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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Dgraph vs MongoDB

Dgraph vs MongoDB

OverviewDecisionsComparisonAlternatives

Overview

MongoDB
MongoDB
Stacks96.6K
Followers82.0K
Votes4.1K
GitHub Stars27.7K
Forks5.7K
Dgraph
Dgraph
Stacks124
Followers221
Votes9
GitHub Stars21.3K
Forks1.6K

Dgraph vs MongoDB: What are the differences?

  1. Data Model: Dgraph uses a graph-based data model, allowing for complex relationships between data entities, while MongoDB uses a document-based data model, which is more suited for hierarchical data structures.
  2. Query Language: Dgraph supports GraphQL as its query language, providing a more intuitive and powerful way to retrieve and manipulate data, whereas MongoDB uses its query language based on JSON-like syntax.
  3. Indexing: Dgraph provides automatic indexing on all predicates (properties) which results in faster queries, while MongoDB requires explicit declaration and management of indexes which can impact query performance if not done efficiently.
  4. Scalability: Dgraph is designed from the ground up to be distributed and horizontally scalable, offering better support for handling large volumes of data and high traffic loads compared to MongoDB which may require more manual effort to scale effectively.
  5. ACID Transactions: MongoDB supports multi-document ACID transactions, ensuring data consistency across multiple operations, while Dgraph lacks full ACID transaction support, though it does offer some level of data consistency through its commitment to eventual consistency.
  6. Community Support: MongoDB has a larger and more established community, providing extensive resources, documentation, and support for users, whereas Dgraph, being a newer technology, may have a smaller community and fewer resources available.

In Summary, Dgraph and MongoDB differ in their data models, query languages, indexing mechanisms, scalability, transaction support, and community resources.

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Advice on MongoDB, Dgraph

George
George

Student

Mar 18, 2020

Needs adviceonPostgreSQLPostgreSQLPythonPythonDjangoDjango

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
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
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
MongoDB
Dgraph
Dgraph

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.

Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.

Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
-
Statistics
GitHub Stars
27.7K
GitHub Stars
21.3K
GitHub Forks
5.7K
GitHub Forks
1.6K
Stacks
96.6K
Stacks
124
Followers
82.0K
Followers
221
Votes
4.1K
Votes
9
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
  • 3
    Graphql as a query language is nice if you like apollo
  • 2
    Low learning curve
  • 2
    Easy set up
  • 1
    Open Source
  • 1
    High Performance

What are some alternatives to MongoDB, Dgraph?

MySQL

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

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

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

SQLite

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

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

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

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.

Neo4j

Neo4j

Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.

RethinkDB

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

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.

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