MongoDB vs QuestDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

QuestDB

Stacks19

Followers50

Votes17

GitHub Stars16.3K

Forks1.5K

MongoDB vs QuestDB: What are the differences?

Data model: MongoDB is a document-oriented NoSQL database, where data is stored in flexible, JSON-like documents. QuestDB, on the other hand, is a time-series database, optimized for handling high volumes of time-stamped data, such as financial market data or IoT sensor data.
Query Language: MongoDB uses a powerful and flexible query language, known as MongoDB Query Language (MQL), which allows developers to perform complex queries on their data. QuestDB, on the other hand, uses a more streamlined SQL-like query language, making it easier for developers familiar with SQL to work with the database.
Performance: MongoDB provides high scalability and availability, with support for horizontal scaling through sharding. It can handle large volumes of data and high write and read loads. QuestDB, on the other hand, is designed specifically for time-series data, providing extremely fast ingestion rates and efficient storage and retrieval of time-stamped data.
Data Compression: MongoDB does not have built-in support for data compression, which can lead to larger storage requirements and slower performance. QuestDB, on the other hand, has built-in support for data compression, reducing storage requirements and improving query performance.
Indexing: MongoDB supports a variety of indexing options, allowing developers to optimize query performance by creating indexes on the fields used in their queries. QuestDB specializes in time-series data and provides built-in indexing and compression techniques specifically tailored for time-series data, further improving query performance.
ACID Compliance: MongoDB supports ACID transactions, providing atomicity, consistency, isolation, and durability for data operations. QuestDB, on the other hand, sacrifices some ACID properties to achieve high write performance, focusing on fast ingestion of time-series data.

In Summary, MongoDB is a document-oriented NoSQL database with a flexible data model, powerful query language, and support for large-scale data management. QuestDB, on the other hand, is a specialized time-series database optimized for handling time-stamped data, with fast ingestion rates, efficient storage, and SQL-like query language.

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Advice on MongoDB, QuestDB

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	QuestDB
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.	QuestDB is an open source database for time series, events, and analytical workloads with a primary focus on performance. It enhances ANSI SQL with time series extensions.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Relational model for time series; SIMD accelerated queries; Time partitioned; Heavy parallelization; Scalable ingestion; Immediate consistency; Time series and relational joins; Native InfluxDB line protocol; Grafana through Postgres wire support; Schema or schema-free; Aggregations and down sampling
Statistics
GitHub Stars 27.7K	GitHub Stars 16.3K
GitHub Forks 5.7K	GitHub Forks 1.5K
Stacks 96.6K	Stacks 19
Followers 82.0K	Followers 50
Votes 4.1K	Votes 17
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	Pros 2 Open source 2 Time-series data analysis 2 No dependencies 2 Postgres wire protocol 2 SQL
Integrations
No integrations available	InfluxDB Java PostgreSQL

What are some alternatives to MongoDB, QuestDB?

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

MongoDB vs QuestDB: What are the differences?

Data model: MongoDB is a document-oriented NoSQL database, where data is stored in flexible, JSON-like documents. QuestDB, on the other hand, is a time-series database, optimized for handling high volumes of time-stamped data, such as financial market data or IoT sensor data.
Query Language: MongoDB uses a powerful and flexible query language, known as MongoDB Query Language (MQL), which allows developers to perform complex queries on their data. QuestDB, on the other hand, uses a more streamlined SQL-like query language, making it easier for developers familiar with SQL to work with the database.
Performance: MongoDB provides high scalability and availability, with support for horizontal scaling through sharding. It can handle large volumes of data and high write and read loads. QuestDB, on the other hand, is designed specifically for time-series data, providing extremely fast ingestion rates and efficient storage and retrieval of time-stamped data.
Data Compression: MongoDB does not have built-in support for data compression, which can lead to larger storage requirements and slower performance. QuestDB, on the other hand, has built-in support for data compression, reducing storage requirements and improving query performance.
Indexing: MongoDB supports a variety of indexing options, allowing developers to optimize query performance by creating indexes on the fields used in their queries. QuestDB specializes in time-series data and provides built-in indexing and compression techniques specifically tailored for time-series data, further improving query performance.
ACID Compliance: MongoDB supports ACID transactions, providing atomicity, consistency, isolation, and durability for data operations. QuestDB, on the other hand, sacrifices some ACID properties to achieve high write performance, focusing on fast ingestion of time-series data.

MongoDB vs QuestDB

Overview