I have a map (HashMap<String, Map<String,String> mapTest) in which I have a loop that does several operations.
HashMap<String, Map<String, String> mapTest = new HashMap<>();
ArrayList<Object> testFinals = new ArrayList<>();
for (Map.Entry<String, Map<String, String>> entry : mapTest.entrySet()) {
// in here I do a lot of things, like another for loops, if's, etc.
//the final juice to get from here is that in each time the loop is executed I have this:
List<Object> resultExp = methodXYZ(String, String, String);
testFinals.addAll(resultExp);
}
- In here, I have to wait before I proceed, since I need the full testFinals filled to advance.
Now, what I need to do is:
1 - This mapTest can have like 400 rows to iterate from.
I want to schedule like 4 threads, and assign like 100 rows of that FOR cycle to thread 1, the next 100 rows of the mapTest to thread 2, and so on.
Already tryed a few solutions, like this one:
ExecutorService taskExecutor = Executors.newFixedThreadPool(4);
while(...) {
taskExecutor.execute(new MyTask());
}
taskExecutor.shutdown();
try {
taskExecutor.awaitTermination(Long.MAX_VALUE, TimeUnit.NANOSECONDS);
} catch (InterruptedException e) {
...
}
but I can't adapt this correctly or a similar working solution to what I have now, with that map iteration.
HashMap is not a thread safe data structure.
When using concurrent, consider that the threads must obtain, hold and relinquish locks on a variable.
This is done at field level -not content.
On short ... The hasmap is locked for access y a specific thread. Not some random entry.
I'm working on an application, that has uses a HashMap to share state. I need to prove via unit tests that it will have problems in a multi-threaded environment.
I tried to check the state of the application in a single thread environment and in a multi-threaded environment via checking the size and elements of the HashMap in both of them. But seems this doesn't help, the state is always the same.
Are there any other ways to prove it or prove that an application that performs operations on the map works well with concurrent requests?
This is quite easy to prove.
Shortly
A hash map is based on an array, where each item represents a bucket. As more keys are added, the buckets grow and at a certain threshold the array is recreated with a bigger size so that its buckets are spread more evenly (performance considerations). During the array recreation, the array becomes empty, which results in empty result for the caller, until the recreation completes.
Details and Proof
It means that sometimes HashMap#put() will internally call HashMap#resize() to make the underlying array bigger.
HashMap#resize() assigns the table field a new empty array with a bigger capacity and populates it with the old items. While this population happens, the underlying array doesn't contain all of the old items and calling HashMap#get() with an existing key may return null.
The following code demonstrates that. You are very likely to get the exception that will mean the HashMap is not thread safe. I chose the target key as 65 535 - this way it will be the last element in the array, thus being the last element during re-population which increases the possibility of getting null on HashMap#get() (to see why, see HashMap#put() implementation).
final Map<Integer, String> map = new HashMap<>();
final Integer targetKey = 0b1111_1111_1111_1111; // 65 535
final String targetValue = "v";
map.put(targetKey, targetValue);
new Thread(() -> {
IntStream.range(0, targetKey).forEach(key -> map.put(key, "someValue"));
}).start();
while (true) {
if (!targetValue.equals(map.get(targetKey))) {
throw new RuntimeException("HashMap is not thread safe.");
}
}
One thread adds new keys to the map. The other thread constantly checks the targetKey is present.
If count those exceptions, I get around 200 000.
It is hard to simulate Race but looking at the OpenJDK source for put() method of HashMap:
public V put(K key, V value) {
if (key == null)
return putForNullKey(value);
//Operation 1
int hash = hash(key.hashCode());
int i = indexFor(hash, table.length);
for (Entry<K,V> e = table[i]; e != null; e = e.next) {
Object k;
if (e.hash == hash && ((k = e.key) == key || key.equals(k))) {
V oldValue = e.value;
e.value = value;
e.recordAccess(this);
return oldValue;
}
}
//Operation 2
modCount++;
//Operation 3
addEntry(hash, key, value, i);
return null;
}
As you can see put() involves 3 operations which are not synchronized. And compound operations are non thread safe. So theoretically it is proven that HashMap is not thread safe.
Its an old thread. But just pasting my sample code which is able to demonstrate the problems with hashmap.
Take a look at the below code, we try to insert 30000 Items into the hashmap using 10 threads (3000 items per thread).
So after all the threads are completed, you should ideally see that the size of hashmap should be 30000. But the actual output would be either an exception while rebuilding the tree or the final count is less than 30000.
class TempValue {
int value = 3;
#Override
public int hashCode() {
return 1; // All objects of this class will have same hashcode.
}
}
public class TestClass {
public static void main(String args[]) {
Map<TempValue, TempValue> myMap = new HashMap<>();
List<Thread> listOfThreads = new ArrayList<>();
// Create 10 Threads
for (int i = 0; i < 10; i++) {
Thread thread = new Thread(() -> {
// Let Each thread insert 3000 Items
for (int j = 0; j < 3000; j++) {
TempValue key = new TempValue();
myMap.put(key, key);
}
});
thread.start();
listOfThreads.add(thread);
}
for (Thread thread : listOfThreads) {
thread.join();
}
System.out.println("Count should be 30000, actual is : " + myMap.size());
}
}
Output 1 :
Count should be 30000, actual is : 29486
Output 2 : (Exception)
java.util.HashMap$Node cannot be cast to java.util.HashMap$TreeNodejava.lang.ClassCastException: java.util.HashMap$Node cannot be cast to java.util.HashMap$TreeNode
at java.util.HashMap$TreeNode.moveRootToFront(HashMap.java:1819)
at java.util.HashMap$TreeNode.treeify(HashMap.java:1936)
at java.util.HashMap.treeifyBin(HashMap.java:771)
at java.util.HashMap.putVal(HashMap.java:643)
at java.util.HashMap.put(HashMap.java:611)
at TestClass.lambda$0(TestClass.java:340)
at java.lang.Thread.run(Thread.java:745)
However if you modify the line Map<TempValue, TempValue> myMap = new HashMap<>(); to a ConcurrentHashMap the output is always 30000.
Another Observation :
In the above example the hashcode for all objects of TempValue class was the same(** i.e., 1**). So you might be wondering, this issue with HashMap might occur only in case there is a collision (due to hashcode).
I tried another example.
Modify the TempValue class to
class TempValue {
int value = 3;
}
Now re-execute the same code. Out of every 5 runs, I see 2-3 runs still give a different output than 30000.
So even if you usually don't have much collisions, you might still end up with an issue. (Maybe due to rebuilding of HashMap, etc.)
Overall these examples show the issue with HashMap which ConcurrentHashMap handles.
I need to prove via unit tests that it will have problems in multithread environment.
This is going to be tremendously hard to do. Race conditions are very hard to demonstrate. You could certainly write a program which does puts and gets into a HashMap in a large number of threads but logging, volatile fields, other locks, and other timing details of your application may make it extremely hard to force your particular code to fail.
Here's a stupid little HashMap failure test case. It fails because it times out when the threads go into an infinite loop because of memory corruption of HashMap. However, it may not fail for you depending on number of cores and other architecture details.
#Test(timeout = 10000)
public void runTest() throws Exception {
final Map<Integer, String> map = new HashMap<Integer, String>();
ExecutorService pool = Executors.newFixedThreadPool(10);
for (int i = 0; i < 10; i++) {
pool.submit(new Runnable() {
#Override
public void run() {
for (int i = 0; i < 10000; i++) {
map.put(i, "wow");
}
}
});
}
pool.shutdown();
pool.awaitTermination(Long.MAX_VALUE, TimeUnit.MILLISECONDS);
}
Is reading the API docs enough? There is a statement in there:
Note that this implementation is not synchronized. If multiple threads
access a hash map concurrently, and at least one of the threads
modifies the map structurally, it must be synchronized externally. (A
structural modification is any operation that adds or deletes one or
more mappings; merely changing the value associated with a key that an
instance already contains is not a structural modification.) This is
typically accomplished by synchronizing on some object that naturally
encapsulates the map. If no such object exists, the map should be
"wrapped" using the Collections.synchronizedMap method. This is best
done at creation time, to prevent accidental unsynchronized access to
the map:
The problem with thread safety is that it's hard to prove through a test. It could be fine most of the times. Your best bet would be to just run a bunch of threads that are getting/putting and you'll probably get some concurrency errors.
I suggest using a ConcurrentHashMap and trust that the Java team saying that HashMap is not synchronized is enough.
Are there any other ways to prove it?
How about reading the documentation (and paying attention to the emphasized "must"):
If multiple threads access a hash map concurrently, and at least one of the threads modifies the map structurally, it must be synchronized externally
If you are going to attempt to write a unit test that demonstrates incorrect behavior, I recommend the following:
Create a bunch of keys that all have the same hashcode (say 30 or 40)
Add values to the map for each key
Spawn a separate thread for the key, which has an infinite loop that (1) asserts that the key is present int the map, (2) removes the mapping for that key, and (3) adds the mapping back.
If you're lucky, the assertion will fail at some point, because the linked list behind the hash bucket will be corrupted. If you're unlucky, it will appear that HashMap is indeed threadsafe despite the documentation.
It may be possible, but will never be a perfect test. Race conditions are just too unpredictable. That being said, I wrote a similar type of test to help fix a threading issue with a proprietary data structure, and in my case, it was much easier to prove that something was wrong (before the fix) than to prove that nothing would go wrong (after the fix). You could probably construct a multi-threaded test that will eventually fail with sufficient time and the right parameters.
This post may be helpful in identifying areas to focus on in your test and has some other suggestions for optional replacements.
You can create multiple threads each adding an element to a hashmap and iterating over it.
i.e. In the run method we have to use "put" and then iterate using iterator.
For the case of HashMap we get ConcurrentModificationException while for ConcurrentHashMap we dont get.
Most probable race condition at java.util.HashMap implementation
Most of hashMaps failing if we are trying to read values while resizing or rehashing step executing. Resizing and rehashing operation executed under certain conditions most commonly if exceed bucket threshold. This code proves that if I call resizing externally or If I put more element than threshold and tend to call resizing operation internally causes to some null read which shows that HashMap is not thread safe. There should be more race condition but it is enough to prove it is not Thread Safe.
Practically proof of race condition
import java.lang.reflect.InvocationTargetException;
import java.lang.reflect.Method;
import java.util.HashMap;
import java.util.Map;
import java.util.stream.IntStream;
public class HashMapThreadSafetyTest {
public static void main(String[] args) {
try {
(new HashMapThreadSafetyTest()).testIt();
} catch (Exception e) {
e.printStackTrace();
}
}
private void threadOperation(int number, Map<Integer, String> map) {
map.put(number, "hashMapTest");
while (map.get(number) != null);
//If code passes to this line that means we did some null read operation which should not be
System.out.println("Null Value Number: " + number);
}
private void callHashMapResizeExternally(Map<Integer, String> map)
throws NoSuchMethodException, InvocationTargetException, IllegalAccessException {
Method method = map.getClass().getDeclaredMethod("resize");
method.setAccessible(true);
System.out.println("calling resize");
method.invoke(map);
}
private void testIt()
throws InterruptedException, NoSuchMethodException, IllegalAccessException, InvocationTargetException {
final Map<Integer, String> map = new HashMap<>();
IntStream.range(0, 12).forEach(i -> new Thread(() -> threadOperation(i, map)).start());
Thread.sleep(60000);
// First loop should not show any null value number untill calling resize method of hashmap externally.
callHashMapResizeExternally(map);
// First loop should fail from now on and should print some Null Value Numbers to the out.
System.out.println("Loop count is 12 since hashmap initially created for 2^4 bucket and threshold of resizing"
+ "0.75*2^4 = 12 In first loop it should not fail since we do not resizing hashmap. "
+ "\n\nAfter 60 second: after calling external resizing operation with reflection should forcefully fail"
+ "thread safety");
Thread.sleep(2000);
final Map<Integer, String> map2 = new HashMap<>();
IntStream.range(100, 113).forEach(i -> new Thread(() -> threadOperation(i, map2)).start());
// Second loop should fail from now on and should print some Null Value Numbers to the out. Because it is
// iterating more than 12 that causes hash map resizing and rehashing
System.out.println("It should fail directly since it is exceeding hashmap initial threshold and it will resize"
+ "when loop iterate 13rd time");
}
}
Example output
No null value should be printed untill thread sleep line passed
calling resize
Loop count is 12 since hashmap initially created for 2^4 bucket and threshold of resizing0.75*2^4 = 12 In first loop it should not fail since we do not resizing hashmap.
After 60 second: after calling external resizing operation with reflection should forcefully failthread safety
Null Value Number: 11
Null Value Number: 5
Null Value Number: 6
Null Value Number: 8
Null Value Number: 0
Null Value Number: 7
Null Value Number: 2
It should fail directly since it is exceeding hashmap initial threshold and it will resizewhen loop iterate 13th time
Null Value Number: 111
Null Value Number: 100
Null Value Number: 107
Null Value Number: 110
Null Value Number: 104
Null Value Number: 106
Null Value Number: 109
Null Value Number: 105
Very Simple Solution to prove this
Here is the code, which proves the Hashmap implementation is not thread safe.
In this example, we are only adding the elements to map, not removing it from any method.
We can see that it prints the keys which are not in map, even though we have put the same key in map before doing get operation.
package threads;
import java.util.HashMap;
import java.util.Map;
import java.util.Random;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
public class HashMapWorkingDemoInConcurrentEnvironment {
private Map<Long, String> cache = new HashMap<>();
public String put(Long key, String value) {
return cache.put(key, value);
}
public String get(Long key) {
return cache.get(key);
}
public static void main(String[] args) {
HashMapWorkingDemoInConcurrentEnvironment cache = new HashMapWorkingDemoInConcurrentEnvironment();
class Producer implements Callable<String> {
private Random rand = new Random();
public String call() throws Exception {
while (true) {
long key = rand.nextInt(1000);
cache.put(key, Long.toString(key));
if (cache.get(key) == null) {
System.out.println("Key " + key + " has not been put in the map");
}
}
}
}
ExecutorService executorService = Executors.newFixedThreadPool(4);
System.out.println("Adding value...");
try {
for (int i = 0; i < 4; i++) {
executorService.submit(new Producer());
}
} finally {
executorService.shutdown();
}
}
}
Sample Output for a execution run
Adding value...
Key 611 has not been put in the map
Key 978 has not been put in the map
Key 35 has not been put in the map
Key 202 has not been put in the map
Key 714 has not been put in the map
Key 328 has not been put in the map
Key 606 has not been put in the map
Key 149 has not been put in the map
Key 763 has not been put in the map
Its strange to see the values printed, that's why hashmap is not thread safe implementation working in concurrent environment.
There is a great tool open sourced by the OpenJDK team called JCStress which is used in the JDK for concurrency testing.
https://github.com/openjdk/jcstress
In one of its sample: https://github.com/openjdk/jcstress/blob/master/tests-custom/src/main/java/org/openjdk/jcstress/tests/collections/HashMapFailureTest.java
#JCStressTest
#Outcome(id = "0, 0, 1, 2", expect = Expect.ACCEPTABLE, desc = "No exceptions, entire map is okay.")
#Outcome(expect = Expect.ACCEPTABLE_INTERESTING, desc = "Something went wrong")
#State
public class HashMapFailureTest {
private final Map<Integer, Integer> map = new HashMap<>();
#Actor
public void actor1(IIII_Result r) {
try {
map.put(1, 1);
r.r1 = 0;
} catch (Exception e) {
r.r1 = 1;
}
}
#Actor
public void actor2(IIII_Result r) {
try {
map.put(2, 2);
r.r2 = 0;
} catch (Exception e) {
r.r2 = 1;
}
}
#Arbiter
public void arbiter(IIII_Result r) {
Integer v1 = map.get(1);
Integer v2 = map.get(2);
r.r3 = (v1 != null) ? v1 : -1;
r.r4 = (v2 != null) ? v2 : -1;
}
}
The methods marked with actor are run concurrently on different threads.
The result for this on my machine is:
Results across all configurations:
RESULT SAMPLES FREQ EXPECT DESCRIPTION
0, 0, -1, 2 3,854,896 5.25% Interesting Something went wrong
0, 0, 1, -1 4,251,564 5.79% Interesting Something went wrong
0, 0, 1, 2 65,363,492 88.97% Acceptable No exceptions, entire map is okay.
This shows that 88% of the times expected values were observed but in around 12% of the times, incorrect results were seen.
You can try out this tool and the samples and write your own tests to verify that concurrency of some code is broken.
As a yet another reply to this topic, I would recommend example from https://www.baeldung.com/java-concurrent-map, that looks as below. Theory is very straigthforwad - for N times we run 10 threads, that each of them increments the value in a common map 10 times. If the map was thread safe, the value should be 100 every time. Example proves, it's not.
#Test
public void givenHashMap_whenSumParallel_thenError() throws Exception {
Map<String, Integer> map = new HashMap<>();
List<Integer> sumList = parallelSum100(map, 100);
assertNotEquals(1, sumList
.stream()
.distinct()
.count());
long wrongResultCount = sumList
.stream()
.filter(num -> num != 100)
.count();
assertTrue(wrongResultCount > 0);
}
private List<Integer> parallelSum100(Map<String, Integer> map,
int executionTimes) throws InterruptedException {
List<Integer> sumList = new ArrayList<>(1000);
for (int i = 0; i < executionTimes; i++) {
map.put("test", 0);
ExecutorService executorService =
Executors.newFixedThreadPool(4);
for (int j = 0; j < 10; j++) {
executorService.execute(() -> {
for (int k = 0; k < 10; k++)
map.computeIfPresent(
"test",
(key, value) -> value + 1
);
});
}
executorService.shutdown();
executorService.awaitTermination(5, TimeUnit.SECONDS);
sumList.add(map.get("test"));
}
return sumList;
}
I am trying to call BatchGetItem to retrieve items from DynamoDB. As input we can get a list of up to 1000 keys (or as little as 1 key). These keys coincide with the hashKey for our DynamoDB table.
Since the BatchGetItem API only takes in up to 100 items per call, I am trying to split up the request into batches of only 100 items each, make the calls in parallel, and then merge the results into a single Set again.
For those unfamiliar with the DynamoDB who could still give advice on an extremely stripped down version (1st example) I'd appreciate it! Otherwise, please see the second more accurate example below.
1st Example - extremely stripped down
public Set<SomeResultType> retrieveSomething(Set<String> someSet) {
ImmutableSet.Builder<SomeResultType> resultBuilder = ImmutableSet.builder();
// FIXME - how to parallelize?
for (List<Map<String, String>> batch : Iterables.partition(someSet, 100)) {
result = callSomeLongRunningAPI(batch);
resultBuilder.addAll(result.getItems());
}
return resultBuilder.build();
}
2nd Example - closer to my actual problem -
Below is a stripped down, dummy version of what I'm currently doing (as such, please forgive formatting / style issues). It currently works and gets all the items, but I can't figure out how to get the batches (see FIXME) to get executed in parallel and end up in a single set. Since performance is pretty important in the system I'm trying to build, any tips would be appreciated in helping this code be more efficient!
public Set<SomeResultType> retrieveSomething(Set<String> someIds) {
if (someIds.isEmpty()) {
// handle this here
}
Collection<Map<String, AttributeValue>> keyAttributes = someIds.stream()
.map(id -> ImmutableMap.<String, AttributeValue>builder()
.put(tableName, new AttributeValue().withS(id)).build())
.collect(ImmutableList.toImmutableList());
ImmutableSet.Builder<SomeResultType> resultBuilder = ImmutableSet.builder();
Map<String, KeysAndAttributes> itemsToProcess;
BatchGetItemResult result;
// FIXME - make parallel?
for (List<Map<String, AttributeValue>> batch : Iterables.partition(keyAttributes, 100)) {
KeysAndAttributes keysAndAttributes = new KeysAndAttributes()
.withKeys(batch)
.withAttributesToGet(...// some attribute names);
itemsToProcess = ImmutableMap.of(tableName, keysAndAttributes);
result = this.dynamoDB.batchGetItem(itemsToProcess);
resultBuilder.addAll(extractItemsFromResults(tableName, result));
}
return resultBuilder.build());
}
Help with either the super stripped down case or the 2nd example would be greatly appreciated! Thanks!
Assume you have a flux of objects with the following structure:
class Element {
String key;
int count;
}
Now imagine those elements flow in a predefined sort order, always in groups of a key, like
{ key = "firstKey", count=123}
{ key = "firstKey", count=1 }
{ key = "secondKey", count=4 }
{ key = "thirdKey", count=98 }
{ key = "thirdKey", count=5 }
.....
What I want to do is create a flux which returns one element for each distinct key and summed count for each key-group.
So basically like a classic reduce for each group, but using the reduce operator does not work, because it only returns a single element and I want to get a flux with one element for each distinct key.
Using bufferUntil might work, but has the drawback, that I have to keep a state to check if the key has changed in comparison to the previous one.
Using groupBy is an overkill, as I know that each group has come to an end once a new key is found, so I don't want to keep anything cached after that event.
Is such an aggregation possible using Flux, without keeping a state outside of the flow?
This is currently (as of 3.2.5) not possible without keeping track of state yourself. distinctUntilChanged could have fit the bill with minimal state but doesn't emit the state, just the values it considered as "distinct" according to said state.
The most minimalistic way of solving this is with windowUntil and compose + an AtomicReference for state-per-subscriber:
Flux<Tuple2<T, Integer>> sourceFlux = ...; //assuming key/count represented as `Tuple2`
Flux<Tuple2<T, Integer>> aggregated = sourceFlux.compose(source -> {
//having this state inside a compose means it will not be shared by multiple subscribers
AtomicReference<T> last = new AtomicReference<>(null);
return source
//use "last seen" state so split into windows, much like a `groupBy` but with earlier closing
.windowUntil(i -> !i.getT1().equals(last.getAndSet(i.getT1())), true)
//reduce each window
.flatMap(window -> window.reduce((i1, i2) -> Tuples.of(i1.getT1(), i1.getT2() + i2.getT2()))
});
That really worked for me! Thanks for that post.
Please note that in the meantime the "compose" method was renamed. You need to use transformDeferred instead.
In my case I have a "Dashboard" object which has an id (stored as UUID) on which I want to group the source flux:
Flux<Dashboard> sourceFlux = ... // could be a DB query. The Flux must be sorted according the id.
sourceFlux.transformDeferred(dashboardFlux -> {
// this stores the dashboardId's as the Flux publishes. It is used to decide when to open a new window
// having this state inside a compose means it will not be shared by multiple subscribers
AtomicReference<UUID> last = new AtomicReference<>(null);
return dashboardFlux
//use "last seen" state so split into windows, much like a `groupBy` but with earlier closing
.windowUntil(i -> !i.getDashboardId().equals(last.getAndSet(i.getDashboardId())), true)
//reduce each window
.flatMap(window -> window.reduce(... /* reduce one window here */));
})
I've worked with ConcurrentHashMaps, but I'm not quite sure if that will cover all of the bases here.
I have a Spring Component. This Component will contain a Map. This will simply be a quick reference for objects in an external service. If the map does not contain a matching String, it will make a call to the external service, retrieve the object, and store it in the mapping. Then other classes can use the mapping for quick retrieval and usage. As such, there are only put() and get() operations performed on the map. Entries are never removed.
That being said, I'm a little concerned that ConcurrentHashMap may not provide the atomic control I'd like. Fetching SomeObject from the external service is potentially expensive. I'd rather not have two separate threads calling in at nearly the same time, resulting in multiple calls for the same value to the external service.
The idea is this:
Map<String, SomeObject> map = Collections.concurrentHashMap(
new HashMap<String, SomeObject>());
public SomeObject getSomeObject(String key){
if (!map.containsKey(key)){
map.put(key, retrieveSomeObjectFromService(key));
}
return map.get(key);
Or this:
Map<String, SomeObject> map = new HashMap<String, SomeObject>();
public SomeObject getSomeObject(String key){
synchronized(map){
if (!map.containsKey(key)){
map.put(key, retrieveSomeObjectFromService(key));
}
}
return map.get(key);
}
The former is certainly simpler, but the latter would ensure that one two or more threads won't try to simultaneously trigger a fetching of the same SomeObject. Alternatively, I suppose I could try locking out only gets that attempt to retrieve a SomeObject that is already in the process of being fetched and does not block retrieving SomeObjects that already exist, but that would require a wait mechanism on the various string values and I'm not sure how to best implement that.
I would suggest you do a little bit of both!
Fast path, just 1 get out of the concurrent hashmap.
Slow path, full sync and lock
private final ConcurrentHashMap<String, Object> map = new ConcurrentHashMap<String, Object>();
private final ReentrantLock lock = new ReentrantLock();
public Object getSomeObject(String key) {
Object value = map.get(key);
if (value == null) {
try {
lock.lock();
value = map.get(key);
if (value == null) {
value = retrieveSomeObjectFromService(key);
map.put(key, value);
}
} finally {
lock.unlock();
}
}
return value;
}
Do you understand why we need the 2nd get inside of the lock? Leaving that out leaves a case where we end up making the inside object twice, and having different copies of it floating around.
Also doing the assign the result to value and nullcheck vs using the contains method - understand why that is better? If we do a .contains then a .get, we just did 2 hashmap lookup. If I just do a get, I can cut my hashmap lookup time in half.
Another version Peter suggested.. less lines of code, but not my personal preference:
private final ConcurrentHashMap<String, Object> map = new ConcurrentHashMap<String, Object>();
public Object getSomeObject(String key) {
Object value = map.get(key);
if (value == null) {
synchronized (map) {
value = map.get(key);
if (value == null) {
value = retrieveSomeObjectFromService(key);
map.put(key, value);
}
}
}
return value;
}