Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Exasol

Stacks15

Followers17

Votes6

Exasol vs MongoDB: What are the differences?

Key Differences between Exasol and MongoDB

Exasol and MongoDB are both popular databases solutions, but they have distinct characteristics that differentiate them from each other.

1. Scalability: Exasol is designed as a massively parallel processing (MPP) database that can scale horizontally to handle large volumes of data and concurrent users. It provides high performance with parallel processing capabilities. In contrast, MongoDB is a NoSQL document-oriented database that uses sharding to scale horizontally. It allows for adding additional servers to distribute the workload but does not provide the same level of parallel processing as Exasol.

2. Data Model: Exasol follows a structured data model with a relational schema that utilizes SQL for querying and data manipulation. It organizes data in tables with predefined columns and data types. On the other hand, MongoDB follows a flexible schema-less data model, where data is stored in JSON-like documents in collections. It allows for dynamic changes to the data structure, making it easier to handle unstructured and evolving data.

3. Query Language: Exasol uses standard SQL as its query language, making it familiar to those with SQL knowledge. It supports advanced SQL capabilities like window functions, subqueries, and joins. MongoDB, on the other hand, uses a powerful and expressive query language called MongoDB Query Language (MQL), which utilizes JSON-like syntax for querying and document manipulation. MQL offers a variety of operators and commands for flexible document querying.

4. ACID Compliance: Exasol is ACID (Atomicity, Consistency, Isolation, Durability) compliant, ensuring data consistency and reliability with full transaction support. It guarantees that all database operations are executed in a reliable and consistent manner. MongoDB, being a NoSQL database, does not provide strict ACID compliance by default. It offers tunable consistency levels, allowing developers to make trade-offs between consistency and availability based on application requirements.

5. Data Replication: Exasol supports data replication for ensuring data availability and fault tolerance. It uses a master-slave replication model, where one master node handles write operations, and multiple slave nodes replicate the data for read operations. MongoDB, on the other hand, uses replica sets for data replication. It also allows for automatic failover and recovery, providing high availability and data redundancy in case of node failures.

6. Indexing: Exasol provides a range of index mechanisms, including single-column, multi-column, bitmap, and text indexes, to optimize query performance. It offers automatic indexing recommendations based on query patterns and data distribution. MongoDB also supports various indexing options, including single-field, compound, multi-key, and geospatial indexes. It provides index intersection and text search capabilities, enhancing query efficiency.

In Summary, Exasol is a highly scalable MPP database with a structured data model and SQL querying capabilities, while MongoDB is a flexible NoSQL document-oriented database with a schema-less data model and a powerful query language. Exasol focuses on high performance and parallel processing, while MongoDB emphasizes flexibility, scalability, and handling unstructured data efficiently.

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Advice on MongoDB, Exasol

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.

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Comments

Detailed Comparison

MongoDB	Exasol
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 an intelligent, self-tuning and resource-efficient database. Use the unrivalled performance of our analytics database and deploy anywhere, whether that’s on cloud, on-premises, or with a hybrid strategy – turning your organization’s insights into real value faster, easier and more cost effectively than ever before.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Unlock analytics as fast as you think; An intelligent, self-tuning and resource-efficient database; Consolidate AI, ML and BI for both standard and advanced analytics, directly in the database – using any data science language; There’s no platform, vendor or architecture lock-in with the Exasol Analytics Database
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 15
Followers 82.0K	Followers 17
Votes 4.1K	Votes 6
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 1 Complex queries 1 Great on old hardware (or new) 1 Cloud 1 On-prem 1 Easy
Integrations
No integrations available	Hadoop

What are some alternatives to MongoDB, Exasol?

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.