jdk.util.concurrent.ConcurrentHashMap

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A hash table supporting full concurrency of retrievals and high expected concurrency for updates. This class obeys the same functional specification as Hashtable, and includes versions of methods corresponding to each method of Hashtable. However, even though all operations are thread-safe, retrieval operations do not entail locking, and there is not any support for locking the entire table in a way that prevents all access. This class is fully interoperable with Hashtable in programs that rely on its thread safety but not on its synchronization details.

Retrieval operations (including get) generally do not block, so may overlap with update operations (including put and remove). Retrievals reflect the results of the most recently completed update operations holding upon their onset. (More formally, an update operation for a given key bears a happens-before relation with any (non-null) retrieval for that key reporting the updated value.) For aggregate operations such as putAll and clear, concurrent retrievals may reflect insertion or removal of only some entries. Similarly, Iterators, Spliterators and Enumerations return elements reflecting the state of the hash table at some point at or since the creation of the iterator/enumeration. They do not throw ConcurrentModificationException. However, iterators are designed to be used by only one thread at a time. Bear in mind that the results of aggregate status methods including size, isEmpty, and containsValue are typically useful only when a map is not undergoing concurrent updates in other threads. Otherwise the results of these methods reflect transient states that may be adequate for monitoring or estimation purposes, but not for program control.

The table is dynamically expanded when there are too many collisions (i.e., keys that have distinct hash codes but fall into the same slot modulo the table size), with the expected average effect of maintaining roughly two bins per mapping (corresponding to a 0.75 load factor threshold for resizing). There may be much variance around this average as mappings are added and removed, but overall, this maintains a commonly accepted time/space tradeoff for hash tables. However, resizing this or any other kind of hash table may be a relatively slow operation. When possible, it is a good idea to provide a size estimate as an optional initialCapacity constructor argument. An additional optional loadFactor constructor argument provides a further means of customizing initial table capacity by specifying the table density to be used in calculating the amount of space to allocate for the given number of elements. Also, for compatibility with previous versions of this class, constructors may optionally specify an expected concurrencyLevel as an additional hint for internal sizing. Note that using many keys with exactly the same hashCode() is a sure way to slow down performance of any hash table. To ameliorate impact, when keys are Comparable, this class may use comparison order among keys to help break ties.

A Set projection of a ConcurrentHashMap may be created (using newKeySet() or newKeySet(int)), or viewed (using keySet(Object) when only keys are of interest, and the mapped values are (perhaps transiently) not used or all take the same mapping value.

A ConcurrentHashMap can be used as scalable frequency map (a form of histogram or multiset) by using LongAdder values and initializing via computeIfAbsent. For example, to add a count to a ConcurrentHashMap<String,LongAdder> freqs, you can use freqs.computeIfAbsent(k -> new LongAdder()).increment();

This class and its views and iterators implement all of the optional methods of the Map and Iterator interfaces.

Like Hashtable but unlike HashMap, this class does not allow null to be used as a key or value.

ConcurrentHashMaps support a set of sequential and parallel bulk operations that, unlike most Stream methods, are designed to be safely, and often sensibly, applied even with maps that are being concurrently updated by other threads; for example, when computing a snapshot summary of the values in a shared registry. There are three kinds of operation, each with four forms, accepting functions with Keys, Values, Entries, and (Key, Value) arguments and/or return values. Because the elements of a ConcurrentHashMap are not ordered in any particular way, and may be processed in different orders in different parallel executions, the correctness of supplied functions should not depend on any ordering, or on any other objects or values that may transiently change while computation is in progress; and except for forEach actions, should ideally be side-effect-free. Bulk operations on Map.Entry objects do not support method setValue.

forEach: Perform a given action on each element. A variant form applies a given transformation on each element before performing the action.

search: Return the first available non-null result of applying a given function on each element; skipping further search when a result is found.

reduce: Accumulate each element. The supplied reduction function cannot rely on ordering (more formally, it should be both associative and commutative). There are five variants:

Plain reductions. (There is not a form of this method for (key, value) function arguments since there is no corresponding return type.)

Mapped reductions that accumulate the results of a given function applied to each element.

Reductions to scalar doubles, longs, and ints, using a given basis value.

These bulk operations accept a parallelismThreshold argument. Methods proceed sequentially if the current map size is estimated to be less than the given threshold. Using a value of Long.MAX_VALUE suppresses all parallelism. Using a value of 1 results in maximal parallelism by partitioning into enough subtasks to fully utilize the ForkJoinPool.commonPool() that is used for all parallel computations. Normally, you would initially choose one of these extreme values, and then measure performance of using in-between values that trade off overhead versus throughput.

The concurrency properties of bulk operations follow from those of ConcurrentHashMap: Any non-null result returned from get(key) and related access methods bears a happens-before relation with the associated insertion or update. The result of any bulk operation reflects the composition of these per-element relations (but is not necessarily atomic with respect to the map as a whole unless it is somehow known to be quiescent). Conversely, because keys and values in the map are never null, null serves as a reliable atomic indicator of the current lack of any result. To maintain this property, null serves as an implicit basis for all non-scalar reduction operations. For the double, long, and int versions, the basis should be one that, when combined with any other value, returns that other value (more formally, it should be the identity element for the reduction). Most common reductions have these properties; for example, computing a sum with basis 0 or a minimum with basis MAX_VALUE.

Search and transformation functions provided as arguments should similarly return null to indicate the lack of any result (in which case it is not used). In the case of mapped reductions, this also enables transformations to serve as filters, returning null (or, in the case of primitive specializations, the identity basis) if the element should not be combined. You can create compound transformations and filterings by composing them yourself under this "null means there is nothing there now" rule before using them in search or reduce operations.

Methods accepting and/or returning Entry arguments maintain key-value associations. They may be useful for example when finding the key for the greatest value. Note that "plain" Entry arguments can be supplied using new AbstractMap.SimpleEntry(k,v).

Bulk operations may complete abruptly, throwing an exception encountered in the application of a supplied function. Bear in mind when handling such exceptions that other concurrently executing functions could also have thrown exceptions, or would have done so if the first exception had not occurred.

Speedups for parallel compared to sequential forms are common but not guaranteed. Parallel operations involving brief functions on small maps may execute more slowly than sequential forms if the underlying work to parallelize the computation is more expensive than the computation itself. Similarly, parallelization may not lead to much actual parallelism if all processors are busy performing unrelated tasks.

All arguments to all task methods must be non-null.

This class is a member of the

Java Collections Framework.

A hash table supporting full concurrency of retrievals and
high expected concurrency for updates. This class obeys the
same functional specification as Hashtable, and
includes versions of methods corresponding to each method of
Hashtable. However, even though all operations are
thread-safe, retrieval operations do not entail locking,
and there is not any support for locking the entire table
in a way that prevents all access.  This class is fully
interoperable with Hashtable in programs that rely on its
thread safety but not on its synchronization details.

Retrieval operations (including get) generally do not
block, so may overlap with update operations (including put
and remove). Retrievals reflect the results of the most
recently completed update operations holding upon their
onset. (More formally, an update operation for a given key bears a
happens-before relation with any (non-null) retrieval for
that key reporting the updated value.)  For aggregate operations
such as putAll and clear, concurrent retrievals may
reflect insertion or removal of only some entries.  Similarly,
Iterators, Spliterators and Enumerations return elements reflecting the
state of the hash table at some point at or since the creation of the
iterator/enumeration.  They do not throw ConcurrentModificationException.
However, iterators are designed to be used by only one thread at a time.
Bear in mind that the results of aggregate status methods including
size, isEmpty, and containsValue are typically
useful only when a map is not undergoing concurrent updates in other threads.
Otherwise the results of these methods reflect transient states
that may be adequate for monitoring or estimation purposes, but not
for program control.

The table is dynamically expanded when there are too many
collisions (i.e., keys that have distinct hash codes but fall into
the same slot modulo the table size), with the expected average
effect of maintaining roughly two bins per mapping (corresponding
to a 0.75 load factor threshold for resizing). There may be much
variance around this average as mappings are added and removed, but
overall, this maintains a commonly accepted time/space tradeoff for
hash tables.  However, resizing this or any other kind of hash
table may be a relatively slow operation. When possible, it is a
good idea to provide a size estimate as an optional initialCapacity constructor argument. An additional optional
loadFactor constructor argument provides a further means of
customizing initial table capacity by specifying the table density
to be used in calculating the amount of space to allocate for the
given number of elements.  Also, for compatibility with previous
versions of this class, constructors may optionally specify an
expected concurrencyLevel as an additional hint for
internal sizing.  Note that using many keys with exactly the same
hashCode() is a sure way to slow down performance of any
hash table. To ameliorate impact, when keys are Comparable,
this class may use comparison order among keys to help break ties.

A Set projection of a ConcurrentHashMap may be created
(using newKeySet() or newKeySet(int)), or viewed
(using keySet(Object) when only keys are of interest, and the
mapped values are (perhaps transiently) not used or all take the
same mapping value.

A ConcurrentHashMap can be used as scalable frequency map (a
form of histogram or multiset) by using LongAdder values and initializing via
computeIfAbsent. For example, to add a count
to a ConcurrentHashMap<String,LongAdder> freqs, you can use
freqs.computeIfAbsent(k -> new LongAdder()).increment();

This class and its views and iterators implement all of the
optional methods of the Map and Iterator
interfaces.

Like Hashtable but unlike HashMap, this class
does not allow null to be used as a key or value.

ConcurrentHashMaps support a set of sequential and parallel bulk
operations that, unlike most Stream methods, are designed
to be safely, and often sensibly, applied even with maps that are
being concurrently updated by other threads; for example, when
computing a snapshot summary of the values in a shared registry.
There are three kinds of operation, each with four forms, accepting
functions with Keys, Values, Entries, and (Key, Value) arguments
and/or return values. Because the elements of a ConcurrentHashMap
are not ordered in any particular way, and may be processed in
different orders in different parallel executions, the correctness
of supplied functions should not depend on any ordering, or on any
other objects or values that may transiently change while
computation is in progress; and except for forEach actions, should
ideally be side-effect-free. Bulk operations on Map.Entry
objects do not support method setValue.


 forEach: Perform a given action on each element.
A variant form applies a given transformation on each element
before performing the action.

 search: Return the first available non-null result of
applying a given function on each element; skipping further
search when a result is found.

 reduce: Accumulate each element.  The supplied reduction
function cannot rely on ordering (more formally, it should be
both associative and commutative).  There are five variants:



 Plain reductions. (There is not a form of this method for
(key, value) function arguments since there is no corresponding
return type.)

 Mapped reductions that accumulate the results of a given
function applied to each element.

 Reductions to scalar doubles, longs, and ints, using a
given basis value.





These bulk operations accept a parallelismThreshold
argument. Methods proceed sequentially if the current map size is
estimated to be less than the given threshold. Using a value of
Long.MAX_VALUE suppresses all parallelism.  Using a value
of 1 results in maximal parallelism by partitioning into
enough subtasks to fully utilize the ForkJoinPool.commonPool() that is used for all parallel
computations. Normally, you would initially choose one of these
extreme values, and then measure performance of using in-between
values that trade off overhead versus throughput.

The concurrency properties of bulk operations follow
from those of ConcurrentHashMap: Any non-null result returned
from get(key) and related access methods bears a
happens-before relation with the associated insertion or
update.  The result of any bulk operation reflects the
composition of these per-element relations (but is not
necessarily atomic with respect to the map as a whole unless it
is somehow known to be quiescent).  Conversely, because keys
and values in the map are never null, null serves as a reliable
atomic indicator of the current lack of any result.  To
maintain this property, null serves as an implicit basis for
all non-scalar reduction operations. For the double, long, and
int versions, the basis should be one that, when combined with
any other value, returns that other value (more formally, it
should be the identity element for the reduction). Most common
reductions have these properties; for example, computing a sum
with basis 0 or a minimum with basis MAX_VALUE.

Search and transformation functions provided as arguments
should similarly return null to indicate the lack of any result
(in which case it is not used). In the case of mapped
reductions, this also enables transformations to serve as
filters, returning null (or, in the case of primitive
specializations, the identity basis) if the element should not
be combined. You can create compound transformations and
filterings by composing them yourself under this "null means
there is nothing there now" rule before using them in search or
reduce operations.

Methods accepting and/or returning Entry arguments maintain
key-value associations. They may be useful for example when
finding the key for the greatest value. Note that "plain" Entry
arguments can be supplied using new
AbstractMap.SimpleEntry(k,v).

Bulk operations may complete abruptly, throwing an
exception encountered in the application of a supplied
function. Bear in mind when handling such exceptions that other
concurrently executing functions could also have thrown
exceptions, or would have done so if the first exception had
not occurred.

Speedups for parallel compared to sequential forms are common
but not guaranteed.  Parallel operations involving brief functions
on small maps may execute more slowly than sequential forms if the
underlying work to parallelize the computation is more expensive
than the computation itself.  Similarly, parallelization may not
lead to much actual parallelism if all processors are busy
performing unrelated tasks.

All arguments to all task methods must be non-null.

This class is a member of the

Java Collections Framework.

jdk.util.concurrent.ConcurrentHashMap

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