Map with iterator java

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.

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Iterate Over a Map in Java

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We’re looking for a new Java technical editor to help review new articles for the site.

1. Overview

In this quick tutorial, we’ll look at the different ways of iterating through the entries of a Map in Java.

Simply put, we can extract the contents of a Map using entrySet(), keySet(), or values(). Since these are all sets, similar iteration principles apply to all of them.

Let’s have a closer look at a few of these.

Further reading:

Guide to the Java 8 forEach

How to Iterate Over a Stream With Indices

Finding the Highest Value in a Java Map

2. Short Introduction to Map‘s entrySet(), keySet(), and values() Methods

Before we iterate through a map using the three methods, let’s understand what these methods do:

• entrySet() – returns a collection-view of the map, whose elements are from the Map.Entry class. The entry.getKey() method returns the key, and entry.getValue() returns the corresponding value
• keySet() – returns all keys contained in this map as a Set
• values() – returns all values contained in this map as a Set

3. Using a for Loop

3.1. Using entrySet()

First, let’s see how to iterate through a Map using the EntrySet:

public void iterateUsingEntrySet(Map map) < for (Map.Entryentry : map.entrySet()) < System.out.println(entry.getKey() + ":" + entry.getValue()); >>

Here, we’re extracting the Set of entries from our Map and then iterating through them using the classical for-each approach.

3.2. Using keySet()

Alternatively, we can first get all keys in our Map using the keySet method and then iterate through the map by each key:

public void iterateUsingKeySetAndForeach(Map map) < for (String key : map.keySet()) < System.out.println(key + ":" + map.get(key)); >>

3.3. Iterating Over Values Using values()

Sometimes, we’re only interested in the values in a map, no matter which keys are associated with them. In this case, values() is our best choice:

public void iterateValues(Map map) < for (Integer value : map.values()) < System.out.println(value); >> 

4. Iterator

Another approach to perform the iteration is using an Iterator. Next, let’s see how the methods work with an Iterator object.

4.1. Iterator and entrySet()

First, let’s iterate over the map using an Iterator and entrySet():

public void iterateUsingIteratorAndEntry(Map map) < Iterator iterator = map.entrySet().iterator(); while (iterator.hasNext()) < Map.Entryentry = iterator.next(); System.out.println(entry.getKey() + ":" + entry.getValue()); > >

Notice how we can get the Iterator instance using the iterator() API of the Set returned by entrySet(). Then, as usual, we loop through the Iterator with iterator.next().

4.2. Iterator and keySet()

Similarly, we can iterate through the Map using an Iterator and keySet():

public void iterateUsingIteratorAndKeySet(Map map) < Iteratoriterator = map.keySet().iterator(); while (iterator.hasNext()) < String key = iterator.next(); System.out.println(key + ":" + map.get(key)); >> 

4.3. Iterator and values()

We can also walk through the map’s values using an Iterator and the values() method:

public void iterateUsingIteratorAndValues(Map map) < Iteratoriterator = map.values().iterator(); while (iterator.hasNext()) < Integer value = iterator.next(); System.out.println("value :" + value); >>

5. Using Lambdas and Stream API

Since version 8, Java has introduced the Stream API and lambdas. Next, let’s see how to iterate a map using these techniques.

5.1. Using forEach() and Lambda

Like most other things in Java 8, this turns out to be much simpler than the alternatives. We’ll just make use of the forEach() method:

public void iterateUsingLambda(Map map) < map.forEach((k, v) ->System.out.println((k + ":" + v))); > 

In this case, we don’t need to convert a map to a set of entries. To learn more about lambda expressions, we can start here.

We can, of course, start from the keys to iterate over the map:

public void iterateByKeysUsingLambda(Map map) < map.keySet().foreach(k ->System.out.println((k + ":" + map.get(k)))); > 

Similarly, we can use the same technique with the values() method:

public void iterateValuesUsingLambda(Map map) < map.values().forEach(v ->System.out.println(("value: " + v))); > 

5.2. Using Stream API

Stream API is one significant feature of Java 8. We can use this feature to loop through a Map as well.

Stream API should be used when we’re planning on doing some additional Stream processing; otherwise, it’s just a simple forEach() as described previously.

Let’s take entrySet() as the example to see how Stream API works:

public void iterateUsingStreamAPI(Map map) < map.entrySet().stream() // . some other Stream processings .forEach(e ->System.out.println(e.getKey() + ":" + e.getValue())); > 

The usage of Stream API with the keySet() and values() methods would be pretty similar to the example above.

6. Conclusion

In this article, we focused on a critical but straightforward operation: iterating through the entries of a Map.

We explored a couple of methods that can only be used with Java 8+, namely Lambda expressions and the Stream API.

As always, the code examples in this article can be found over on GitHub.

Slow MySQL query performance is all too common. Of course it is. A good way to go is, naturally, a dedicated profiler that actually understands the ins and outs of MySQL.

The Jet Profiler was built for MySQL only, so it can do things like real-time query performance, focus on most used tables or most frequent queries, quickly identify performance issues and basically help you optimize your queries.

Critically, it has very minimal impact on your server’s performance, with most of the profiling work done separately — so it needs no server changes, agents or separate services.

Basically, you install the desktop application, connect to your MySQL server, hit the record button, and you’ll have results within minutes:

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