Java HashMap Implementation and Performance

In this article, we are going to explore more about the implementation and performance of HashMap. Before we look into the performance of HashMap, first we will understand its implementation. HashMap is one of most frequently asked interview question for Java developers.

Internal Storage:

In Java HashMap<K,V> class implements Map<K,V>. The main methods of this interface are:

  • V put(K key, V value)
  • V get(Object key)
  • V remove(Object key)
  • Boolean containsKey(Object key)

HashMaps use an inner class  Entry<K, V> to store the data in nodes.  HashMap stores data into multiple singly linked lists of entries  called buckets. This entry is a simple key-value pair with two extra data:

  • a reference to another Entry so that a HashMap can store entries like singly linked lists
  • a hash value that represents the hash value of the key. This hash value is stored to avoid the computation of the hash every time the HashMap needs it.

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(default load factor .75) 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.

The Map usually acts as a bucket of bins, when the bins gets too large, they are transformed into bins of TreeNodes similar to java.util.TreeMap. Tree bins are ordered primarily by hashcode, if two elements has same hashcode then compareTo() method of Comparable<C> interface is used to ordering. Java 8 improvement

Java 8 Improvement

HashMap implementation in Java provides constant time performance O(1) for get() and put() methods in the ideal case when the Hash function distributes the objects evenly among the buckets. In Java 8, you still have an array but it now stores Nodes that contains the exact same information as Entries and therefore are also linked lists:

Below is the Node implementation in Java 8:


Well, Nodes can be extended to TreeNodes. A TreeNode is a balanced tree structure that maintains O(long(n)) complexity for add, delete or get. TreeNode also ensures that their length is always log(n) despite new adds or removes of nodes.

Thus, performance of HashMap degrades gracefully if hashCode() method is not used properly, where it returns values that are poorly distributed or those in which many keys share a hashCode.

To address this issue in Java 8 Map transforms it into bins of TreeNode, each structured similarly to those in java.util.TreeMap once the threshold(TREEIFY_THRESHOLD) is reached. Which means HashMap starts with storing Entry objects in bins of linked list but after the number of items in a Map becomes larger than a certain threshold, it is transformed from bins of linked list to TreeNode, this improves the worst case performance from O(n) to O(log n). And when they become too small (due to removal or resizing) they are converted back to plain bins.


Java 8 features worth reading

HashMap Bucket Resizing Overhead

If you need to store huge amount of data (millions), you should create your HashMap with an initial capacity close to your expected volume.

For default initial capacity whenever bucket in HashMap reaches its load factor .75 i.e. (16 * .75) 12th element, it recreates a new inner array with double its capacity i.e. 32 in this case. And when it resizes itself all the hash codes are again generated which is called rehashing. Its a very costly operation as every time it reaches its load factor it resizes itself.

If we are using HashMap for storing small values then we don’t have to worry. But if we are going to store very large amount of data let say 2 million records, then imagine how many times it has to rehash the whole structure. At low volume the full recreation of the inner array is fast but at high volume it can takes seconds to minutes. By initially setting your expected size, you can avoid these costly operations. Also should make sure to use full capacity else you will waste lot of memory as unused blocks gets allocated in memory.


For simple use cases, you don’t need to understand internal working of HashMap as you wont see difference between O(1) or O(n) or O(log (n)). But its always good to know the implementation details being Java developer. But at high volume it becomes very important to know how it works and to understand the importance of the hashcode() method.

I hope this article helped you to have a deep understanding of the HashMap implementation.

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