Question 1

What is a hash table, and why is lookup constant time?

Accepted Answer

A structure that turns a key into an **array index** by computing a number from it, so finding an item takes roughly the same time whether the table holds ten entries or ten million.

**The mechanism.** A **hash function** takes a key of any kind and produces a fixed-size number. Reduce that number modulo the array size and you have a **bucket index**. Storing and retrieving both compute the same index, so lookup is one computation plus one array access — **O(1) on average**, regardless of size. This is the data structure behind dictionaries, maps, objects, sets and database indexes of the hash variety.

**The complication: collisions.** Two different keys can produce the same bucket, and by the pigeonhole principle they must eventually. Two standard resolutions:

**Chaining**, where each bucket holds a list of entries, searched linearly.

**Open addressing**, where a colliding entry is placed in another slot by a defined probing sequence.

**Why "on average" matters.** If many keys land in one bucket, lookup degrades towards **O(n)**. This is a real attack — **hash flooding**, where crafted inputs deliberately collide to make a server's request handling quadratic — which is why language runtimes now use randomised hash seeds.

**Load factor and resizing.** As the table fills, collisions rise. When occupancy passes a threshold, typically around 0.7, the table **allocates a larger array and rehashes everything**. That single insertion is expensive; **amortised** over many insertions the cost per operation remains constant, which is the honest statement of the guarantee.

**What a good hash function needs:** speed, and distributing keys evenly so similar inputs land far apart.

**The practical consequences:**

**Keys must be hashable and stable.** Mutating an object after using it as a key puts it in the wrong bucket and makes it unfindable — a classic bug.

**Order is not guaranteed** by the structure, though some languages now preserve insertion order separately.

**Tree-based maps** are the alternative when you need ordering or range queries, at O(log n).

Question 2

What is Big O notation actually telling you?

Accepted Answer

Big O describes how an algorithm's cost grows as the input grows. It is a statement about the shape of the growth curve, not about speed in any absolute sense. What it deliberately ignores: Constant

Question 3

When does a database index hurt performance?

Accepted Answer

Indexes speed up reads by maintaining an ordered structure alongside the table, and that structure has to be kept correct on every write. Every insert, update and delete must update every affected

Question 4

When should you use an array rather than a linked list?

Accepted Answer

Almost always — and the textbook answer, which favours linked lists for insertion and deletion, is misleading on modern hardware because it ignores how memory actually behaves. The theoretical

Question 5

What are sharding and partitioning?

Accepted Answer

Both split a large dataset into pieces. Partitioning divides a table within one database; sharding distributes data across separate database instances. The distinction matters because one is an

Question 6

What techniques can I use to tune the performance of MongoDB?

Accepted Answer

MongoDB performance tuning techniques include indexing, sharding, schema design, connection pooling, query optimization, and hardware optimization. Each method enhances database efficiency and responsiveness.

Question 7

Why is my PostgreSQL query still slow after adding an index?

Accepted Answer

An index existing and an index being used are different things. The planner chooses a strategy based on its cost estimates, and it will ignore an index whenever it believes a sequential scan will be

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