Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Greenplum Database

Stacks45

Followers111

Votes0

GitHub Stars6.2K

Forks1.7K

Greenplum Database vs MongoDB: What are the differences?

Introduction:

Greenplum Database and MongoDB are both popular databases used for different purposes. Greenplum Database is a massively parallel processing (MPP) database designed for handling large-scale analytical workloads, while MongoDB is a document-oriented database designed for flexibility and scalability. These databases have significant differences in various aspects, including their data models, query languages, and scaling capabilities.

Data Model:

Greenplum Database follows a relational data model, where data is organized in tables with rows and columns. It supports structured data and enforces strong consistency and data integrity through primary and foreign key constraints. On the other hand, MongoDB follows a document data model, where data is stored in flexible, semi-structured JSON-like documents. It allows storing nested data structures and provides high flexibility in schema design.

Query Language:

Greenplum Database supports SQL as its query language, which is a widely used language for relational databases. It provides a rich set of SQL features for querying and manipulating structured data. MongoDB, on the other hand, uses a query language based on JSON documents. It supports a powerful and expressive query language that can handle complex data structures and provide powerful aggregation capabilities.

Scalability:

Greenplum Database is designed for scalable analytics and can scale horizontally by adding more compute nodes. It leverages parallel processing to distribute data and work across multiple nodes, allowing for high-performance analytics on large datasets. On the other hand, MongoDB is designed for horizontal scalability as well, but it achieves scalability through sharding, where data is partitioned and distributed across multiple servers. This allows MongoDB to handle high write and read workloads across multiple nodes.

Storage:

Greenplum Database uses a row-oriented storage format, where data is stored in rows on disk. This format is optimized for analytical workloads that involve scanning large amounts of data. MongoDB, on the other hand, uses a document-oriented storage format, where data is stored in JSON-like documents. This format provides flexibility in querying and updating specific fields within documents, making it suitable for applications with frequent updates and dynamic schema requirements.

Indexing:

Greenplum Database supports various indexing techniques like B-tree, bitmap, and hash indexes to improve query performance on large datasets. These indexes are optimized for analytical queries and can significantly speed up data retrieval. MongoDB also supports indexing but offers additional indexing options like text indexes and geospatial indexes. These indexes are useful for text search and geospatial queries, making MongoDB suitable for applications that require advanced indexing capabilities.

Concurrency Control:

Greenplum Database provides strong concurrency control mechanisms like multiversion concurrency control (MVCC) to ensure data consistency in a parallel processing environment. It supports ACID (Atomicity, Consistency, Isolation, Durability) properties for transactions, allowing multiple concurrent users to access and modify the data safely. MongoDB, on the other hand, provides weaker concurrency guarantees and does not support multi-document transactions by default. It focuses more on scalability and availability rather than strong consistency.

In Summary, Greenplum Database and MongoDB differ significantly in their data models, query languages, scalability approaches, storage formats, indexing capabilities, and concurrency control mechanisms. While Greenplum Database is optimized for large-scale analytics with a relational data model and SQL query language, MongoDB is designed for flexible document storage with JSON-based queries and horizontal scalability through sharding. Both databases have their strengths and are suitable for different use cases.

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Advice on MongoDB, Greenplum Database

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	Greenplum Database
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 massively parallel processing (MPP) database server with an architecture specially designed to manage large-scale analytic data warehouses and business intelligence workloads. It is based on PostgreSQL open-source technology.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Core SQL Conformance; MPP Architecture; Innovative Query Optimization; Polymorphic Data Storage; Integrated In-Database Analytics
Statistics
GitHub Stars 27.7K	GitHub Stars 6.2K
GitHub Forks 5.7K	GitHub Forks 1.7K
Stacks 96.6K	Stacks 45
Followers 82.0K	Followers 111
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	PostgreSQL Kong Slick Heroku Apache Hive Clever Cloud Couchbase Sequelize Sails.js Metabase

What are some alternatives to MongoDB, Greenplum Database?

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.