Java Hash-Map: Part 1

Data Structure

• A HashMap uses an array to store entries, with each position in the array called a bucket.
• Each bucket contains a linked list (or binary tree in high collision scenarios) of nodes, each storing a key-value pair, the key's hash code, and a reference to the next node in the list.

Hash Function

• The hash function computes an index in the array from the key's hash code.
• The goal is to distribute entries uniformly across the array to minimize collisions.
• The hash function applies additional operations to the key's hashCode() to ensure a better distribution of hash values.

Operations

• Put operation:
  • Compute hash code of the key.
  • Determine the bucket index by performing a modulo operation with the array's length.
  • If the bucket is empty, create a new node with the key-value pair and place it in the bucket.
  • If the bucket is not empty, traverse the linked list (or binary tree) to find the node with the same key, update its value, or append the new node to the end of the list.
• Get operation:
  • Compute hash code of the key.
  • Determine the bucket index.
  • Traverse the linked list (or binary tree) to find the node with the matching key and return its value.
• Remove operation:
  • Compute hash code of the key.
  • Determine the bucket index.
  • Traverse the linked list (or binary tree) to find the node with the matching key, remove it, and adjust the links accordingly.

Collision Handling

• Chaining: each bucket contains a linked list of entries that have the same hash code.
• Tree-Based Resolution: in Java 8 and later, if the number of entries in a bucket exceeds a certain threshold, the linked list is converted to a balanced binary tree (TreeBin) to improve performance.

Resizing

• Load factor determines when to resize the HashMap.
• The default load factor is 0.75, meaning the HashMap will resize when it is 75% full.
• When resizing is triggered, the array's capacity is doubled, and all existing entries are rehashed and redistributed to the new array.

Key Concepts

• A good hash function is crucial for performance, as it reduces the number of collisions.
• The load factor controls the trade-off between space and time.
• Collision handling mechanisms, such as chaining and tree-based resolutions, are used to manage collisions effectively.

Advantages

• Fast access: HashMap provides average O(1) time complexity for get and put operations due to its efficient hashing mechanism and array-based structure.
• Flexible: it dynamically resizes to accommodate more entries while maintaining efficient access times.

Disadvantages

• Memory overhead: the underlying array and linked list/tree structures consume additional memory.
• Potential for poor performance: if the hash function is not well-designed or if the load factor is set too high, performance can degrade significantly due to increased collisions and longer chains/trees.

Cache Implementations

• LRU Cache (Least Recently Used) evicts the least recently accessed item when the cache reaches its maximum capacity, assuming that items not used recently are less likely to be used in the near future.
• LRU Cache implementation uses a HashMap for O(1) access time and a doubly linked list to maintain the order of access.
• Example: Web browsers use LRU caches to store recently accessed web pages.

LFU Cache (Least Frequently Used)

• LFU Cache evicts the least frequently accessed item, assuming that items accessed less frequently are less likely to be accessed in the future.
• LFU Cache implementation uses a HashMap to store the frequency of access for each item and another data structure (e.g., min-heap or frequency list) to track items with the lowest access frequency.
• Example: Database query caching where queries executed less frequently are evicted to make space for more frequently executed queries.

Concurrent Access with HashMap

• ConcurrentHashMap is a thread-safe implementation of HashMap designed for concurrent access.
• ConcurrentHashMap segments the underlying data structure to allow multiple threads to read and write concurrently without blocking each other excessively.
• Example: A real-time analytics system where multiple threads need to update counters or metrics simultaneously without blocking each other.

Indexing and In-Memory Databases

Inverted Index

• An inverted index maps terms to the documents in which they appear, enabling efficient full-text searches.
• Inverted index implementation uses a HashMap to quickly retrieve the list of documents associated with a given term.
• Example: Search engines like Google use inverted indexes to quickly retrieve documents containing search terms.

In-Memory Key-Value Stores

• In-memory key-value stores, such as Redis, use hash-based data structures to provide fast access to data stored in memory.
• Implementation uses HashMaps to store key-value pairs, providing O(1) average-time complexity for get and set operations.
• Example: Caching user session data in web applications to provide fast access and reduce database load.

Symbol Tables and Compilers

Symbol Table

• A symbol table is used by a compiler to store information about variables, function names, and other identifiers.
• HashMaps provide fast lookup times to check for the existence of symbols and retrieve associated information.
• Example: Modern compilers like GCC and LLVM use symbol tables to manage variables and functions during compilation.

Bloom Filters

Approximate Membership Queries

• A Bloom filter is a probabilistic data structure used to test whether an element is a member of a set.
• Implementation uses multiple hash functions to map elements to a bit array.
• Example: Web crawlers use Bloom filters to track URLs that have already been visited, reducing the need for repeated lookups and improving efficiency.

Sharded Data Structures

Sharding

• Sharding is a technique used to distribute data across multiple machines to improve scalability and performance.
• Consistent hashing is often used to determine the shard for each data item.
• Implementation uses a HashMap to map keys to shard identifiers, ensuring minimal data movement when adding or removing a shard.
• Example: Distributed databases like Amazon DynamoDB use sharding to distribute data across multiple nodes, ensuring high availability and scalability.