MongoDB Interview Questions and Answers- Part 5

Answer:

In MongoDB, a capped collection is a special type of collection that has a fixed size and follows a first-in, first-out (FIFO) data storage mechanism. It is primarily used for scenarios where you need a high-performance, low-overhead way to store and manage a stream of data, such as logs or event records.

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In MongoDB, you can perform join operations using a feature called “aggregation.” Unlike traditional SQL databases, MongoDB uses a different approach for data modeling, and joins are not as straightforward. Instead of traditional joins, MongoDB provides the $lookup aggregation stage to perform similar operations.

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Configuring the cache size in MongoDB primarily involves adjusting the WiredTiger cache, which is the storage engine used by MongoDB since version 3.0. The WiredTiger storage engine manages the data cache, so you can control the cache size by modifying the storage.wiredTiger.engineConfig.cacheSizeGB option in your MongoDB configuration file or through runtime settings using the MongoDB shell.

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Below are the aggregate functions of MongoDB:

• Sum
• Push
• Last
• Min
• Max
• AVG
• First
• addTo Set

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Following are the datatypes of MongoDB:

• String
• Array
• Date
• Integer
• Double
• Timestamp
• Boolean
• Regular Expression

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MongoDB and RDBMS (Relational Database Management System) are two different types of database systems, each with its own set of characteristics and use cases. Here’s a comparison of the two:

1. Data Model:
   • MongoDB: MongoDB is a NoSQL database, which means it uses a document-oriented data model. It stores data in collections, where each collection contains documents in BSON (Binary JSON) format. Documents can have varying structures within the same collection.
   • RDBMS: RDBMS follows a tabular data model. It stores data in tables with fixed schemas, where each table consists of rows and columns. Data must conform to the predefined schema.
2. Schema:
   • MongoDB: MongoDB is schema-less, allowing for flexible and dynamic data structures. Fields within a document can vary from one document to another within the same collection.
   • RDBMS: RDBMS enforces a strict schema with predefined table structures, data types, and relationships between tables. Any changes to the schema require careful planning and potentially involve data migration.
3. Query Language:
   • MongoDB: MongoDB uses a query language that is JSON-based and supports a wide range of query operators. It is designed to work with documents and offers powerful querying capabilities.
   • RDBMS: RDBMS systems use SQL (Structured Query Language) for querying and managing data. SQL is standardized and has been widely adopted for relational databases.
4. Scalability:
   • MongoDB: MongoDB is designed for horizontal scalability, making it suitable for handling large amounts of data and high-velocity workloads. It can distribute data across multiple servers or clusters.
   • RDBMS: Traditional RDBMS systems are typically scaled vertically by upgrading hardware. Scaling out (horizontally) can be more challenging and may involve replication and sharding.
5. ACID Compliance:
   • MongoDB: MongoDB offers tunable levels of consistency and can provide ACID (Atomicity, Consistency, Isolation, Durability) guarantees for specific operations or configurations.
   • RDBMS: Most RDBMS systems are ACID-compliant by default, ensuring strong data consistency and reliability.
6. Use Cases:
   • MongoDB: MongoDB is often used for applications with rapidly changing data or unstructured data, such as social media platforms, content management systems, and real-time analytics.
   • RDBMS: RDBMS is well-suited for applications with structured data and complex relationships, such as financial systems, e-commerce platforms, and inventory management.

Answer:

In MongoDB, indexes are essential for improving query performance by allowing the database to quickly locate and retrieve documents. There are several types of indexes available:

1. Single Field Index: This is the most common type of index, where an index is created on a single field in a document. It is used to speed up queries that filter or sort based on that specific field.
2. Compound Index: A compound index is created on multiple fields within a document. It can be useful when queries involve multiple fields in their filtering or sorting criteria. The order of fields in a compound index can affect query optimization.
3. Text Index: Text indexes are designed for full-text search. They allow you to perform text-based queries, including searching for words and phrases in text fields. MongoDB uses the text index for text search queries and can rank results by relevance.
4. Geospatial Index: Geospatial indexes are used to support geospatial queries on location data. They enable efficient retrieval of documents based on their geographic coordinates, making it suitable for location-based applications.
5. Hashed Index: Hashed indexes are used for exact matching on a single field and can be useful when you want to distribute data uniformly across shards in a sharded MongoDB cluster. Hashed indexes can help with load balancing.
6. Wildcard Index: Starting from MongoDB 4.2, wildcard indexes allow indexing fields in embedded documents using wildcard characters. This can be useful when you want to index fields with dynamic or variable keys.
7. Sparse Index: Sparse indexes only index documents that contain the indexed field. They are helpful when you have a large collection, and most documents do not have the indexed field. Sparse indexes reduce index size and improve query performance for documents with the indexed field.
8. Unique Index: Unique indexes ensure that the indexed field contains unique values across all documents in a collection. They can be applied to a single field or a compound set of fields.
9. TTL (Time-To-Live) Index: TTL indexes allow you to automatically remove documents from a collection after a certain period. They are useful for implementing data retention policies and managing time-based data.
10. Collation Index: Collation indexes allow you to specify the collation rules for string comparison in a specific index. This is useful for supporting multilingual text search and sorting based on specific language rules.

Answer:

The term “ACID” refers to the transactional properties of a database, although MongoDB’s transactions work a bit differently from traditional relational databases. ACID stands for Atomicity, Consistency, Isolation, and Durability, and it’s a set of properties that ensure the reliability of database transactions.

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CRUD in MongoDB, as in many other database systems, stands for Create, Read, Update, and Delete. These are the fundamental operations that can be performed on data within a MongoDB database.

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A storage engine is a crucial component responsible for managing how data is stored on disk and how it is accessed and manipulated. MongoDB supports several storage engines, each with its own characteristics and capabilities.

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No, one cannot configure the cache size for MMAPv1 as MMAPv1 does not allow configuring the cache size.

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“Splitting” typically refers to the process of dividing or splitting data across multiple chunks in a sharded cluster. MongoDB uses sharding to horizontally scale data across multiple servers or nodes, allowing you to manage large datasets and high write and read workloads.

Answer:

MongoDB offers two primary data modeling approaches:

1. Embedded Data Model: In this model, related pieces of information are stored within a single database record. This design reduces the need for frequent database queries when retrieving or updating data. This approach is often referred to as a denormalized data model.
2. Normalized Data Model: This model employs the traditional reference technique, where a child document references a parent document using their unique _id field.

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A Relational Database Management System (RDBMS) is a type of database management system that organizes and stores data in a structured manner, using tables with rows and columns. It is a software application or system that allows users to create, manage, and manipulate relational databases.

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A Replica Set requires at least three nodes, consisting of one primary node and two secondary nodes. In the event that the primary node becomes unavailable, a secondary node will be designated as the new primary node through a procedure known as Replica Set Elections.

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A Shard Key is a field or combination of fields used to distribute data across multiple servers or shards in a sharded cluster. Sharding is a technique used to horizontally scale MongoDB databases, allowing them to handle large amounts of data and high write and read workloads.
The Shard Key determines how data is distributed among the different shards in the cluster. MongoDB uses the value of the Shard Key to determine which shard should store a specific document. It’s crucial to choose an appropriate Shard Key because it can significantly impact the performance and scalability of your MongoDB cluster.

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Journaling is a feature that helps ensure data durability and recoverability in the event of a server crash or unexpected shutdown. Journaling is a write-ahead log that records all the changes or modifications made to the database before they are applied to the data files.

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No, MongoDB databases do not have a rigid schema like traditional relational databases. MongoDB is a NoSQL database system that uses a flexible, schema-less data model. In MongoDB, data is stored in BSON (Binary JSON) format, and each document in a collection can have its own structure. This means that different documents in the same collection can have different fields and field types.

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Change streams in MongoDB provide a real-time, event-driven mechanism to track changes to the data in a collection. It allows applications to react to changes, such as insertions, updates, or deletions, in real-time by subscribing to a change stream. This feature is valuable for building reactive and event-driven applications.

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MongoDB is considered a schema-less database, which means that the structure of documents in a collection can evolve over time. It can accommodate new fields or changes in field types without affecting existing data. MongoDB’s flexible schema allows for easy schema evolution.