5 Tricky Interview Questions on Hashmap | Java 8
- How Hashmap is stored in memory — Memory Structure ?
- What is Hash Collision during hashmap.put() operation ?
- What is the significance of equals() and hashcode() in Hashmap ?
- Why the searching time-complexity of HashMap is O(1) ?
- What is Hashmap treefy threshold and why it is introduced in Java 8 ?
Watch the video, to know find out answers to these questions.
This video is a comprehensive guide to HashMap related interview questions and enhacenments that are done to HashMap internal working in Java 8.
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- Memory Structure of Hashmap:
A Hashmap is typically implemented as an array of linked lists or as an array of balanced binary search trees (from Java 8 onwards, when the threshold is crossed). The array provides the buckets to store key-value pairs. Each bucket can hold multiple entries (either in a linked list or a tree) to handle hash collisions. The index of the array is calculated using the hash code of the key.
2. Hash Collision during hashmap.put():
Hash collision occurs when two different keys have the same hash code. In a Hashmap, when a collision occurs during the put() operation, the new key-value pair is added to the existing bucket. If the bucket uses a linked list, the new entry is appended to the list. If the bucket uses a tree (after reaching the treefy threshold), the tree structure is used to handle collisions.
3. Significance of equals() and hashCode() in Hashmap:
The equals() and hashCode() methods are crucial for proper functioning of a Hashmap. The hashCode() method is used to calculate the hash code of a key, which determines the index of the bucket where the key-value pair is stored. The equals() method is used to compare keys for equality when there is a hash collision. It helps identify if two keys are the same or different, enabling proper retrieval of values from the Hashmap.
4. Searching time-complexity of HashMap is O(1):
The searching time-complexity of a Hashmap is O(1) on average, which means it has constant-time performance for most operations. This is possible because Hashmap uses a hashing technique to calculate the index where a key-value pair is stored. With a properly distributed hash function, the time complexity to retrieve a value is independent of the number of elements stored in the Hashmap.
5. Hashmap treefy threshold and its introduction in Java 8:
The “treefy threshold” is the number of entries a bucket can hold before it is converted from a linked list to a balanced binary search tree. In Java 8, an optimization was introduced to convert a bucket to a tree structure when the number of entries in that bucket exceeds a certain threshold (usually 8). This was done to improve the worst-case performance of the Hashmap, as a long linked list can lead to degraded search performance. By using a tree, the time complexity for search operations in a bucket is reduced from O(n) to O(log n), resulting in more predictable and improved performance.
