Nosql Document Db: Overview, Use Cases, and Best Practices

Document databases are a type of NoSQL database that store data in a JSON-like format.

They are particularly useful for handling semi-structured data, which doesn't fit neatly into traditional relational database structures.

Document databases are designed to be flexible and scalable, making them a great choice for applications with high traffic or rapidly changing data.

One of the key benefits of document databases is their ability to store and retrieve data in a single operation, reducing the complexity of database operations.

A NoSQL document database stores data as JSON-like documents, rather than rows, columns, and tables like traditional SQL databases.

This type of database is perfect for situations where you need to store data with varying structures, like a book with different fields such as author, title, and number of pages.

For example, in a relational database, you'd have empty columns for books with less information, but in a document database, each book is stored as a separate document with all its necessary information.

Here are some benefits of NoSQL document databases:

Document databases like FerretDB offer even more advantages, such as being open-source and compatible with MongoDB, allowing you to run MongoDB workloads on PostgreSQL.

FerretDB is an open-source document database alternative to MongoDB with PostgreSQL as the backend. It was born out of a need to offer a truly open-source alternative to MongoDB after its switch to SSPL in 2018.

FerretDB is already gaining traction and being leveraged by users seeking freedom away from vendor lock-in associated with MongoDB. This is because it translates documents using MongoDB's BSON format to JSONB in PostgreSQL, allowing users to run MongoDB workloads on PostgreSQL.

FerretDB converts MongoDB wire protocols to SQL in PostgreSQL, making it possible to use similar syntax and query language as MongoDB. An insert statement and query in FerretDB looks like MongoDB, but it's actually FerretDB using PostgreSQL under the hood.

Experienced PostgreSQL users can manage FerretDB using all the extensions and administrative features already available in PostgreSQL, such as replication, backup, and monitoring. This flexibility and ease-of-use associated with MongoDB is still available.

FerretDB is also working on improving performance by pushing more queries to the backend.

A NoSQL document database like Couchbase is built on a highly distributed architecture, with data sharded across machines in a cluster.

This architecture allows for horizontal scaling, which means the system can easily handle increased traffic by adding more machines to the cluster.

Each machine in the cluster runs two primary processes: a data manager and a cluster manager.

The data manager handles the actual data in the partition, while the cluster manager deals with intranode operations.

Couchbase uses hash sharding, which ensures that data is distributed uniformly across all nodes in the cluster.

The system defines 1,024 partitions, and once a document's key is hashed into a specific partition, that's where the document lives.

Each partition is assigned to a specific node in the cluster.

If nodes are added or removed, the system rebalances itself by migrating partitions from one node to another.

This means there is no single point of failure in a Couchbase system, as all partition servers in a Couchbase cluster are equal and responsible for only that portion of the data assigned to it.

Documents are placed into buckets, and documents in one bucket are isolated from documents in other buckets from the perspective of indexing and querying operations.

A new bucket can be created with a specified number of replicas, up to three, which ensures system resilience in case of a server crash.

The system will detect the crash, locate the replicas of the documents that lived on the crashed system, and promote them to active status.

This is made possible by the cluster map, which defines the topology of the cluster, and is updated in response to changes in the cluster.

Relational databases are a type of traditional database that stores data in separate tables defined by the programmer. Each object appears in multiple tables, making it necessary to use join statements to get the desired result from the database.

In a relational database, the structure is predefined and rigid, with tables, columns, and rows. This can be limiting when working with complex data structures.

Relational databases are best suited for handling structured data and are often used in traditional business applications. They offer higher consistency guarantees, but this comes at the cost of performance and availability.

Here's a comparison of relational databases and document databases:

Document databases offer several key features that make them an attractive choice for developers. Here are some of the most notable ones:

Document databases store data in documents, which map easily to objects in popular programming languages, allowing developers to quickly develop their applications.

Data is stored in documents, not tables or graphs, making it easy to work with in various programming languages.

Flexible schema is another key feature of document databases. This means that not all documents in a collection need to have the same fields, making it easier to evolve and change data structures.

Some document databases even support schema validation, allowing you to lock down the schema if needed.

Distributed and resilient design is a hallmark of document databases. This allows for horizontal scaling, which is typically cheaper than vertical scaling, and provides resiliency through replication.

Horizontal scaling also makes document databases highly available, even in the event of node failures.

Document databases come with a query language that allows developers to execute CRUD operations on the database. This makes it easy to access and manipulate data.

Developers can query for documents based on unique identifiers or field values.

Some of the benefits of using document databases include high scalability, easy scalability, and consistent high performance. These benefits make document databases an attractive choice for applications that require rapid growth and high availability.

Here are some examples of document data models:

Working with NoSQL Document DB is a breeze, especially when compared to relational databases. Documents map to data structures in most popular programming languages, making it easy for developers to work with data directly in their applications.

In a document database, you can store related data in a single document, eliminating the need for manual data splitting and joining. This intuitive approach makes it easier to interact with the database and model the data.

Data retrieval is also faster and more efficient, as you can query a single document to get all the information you need in a single query.

To get started with document databases, you'll want to understand the basics of how they work. Documents are stored as semi-structured data, meaning they can contain various data types such as numbers, strings, objects, and arrays.

In a document database, data is stored in a single document, making it easy to work with and retrieve information. This is in contrast to relational databases, where data is split across multiple tables.

One of the key benefits of document databases is their flexibility. You can store data in any format necessary for that record, without a fixed schema. For example, a book document might contain information on its author, publication, genre, and movie adaptation.

To get started with document databases, you can practice with open-source options like Ferret, which is compatible with MongoDB wire protocols and queries. To install Ferret, check out their installation guide.

Here are some key things to consider when getting started with document databases:

By understanding these basics and getting hands-on experience, you'll be well on your way to working effectively with NoSQL document databases.

Vbuckets are essentially owners of a subset of the key space in a Couchbase cluster. They're responsible for distributing data across the cluster and supporting replicas on multiple nodes.

Every document ID belongs to a vBucket. This is determined by a hashing function that takes the document ID as input and outputs a vBucket identifier.

A mapping function is used to calculate the vBucket to which a given document belongs. This function is a hashing function in Couchbase Server.

The vBucket identifier is then used to look up the server that hosts that vBucket. This is done by consulting a table that contains one row per vBucket, pairing the vBucket to its hosting server.

A server can be responsible for multiple vBuckets. This means a server can host more than one vBucket.

Working with NoSQL Document DB can be a challenge, especially when it comes to data quality. Relational databases offer little to validate the schema of documents, leaving you with no way to apply quality controls against your JSON data.

This can lead to poor data quality, which is a major issue in any database. You still need to define a schema for your regular tabular data, which can be a hassle.

Rigid tables can be a problem, especially when your application's features evolve. You may need to alter your tables, which can be a time-consuming process.

CRUD operations are a fundamental aspect of any database, and document databases are no exception. They typically have an API or query language that allows developers to execute CRUD operations, including creating, reading, updating, and deleting documents.

Each document has a unique identifier, making it easy to query and retrieve specific data. Indexes can be added to the database to increase read performance, allowing for faster retrieval of data.

Here are the basic CRUD operations that document databases support:

In terms of performance, document databases excel in handling large amounts of data and high traffic loads. With the ability to query and update nested objects in a single atomic operation, they are particularly well-suited for applications that involve complex data structures.

CRUD operations are the foundation of any database, and document databases are no exception. They allow developers to create, read, update, and delete documents in the database.

You can create new documents in the database, each with a unique identifier. This is a crucial aspect of CRUD operations, as it enables you to store and manage data effectively.

Indexes can be added to the database to increase read performance. This is especially important when dealing with large datasets, as it can significantly speed up query execution.

Existing documents can be updated in whole or in part, allowing you to modify specific fields or the entire document. This flexibility is essential for maintaining accurate and up-to-date data.

Documents can be deleted from the database, which is necessary for removing redundant or outdated information. This ensures that your database remains clutter-free and efficient.

Here's a summary of CRUD operations:

Document databases are highly performant, especially when working with nested objects and documents, allowing for easy querying and updating in a single atomic operation.

They're perfect for applications that require handling numerous data types and structures, such as content management systems, social media apps, and real-time analytics.

With horizontal scaling, document databases can handle large amounts of data and high traffic loads by spreading them across multiple distributed nodes.

This means no more worrying about slow performance or bottlenecks, especially when dealing with complex data structures.

Couchbase Server, for instance, is designed for massively concurrent data use and provides consistent sub-millisecond response times, ensuring an enjoyable experience for application users.

It automatically spreads the workload across all servers to maintain consistent performance and reduce bottlenecks, making it an ideal choice for applications with high traffic loads.

In fact, the YCSB benchmark showed that Couchbase has the lowest latencies and the highest throughput, making it a top performer among NoSQL technologies.

To ensure consistency, it's essential to execute read/write operations on the primary nodes only, which is exactly what Couchbase Server does.

This approach ensures data consistency while still leveraging all the nodes in the cluster, making it a win-win for performance and consistency.

By doing so, Couchbase Server can support more users with fewer servers, reducing the need for additional infrastructure and saving costs.

Low performance can be a major issue when it comes to CRUD operations, especially when dealing with large datasets. Most relational databases don't maintain statistics on JSON data, making it difficult to optimize queries.

This lack of statistics prevents the query planner from optimizing queries against documents. As a result, you're left to tune your queries on your own, which can be a time-consuming and frustrating process.

The absence of statistics also means you can't rely on the database to provide performance insights, forcing you to dig deeper into query execution plans and other low-level details.

Document databases are incredibly versatile and can be used in a variety of applications, from content management to analytics platforms.

They're perfect for developing content management systems, such as blogs and streaming video platforms, where each piece of information is stored as a single document, making database maintenance and growth easier.

Document databases are also great for creating book databases, where information about books can be efficiently organized and retrieved due to the document's hierarchical structure.

They're frequently used in catalogs, where their quick reading speed comes in handy when working with catalogs that include hundreds of qualities.

In fact, document databases can be used in a wide range of applications, including:

Developers also love using document databases for application development, as they allow for high coding velocity and great agility in the building process, thanks to the use of JSON format for modeling data.

Amazon's managed document database service, Amazon DocumentDB, provides scalability, availability, and performance for document-based applications, and is compatible with MongoDB.

MongoDB is a widely used open-source document database that provides high scalability, flexibility, and robust querying capabilities, making it suitable for various applications.

Cosmos DB is a globally distributed, multi-model database service that supports a document data model, offering automatic scaling, high availability, and low latency.

ArangoDB is a multi-model database that supports document, graph, and key-value data models, providing a unified query language for seamless work with different data models.

Couchbase Server is a distributed document database designed for performance, scalability, and high availability, offering a flexible data model for storing and retrieving JSON documents.

CouchDB is an open-source document database with a distributed architecture, providing seamless replication and conflict resolution for offline-first applications.

MongoDB is the most popular document database, with its rich query language, complex query support, and ease-of-use contributing to its widespread adoption.

Mongo is a widely used open-source document database that provides high scalability, flexibility, and robust querying capabilities. It supports a rich set of features, making it suitable for various applications.

MongoDB is the most popular document database, with a rich query language and complex query support that play a favorable role in its popularity. It's also included in popular JavaScript stacks like MEAN and MERN, which is widely used for web application development.

MongoDB is designed to be horizontally scalable, handling large volumes of data and traffic by sharding data across multiple nodes. It also enables high availability through replica sets that provide redundancy and failover.

In the early days, MongoDB didn't support ACID transactions, but since version 4.0, it has added support for multi-document ACID transactions, and version 4.2 added support for distributed multi-document ACID transactions. This makes MongoDB a more robust option for applications that require strong consistency guarantees.

MongoDB has a strong ecosystem of tools and services, including MongoDB Atlas, a fully managed cloud database service, and MongoDB Compass, a database GUI. It also has drivers available for a large number of programming languages, making it possible for developers to work in their preferred programming language.

Couchbase is an open-source NoSQL distributed multi-model database built and optimized for interactive applications. It uses a flexible JSON model that doesn't require a fixed data model and can be modified on the fly.

Couchbase offers its own unique query language called N1QL, a sort of SQL-like query language for JSON. This allows for complex operations like joining, filtering, aggregating, ordering, and more.

A typical query in Couchbase looks like this, making it more akin to SQL than MongoDB's query language. The query language allows for a lot of the same operations you would do in SQL.

Couchbase is built for easy scalability, replication, and failover using a distributed architecture with sharding and load balancing. This makes it suitable for high-performance applications.

The primary unit of data storage in Couchbase Server is a JSON document, which makes your application free of rigidly-defined relational database tables. This allows for flexible schema migrations.

Couchbase Server provides a JavaScript-based query engine to find records based on field values. This makes it easy to index and query binary data stored in documents.

Couchbase SDKs are language-specific SDKs provided by Couchbase, responsible for communicating with the Couchbase Server. They automatically read and write data to the right node in a cluster, even if the database topology changes.

All Couchbase SDKs automatically direct requests to still-functioning nodes if your cluster experiences server failure. This ensures high uptime and availability.