DEVPREP CODEX · #12

MongoDB Interview Questions

171 curated questions graded from Foundations (Easy) to Practical Patterns (Medium) and Internals & Architecture (Hard).

TOTAL QUESTIONS

171

THEORY QUESTIONS

150

IMPLEMENTATION FOLIOS

FREE QUESTIONS

5 / Level

Easy Level·57 Questions Total

Foundations & Core Concepts

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Q1What is MongoDB and how does it differ from traditional RDBMS?

Definition: MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents (BSON format).
Schema Difference: Traditional RDBMS uses a rigid schema with tables, rows, and columns. MongoDB uses a schema-less structure with collections and documents, allowing varying structures within the same collection.
Relationships: RDBMS relies heavily on normalized tables and relations via foreign keys and joins. MongoDB relies on denormalized embedded documents or manual references, optimizing for read-heavy operations.
Scalability: RDBMS typically scales vertically (adding hardware power), while MongoDB scales horizontally (sharding across multiple servers).

Q2What is BSON and how is it different from JSON?

Definition: BSON stands for Binary JSON. It is a binary-serialized representation of JSON-like documents.
Size & Efficiency: BSON is designed for high speed and space efficiency. It is faster to parse and traverse compared to JSON.
Data Types: BSON extends JSON by supporting additional data types such as Date, ObjectId, BinData (binary data), Double, and 32-bit/64-bit integers. JSON only natively supports strings, numbers, booleans, null, arrays, and objects.

Q3Explain the structure of a MongoDB ObjectId.

Definition: A unique 12-byte identifier automatically generated as the primary key (_id) for each document.
Byte Breakdown:
- First 4 bytes: Unix timestamp (seconds since epoch) representing the creation time.
- Next 5 bytes: Random value unique to the machine and process (ensures uniqueness across distributed systems).
- Last 3 bytes: An incrementing counter, initialized with a random value.
Implication: ObjectIds are naturally ordered by creation time, allowing sorting by _id to act as a chronological sort.

Q4What is a dynamic schema and what are its advantages?

Concept: MongoDB collections do not enforce document structure. Documents in the same collection can have different fields, types, and nested nesting.
Advantages:
- Agility: Rapid development cycles since database migrations or schema alterations (ALTER TABLE) are not required.
- Polymorphism: Ability to represent diverse entities or semi-structured data without complex join tables.
- No Null Fields: Eliminates the need to store null values for optional attributes, saving storage space.

Q5What are collections and documents in MongoDB?

Collection: Equivalent to an RDBMS table. It is a grouping of MongoDB documents. Collections do not enforce schemas but can have schema validation rules if configured.
Document: Equivalent to an RDBMS row. It is the basic unit of data in MongoDB, composed of key-value pairs stored in BSON format.

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Medium Level·57 Questions Total

Mechanisms & Practical Patterns

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Q1Explain the components of MongoDB aggregation framework ($match, $project, $group, $unwind, $lookup, $out).

$match: Filters documents to pass only matching documents to the next stage (equivalent to SQL WHERE). Must be placed as early as possible to utilize indexes.
$project: Reshapes documents by adding, renaming, or removing fields (equivalent to SQL SELECT).
$group: Groups documents by a specified key and performs accumulative operations (like $sum, $avg, $min, $max) on the grouped data.
$unwind: Deconstructs an array field from input documents to output a document for each element of the array.
$lookup: Performs a left outer join to another collection in the same database to filter/add data.
$out: Writes the resulting documents of the aggregation pipeline to a new or existing collection (must be the final stage).

Q2What is the difference between `$merge` and `$out` stages in aggregation?

$out:
- Overwrites the entire output collection if it exists, or creates a new one.
- Cannot write to a sharded collection.
- Replaces the collection atomically upon pipeline completion.
$merge:
- Introduces on-demand incremental updates. It can merge results (insert, update, discard, or fail on matches) into an existing target collection.
- Can write to sharded or unsharded collections, even across different databases.

Q3How does `$lookup` perform a join? What are its performance characteristics and index requirements?

Mechanism: Acts as a nested loop join. For each document in the source collection, it queries the target collection.
Performance: Can be extremely slow if the target collection is large.
Index Requirements: The joined foreign field on the target collection must be indexed. If not, MongoDB must perform a full collection scan on the target collection for every single document processed by $lookup.

Q4What is the difference between single-field indexing and compound indexing regarding query coverage?

Single-Field Index: Indexes a single field (e.g., { a: 1 }). Optimizes queries filtering or sorting on a.
Compound Index: Indexes multiple fields (e.g., { a: 1, b: -1 }). Optimizes queries filtering on a, or a and b.
Index Prefix Rule: A compound index { a, b } covers queries on a but not queries filtering only on b. To optimize queries on b, a separate single-field or compound index starting with b is required.

Q5Explain the performance implications of indexing array fields (multikey indexes).

Multikey Index: Automatically created when an index is created on a field containing an array.
Under the Hood: MongoDB creates separate index keys for every single element in the array.
Implications:
- High write overhead: If a document has an array with 100 elements, inserting it creates 100 index entries.
- Storage bloat: Multikey indexes occupy significantly more RAM and disk storage.
- Limit: A compound multikey index cannot have more than one array field indexed in the index definition to prevent exponential explosion of index entries.

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Hard Level·57 Questions Total

Internals & System Architecture

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Q1Explain WiredTiger storage engine internals, specifically MVCC, page structure, checkpointing, and reconciliation.

Multi-Version Concurrency Control (MVCC): WiredTiger handles concurrent transactions by keeping multiple versions of modified documents. Readers get a point-in-time snapshot, avoiding read locks while writes occur concurrently.
Page Structure: Data is organized in B-Tree pages (internal and leaf pages) in memory and on disk. Modifications are stored as in-memory "update lists" appended to the original on-disk page layout.
Checkpointing: Every 60 seconds (or when journal writes reach 2GB), WiredTiger flushes all dirty in-memory pages to disk, creating a crash-consistent checkpoint.
Reconciliation: During checkpointing, the engine combines the in-memory update lists with the old on-disk page format, compiles them into a new layout, compresses it, and writes clean, new pages to disk.

Q2What is read concern `snapshot` and how does it guarantee read isolation in multi-document transactions?

Concept: Only available within multi-document sessions/transactions.
Guarantee: It provides strict snapshot isolation. The query engine reads data from a synchronized, point-in-time snapshot of the database across all replica set nodes.
Mechanism: Ensures that all operations inside the transaction observe a single, consistent state of the database. Even if other clients commit transactions concurrently, the transaction's view remains completely isolated and unchanged.

Q3How does the election protocol version 1 (pv1) prevent split-brain scenarios in a distributed replica set?

Split-Brain: A situation where network partitions cause two sub-groups of a replica set to both think they have an active primary, leading to divergent write logs.
PV1 Solution: Uses strict raft-like consensus. A candidate node must receive a majority vote of all voting nodes (e.g., at least 2 out of 3, or 3 out of 5) to become primary.
Double Voting Prevention: A voter can only vote once per election term (using term increment counters). If a network partition occurs, only the partition containing a true majority can elect a primary; the minority partition is physically blocked from electing a primary.

Q4Explain the step-down process of a Primary node and how to configure priority and votes parameters.

Step-Down: An administrative command (replSetStepDown) or automatic behavior where the active primary relinquishes its role and becomes a secondary.
Trigger: Happens during maintenance, or when the primary detects that it has lost connectivity to a majority of the replica set nodes.
Configuration:
- priority: A number (0 to 1000) indicating preference for primary. Secondary nodes with priority: 0 can never become primary.
- votes: A boolean (0 or 1) indicating voting rights. Up to 7 members in a replica set can have votes.

Q5How does MongoDB handle node synchronization from scratch (Initial Sync)?

Phase 1: Metadata & Cloning: The syncing node creates database structures, drops existing databases, clones all collections and indexes from a sync source node, while concurrently logging any new oplog writes occurring on the sync source.
Phase 2: Oplog Application: The syncing node applies the recorded oplog writes to bring its local dataset up to speed.
Pitfalls: If the sync source's oplog rolls over (overwrites older records) before Phase 1 completes, the sync fails, requiring a complete restart.

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