Amazon QLDB vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Amazon QLDB

Stacks5

Followers17

Votes0

Amazon QLDB vs MongoDB: What are the differences?

Data Model: Amazon QLDB utilizes a unique document-oriented model that allows users to organize data in a more flexible and scalable way compared to MongoDB's JSON-based document model.
Consistency: Amazon QLDB maintains a central ledger that provides an immutable and tamper-proof history of all changes, ensuring strong consistency across all transactions, while MongoDB offers eventual consistency by default.
Use Cases: Amazon QLDB is specifically designed for applications that require verifiable transactions and audit trails, such as financial systems, supply chain management, and regulatory compliance, whereas MongoDB is more suitable for general-purpose applications with flexible schemas.
Query Language: Amazon QLDB uses its proprietary PartiQL language, which is similar to SQL and offers advanced query capabilities for retrieving and analyzing data in the ledger, while MongoDB uses a query language based on JSON-like syntax for data manipulation.
Scaling: Amazon QLDB automatically scales read and write capacity to handle varying workloads, making it ideal for applications with unpredictable traffic patterns, whereas MongoDB requires manual sharding and scaling configurations for large-scale deployments.
Integration: Amazon QLDB seamlessly integrates with other AWS services such as Lambda, S3, and IAM, enabling users to build serverless applications with ease, whereas MongoDB offers a wide range of third-party integrations and connectors for various platforms and frameworks.

In Summary, Amazon QLDB and MongoDB differ in their data models, consistency, use cases, query languages, scaling capabilities, and integration options.

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Advice on MongoDB, Amazon QLDB

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

MongoDB	Amazon QLDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.	It is a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log ‎owned by a central trusted authority. It can be used to track each and every application data change and maintains a complete and verifiable history of changes over time.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Immutable and Transparent; Cryptographically Verifiable; Serverless; Easy to Use; Streaming Capability
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 5
Followers 82.0K	Followers 17
Votes 4.1K	Votes 0
Pros & Cons
Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language	No community feedback yet
Integrations
No integrations available	AWS Lambda Amazon Redshift Amazon Kinesis Amazon Elasticsearch Service

What are some alternatives to MongoDB, Amazon QLDB?

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Amazon QLDB vs MongoDB: What are the differences?

Data Model: Amazon QLDB utilizes a unique document-oriented model that allows users to organize data in a more flexible and scalable way compared to MongoDB's JSON-based document model.
Consistency: Amazon QLDB maintains a central ledger that provides an immutable and tamper-proof history of all changes, ensuring strong consistency across all transactions, while MongoDB offers eventual consistency by default.
Use Cases: Amazon QLDB is specifically designed for applications that require verifiable transactions and audit trails, such as financial systems, supply chain management, and regulatory compliance, whereas MongoDB is more suitable for general-purpose applications with flexible schemas.
Query Language: Amazon QLDB uses its proprietary PartiQL language, which is similar to SQL and offers advanced query capabilities for retrieving and analyzing data in the ledger, while MongoDB uses a query language based on JSON-like syntax for data manipulation.
Scaling: Amazon QLDB automatically scales read and write capacity to handle varying workloads, making it ideal for applications with unpredictable traffic patterns, whereas MongoDB requires manual sharding and scaling configurations for large-scale deployments.
Integration: Amazon QLDB seamlessly integrates with other AWS services such as Lambda, S3, and IAM, enabling users to build serverless applications with ease, whereas MongoDB offers a wide range of third-party integrations and connectors for various platforms and frameworks.

In Summary, Amazon QLDB and MongoDB differ in their data models, consistency, use cases, query languages, scaling capabilities, and integration options.

Amazon QLDB vs MongoDB

Overview