MongoDB vs eXist-db

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

eXist-db

Stacks8

Followers17

Votes0

MongoDB vs eXist-db: What are the differences?

Introduction:

MongoDB and eXist-db are both document-oriented databases, but they have key differences that set them apart.

Data Structure: MongoDB stores data in a flexible and schema-less format. It uses BSON (Binary JSON) to represent data, allowing for easy document handling and dynamic fields. On the other hand, eXist-db stores data in XML format. It enforces a specific structure for documents using XML schemas, providing validation and ensuring data integrity.
Querying Language: MongoDB uses a query language called MongoDB Query Language (MQL). MQL is a rich and expressive language that allows for complex queries, including projections, aggregations, and geospatial queries. In contrast, eXist-db uses a query language called XQuery, which is specifically designed for querying XML data. XQuery enables powerful XML querying and transformation capabilities, including support for XPath expressions.
Replication and Sharding: MongoDB has built-in support for replication, allowing data to be distributed across multiple replica sets for high availability and fault tolerance. It also supports sharding, which enables horizontal scaling by distributing data across multiple shards. eXist-db, on the other hand, does not have built-in replication or sharding capabilities. Replication and sharding would need to be implemented manually if required.
Indexing: MongoDB provides a wide range of indexing options to optimize query performance, including single-field indexes, compound indexes, geospatial indexes, text indexes, and more. eXist-db also supports indexing but primarily focuses on XML indexing using XPath expressions for efficient retrieval of XML data.
Transactions: MongoDB supports multi-document ACID transactions, allowing multiple operations to be grouped together atomically. This provides data consistency and integrity in the face of concurrent operations. eXist-db does not have built-in support for ACID transactions. While it provides locking mechanisms to ensure data consistency, it does not support transactional operations across multiple documents.
Support for Programming Languages: MongoDB has official drivers available for a wide range of programming languages, including Java, Python, JavaScript, C#, Ruby, and more. These drivers provide native connectivity and APIs for interacting with MongoDB. eXist-db primarily focuses on Java, as it is developed in Java and provides an extensive Java API. While there are community-contributed libraries for other languages, the official support may be limited.

In Summary, MongoDB and eXist-db differ in their data structure (MongoDB using BSON and eXist-db using XML), querying language (MongoDB using MQL and eXist-db using XQuery), support for replication and sharding (MongoDB has built-in support while eXist-db does not), indexing options (MongoDB offering a wider range), support for transactions (MongoDB supporting ACID transactions while eXist-db does not), and support for programming languages (MongoDB having official drivers for multiple languages).

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Advice on MongoDB, eXist-db

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	eXist-db
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 known as High-performance native XML database engine and all-in-one solution for application building.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Native XML Database; Rich Application server; FullText indexing; NoSQL; XQuery Processor; XML processing stack; betterForm XForms integration
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 8
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	Meteor MEAN Datadog Clever Cloud Mongoose Sails.js Metabase

What are some alternatives to MongoDB, eXist-db?

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 eXist-db: What are the differences?

Introduction:

MongoDB and eXist-db are both document-oriented databases, but they have key differences that set them apart.

Data Structure: MongoDB stores data in a flexible and schema-less format. It uses BSON (Binary JSON) to represent data, allowing for easy document handling and dynamic fields. On the other hand, eXist-db stores data in XML format. It enforces a specific structure for documents using XML schemas, providing validation and ensuring data integrity.
Querying Language: MongoDB uses a query language called MongoDB Query Language (MQL). MQL is a rich and expressive language that allows for complex queries, including projections, aggregations, and geospatial queries. In contrast, eXist-db uses a query language called XQuery, which is specifically designed for querying XML data. XQuery enables powerful XML querying and transformation capabilities, including support for XPath expressions.
Replication and Sharding: MongoDB has built-in support for replication, allowing data to be distributed across multiple replica sets for high availability and fault tolerance. It also supports sharding, which enables horizontal scaling by distributing data across multiple shards. eXist-db, on the other hand, does not have built-in replication or sharding capabilities. Replication and sharding would need to be implemented manually if required.
Indexing: MongoDB provides a wide range of indexing options to optimize query performance, including single-field indexes, compound indexes, geospatial indexes, text indexes, and more. eXist-db also supports indexing but primarily focuses on XML indexing using XPath expressions for efficient retrieval of XML data.
Transactions: MongoDB supports multi-document ACID transactions, allowing multiple operations to be grouped together atomically. This provides data consistency and integrity in the face of concurrent operations. eXist-db does not have built-in support for ACID transactions. While it provides locking mechanisms to ensure data consistency, it does not support transactional operations across multiple documents.
Support for Programming Languages: MongoDB has official drivers available for a wide range of programming languages, including Java, Python, JavaScript, C#, Ruby, and more. These drivers provide native connectivity and APIs for interacting with MongoDB. eXist-db primarily focuses on Java, as it is developed in Java and provides an extensive Java API. While there are community-contributed libraries for other languages, the official support may be limited.

MongoDB vs eXist-db

Overview