Hey there! Hashmaps are one of the most useful data structures you‘ll encounter in your Java journey. In this comprehensive guide, I‘m going to walk you through everything you need to know about HashMaps – how they function, why they offer terrific performance, and how exactly you can leverage their capabilities in your own code.
Whether you‘re a beginner looking to learn the HashMap basics or an experienced programmer hoping to gain a deeper understanding, this article has you covered! Let‘s get started.
Why Are HashMaps Important?
Hashmaps provide a flexible data structure to store key-value pairs and offer constant-time lookups. This efficiency makes them indispensable when:
- Building caches
- Storing user profiles in web apps
- Adding metadata or related data in programs
- Implementing dictionaries/maps needed in algorithms
The key capability enabling these use cases is fast retrieval. And HashMaps achieve this through a technique called hashing.
Hashing refers to converting an object into a representational fixed-length integer value. Based on this hash, the object can be mapped to an index location in an array. With hashes deriving array positions, values can then be stored and looked up in O(1) time!
This beats out linear searches in simple arrays or lists which take O(N) time. And those slow lookups quickly compound problems as data grows. No such issues with HashMaps!
How Do HashMaps Work In Java?
Internally, HashMaps in Java use:
- Hashing – Keys are converted to integer hashes used as indices
- Buckets – Each index points to a bucket location which holds values
- Linked Lists – Collisions are handled through chaining with linked lists
This enables low constant time costs for lookup, insert and delete operations in the average case.
But how does this all come together? Let‘s go through it step-by-step.
Hashing Functions
A hash function takes in a key and outputs a whole number. This transforms arbitrary keys into numeric indices mapped to fixed-size bucket locations.
index = hash(key);Common hash functions used include:
- Modulus – Remainder of key divided by array size
- Folding – Divide key bits into chunks and combine
- Mid-Square – Square the key and extract middle bits
Hash codes should distribute keys uniformly to avoid collisions. Prime modular tends to offer the best real-world distribution.
HashMap Arrays
Once keys are hashed, the next piece is the backing array. HashMaps internally maintain an array of buckets where values are actually stored.
Based on the hash, a key is mapped to an index in this array. Then the value can be written to or read from this bucket location.
Hashing maps keys to indices in a bucket array – Source
But what happens if multiple keys hash to the same bucket? Enter chaining…
Chaining with Linked Lists
When two different keys generate the same hash code, a collision occurs. The hash points them to the same bucket location.
HashMaps handle these collisions by chaining – using linked lists called bins at each array index.
So values with colliding keys are stored as links in a list rather than directly at array slots. This helps maintain efficient constant lookups.
Collisions resolved through linked list chaining – Source
Analysis
Now how does this translate to computational efficiency?
Time Complexity
- Insert – O(1)
- Search – O(1)
- Delete – O(1)
With chaining, collisions lead to linked list or tree operations. But in general, we still get constant time performance.
Space Complexity
- O(N) overall
Where N is the total number of key-value entries – the capacity required is linear.
Let‘s now see HashMap usage!
Using HashMaps in Java
First, import HashMap:
import java.util.HashMap;Then create a HashMap specifying key-value data types:
HashMap<String, Integer> map = new HashMap<>();Next, try out some essential methods:
Insert
map.put("John", 26); Retrieve
int johnsAge = map.get("John");Check Existence
if(map.containsKey("John")) {
// ...
}Delete
map.remove("John");Iterate
for(String key : map.keySet()) {
int value = map.get(key);
// ...
}Hope this gives you a sense of HashMap possibilities! Now let‘s dig deeper.
Customizing Hashing Behavior
When using custom objects as keys, overriding hashCode() and equals() is useful. This lets you control object hashing and equality checks.
hashCode()
By overriding hashCode(), you can customize how your class instances are hashed and mapped to buckets.
@Override
public int hashCode() {
int hash = 7;
hash = 31 * hash + age;
hash = 31 * hash + name.toLowerCase().hashCode();
return hash;
}Here based on age and a name field, we generate a 32-bit int hash code. This then scatters Person objects reasonably uniformly across buckets.
equals()
The equals() method checks for logical equality between two objects. It‘s used internally in HashMap methods.
So when using custom classes as keys, equals() should compare based on their logical data value, not reference identity.
@Override
public boolean equals(Object o) {
if(o == this)
return true;
if(!(o instanceof Person))
return false;
Person p = (Person)o;
return name.equals(p.getName())
&& age == p.getAge();
}Now when comparing people keys, equal person instances return true based on equal name and age.
Overriding these two methods allows you to enable custom class instances as HashMap keys with tuned behavior.
Handling Collisions
What happens when two keys generate the same integer hash code?
You‘ll run into a collision when different keys hash to the same bucket index. As we covered earlier, HashMaps handle these through chaining with linked lists.
But from Java 8 onwards, an alternative tree-based collision resolution was also introduced.
If a particular bucket sees too many collisions, meaning a long chain of links, a self-balancing tree is used instead to avoid efficiency loss. This keeps operations logarithmic even in extreme collision scenarios.
Tree-based collision handling – Source
So in summary, HashMaps handle collisions through:
- Chaining with linked lists
- Switching to trees if chain grows large
Avoiding collisions helps optimize performance. But Java‘s models account for clashes gracefully!
HashMap vs Other Structures
How do HashMaps compare to other data structures? Let‘s evaluate them against arrays and lists.
| Operation | Array | ArrayList | HashMap |
|---|---|---|---|
| Read | O(1) | O(1) | O(1) |
| Insert | O(N) | O(1) | O(1) |
| Delete | O(N) | O(N) | O(1) |
| Search | O(N) | O(N) | O(1) |
- Arrays allow fast access by index but slow search and deletion
- ArrayLists add fast insertion compared to arrays
- But HashMaps provide constant access, search, insert and deletes!
This positions them as an extremely versatile structure – great for caching, metadata storage, search applications and more.
Storing Null Values
Java‘s HashMap handles nulls in a convenient manner:
- Can contain exactly 1 null key
- Can contain multiple null values
- null key stored in the 0th bucket
Being able to represent missing or unknown data as nulls further extends use cases for HashMaps.
Advanced Usage
Let‘s discuss some best practices and advanced functionality.
Initial Capacity
By default, HashMaps start with 16 buckets. But you can specify an initial capacity – ideal when you know approx data volumes.
HashMap map = new HashMap(64); This eliminates unnecessary resizing by allocating suitable capacity upfront.
Load Factor
As more entries get added, at some threshold of fullness, the HashMap needs to resize to avoid collisions. This threshold is set by the load factor (default 0.75).
Tuning load factor higher/lower impacts resize frequency and collision likelihoods.
HashMap map = new HashMap(16, 0.85);Here we set larger factor anticipating frequent inserts.
Ordering Guarantees
LinkedHashMaps preserve insert order iteration.
TreeMaps maintain keys in sorted ascending order based on logic provided or natural ordering.
Synchronization
HashMaps themselves are not thread-safe. But Collections provide synchronized wrappers – Collections.synchronizedMap() which handle concurrent access.
When Should You Use HashMaps?
With their versatility and performance, HashMaps have become integral in most Java programs today. You would want to leverage HashMaps for:
✅ Building application caches
✅ Storing user sessions/preferences
✅ Relationally grouping entities like products-orders
✅ Passing data through web services
✅ Serving as lookup directories
✅ Backing key-based sorting/ordering
✅ Algorithm map representations
And more! Hashes truly unlock efficient access in diverse domains.
Hope you now have an in-depth understanding of this immensely capable data structure! Let‘s round up the key takeaways:
Summary of HashMaps in Java
- Enable fast O(1) object lookups through key-value mapping
- Use hashing functions to map keys to array indices
- Handle collisions through chaining and balanced trees
- Override hashCode() and equals() for custom classes
- Parameterize capacity, load factors and ordering behavior
- Synchronize for multi-threaded access if needed
And with that, we‘ve covered everything from theory to application of hashmaps in Java! I aimed to provide an expert-level guided tour you can continually reference. Feel free to hit me up in the comments for any other hashmap-related questions.
Happy coding!
