Merge two List value maps
in my case is even more complicated since the map is created inside an iterator, so I need to merge the last map created with the new one in every iteration. Thanks but still dont see how can works in my example. Maybe can you provide an example? I really dont seen it. Thanks.
3 Answers 3
The general idea is the same as in this post. You create a new map from the first map, iterate over the second map and merge each key with the first map thanks to merge(key, value, remappingFunction) . In case of conflict, the remapping function is applied: in this case, it takes the two lists and merges them; if there is no conflict, the entry with the given key and value is put.
Map> mx = new HashMap<>(map1); map2.forEach((k, v) -> mx.merge(k, v, (l1, l2) -> < Listl = new ArrayList<>(l1); l.addAll(l2); return l; >)); An alternative merge function would be (l1, l2) -> Stream.concat(l1.stream(),l2.stream()).collect(toList()) . That’s the simplest Java has so far (without 3rd party libraries).
more than complexity it´s matter to me the speed. I guess is true that is less verbose but more expensive
You could try this, which gradually flattens the structure until you have a stream of tuples of the maps keys versus the lists values:
Map> result = Stream.of(map1,map2) // Stream Another option would avoid the internal streaming of the lists by using Collectors.reducing() as a second parameter of groupingBy, I guess. However, I would consider the accepted answer first
Class HashMap
Type Parameters: K — the type of keys maintained by this map V — the type of mapped values All Implemented Interfaces: Serializable , Cloneable , Map Direct Known Subclasses: LinkedHashMap , PrinterStateReasons
Hash table based implementation of the Map interface. This implementation provides all of the optional map operations, and permits null values and the null key. (The HashMap class is roughly equivalent to Hashtable , except that it is unsynchronized and permits nulls.) 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 implementation provides constant-time performance for the basic operations ( get and put ), assuming the hash function disperses the elements properly among the buckets. Iteration over collection views requires time proportional to the «capacity» of the HashMap instance (the number of buckets) plus its size (the number of key-value mappings). Thus, it’s very important not to set the initial capacity too high (or the load factor too low) if iteration performance is important.
An instance of HashMap has two parameters that affect its performance: initial capacity and load factor. The capacity is the number of buckets in the hash table, and the initial capacity is simply the capacity at the time the hash table is created. The load factor is a measure of how full the hash table is allowed to get before its capacity is automatically increased. When the number of entries in the hash table exceeds the product of the load factor and the current capacity, the hash table is rehashed (that is, internal data structures are rebuilt) so that the hash table has approximately twice the number of buckets.
As a general rule, the default load factor (.75) offers a good tradeoff between time and space costs. Higher values decrease the space overhead but increase the lookup cost (reflected in most of the operations of the HashMap class, including get and put ). The expected number of entries in the map and its load factor should be taken into account when setting its initial capacity, so as to minimize the number of rehash operations. If the initial capacity is greater than the maximum number of entries divided by the load factor, no rehash operations will ever occur.
If many mappings are to be stored in a HashMap instance, creating it with a sufficiently large capacity will allow the mappings to be stored more efficiently than letting it perform automatic rehashing as needed to grow the table. Note that using many keys with 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.
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:
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 method, 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: the fail-fast behavior of iterators should be used only to detect bugs.
This class is a member of the Java Collections Framework.
Java merge map values
Hash table based implementation of the Map interface. This implementation provides all of the optional map operations, and permits null values and the null key. (The HashMap class is roughly equivalent to Hashtable, except that it is unsynchronized and permits nulls.) 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 implementation provides constant-time performance for the basic operations (get and put), assuming the hash function disperses the elements properly among the buckets. Iteration over collection views requires time proportional to the «capacity» of the HashMap instance (the number of buckets) plus its size (the number of key-value mappings). Thus, it’s very important not to set the initial capacity too high (or the load factor too low) if iteration performance is important. An instance of HashMap has two parameters that affect its performance: initial capacity and load factor. The capacity is the number of buckets in the hash table, and the initial capacity is simply the capacity at the time the hash table is created. The load factor is a measure of how full the hash table is allowed to get before its capacity is automatically increased. When the number of entries in the hash table exceeds the product of the load factor and the current capacity, the hash table is rehashed (that is, internal data structures are rebuilt) so that the hash table has approximately twice the number of buckets. As a general rule, the default load factor (.75) offers a good tradeoff between time and space costs. Higher values decrease the space overhead but increase the lookup cost (reflected in most of the operations of the HashMap class, including get and put). The expected number of entries in the map and its load factor should be taken into account when setting its initial capacity, so as to minimize the number of rehash operations. If the initial capacity is greater than the maximum number of entries divided by the load factor, no rehash operations will ever occur. If many mappings are to be stored in a HashMap instance, creating it with a sufficiently large capacity will allow the mappings to be stored more efficiently than letting it perform automatic rehashing as needed to grow the table. Note that using many keys with 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. 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:
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 method, 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: the fail-fast behavior of iterators should be used only to detect bugs. This class is a member of the Java Collections Framework.
Nested Class Summary
Nested classes/interfaces inherited from class java.util.AbstractMap
Nested classes/interfaces inherited from interface java.util.Map
Constructor Summary
Constructs an empty HashMap with the default initial capacity (16) and the default load factor (0.75).
Merging two Maps
I have two maps whose keys are String s and whose values are Set
If you have the possiblity to use Guavas Multimap, you can simply avoid the problem, and merging is as simple as putAll(Multimap other).
11 Answers 11
You can do this with a stream fairly easily:
Map> merged = Stream.of(first, second) .map(Map::entrySet) .flatMap(Set::stream) .collect(Collectors.toMap(Entry::getKey, Entry::getValue, (a, b) -> < HashSetboth = new HashSet<>(a); both.addAll(b); return both; >)); This splits the maps into their Entry s and then joins them with a Collector which resolves duplicates by adding both values to a new HashSet .
This also works for any number of maps.
Some variations which produce the same result:
Stream.of(first, second).flatMap(m -> m.entrySet().stream()) .collect(. ); Stream.concat(first.entrySet().stream(), second.entrySet().stream()) .collect(. ); //from comment by Aleksandr Dubinsky The third parameter for Collectors.toMap is not necessary if there are no duplicate keys.
There is another Collectors.toMap with a fourth parameter that lets you decide the type of the Map collected into.