SQLite vs MongoDB: Comparing Two of the Most Popular Databases

Choosing the right database management system (DBMS) is a crucial decision that can make or break your application. With so many options to pick from, how do you decide which one is best suited for your needs?

Two of the most popular choices are SQLite and MongoDB – but they differ significantly in their architecture, capabilities, and use cases. In this comprehensive guide, we will dive deep into their key differences, strengths, and weaknesses to help you make an informed decision.

Let’s dive in!

SQLite vs. MongoDB – A Quick Intro

Before we contrast these two databases, let’s first understand what they are:

What is SQLite?

SQLite is an embedded, open-source relational database management system. It implements a self-contained, zero-configuration, serverless SQL database engine. SQLite stores the entire database (definitions, tables, indices, and data) in a single cross-platform file on the host machine.

With SQLite, you don’t need a separate server process or complicated setup. The database file can be directly integrated into an application. This makes SQLite a popular choice for embedding lightweight database functionality into desktop applications, mobile apps, browsers and other standalone programs.

What is MongoDB?

MongoDB is a popular open-source NoSQL document database. Instead of tables and rows like a relational database, MongoDB makes use of flexible JSON-like documents to store data.

These documents are stored in BSON (a binary representation of JSON) format. MongoDB provides horizontal scalability and high availability out of the box. It can handle huge volumes of rapidly changing unstructured or semi-structured data across distributed clusters.

Now that you have a basic idea about both databases, let’s explore them in more detail and contrast their architectures, data models, features, use cases and more.

Key Differences Between SQLite vs. MongoDB

SQLite and MongoDB have fundamentally different database architectures. This leads to significant differences in capabilities, performance tradeoffs, usage scenarios and more.

Here’s an overview of their key differences:

1. SQLite vs MongoDB – Database Architecture

• SQLite has a simple, lightweight embedded architecture. There is no standalone database server process. The entire database is contained in a single cross-platform disk file.
• MongoDB employs a client-server architecture. The database runs as a standalone daemon process or a distributed cluster of processes. Clients connect to this database server to read/write data.

2. SQLite vs MongoDB – Data Model

• SQLite is a relational database, adhering to an RDBMS data model. Data is organized into tables, rows, columns and indices. Foreign key constraints enforce data integrity.
• MongoDB employs a document data model. Data is stored as flexible JSON-like documents rather than tables with rigid schemas. Related data can be nested within documents.

3. SQLite vs MongoDB – Schema Flexibility

• SQLite requires a predefined schema. You need to determine and declare the structure of tables and relationships upfront. Modifying schemas is restricted.
• MongoDB is schema-less. Documents in a collection can have different structures and new fields can be added dynamically. This provides more flexibility for changing data.

4. SQLite vs MongoDB – Scaling

• SQLite is designed for lightweight, low-concurrency workloads. It provides limited support for scaling to multiple servers due to its serverless architecture.
• MongoDB provides horizontal scalability out of the box. It can scale by adding more shards and servers to handle large datasets and traffic.

5. SQLite vs MongoDB – SQL Support

• SQLite provides full support for SQL queries including joins, group by, order by etc. It adheres closely to the SQL standard.
• MongoDB uses its own query language. While powerful, it does not provide complete SQL capabilities like joins between collections.

6. SQLite vs MongoDB – Transactions

• SQLite provides ACID compliant transactions with features like serialized isolation, durable writes etc. This ensures data consistency.
• MongoDB added multi-document ACID transactions only recently in v4.0. Transactions have limitations in sharding environments.

7. SQLite vs MongoDB – Administration

• SQLite databases require minimal administration or maintenance since they are embedded files rather than client-server systems.
• MongoDB needs dedicated administration for production deployments – security, access control, backup/recovery, replication, sharding etc.

This covers some of the key differences in a nutshell. Now let’s do a deeper comparison across various parameters:

Detailed Feature Comparison Between SQLite vs MongoDB

Let’s now compare the individual features that make SQLite and MongoDB stand out.

Database Architecture

One of the biggest differences between SQLite and MongoDB is their architectural design, which has cascading effects on capabilities like scalability, concurrency, administration and more.

SQLite Architecture

SQLite implements an embedded, zero-configuration database engine. There is no standalone server process like traditional client-server databases. The entire database instance – including tables, indices, triggers, views and data – is stored in a single cross-platform disk file on the host machine.

This file acts as a database that can be directly integrated into an application. The application handles database operations by linking against the SQLite library and accessing the local database file.

The key highlights of SQLite’s architecture:

• Serverless – No standalone database server. Directly embedded into the application.
• Single File – Entire database is stored in a local disk file on the host.
• Self-Contained – Requires minimal setup or configuration.
• Portable – Database file can be freely copied and moved between platforms.
• Embedded – Linked as a library and integrated within the application process.
• ACID Compliant – Supports atomic, consistent, isolated and durable transactions by default.
• Single-User – Designed for single-process access. Limited support for concurrency.

This simple, file-based architecture makes SQLite ideal for embedding lightweight database functionality directly into an application. Since it uses local files, administration overhead is minimal compared to running a separate database server process.

However, the single-file design also means SQLite has limited support for multi-user concurrency and cannot leverage multiple servers for high availability or scalability.

MongoDB Architecture

In contrast to SQLite, MongoDB employs a traditional client-server architecture. The database runs as a standalone daemon process or a cluster of processes according to your deployment.

Applications connect to this MongoDB server acting as clients. All data is stored in the centralized database. The clients do not directly access the filesystem for storage.

Here are some key aspects of MongoDB’s architecture:

• Client-Server – MongoDB runs as a standalone or distributed database server. Clients connect to this server.
• Daemon Process – Database runs as a persistent background process or cluster of processes.
• Centralized Data – All data is stored in the database rather than separate application files.
• Distributed – Supports automatic sharding i.e. horizontal partitioning and distribution of data across machines.
• Highly Scalable – Can scale vertically by adding RAM/CPUs and horizontally by adding more shards and servers.
• High Availability – Replica sets with automatic failover provide redundancy and 24/7 uptime.

This architecture allows MongoDB to support crucial enterprise-grade capabilities – scalability, high availability, security, access control and more. However, it also means added complexity for administration and deployment compared to SQLite.

Data Model

SQLite and MongoDB adhere to fundamentally different paradigms for organizing and storing data. This has a far reaching impact on how developers interact with each database.

SQLite Data Model

SQLite follows the relational model for structuring data. Information is stored in tables containing rows and columns. Tables can be linked through foreign key relationships forming a network of interrelated entities.

For example, an ecommerce application might have:

• Customers table with columns like customer_id, name, email, address etc.
• Orders table with columns like order_id, customer_id, amount, status etc.
• Order_Items table linking orders to purchased products.

This rigid, predefined structure has some key implications:

• Tables, columns and data types must be declared upfront in CREATE TABLE statements.
• Rows contain data corresponding to the fixed columns.
• Changing the schema by adding/removing columns is restricted.
• Related tables are linked through foreign key constraints.
• JOINS are required to query across tables.

This traditional relational model provides simplicity, stability and structure. But it lacks flexibility as application requirements evolve.

MongoDB Data Model

Unlike SQLite, MongoDB does not adhere to a tabular relational structure. It uses a more flexible document data model.

In MongoDB, data records are stored as BSON documents (Binary JSON) rather than rows in tables. These documents are analogous to JSON objects in Javascript.

For example, a blog site might store posts like:

{
  "_id": "asd123",
  "title": "Welcome to My Blog", 
  "content": "This is my first post...",
  "published_date": ISODate("2020-01-01"),
  "tags": ["announcements", "events"],
  "comments": [    
    {
      "username": "Jack", 
      "message": "Interesting post!" 
    },
    {
      "username": "Jill",
      "message": "Keep up the good work."
    }
   ]
}

And comments like:

{
  "_id": "qwe234",
  "post_id": "asd123", 
  "username": "Rae",
  "message": "Congratulations on the launch!"
}

Related documents are typically embedded within each other rather than normalized across separate tables.

This flexible structure provides some key advantages:

• Documents don’t need a predefined schema. New fields can be added dynamically.
• Hierarchical data with nested objects/arrays can be easily modeled.
• No complex joins are required to retrieve related data.
• Better performance for read-heavy workflows.

However, modeling complex many-to-many relationships is trickier compared to relational databases.

Schema Flexibility

The structural principles of SQLite and MongoDB have clear implications on schema flexibility and dealing with changing data models.

SQLite requires schemas to be predefined upfront. Changing the structure by adding/removing columns is restricted. MongoDB however is schema-less so structure can evolve dynamically.

SQLite Schemas

In SQLite, the database schema with details of all tables must be specified in CREATE TABLE statements before inserting any data:

CREATE TABLE Post(
  id INTEGER PRIMARY KEY,
  title TEXT,
  content TEXT,
  published DATE
);

CREATE TABLE Comment(
  id INTEGER PRIMARY KEY,
  post_id INTEGER,
  message TEXT,
  FOREIGN KEY(post_id) REFERENCES Post(id)
);

If you later want to alter the structure, SQLite only allows adding new columns:

ALTER TABLE Post ADD COLUMN view_count INTEGER DEFAULT 0;

Deleting or modifying existing columns is not possible without losing data. The rigid schema forces you to plan ahead.

MongoDB Schemas

MongoDB does not enforce any schema for documents. You can freely add or remove fields dynamically:

// Initial document
{
  title: "My First Post",
  content: "Hello World!" 
}

// Changed later by adding new fields
{
  title: "My First Post",
  content: "Hello World!",
  viewCount: 123,
  tags: ["technology", "programming"]
}  

There is no need to declare any structure upfront like SQLite. This flexibility allows MongoDB schemas to evolve with changing application requirements easily.

However, some structure is still necessary for consistency. MongoDB supports JSON Schema validation to define optional expected structures that documents should conform to. However the database will not enforce it unless configured to do so.

Scaling

The architectures of SQLite and MongoDB lead to vast differences in their scalability patterns – how well they can handle growth in workload and datasets.

SQLite Scalability

SQLite is designed to scale up to a limited extent for single-user applications that require local data storage. But it has little ability to scale out across multiple servers due to its embedded architecture.

Some factors affecting SQLite’s scalability:

• Disk I/O tends to be the main bottleneck for embedded databases rather than compute or memory resources. Writes can be slow on rotational disks compared to memory or SSDs.
• The entire database is contained in a single disk file that can grow to terabytes in size. Larger databases have slower access times.
• Being restricted to single-process access, SQLite has limited concurrency capabilities. Only one write operation is allowed at a time.
• There is no support for distributing load or storage across multiple nodes.
• Common scaling techniques like sharding, replication, failover, etc are not feasible.

For these reasons, SQLite works best for small, low-concurrency workloads. It can handle thousands to millions of records acceptably but does not work well for massive user bases or internet-scale multi-tenant applications.

MongoDB Scalability

In contrast, MongoDB is designed to scale out on distributed infrastructure for internet-scale workloads easily. The client-server architecture readily allows scaling both vertically and horizontally.

• Vertical Scaling – You can upgrade RAM, SSDs, and processors or use faster machines to allow more operations from a single MongoDB instance.
• Sharding – MongoDB provides auto-sharding to split data across clusters of machines transparently. Just add more cheap commodity servers, and MongoDB will take care of distributing load using the shard key.
• Replication – Data can be replicated across multiple nodes for redundancy. Replica sets support automatic failover.
• Load Balancing – Smart clients and drivers with awareness of data location can distribute read/write operations across replicas.

Together these capabilities allow MongoDB to scale out linearly and sustain massive workloads across clusters of hundreds of servers or more. Auto-sharding and replication also provide high availability benefits. Such capabilities would be impossible for SQLite due to its embedded architecture.

SQL Support

SQLite supports the SQL standard for operating on relational data using declarative queries. MongoDB provides its own query interface.

SQLite and SQL

SQLite allows manipulating data via full-featured SQL statements like any traditional RDBMS. All common SQL operations are supported:

-- Create table
CREATE TABLE Post(
  id INTEGER PRIMARY KEY, 
  title TEXT,
  content TEXT
);

-- Insert rows
INSERT INTO Post VALUES (1, 'Post 1', 'Content 1');

-- Select with filters
SELECT * FROM Post WHERE title = 'Post 1'; 

-- Joins across tables 
SELECT * FROM Post 
JOIN Comment ON Post.id = Comment.post_id;

-- Group By, Order By, Limit etc
SELECT author, COUNT(*) AS num_posts 
FROM Post
GROUP BY author 
ORDER BY num_posts DESC 
LIMIT 5;

SQLite implements most of the SQL standard at a strong level of compliance. This makes it highly interoperable with other systems like scripts, ORMs, BI tools etc that rely on standard SQL capabilities.

However, SQLite does omit some esoteric features like RIGHT OUTER JOIN, WITH clauses etc. It also has limited support for ALTER TABLE statements that modify schema. But SQLite provides all commonly used SQL functionality.

MongoDB Query Language

MongoDB uses its own JSON-oriented query language rather than SQL. The syntax is designed to work easily with application code.

For simple CRUD operations:

// Insert document
db.posts.insert({
  title: "Hello MongoDB",
  content: "Intro to Mongo..."
})

// Find documents
db.posts.find({title: "Hello MongoDB"}) 

// Update document
db.posts.update({title: "Hello MongoDB"}, {
  $set: {views: 156}  
})

// Delete document
db.posts.remove({title: "Hello MongoDB"})

Additional query operators like $lookup, $match, $project, $group etc provide more advanced transformations and aggregations:

db.posts.aggregate([
  { 
    $lookup: {
      from: comments,
      localField: _id,
      foreignField: post_id,
      as: comments
    }
  },
  {
    $project: {
      title: 1,
      commentCount: {$size: "$comments"} 
    }
  }
])  

However, MongoDB does not directly support joins across collections, subqueries, window functions etc unlike SQL databases. The query language has gaps in functionality that must be worked around.

Transactions

Transactions allow combining multiple operations into an atomic unit that succeeds or fails together. This ensures consistency in multi-step updates.

SQLite supports full ACID compliant transactions. MongoDB had major gaps in transaction support until recently.

SQLite Transactions

SQLite transactions have proper ACID semantics:

• Atomicity – All statements in a transaction succeed or fail as a single unit. Partial commits are not possible.
• Consistency – Transactions move the database from one valid state to another without breaking integrity constraints.
• Isolation – Concurrent transactions are isolated from uncommitted changes by others. SQLite supports serializable isolation.
• Durability – Once committed, a transaction is persisted even if there are crashes or errors.

SQLite transactions provide critical data consistency guarantees:

-- Start transaction
BEGIN;

-- Steps executed atomically  
INSERT INTO Post VALUES (1, 'New Post', 'Content here');
INSERT INTO Comment VALUES (1, 1, 'Great Post!', 'Jack');

-- If no error, commit
COMMIT;

-- Or roll back on error
ROLLBACK; 

This strong transaction support makes SQLite an excellent choice for applications that require reliable data integrity, especially in multi-user environments.

However, one caveat is that SQLite allows only one writer at a time even in transactions. Multiple transactions cannot simultaneously write to the database. So performance may suffer under heavy write contention.

MongoDB Transactions

For a long time, MongoDB lacked true multi-document ACID transactions. This made it eventually consistent but prone to anomalies when modifying related data across multiple documents.

However, MongoDB 4.0 (released in 2018) finally added support for multi-document transactions by implementing multi-document writes under snapshot isolation:

• All operations in a transaction see a consistent snapshot of data throughout execution.
• No intervening updates are visible during the transaction.
• On commit, all writes are atomically applied.

This model prevents dirty reads, dirty writes etc. But it is not serializable isolation since phantoms reads can occur.

Here is how transactions can be used:

// Start transaction session
const session = db.startSession();
session.startTransaction();

try {
  // Modify multiple documents
  db.posts.updateOne({...}, {...}, {session});
  
  db.comments.updateOne({...}, {...}, {session});  

  // Commit on success
  await session.commitTransaction();

} catch (error) {
  // Abort and rollback 
  await session.abortTransaction();
}

So MongoDB now provides ACID transactions. However, there are still limitations:

• Transactions cannot span multiple shards. Only documents belonging to the same shard can be transacted atomically.
• No transactional DDL statements. Schema modifications still remain eventually consistent.

Due to these gaps, complex transactions may need to be coordinated from the application layer if sharding is involved.

Indexing and Performance

Proper indexing is crucial for achieving good performance in read-heavy workloads. Both SQLite and MongoDB provide comprehensive indexing capabilities.

SQLite Indexing

SQLite supports standard B-Tree indices to efficiently locate records without scanning entire tables:

-- Create index
CREATE INDEX idx_title ON Posts(title);

-- Indexes used automatically
SELECT * FROM Posts WHERE title = 'Post 1';

SQLite indexes provide:

• Speed – Queries leverage indexes to avoid full table scans.
• Optimized – The query planner automatically chooses the best indexes based on usage statistics.
• Flexible – Indices can be created on any columns like id, title, timestamps etc.
• Compound – Indices can span multiple columns for efficient multi-column queries.

With proper indices, SQLite can execute queries over millions of records at high performance. It is very efficient for fast indexed access.

However, being an embedded database, disk I/O can become a bottleneck if the working set does not fit in memory. SSD storage is recommended for large workloads.

MongoDB Indexing

MongoDB also provides secondary indexes to improve query performance and support efficient sorts/ranges:

// Create index on `title` field 
db.posts.createIndex({title: 1}) 

// Index automatically used
db.posts.find({title: 'Post 1'})

MongoDB has some special index types and capabilities:

• Multi-Key Indexes – Indices can span array fields for efficient searches within them.
• Geospatial Indexes – Special indices for geospatial queries, location data.
• Text Indexes – Index content for fast text searches across string fields.
• TTL Indices – Automatically expire old documents based on timestamp indices.

Due to the document model, MongoDB avoids expensive table scan operations. The entire document is stored contiguously for fast lookups by primary key. This applies even if data is distributed across shards.

MongoDB also provides real-time analyzed query profiling. Overall, MongoDB indexing provides powerful capabilities given its document data model.

Data Types

The data types and constraints supported by each database impact what kind of data they are most suited for.

SQLite Data Types

SQLite supports standard SQL data types for storing relational data:

• Integer – For whole numbers like ID fields.
• Real – Floating point values with decimals.
• Text – Character data like strings and text content.
• Blob – Binary data like images, files.
• Date / Time – Date and time values.

Additionally, SQLite enforces type safety and constraints:

• Data type specified for each column.
• Values constrained to allowed types and ranges.
• NOT NULL constraint makes a value mandatory.
• UNIQUE constraint prevents duplicate values in a column.
• FOREIGN KEYS enforce referential integrity across tables.
• CHECK constraints validate against custom criteria.

This makes SQLite ideal for well-structured data with clear requirements. Flexible schemaless data may not be a good fit.

MongoDB Data Types

Since MongoDB is schemaless, documents can freely contain any valid BSON data types without restriction:

• String – UTF-8 strings of any length.
• Integer – 64-bit integers.
• Boolean – True/false values.
• Decimal – High-precision decimal numbers.
• Object / Document – Embedded child documents.
• Array – Lists of any other types.
• Date – Date and time values.
• ObjectId – 12-byte unique identifiers.
• Binary – Blobs, files.

Values are not restricted to predefined columns or types. Validation can be implemented in application code if required.

MongoDB provides greater flexibility for semi-structured data of varying shapes compared to SQLite’s rigidity.

Tooling and Ecosystem

The availability of complementary tools, libraries and services is key for efficiently developing applications with a database.

SQLite Ecosystem

As an embedded software library, SQLite itself provides core data storage capabilities but limited external tooling:

• The sqlite3 module for Python provides a convenient wrapper for using SQLite in applications. Similar standard libraries exist across many languages.
• DB Browser for SQLite is an open-source visual tool for browsing, querying and managing SQLite databases.
• SQLite works seamlessly with Object Relational Mappers (ORMs) like SQLAlchemy that provide an application-level abstraction layer and object model for using SQL in application code.
• It can also connect to various programming languages and BI tools like Tableau and Power BI for analysis and reporting.

Overall the ecosystem is limited compared to full-fledged database platforms – the focus is on embedded programmatic use rather than enterprise-grade tooling and administration.

MongoDB Ecosystem

MongoDB offers a robust and rapidly expanding ecosystem for development and management:

• MongoDB Compass is the native GUI for visualization, query building and analysis.
• Mongoose provides a schema-based ODM for modeling MongoDB data in Node.js applications.
• Client libraries with built-in connection pooling, retry logic etc exist for languages like Python, Java, .NET etc.
• MongoDB Charts allows building visualizations and dashboards using MongoDB data.
• MongoDB BI Connector provides integration with Business Intelligence tools.
• MongoDB Realm allows building mobile apps with data syncing.

The extensive ecosystem unlocks a breadth of capabilities beyond just the core database. These tools cater to both developers and administrators.

Administration

Since SQLite runs embedded within applications, it requires minimal dedicated administration. MongoDB instances, on the other hand, need specialized DBA skills for production use.

SQLite Administration

As an embeddable file-based database, SQLite entails almost zero administration overhead. Some simple practices are advised:

• Encapsulate database operations in repositories/services rather than directly accessing files.
• Use connection pooling instead of opening a connection per query.
• Employ parameterized queries instead of concatenating values.
• Follow language best practices for using SQLite wrappers and ORMs.
• Validate inputs and use strict typing to prevent bugs.
• Use plain disk files rather than ephemeral in-memory databases for persistence.
• Back up the database file regularly.

That’s all – no complex deployments, security config, user management, sharding, replication etc. Administration is focused on application-level use rather than managing a dedicated database server.

MongoDB Administration

MongoDB is designed for production deployments powering internet-scale applications. Managing it requires specialized database admin skills.

Typical responsibilities include:

• Installation, upgrades, configuration tuning.
• User access control, authentication and authorization.
• Database security – network encryption, TLS certificates etc.
• Backup and point-in-time recovery procedures.
• Monitoring performance metrics and logs.
• Database sizing and maintenance operations.
• Replication setup for high availability.
• Sharding configuration and management.
• Deployments on-premise or on the cloud (Atlas).

A skilled DBA is essential for managing large MongoDB deployments just like with traditional RDBMS systems.

When to Use SQLite vs MongoDB?

Now that we’ve compared SQLite and MongoDB in-depth across various criteria, let’s summarize when each one is the most optimal choice.

Use Cases for SQLite

SQLite works best when:

• You need a simple local data store for standalone desktop or mobile applications. SQLite shines for internal data management in kiosks, smartphones, IoT devices etc.
• Strong ACID transactions are required to maintain consistency across multiple operations. SQLite provides robust support for transactional workflows.
• Your data is structured and well-defined. The rigid schema and typing discipline of SQLite keeps it consistent and predictable.
• Simplicity and low overhead is valued over advanced functionality. SQLite has a lightweight embedded footprint.
• Concurrency needs are low. SQLite performance suffers under heavy concurrent write load.
• Data volumes are small – thousands to millions of records. SQLite files become slow with terabyte-scale data.
• Portability of the entire database as a single file is beneficial. SQLite makes it trivial to copy, migrate or backup the data.

So SQLite fits best for standalone applications that need a simple local data store that provides transactions and portability without major scalability needs.

Use Cases for MongoDB

MongoDB is the preferred choice when:

• Data schemas are fluid and subject to frequent changes. The flexibility of schemaless documents caters to evolving requirements.
• Developers need to iterate and modify data models rapidly as features change. MongoDB allows dynamically adapting schemas without migrations.
• Data has varied, rich and hierarchical relationships. Related records can be embedded for performance rather than needing slow SQL joins.
• Large volumes of rapidly changing unstructured or semi-structured data needs to be managed.
• High scalability and throughput is required for internet-scale applications. MongoDB provides horizontal scaling through sharding.
• Persistence, redundancy and 24/7 availability is critical. MongoDB replication provides robust high availability.
• Reporting, analysis and complex aggregation pipelines are common. MongoDB provides extensive operators and indexing.

So MongoDB fits best for large applications managing massive, ever-changing semi-structured or unstructured data with strict scalability, uptime and performance demands.

So Which Database Would You Choose?

• SQLite is ideal for standalone applications needing simple transactional data storage and portability.
• MongoDB works best for large internet-scale applications managing massive volumes of dynamic, richly structured data with demanding scalability and uptime requirements.

SQLite and MongoDB have fundamentally different architectures, data models and capabilities optimized for divergent use cases.

SQLite offers a simple, portable relational data store for embedding within standalone desktop and mobile applications. It provides SQL and transactions local to each instance.

MongoDB provides a flexible JSON-based document store designed for internet-scale access, rich data relationships, and horizontal scalability across distributed clusters.

As your application evolves, you may even leverage both for different needs – SQLite for small local data and MongoDB for large-scale cloud data synchronization for example.