- public class IdentityHashMap
- extends AbstractMap
- implements Map, Serializable, Cloneable
This class implements the Map interface with a hash table, using reference-equality(has to be the same instance of the object) in place of object-equality(two individual objects which are equal in every respect) when comparing keys (and values). In other words, in an IdentityHashMap, two keys k1 and k2 are considered equal if and only if (k1==k2). (In normal Map implementations (like HashMap) two keys k1 and k2 are considered equal if and only if (k1==null ? k2==null : k1.equals(k2)))
This class is not a general-purpose Map implementation! While this class implements the Map interface, it intentionally violates Map's general contract, which mandates the use of the equals method when comparing objects. This class is designed for use only in the rare cases wherein reference-equality semantics are required (generally when checking for single-copies – Consider an algorithm to find a loop in a linked list).
A typical use of this class is topology-preserving object graph transformations, such as serialization or deep-copying. To perform such a transformation, a program must maintain a “node table” that keeps track of all the object references that have already been processed. The node table must not equate distinct objects even if they happen to be equal. Another typical use of this class is to maintain proxy objects. For example, a debugging facility might wish to maintain a proxy object for each object in the program being debugged.
This class provides all of the optional map operations, and permits null values and the null key. This class makes no guarantees as to the order of the map; in particular, it does not guarantee that the order will remain constant over time.
This class provides constant-time performance for the basic operations (get and put), assuming the system identity hash function (System.identityHashCode(Object)) disperses elements properly among the buckets.
This class has one tuning parameter (which affects performance but not semantics): expected maximum size. This parameter is the maximum number of key-value mappings that the map is expected to hold. Internally, this parameter is used to determine the number of buckets initially comprising the hash table. The precise relationship between the expected maximum size and the number of buckets is unspecified.
If the size of the map (the number of key-value mappings) sufficiently exceeds the expected maximum size, the number of buckets is increased Increasing the number of buckets (“rehashing”) may be fairly expensive, so it pays to create identity hash maps with a sufficiently large expected maximum size. On the other hand, iteration over collection views requires time proportional to the the number of buckets in the hash table, so it pays not to set the expected maximum size too high if you are especially concerned with iteration performance or memory usage.
Note that this implementation is not synchronized. If multiple threads access this 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:
Map m = Collections.synchronizedMap(new HashMap(...));
The iterators returned by all of this class’s “collection view methods” are fail-fast: if the map is structurally modified at any time after the iterator is created, in any way except through the iterator’s own remove or add methods, the iterator will throw a ConcurrentModificationException. Thus, in the face of concurrent modification, the iterator fails quickly and cleanly, rather than risking arbitrary, non-deterministic behavior at an undetermined time in the future.
Note that the fail-fast behavior of an iterator cannot be guaranteed as it is, generally speaking, impossible to make any hard guarantees in the presence of unsynchronized concurrent modification. Fail-fast iterators throw ConcurrentModificationException on a best-effort basis. Therefore, it would be wrong to write a program that depended on this exception for its correctness: fail-fast iterators should be used only to detect bugs.
Implementation note: This is a simple linear-probe hash table, as described for example in texts by Sedgewick and Knuth. The array alternates holding keys and values. (This has better locality for large tables than does using separate arrays.) For many JRE implementations and operation mixes, this class will yield better performance than HashMap (which uses chaining rather than linear-probing).
Chaining
Hash collision resolved by chaining.
In the simplest chained hash table technique, each slot in the array references a linked list of inserted records that collide to the same slot. Insertion requires finding the correct slot, and appending to either end of the list in that slot; deletion requires searching the list and removal.
Chained hash tables have advantages over open addressed hash tables in that the removal operation is simple and resizing the table can be postponed for a much longer time because performance degrades more gracefully even when every slot is used. Indeed, many chaining hash tables may not require resizing at all since performance degradation is linear as the table fills. For example, a chaining hash table containing twice its recommended capacity of data would only be about twice as slow on average as the same table at its recommended capacity.
Chained hash tables inherit the disadvantages of linked lists. When storing small records, the overhead of the linked list can be significant. An additional disadvantage is that traversing a linked list has poor cache performance.
Alternative data structures can be used for chains instead of linked lists. By using a self-balancing tree, for example, the theoretical worst-case time of a hash table can be brought down to O(log n) rather than O(n). However, since each list is intended to be short, this approach is usually inefficient unless the hash table is designed to run at full capacity or there are unusually high collision rates, as might occur in input designed to cause collisions. Dynamic arrays can also be used to decrease space overhead and improve cache performance when records are small.
Some chaining implementations use an optimization where the first record of each chain is stored in the table. Although this can increase performance, it is generally not recommended: chaining tables with reasonable load factors contain a large proportion of empty slots, and the larger slot size causes them to waste large amounts of space.
Open addressing
Hash collision resolved by linear probing (interval=1).
Open addressing hash tables store the records directly within the array. This approach is also called closed hashing. A hash collision is resolved by probing, or searching through alternate locations in the array (the probe sequence) until either the target record is found, or an unused array slot is found, which indicates that there is no such key in the table. Well known probe sequences include:
- linear probing
- in which the interval between probes is fixed–often at 1.
- quadratic probing
- in which the interval between probes increases linearly (hence, the indices are described by a quadratic function).
- double hashing
- in which the interval between probes is fixed for each record but is computed by another hash function.
The main tradeoffs between these methods are that linear probing has the best cache performance but is most sensitive to clustering, while double hashing has poor cache performance but exhibits virtually no clustering; quadratic probing falls in-between in both areas. Double hashing can also require more computation than other forms of probing. Some open addressing methods, such as last-come-first-served hashing and cuckoo hashing move existing keys around in the array to make room for the new key. This gives better maximum search times than the methods based on probing.
A critical influence on performance of an open addressing hash table is the load factor; that is, the proportion of the slots in the array that are used. As the load factor increases towards 100%, the number of probes that may be required to find or insert a given key rises dramatically. Once the table becomes full, probing algorithms may even fail to terminate. Even with good hash functions, load factors are normally limited to 80%. A poor hash function can exhibit poor performance even at very low load factors by generating significant clustering. What causes hash functions to cluster is not well understood, and it is easy to unintentionally write a hash function which causes severe clustering.
