
#Override
public LazyishSeq get() {
return createSequence(seed);
}
}
static LazyishSeq naturals() {
return Incrementing.createSequence(1);
}
static LazyishSeq drop(
final int n,
final LazyishSeq lazySeqArg) {
LazyishSeq lazySeq = lazySeqArg;
for( int i = n; i > 0 && null != lazySeq; i -= 1) {
lazySeq = lazySeq.tail();
}
return lazySeq;
}
static int nth(final int n, final LazyishSeq lazySeq) {
return drop(n, lazySeq).head();
}

In your method
static int nth(final int n, final LazyishSeq lazySeq) {
return drop(n, lazySeq).head();
}
the parameter variable lazySeq hold a reference to the first element of your sequence during the entire drop operation. This prevents the entire sequence from getting garbage collected.
In contrast, with
public void dropTest() {
assertThat( drop(N, naturals()).head(), is(N+1));
}
the first element of your sequence is returned by naturals() and directly passed to the invocation of drop, thus removed from the operand stack and does not exist during the execution of drop.
Your attempt to set the parameter variable to null, i.e.
static int nth(final int n, /*final*/ LazyishSeq lazySeqArg) {
final LazyishSeq lazySeqLocal = lazySeqArg;
lazySeqArg = null;
return drop(n,lazySeqLocal).head();
}
does not help, as now, the lazySeqArg variable is null, but the lazySeqLocal holds a reference to the first element.
A local variable does not prevent garbage collection in general, the collection of otherwise unused objects is permitted, but that doesn’t imply that a particular implementation is capable of doing it.
In case of the HotSpot JVM, only optimized code will get rid of such unused references. But here, nth is not a hot spot, as the heavy things happen within drop method.
This is the reason why the same issue does not appear at the drop method, despite it also holds a reference to the first element in its parameter variable. The drop method contains the loop doing the actual work, hence, is very likely to get optimized by the JVM, which may cause it to eliminate unused variables, allowing the already processed part of the sequence to become collected.
There are many factors which may affect the JVM’s optimizations. Besides the different shape of the code, it seems that that rapid memory allocations during the unoptimized phase may also reduce the optimizer’s improvements. Indeed, when I run with -Xcompile, to forbid interpreted execution altogether, both variants run successfully, even int N = (int)1e9 is no problem anymore. Of course, forcing compilation raises the startup time.
I have to admit that I do not understand why the mixed mode performs that much worse and I’ll investigate further. But generally, you have to be aware that the efficiency of the garbage collector is implementation dependent, so objects collected in one environment may stay in memory in another.

Clojure implements a strategy for dealing with this sort of scenario which it calls "locals clearing". There's support for it in the compiler that makes it kick in automatically where required in pure Clojure code (unless disabled at compilation time – this is sometimes useful for debugging). Clojure does also clear locals in various places in its Java runtime, however, and the way it does that could be used in Java libraries and possibly even application code, though it would undoubtedly be somewhat cumbersome.
Before I get into what Clojure does, here's a short summary of what is going on in this example:
nth(int, LazyishSeq) is implemented in terms of drop(int, LazyishSeq) and LazyishSeq.head().
nth passes both its arguments to drop and has no further use for them.
drop can easily be implemented so as to avoid holding on to the head of the passed-in sequence.
Here nth still holds on to the head of its sequence argument. The runtime may potentially discard that reference, but it is not guaranteed that it will.
The way Clojure deals with this is by clearing the reference to the sequence explicitly before control is handed off to drop. This is done using a rather elegant trick (link to the below snippet on GitHub as of Clojure 1.9.0):
// clojure/src/jvm/clojure/lang/Util.java
/**
* Copyright (c) Rich Hickey. All rights reserved.
* The use and distribution terms for this software are covered by the
* Eclipse Public License 1.0 (http://opensource.org/licenses/eclipse-1.0.php)
* which can be found in the file epl-v10.html at the root of this distribution.
* By using this software in any fashion, you are agreeing to be bound by
* the terms of this license.
* You must not remove this notice, or any other, from this software.
**/
// … beginning of the file omitted …
// the next line is the 190th in the file as of Clojure 1.9.0
static public Object ret1(Object ret, Object nil){
return ret;
}
static public ISeq ret1(ISeq ret, Object nil){
return ret;
}
// …
Given the above, the call to drop inside nth can be changed to
drop(n, ret1(lazySeq, lazySeq = null))
Here lazySeq = null is evaluated as an expression before control is transferred to ret1; the value is null and there is also the side effect of setting the lazySeq reference to null. The first argument to ret1 will have been evaluated by this point, however, so ret1 receives the reference to the sequence in its first argument and returns it as expected, and that value is then passed to drop.
Thus drop receives the original value held by the lazySeq local, but the local itself is cleared before control is transferred to drop.
Consequently nth no longer holds on to the head of the sequence.

Additional 'if checks' if the value is already set up - what is faster, what uses more resources?

Assume that we have a given interface:
public interface StateKeeper {
public abstract void negateWithoutCheck();
public abstract void negateWithCheck();
}
and following implementations:
class StateKeeperForPrimitives implements StateKeeper {
private boolean b = true;
public void negateWithCheck() {
if (b == true) {
this.b = false;
}
}
public void negateWithoutCheck() {
this.b = false;
}
}
class StateKeeperForObjects implements StateKeeper {
private Boolean b = true;
#Override
public void negateWithCheck() {
if (b == true) {
this.b = false;
}
}
#Override
public void negateWithoutCheck() {
this.b = false;
}
}
Moreover assume that methods negate*Check() can be called 1+ many times and it is hard to say what is the upper bound of the number of calls.
The question is which method in both implementations is 'better'
according to execution speed, garbage collection, memory allocation, etc. -
negateWithCheck or negateWithoutCheck?
Does the answer depend on which from the two proposed
implementations we use or it doesn't matter?
Does the answer depend on the estimated number of calls? For what count of number is better to use one or first method?

There might be a slight performance benefit in using the one with the check. I highly doubt that it matters in any real life application.
premature optimization is the root of all evil (Donald Knuth)
You could measure the difference between the two. Let me emphasize that these kind of things are notoriously difficult to measure reliably.
Here is a simple-minded way to do this. You can hope for performance benefits if the check recognizes that the value doesn't have to be changed, saving you an expensive write into the memory. So I have changed your code accordingly.
interface StateKeeper {
public abstract void negateWithoutCheck();
public abstract void negateWithCheck();
}
class StateKeeperForPrimitives implements StateKeeper {
private boolean b = true;
public void negateWithCheck() {
if (b == false) {
this.b = true;
}
}
public void negateWithoutCheck() {
this.b = true;
}
}
class StateKeeperForObjects implements StateKeeper {
private Boolean b = true;
public void negateWithCheck() {
if (b == false) {
this.b = true;
}
}
public void negateWithoutCheck() {
this.b = true;
}
}
public class Main {
public static void main(String args[]) {
StateKeeper[] array = new StateKeeper[10_000_000];
for (int i=0; i<array.length; ++i)
//array[i] = new StateKeeperForObjects();
array[i] = new StateKeeperForPrimitives();
long start = System.nanoTime();
for (StateKeeper e : array)
e.negateWithCheck();
//e.negateWithoutCheck();
long end = System.nanoTime();
System.err.println("Time in milliseconds: "+((end-start)/1000000));
}
}
I get the followings:
check no check
primitive 17ms 24ms
Object 21ms 24ms
I didn't find any performance penalty of the check the other way around when the check is always superfluous because the value always has to be changed.
Two things: (1) These timings are unreliable. (2) This benchmark is far from any real life application; I had to make an array of 10 million elements to actually see something.
I would simply pick the function with no check. I highly doubt that in any real application you would get any measurable performance benefit from the function that has the check but that check is error prone and is harder to read.

Short answer: the Without check will always be faster.
An assignment takes a lot less computation time than a comparison. Therefore: an IF statement is always slower than an assignment.
When comparing 2 variables, your CPU will fetch the first variable, fetch the second variable, compare those 2 and store the result into a temporary register. That's 2 fetches, 1 compare and a 1 store.
When you assign a value, your CPU will fetch the value on the right hand of the '=' and store it into the memory. That's 1 fetch and 1 store.

In general, if you need to set some state, just set the state. If, on the otherhand, you have to do something more - like log the change, inform about the change, etc. - then you should first inspect the old value.
But, in the case when methods like the ones you provided are called very intensely, there may be some performance difference in checking vs non-checking (whether the new value is different). Possible outcomes are:
1-a) check returns false
1-b) check returns true, value is assigned
2) value is assigned without check
As far as I know, writing is always slower than reading (all the way down to register level), so the fastest outcome is 1-a. If your case is that the most common thing that happens is that the value will not be changed ('more than 50%' logic is just not good enough, the exact percentage has to be figured out empirically) - then you should go with checking, as this eliminates redundant writing operation (value assignment). If, on the other hand, value is different more than often - assign it without checking.
You should test your concrete cases, do some profiling, and based on the result determine the best implementation. There is no general "best way" for this case (apart from "just set the state").
As for boolean vs Boolean here, I would say (off the top of my head) that there should be no performance difference.

Only today I've seen few answers and comments repeating that
Premature optimization is the root of all evil
Well obviously one if statement more is one thing more to do, but... it doesn't really matter.
And garbage collection and memory allocation... not an issue here.

I would generally consider the negateWithCheck to be slightly slower due there always being a comparison. Also notice in the StateKeeperOfObjects you are introducing some autoboxing. 'true' and 'false' are primitive boolean values.
Assuming you fix the StateKeeperOfObjects to use all objects, then potentially, but most likely not noticeable.
The speed will depend slightly on the number of calls, but in general the speed should be considered to be the same whether you call it once or many times (ignoring secondary effects such as caching, jit, etc).
It seems to me, a better question is whether or not the performance difference is noticeable. I work on a scientific project that involves millions of numerical computations done in parallel. We started off using Objects (e.g. Integer, Double) and had less than desirable performance, both in terms of memory and speed. When we switched all of our computations to primitives (e.g. int, double) and went over the code to make sure we were not introducing anything funky through autoboxing, we saw a huge performance increase (both memory and speed).
I am a huge fan of avoiding premature optimization, unless it is something that is "simple" to implement. Just be wary of the consequences. For example, do you have to represent null values in your data model? If so, how do you do that using a primitive? Doubles can be done easily with NaN, but what about Booleans?

negateWithoutCheck() is preferable because if we consider the number of calls then negateWithoutCheck() has only one call i.e. this.b = false; where as negateWithCheck() has one extra with previous one.

Simple Java String cache with expiration possibility

I am looking for a concurrent Set with expiration functionality for a Java 1.5 application. It would be used as a simple way to store / cache names (i.e. String values) that expire after a certain time.
The problem I'm trying to solve is that two threads should not be able to use the same name value within a certain time (so this is sort of a blacklist ensuring the same "name", which is something like a message reference, can't be reused by another thread until a certain time period has passed). I do not control name generation myself, so there's nothing I can do about the actual names / strings to enforce uniqueness, it should rather be seen as a throttling / limiting mechanism to prevent the same name to be used more than once per second.
Example:
Thread #1 does cache.add("unique_string, 1) which stores the name "unique_string" for 1 second.
If any thread is looking for "unique_string" by doing e.g. cache.get("unique_string") within 1 second it will get a positive response (item exists), but after that the item should be expired and removed from the set.
The container would at times handle 50-100 inserts / reads per second.
I have really been looking around at different solutions but am not finding anything that I feel really suites my needs. It feels like an easy problem, but all solutions I find are way too complex or overkill.
A simple idea would be to have a ConcurrentHashMap object with key set to "name" and value to the expiration time then a thread running every second and removing all elements whose value (expiration time) has passed, but I'm not sure how efficient that would be? Is there not a simpler solution I'm missing?

Google's Guava library contains exactly such cache: CacheBuilder.

How about creating a Map where the item expires using a thread executor
//Declare your Map and executor service
final Map<String, ScheduledFuture<String>> cacheNames = new HashMap<String, ScheduledFuture<String>>();
ScheduledExecutorService executorService = Executors.newSingleThreadScheduledExecutor();
You can then have a method that adds the cache name to your collection which will remove it after it has expired, in this example its one second. I know it seems like quite a bit of code but it can be quite an elegant solution in just a couple of methods.
ScheduledFuture<String> task = executorService.schedule(new Callable<String>() {
#Override
public String call() {
cacheNames.remove("unique_string");
return "unique_string";
}
}, 1, TimeUnit.SECONDS);
cacheNames.put("unique_string", task);

A simple unique string pattern which doesn't repeat
private static final AtomicLong COUNTER = new AtomicLong(System.currentTimeMillis()*1000);
public static String generateId() {
return Long.toString(COUNTER.getAndIncrement(), 36);
}
This won't repeat even if you restart your application.
Note: It will repeat after:
you restart and you have been generating over one million ids per second.
after 293 years. If this is not long enough you can reduce the 1000 to 100 and get 2930 years.

It depends - If you need strict condition of time, or soft (like 1 sec +/- 20ms).
Also if you need discrete cache invalidation or 'by-call'.
For strict conditions I would suggest to add a distinct thread which will invalidate cache each 20milliseconds.
Also you can have inside the stored key timestamp and check if it's expired or not.

Why not store the time for which the key is blacklisted in the map (as Konoplianko hinted)?
Something like this:
private final Map<String, Long> _blacklist = new LinkedHashMap<String, Long>() {
#Override
protected boolean removeEldestEntry(Map.Entry<String, Long> eldest) {
return size() > 1000;
}
};
public boolean isBlacklisted(String key, long timeoutMs) {
synchronized (_blacklist) {
long now = System.currentTimeMillis();
Long blacklistUntil = _blacklist.get(key);
if (blacklistUntil != null && blacklistUntil >= now) {
// still blacklisted
return true;
} else {
// not blacklisted, or blacklisting has expired
_blacklist.put(key, now + timeoutMs);
return false;
}
}
}

On using Enum based Singleton to cache large objects (Java)

Is there any better way to cache up some very large objects, that can only be created once, and therefore need to be cached ? Currently, I have the following:
public enum LargeObjectCache {
INSTANCE;
private Map<String, LargeObject> map = new HashMap<...>();
public LargeObject get(String s) {
if (!map.containsKey(s)) {
map.put(s, new LargeObject(s));
}
return map.get(s);
}
}
There are several classes that can use the LargeObjects, which is why I decided to use a singleton for the cache, instead of passing LargeObjects to every class that uses it.
Also, the map doesn't contain many keys (one or two, but the key can vary in different runs of the program) so, is there another, more efficient map to use in this case ?

You may need thread-safety to ensure you don't have two instance of the same name.
It does matter much for small maps but you can avoid one call which can make it faster.
public LargeObject get(String s) {
synchronized(map) {
LargeObject ret = map.get(s);
if (ret == null)
map.put(s, ret = new LargeObject(s));
return ret;
}
}

As it has been pointed out, you need to address thread-safety. Simply using Collections.synchronizedMap() doesn't make it completely correct, as the code entails compound operations. Synchronizing the entire block is one solution. However, using ConcurrentHashMap will result in a much more concurrent and scalable behavior if it is critical.
public enum LargeObjectCache {
INSTANCE;
private final ConcurrentMap<String, LargeObject> map = new ConcurrentHashMap<...>();
public LargeObject get(String s) {
LargeObject value = map.get(s);
if (value == null) {
value = new LargeObject(s);
LargeObject old = map.putIfAbsent(s, value);
if (old != null) {
value = old;
}
}
return value;
}
}
You'll need to use it exactly in this form to have the correct and the most efficient behavior.
If you must ensure only one thread gets to even instantiate the value for a given key, then it becomes necessary to turn to something like the computing map in Google Collections or the memoizer example in Brian Goetz's book "Java Concurrency in Practice".