MongoDB vs DynamoDB – Comparing NoSQL Databases
Databases serve as a crucial architectural element in numerous applications and services. Organizations traditionally favored relational databases like SQL Server, Oracle, MySQL, and Postgres. These databases use tables and structured languages to enforce data integrity but have limitations.
MongoDB and DynamoDB are NoSQL databases; NoSQL databases, also known as non-relational databases, specialize in managing extensive amounts of varied data, i.e., unstructured data for real-time web applications. While they may differ in data integrity, their key advantage is seamless scalability across multiple servers. Major companies use these databases widely for massive data storage and high query loads. They can accommodate various data types and provide flexibility for different use cases, with a strong focus on horizontal scalability.
NoSQL databases have gained such popularity that major companies depend on them to store hundreds of terabytes of data and process millions of queries per second.
Let’s look at the two popular options, DynamoDB and MongoDB.
What is MongoDB?
MongoDB uses BSON as an internal format for document storage. BSON is a binary representation of JSON, fully encompassing all JSON features while supporting additional data types. It offers improved compression efficiency and enhanced ease of parsing. In MongoDB, collections have the flexibility of optional schema validation for new documents.
MongoDB is a general-purpose database capable of handling multiple workloads and serving various purposes within an application. One of its key strengths lies in its flexible schema design, which eliminates the need for a fixed schema to define how to store data. Additionally, it offers scalability in both vertical and horizontal dimensions. Security is a priority, with built-in features for authentication and authorization. Its document model seamlessly aligns with objects in application code, simplifying data management processes.
Migration: You can use “mongorestore” to migrate from a self-hosted deployment to MongoDB Atlas, the fully managed service for MongoDB deployments in the cloud. The “mongorestore” utility restores a binary backup initially created by “mongodump.”
- High Speed and Higher Availability: MongoDB is a document-oriented database solution with features like replication and GridFS, contributing to improved data availability. It offers efficient document access through indexing. It exhibits exceptional performance, performing up to 100 times faster than traditional relational databases.
- Flexibility: MongoDB has flexible database schemas that enable you to insert data without concerning yourself with strict matching criteria for data types. MongoDB offers a broader range of native data types than DynamoDB and allows for the nesting of documents.
- Data Model: MongoDB provides support for both regular JSON and advanced BSON data models, including data types like int, long, date, timestamp, geospatial, floating-point, and Decimal128.
- Cost: MongoDB offers a free, open-source version. They’ve recently introduced a pay-as-you-go, serverless pricing option for MongoDB Atlas, their managed cloud service.
- Sharding: MongoDB mitigates server challenges related to large datasets by implementing sharding. This mechanism partitions and allocates data across multiple servers for processing, all without causing disruptions to ongoing activities.
- Technical Support: MongoDB provides technical support, including community forums, Atlas, Cloud Manager, Enterprise, and Ops Manager. If any problems arise, the expert customer support team is readily available to assist clients.
- Memory Use: MongoDB relies on keeping its working set in RAM to achieve satisfactory performance. This dependency on RAM renders MongoDB costly for a wide range of use cases.
- Data Duplication: Data duplication may arise in MongoDB compared to relational databases due to its use of nested documents rather than normalized tables. This is often due to the need for denormalization, as MongoDB doesn’t facilitate high-performance JOIN operations. MongoDB’s approach centers around storing related data together, eliminating the need for JOINS but potentially increasing data sizes and associated expenses.
- Indexing: MongoDB supports simple and complex compound indexes with multiple document attributes. Like most databases, poorly designed or absent indexes can slow down read and write operations since the index must be updated each time a new document gets inserted into a collection.
- Joins: Performing document joins in MongoDB can be challenging, primarily because it lacks native join support, a feature commonly found in relational databases.
Users can implement join functionality by manually adding code. However, fetching data from multiple collections necessitates multiple queries, potentially resulting in fragmented code and time-consuming operations.
What is DynamoDB?
DynamoDB is a fast, flexible NoSQL database developed by Amazon. It is well-suited for any application that demands consistent latency at any scale. DynamoDB, a fully managed NoSQL database, is well-suited for document and key-value data models.
It can seamlessly scale on-demand to support unlimited read and write operations while maintaining response times within single-digit milliseconds.
This serverless database horizontally scales to support tables of any size, making it well-suited for large-scale performance. Furthermore, its query performance remains consistent even as the database size grows when querying by key. Additionally, it offers a flexible schema, allowing you to easily adjust table structures as your requirements evolve without requiring extensive schema changes.
DynamoDB maintains continuous backups of your data to prevent any loss, and it incorporates data encryption to enhance security, making it particularly well-suited for enterprise applications with stringent security demands.
- Customizable: The DynamoDB can be customized to meet your application’s requirements.
- Speed: DynamoDB consistently provides exceptional performance, ensuring fast response times whether you store a few records or query it frequently by key.
- Scalability: DynamoDB scales to handle increased traffic levels without any performance degradation.
- Low Latency: DynamoDB ensures consistent, low-latency responses in the single-digit millisecond range, rendering it well-suited for real-time applications and high-demand workloads.
- Pricing: DynamoDB employs a pay-as-you-go, throughput-based pricing approach, where multiple variables can impact costs. This model enables cost optimization in line with workload changes but may introduce pricing unpredictability.
- Seamless Data Replication: DynamoDB’s default configuration involves replicating table data across three availability zones within a single region. This setup ensures robust disaster recovery capabilities and minimizes service disruptions.
Furthermore, DynamoDB provides the option to employ global tables, which enable data replication across multiple regions spanning different geographical locations. This multi-regional replication enhances redundancy, offering an additional layer of data protection.
Data replication occurs almost instantaneously, facilitating seamless failover in a disaster, all while minimizing data loss.
- Fully Managed: DynamoDB is a fully managed NoSQL database service that relieves users of the responsibility of maintaining the underlying infrastructure. This approach enables users to concentrate on application development. At the same time, AWS takes care of tasks like ensuring high availability, managing database upgrades and maintaining the physical infrastructure in their data centers.
- Limited Query Language: DynamoDB offers a more constrained query language than MongoDB. This limitation arises from DynamoDB’s nature as a key-value store rather than a comprehensive document database. In DynamoDB, each record is characterized by two primary keys: partition and sort keys. When conducting queries in it, you must provide at least one partition key, and you can specify a single value or a range for the sort key; this encompasses the entirety of the query language’s capabilities.
- Difficult To Predict Costs: DynamoDB enables users to choose an appropriate capacity allocation method depending on their use case. Users can opt for the provisioned capacity model for applications with predictable traffic and requests. This model allocates a specified number of read and write units, ensuring that resources remain available even during periods of low utilization.
- On the other hand, the on-demand capacity allocation model automatically adjusts the read and write capacity in response to the number of requests sent to the database service. This model is well-suited for applications that experience unpredictable spikes in requests. While the on-demand model provides seamless scaling, it has the drawback of potentially unpredictable and higher costs.
- Unable To Use Table Joins: DynamoDB imposes limitations on querying data within its tables and restricts the complexity of queries. One notable restriction is the inability to query information from multiple tables, as DynamoDB does not support table joins. This limitation can be a significant drawback, as it prevents developers from executing complex queries on the data, which may be achievable in other competitive database products.
Side-by-Side Comparison of MongoDB and DynamoDB
|MongoDB is an open-source database from MongoDB Inc. that can be deployed in various environments, including most cloud platforms or on-premises.
|DynamoDB is an integral part of the Amazon Web Services (AWS) ecosystem and is exclusively designed for use within the AWS cloud environment.
|MongoDB can be self-managed or fully managed using MongoDB Atlas, which offers a database-as-a-service solution.
|DynamoDB is a fully managed solution; Amazon is responsible for server updates, patch updates, and hardware provisioning.
|Developers must allocate additional time at the outset to configure security settings for MongoDB, particularly when self-managing due to MongoDB’s default configurations allowing unrestricted and direct data access without authentication. The authentication remains turned off by default, which may pose security risks if not configured correctly.
|DynamoDB initially implements security in a restrictive manner and integrates seamlessly with the AWS Identity and Access Management (IAM) Policy framework.
|MongoDB’s database structure is built upon JSON-like documents consisting of collections, keys, values, and nested documents.
|In DynamoDB’s database structure, you can use either blobs or documents as values.
|MongoDB permits the presence of up to 64 mutable indexes per collection, offering the flexibility for the document’s structure to change dynamically.
|DynamoDB accommodates a maximum of 20 mutable global indexes per table, independent of the underlying data, and up to 5 local indexes; this cannot be altered once the table is created.
|Data Types and Sizes
|MongoDB supports multiple data types, like regular JSON and advanced BSON data types (int, long, date, timestamp, geospatial, floating point, and decimal128), and permits document sizes of up to 16MB.
|DynamoDB offers restricted support for data types (number, string, and binary only) and allows item sizes of up to 400 KB.
|MongoDB features a versatile query language that can be employed in various ways, encompassing single keys, ranges, graph traversals, joins, and beyond.
|DynamoDB’s querying capabilities are exclusively accessible through local secondary indexes (LSI) and global secondary indexes (GSI).
|MongoDB follows a fixed pricing model, covering RAM, I/O, storage, and administrative expenses. It’s suitable for predictable workloads, but costs can be less predictable.
|DynamoDB employs a flexible pricing model based on actual usage, with additional charges for various features like backup and restore; this can lead to increased variability.
|Organizations use MongoDB for various purposes, including mobile applications, content management systems (CMSs), and its proficiency in scalability and caching.
|DynamoDB is used in gaming and the Internet of Things (IoT) sectors.
|MongoDB offers strong consistency, built-in schema control, data validation, and ACID transactions covering documents, indexes, and backups, supporting up to 1,000 operations per transaction.
|DynamoDB uses eventual consistency, may require handling potentially outdated data, lacks native data validation, and has limited support for strongly consistent reads; ACID transactions apply only to table data, not indexes or backups, with a maximum of 100 writes per transaction.
|“Mongorestore” and “Mongodump” are handy for creating backups with BSON data dumps suitable for small deployments. MongoDB Atlas offers diverse backup options, including on-demand, continuous, and snapshot backups. Continuous backups are fully managed and provide real-time data backup, while snapshot backups offer a cost-effective alternative. Both options allow direct querying of backups, saving time and resources by avoiding data restoration.
|DynamoDB also provides continuous backups. Still, these backups do not support direct querying.
Furthermore, there is an additional cost associated with restoring backups. Additionally, backups don’t include specific configurations and require manual reconfiguration.
When to use MongoDB v/s DynamoDB?
MongoDB offers robust query capabilities, making it ideal for complex queries, text searches, geospatial data, and aggregations. Supported by a vibrant community, it provides extensive resources and multi-language support. Its schema-less design allows for swift document additions, which speed up the development, specifically when your project’s schema evolves with frequent changes, eliminating the need for constant document reformatting.
DynamoDB – It could be the optimal selection if your requirements demand high-performance and low-latency access to key-value data. It is preferred when your organization is integrated with Amazon services.
Which NoSQL database should you go with?
DynamoDB and MongoDB are highly successful contemporary replacements for conventional database systems like MySQL, PostgreSQL, and others. When choosing your database, evaluating scalability, user needs, deployment approach, storage requirements, and feature set is crucial.
MongoDB and DynamoDB are robust NoSQL databases that effectively address various user requirements. It’s essential to carefully assess whether a particular database aligns with your use case. Each database offers unique advantages, so it’s necessary to consider your long-term cloud strategy and your application’s precise needs when deciding which NoSQL database to choose.
Jilesh Patadiya, the visionary Founder and Chief Technology Officer (CTO) behind AccuWeb.Cloud. Founder & CTO at AccuWebHosting.com. He shares his web hosting insights on the AccuWeb.Cloud blog. He mostly writes on the latest web hosting trends, WordPress, storage technologies, and Windows and Linux hosting platforms.