Is a Java hashmap search really O(1)?

I've seen some interesting claims on SO re Java hashmaps and their O(1) lookup time. Can someone explain why this is so? Unless these hashmaps are vastly different from any of the hashing algorithms I was bought up on, there must always exist a dataset that contains collisions.

In which case, the lookup would be O(n) rather than O(1).

Can someone explain whether they are O(1) and, if so, how they achieve this?

A particular feature of a HashMap is that unlike, say, balanced trees, its behavior is probabilistic. In these cases its usually most helpful to talk about complexity in terms of the probability of a worst-case event occurring would be. For a hash map, that of course is the case of a collision with respect to how full the map happens to be. A collision is pretty easy to estimate.

>p_collision = n / capacity

So a hash map with even a modest number of elements is pretty likely to experience at least one collision. Big O notation allows us to do something more compelling. Observe that for any arbitrary, fixed constant k.

>O(n) = O(k * n)

We can use this feature to improve the performance of the hash map. We could instead think about the probability of at most 2 collisions.

> p_{collision x 2} = (n / capacity)²

This is much lower. Since the cost of handling one extra collision is irrelevant to Big O performance, we've found a way to improve performance without actually changing the algorithm! We can generalzie this to

> p_{collision x k} = (n / capacity)^k

And now we can disregard some arbitrary number of collisions and end up with vanishingly tiny likelihood of more collisions than we are accounting for. You could get the probability to an arbitrarily tiny level by choosing the correct k, all without altering the actual implementation of the algorithm.

We talk about this by saying that the hash-map has O(1) access with high probability

You seem to mix up worst-case behaviour with average-case (expected) runtime. The former is indeed O(n) for hash tables in general (i.e. not using a perfect hashing) but this is rarely relevant in practice.

Any dependable hash table implementation, coupled with a half decent hash, has a retrieval performance of O(1) with a very small factor (2, in fact) in the expected case, within a very narrow margin of variance.

In Java, how HashMap works?

• Using hashCode to locate the corresponding bucket [inside buckets container model].
• Each bucket is a list (or tree starting from Java 8) of items residing in that bucket.
• The items are scanned one by one, using equals for comparison.
• When adding more items, the HashMap is resized once a certain load percentage is reached.

So, sometimes it will have to compare against a few items, but generally, it's much closer to O(1) than O(n).
For practical purposes, that's all you should need to know.

Remember that o(1) does not mean that each lookup only examines a single item - it means that the average number of items checked remains constant w.r.t. the number of items in the container. So if it takes on average 4 comparisons to find an item in a container with 100 items, it should also take an average of 4 comparisons to find an item in a container with 10000 items, and for any other number of items (there's always a bit of variance, especially around the points at which the hash table rehashes, and when there's a very small number of items).

So collisions don't prevent the container from having o(1) operations, as long as the average number of keys per bucket remains within a fixed bound.

I know this is an old question, but there's actually a new answer to it.

You're right that a hash map isn't really O(1), strictly speaking, because as the number of elements gets arbitrarily large, eventually you will not be able to search in constant time (and O-notation is defined in terms of numbers that can get arbitrarily large).

But it doesn't follow that the real time complexity is O(n)--because there's no rule that says that the buckets have to be implemented as a linear list.

In fact, Java 8 implements the buckets as TreeMaps once they exceed a threshold, which makes the actual time O(log n).

O(1+n/k) where k is the number of buckets.

If implementation sets k = n/alpha then it is O(1+alpha) = O(1) since alpha is a constant.

If the number of buckets (call it b) is held constant (the usual case), then lookup is actually O(n).
As n gets large, the number of elements in each bucket averages n/b. If collision resolution is done in one of the usual ways (linked list for example), then lookup is O(n/b) = O(n).

The O notation is about what happens when n gets larger and larger. It can be misleading when applied to certain algorithms, and hash tables are a case in point. We choose the number of buckets based on how many elements we're expecting to deal with. When n is about the same size as b, then lookup is roughly constant-time, but we can't call it O(1) because O is defined in terms of a limit as n → ∞.

We've established that the standard description of hash table lookups being O(1) refers to the average-case expected time, not the strict worst-case performance. For a hash table resolving collisions with chaining (like Java's hashmap) this is technically O(1+α) with [a good hash function][1], where α is the table's load factor. Still constant as long as the number of objects you're storing is no more than a constant factor larger than the table size.

It's also been explained that strictly speaking it's possible to construct input that requires O(n) lookups for any deterministic hash function. But it's also interesting to consider the worst-case expected time, which is different than average search time. Using chaining this is O(1 + the length of the longest chain), for example Θ(log n / log log n) when α=1.

If you're interested in theoretical ways to achieve constant time expected worst-case lookups, you can read about [dynamic perfect hashing][2] which resolves collisions recursively with another hash table!

[1]: http://en.wikipedia.org/wiki/SUHA "Simple Uniform Hashing Assumption" [2]: http://courses.csail.mit.edu/6.897/spring03/scribe_notes/L2/lecture2.pdf "MIT Adv. Algs. Lecture Notes"

It is O(1) only if your hashing function is very good. The Java hash table implementation does not protect against bad hash functions.

Whether you need to grow the table when you add items or not is not relevant to the question because it is about lookup time.

Elements inside the HashMap are stored as an array of linked list (node), each linked list in the array represents a bucket for unique hash value of one or more keys.
While adding an entry in the HashMap, the hashcode of the key is used to determine the location of the bucket in the array, something like:

location = (arraylength - 1) & keyhashcode

Here the & represents bitwise AND operator.

For example: 100 & "ABC".hashCode() = 64 (location of the bucket for the key "ABC")

During the get operation it uses same way to determine the location of bucket for the key. Under the best case each key has unique hashcode and results in a unique bucket for each key, in this case the get method spends time only to determine the bucket location and retrieving the value which is constant O(1).

Under the worst case, all the keys have same hashcode and stored in same bucket, this results in traversing through the entire list which leads to O(n).

In the case of java 8, the Linked List bucket is replaced with a TreeMap if the size grows to more than 8, this reduces the worst case search efficiency to O(log n).

This basically goes for most hash table implementations in most programming languages, as the algorithm itself doesn't really change.

If there are no collisions present in the table, you only have to do a single look-up, therefore the running time is O(1). If there are collisions present, you have to do more than one look-up, which drives down the performance towards O(n).

It depends on the algorithm you choose to avoid collisions. If your implementation uses separate chaining then the worst case scenario happens where every data element is hashed to the same value (poor choice of the hash function for example). In that case, data lookup is no different from a linear search on a linked list i.e. O(n). However, the probability of that happening is negligible and lookups best and average cases remain constant i.e. O(1).

Only in theoretical case, when hashcodes are always different and bucket for every hash code is also different, the O(1) will exist. Otherwise, it is of constant order i.e. on increment of hashmap, its order of search remains constant.

Of course the performance of the hashmap will depend based on the quality of the hashCode() function for the given object. However, if the function is implemented such that the possibility of collisions is very low, it will have a very good performance (this is not strictly O(1) in every possible case but it is in most cases).

For example the default implementation in the Oracle JRE is to use a random number (which is stored in the object instance so that it doesn't change - but it also disables biased locking, but that's an other discussion) so the chance of collisions is very low.

Academics aside, from a practical perspective, HashMaps should be accepted as having an inconsequential performance impact (unless your profiler tells you otherwise.)