MongoDB and SQLite are widely used database management systems because of their features and functionality. MongoDB allows you to handle structured and unstructured data of high volume effectively. SQLite, on the other hand, is an RDBMS that helps you handle small and medium-volume datasets.
This article compares MongoDB vs SQLite and the key differences between these two databases. You can choose to use any of these depending on the data demands of your project.
What is MongoDB?
MongoDB is a NoSQL multi-cloud database that uses a document-oriented format. Here, your data is stored in BSON format, the binary-encoded version of JSON documents. This data type enables faster querying and indexing, especially of nested data structures, making MongoDB a highly efficient database.
The data objects in MongoDB are called collections and documents. Collections are like tables in relational databases and contain a set of documents. Each document is created using fields that are key-value pairs and are similar to columns in relational databases. The documents support nested data structures, enabling you to store data records related to each other in a complex manner within the same document. As a result, it becomes easier for you to fetch these data records and work on them, making MongoDB more capable than relational databases.
MongoDB enables the storage of structured and unstructured data in the same document. You can access the data stored in MongoDB directly in JSON format. The data retrieved can be queried in different programming languages, including C, C#, Java, Python, or Scala.
You can easily deal with large volumes of data while using MongoDB. This is done through sharding, which is the process of splitting large datasets into smaller parts and storing them across multiple servers. It speeds up query execution as data load gets distributed across different servers.
Key Features of MongoDB
Some key features of MongoDB are as follows:
- Document-Oriented: In MongoDB, all the data is stored in documents as key-value pairs, which are easier to access than data records stored in tables. This makes the database suitable for faster data retrieval operations.
- Schema-less Database: A schema-less database does not have a predefined rigid schema and allows you to organize data flexibly. MongoDB is a schema-less database that enables you to store multiple documents that may or may not be similar to each other in a single collection.
- Indexing: MongoDB facilitates indexing all the fields in documents by allocating primary and secondary indices. This makes it easier to search for specific data records.
- Aggregation: You can perform grouped operations on data points in MongoDB and fetch a single outcome. To accomplish this, MongoDB enables you to use an aggregation pipeline, map-reduce function, and single-purpose aggregation method.
- Replication: You can maintain multiple copies of your data through MongoDB's data replication feature, ensuring data availability and durability.
What is SQLite?
SQLite is an SQL database engine written in the C programming language that supports text, integer, null, real, and blob data types. It acts as an embedded database, and you can use it directly within several applications for data storage without the need for a separate server.
Instead of storing data in different formats, SQLite stores data directly in a single file. You can create a complete SQL database with multiple tables, indices, triggers, and views in a single disk file called the ‘main database file.’ Any additional information during the transaction is stored in a separate file called ‘rollback journal’ or in a write-ahead log file if SQLite is in WAL mode.
SQLite is ACID-compliant and performs well even in low-memory ecosystems. When all features are enabled, it acts as a compact library with a size of up to 750 KiB, suitable for use in smaller applications. The performance of SQLite gets better with the increase in memory, but it can perform well even in low-memory ecosystems.
Key Features of SQLite
The important features of SQLite are as follows:
- Open-source: SQLite is open-source, and you do not have to pay any licensing fees to use it.
- Zero-configuration: SQLite does not require detailed configuration or setup procedures for initial installation.
- Serverless: You do not need to use different servers to operate SQLite, which simplifies infrastructure management.
- Cross-platform: It can be used on various platforms, including Android, iOS, Linux, Mac, Solaris, and Windows.
- Self-contained: SQLite is a self-contained software service with very less dependencies. It does not use external libraries or interfaces except for a few C library calls.
Key Differences Between MongoDB vs SQLite
Here is a list of some features comparing MongoDB vs SQLite:
Data Model
MongoDB is a NoSQL database system that helps you to store data in JSON-like document format. As a result, you can use it to store and query structured, semi-structured, or unstructured data.
SQLite, on the other hand, is a relational database that allows you to store structured data in tabular format.
Data Types
MongoDB supports various data types, including string, date, integer, boolean, null, array, object, and binary data. Unlike MongoDB, SQLite supports fewer data types, such as null, integer, text, blob, and real numbers.
Scalability
MongoDB facilitates scaling techniques such as replication and sharding to accommodate increased data volume. In replication, you can create copies of your database or database nodes. Each cluster node consists of data copies, and if any of the nodes is non-functional, other nodes fulfill the client request.
In sharding, data is distributed across multiple nodes in a cluster, with each node containing only a small part of the data. This technique enhances storage capacity more efficiently, as each node is responsible for processing only the data that is stored in it.
Alternatively, SQLite has limited scalability. This is because SQLite allows you to store data in a single disk file, and you may find it difficult to adjust its storage capacity to support the increase in data volume.
Concurrency
Database concurrency enables two or more users to access and query data simultaneously. MongoDB is a highly concurrent database because it facilitates concurrent read and write operations. You can use it to develop highly functional applications that require the involvement of multiple experts. It provides multi-granularity locking and concurrency control to avoid modification of the same data simultaneously.
In contrast to MongoDB, SQLite offers limited concurrency. It allows multiple readers but only one writer at a time to modify the database.
Resource Utilization
MongoDB's distributed architecture and high concurrency enable querying, reading, and writing large volumes of data simultaneously. This utilizes a large amount of CPU resources. The sharding and replication techniques further increase memory consumption.
On the other hand, SQLite offers single-threading, allowing you to query one data at a time, resulting in minimal CPU consumption. You can also opt for multi-threading according to your requirements. Moreover, SQLite is suitable for small- or medium-volume datasets, which involve less processing than large datasets.
Deployment Strategies
MongoDB offers a community edition that you can deploy locally or on the cloud. You can use MongoDB Atlas, which is fully automated and can be deployed on AWS, GCP, or Microsoft Azure. Lastly, you can also use the self-hosted Enterprise edition.
SQLite, on the other hand, can be deployed on-cloud as well as on-premise. You have to deploy it manually or using third-party services.
Use Cases
MongoDB is a highly efficient database used for many purposes, including customer data management, payment processing, and operational analytics. It is also used in mobile apps, IoT devices, product catalogs, content management, and real-time analytics.
Contrarily, SQLite is used in mobile phones, kitchen appliances, thermostats, automobiles, remote sensors, and drones. It is used as an on-disk file format for desktop applications such as media cataloging and editing suites, version control systems, CAD packages, and record-keeping programs. You can also use it as a database engine for low and medium-traffic websites.
Costs
There are two main versions of MongoDB. One is Atlas Cloud, and the other is an enterprise-advanced version. The MongoDB Atlas cloud version is categorized into free, dedicated, and serverless versions. While using these services, you are charged according to the cluster tier you are using and the cloud in which you deploy MongoDB.
SQLite is open-source and can be used for commercial or private purposes without paying license fees. However, you will have to pay to access professional support and extension products.
Tabular Comparison of MongoDB vs SQLite Server
A tabular comparison between SQLite vs MongoDB is as follows:
Factors to Consider When Choosing MongoDB or SQLite Server
Here are some factors you should consider when using MongoDB vs SQLite databases.
Data Structure
MongoDB enables you to store data as binary JSON (BSON) documents, which are flexible and schema-less data structures. As a result, you can store and query structured, semi-structured, and unstructured data. It allows you to handle nested data easily and provides flexibility to add new fields because of its dynamic schema.
SQLite is a relational database that helps you handle structured data records related to each other. It supports simpler data structures and has a fixed schema.
Depending on the data structure you are working with, you can use MongoDB or SQLite.
Scalability
MongoDB is a highly scalable database as it supports sharding and replication techniques. Thus, it can be used to build applications that require the usage of large datasets.
SQLite is suitable for small and medium datasets but cannot handle large datasets due to single-file data storage.
According to a StackOverflow survey, 3816 developers who worked with SQLite in 2024 want to work with MongoDB. As a result, you should utilize MongoDB for large datasets that require distributed scaling and complex querying and SQLite for smaller datasets with limited resource requirements.
Deployment Ecosystem
Both MongoDB and SQLite can be deployed on the cloud or on-premise, according to your preferences. However, you can deploy MongoDB on platforms such as AWS, GCP, or Microsoft Azure. SQLite is self-sufficient as it depends less on external libraries and can be ported easily to most data systems. You can use MongoDB or SQLite based on your deployment choices and available resources.
You can integrate data from your source systems into MongoDB or SQLite databases to leverage their effective features. For this, you can take the assistance of third-party data integration tools like Airbyte. Let’s see how Airbyte can help you reap the benefits of MongoDB and SQLite functionalities.
Integrate Your Data into MongoDB or SQLite with Airbyte
Airbyte is an ELT data integration platform that helps you collect data from different sources and consolidate it into a centralized location. It offers an extensive library of 400+ connectors, allowing you to integrate data from different sources into preferred destinations, including MongoDB or SQLite databases. This unified data can be further utilized for various purposes, including analytics, developing AI and ML applications, or fulfilling any data-driven objective in your organization.
Some of the important features of Airbyte are as follows:
- Support for Custom Connectors: Airbyte allows you to build custom connectors using a connector builder, low-code connector development kit (CDK), Python CDK, or Java CDK.
- GenAI Workflow Management: You can streamline your AI workflows with Airbyte, as it allows you to load structured and unstructured data directly to vector store destinations.
- Developer Friendly Pipeline Building: PyAirbyte is a library that allows you to utilize Airbyte connectors in the Python ecosystem. You can extract data using PyAirbyte from different sources and load it to SQL caches such as Postgres or Snowflake.
- Custom Transformations Using dbt: After syncing data, you can integrate Airbyte with dbt to transform your raw data into a form suitable for data analysis, business intelligence, or any other application.
Conclusion
MongoDB vs SQLite are both highly efficient and popular databases that fulfill different requirements. This blog gives a detailed explanation of the key differences between these databases. Based on these differences, you can make an informed choice of database to achieve your data-driven objectives.