Liquibase vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Liquibase

Stacks638

Followers648

Votes70

GitHub Stars5.3K

Forks1.9K

Liquibase vs MongoDB: What are the differences?

Introduction

Liquibase and MongoDB are both popular database technologies used in software development. Liquibase is an open-source tool for database schema management, while MongoDB is a NoSQL document-oriented database. Despite both being used to store and manage data, there are several key differences between Liquibase and MongoDB.

Data Model: Liquibase is designed to work with traditional SQL databases and follows a relational data model where data is organized into tables with predefined schemas. On the other hand, MongoDB is a NoSQL database that uses a flexible document model where data is stored in documents with dynamic schemas, allowing for more varied and unstructured data storage.
Query Language: Liquibase uses SQL (Structured Query Language) as its primary query language. SQL provides a structured and consistent way of querying and manipulating relational data in a database. MongoDB, on the other hand, uses a flexible query language called MongoDB Query Language (MQL), which allows for complex queries on the document-based data model.
Scalability: Liquibase primarily focuses on managing database schemas and version control. It doesn't have built-in scalability features, but it can be used with databases that offer scalability options. MongoDB, being a NoSQL database, is built for scalability and can handle large amounts of data and high traffic loads by distributing data across multiple servers.
Schema Flexibility: Liquibase requires a predefined schema for the database tables it manages. This means that the structure of the data is fixed and any changes to the schema require manual modifications. MongoDB, on the other hand, has a flexible schema that allows developers to store varying and evolving data structures within the same collection without requiring predefined schemas.
ACID Compliance: Liquibase, being a tool for managing traditional SQL databases, ensures ACID (Atomicity, Consistency, Isolation, Durability) compliance by leveraging the transaction capabilities provided by the underlying database system. MongoDB, being a NoSQL database, relaxes ACID guarantees in favor of performance and scalability, offering eventual consistency instead.
Replication and Sharding: Liquibase relies on the replication and sharding features provided by the underlying database system for high availability and scalability. It doesn't have built-in replication and sharding capabilities. MongoDB, on the other hand, has built-in support for replication and sharding, allowing for automatic failover and distributed data storage across multiple servers.

In Summary, Liquibase is a schema management tool for SQL databases that follows a relational data model, while MongoDB is a flexible NoSQL document-oriented database with built-in scalability features.

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Manual

Advice on MongoDB, Liquibase

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	Liquibase
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.	Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Supports code branching and merging;Supports multiple developers;Supports multiple database types;Supports XML, YAML, JSON and SQL formats;Supports context-dependent logic;Cluster-safe database upgrades;Generate Database change documentation;Rollbacks;Generate Database "diff's";Run through your build process, embedded in your application or on demand;Automatically generate SQL scripts for DBA code review;Does not require a live database connection;Stored logic
Statistics
GitHub Stars 27.7K	GitHub Stars 5.3K
GitHub Forks 5.7K	GitHub Forks 1.9K
Stacks 96.6K	Stacks 638
Followers 82.0K	Followers 648
Votes 4.1K	Votes 70
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 18 Many DBs supported 18 Great database tool 12 Easy setup 8 Database independent migration scripts 5 Database version controller Cons 5 Documentation is disorganized 5 No vendor specifics in XML format - needs workarounds
Integrations
No integrations available	Amazon RDS for MariaDB Travis CI SAP HANA Oracle PostgreSQL Sybase jFrog GitHub Actions Firebird IBM DB2

What are some alternatives to MongoDB, Liquibase?

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.

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

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.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

Related Comparisons

Liquibase vs MongoDB: What are the differences?

Introduction

Data Model: Liquibase is designed to work with traditional SQL databases and follows a relational data model where data is organized into tables with predefined schemas. On the other hand, MongoDB is a NoSQL database that uses a flexible document model where data is stored in documents with dynamic schemas, allowing for more varied and unstructured data storage.
Query Language: Liquibase uses SQL (Structured Query Language) as its primary query language. SQL provides a structured and consistent way of querying and manipulating relational data in a database. MongoDB, on the other hand, uses a flexible query language called MongoDB Query Language (MQL), which allows for complex queries on the document-based data model.
Scalability: Liquibase primarily focuses on managing database schemas and version control. It doesn't have built-in scalability features, but it can be used with databases that offer scalability options. MongoDB, being a NoSQL database, is built for scalability and can handle large amounts of data and high traffic loads by distributing data across multiple servers.
Schema Flexibility: Liquibase requires a predefined schema for the database tables it manages. This means that the structure of the data is fixed and any changes to the schema require manual modifications. MongoDB, on the other hand, has a flexible schema that allows developers to store varying and evolving data structures within the same collection without requiring predefined schemas.
ACID Compliance: Liquibase, being a tool for managing traditional SQL databases, ensures ACID (Atomicity, Consistency, Isolation, Durability) compliance by leveraging the transaction capabilities provided by the underlying database system. MongoDB, being a NoSQL database, relaxes ACID guarantees in favor of performance and scalability, offering eventual consistency instead.
Replication and Sharding: Liquibase relies on the replication and sharding features provided by the underlying database system for high availability and scalability. It doesn't have built-in replication and sharding capabilities. MongoDB, on the other hand, has built-in support for replication and sharding, allowing for automatic failover and distributed data storage across multiple servers.

Liquibase vs MongoDB

Overview

Liquibase vs MongoDB: What are the differences?