Overview

IBM DB2

Stacks245

Followers254

Votes19

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

IBM DB2 vs MongoDB: What are the differences?

Introduction

In this article, we will discuss the key differences between IBM DB2 and MongoDB. Both of these software solutions are widely used in the field of database management, but they have distinct characteristics that set them apart from each other.

1. Data Model:

IBM DB2 is a relational database management system (RDBMS), which means it organizes data into tables with predefined schemas, and relationships between tables are established using foreign keys. On the other hand, MongoDB is a document-oriented database, where data is stored in flexible, JSON-like documents without any predefined schema or structure. This allows for more easily handling of unstructured or semi-structured data in MongoDB.

2. Scalability:

When it comes to scalability, IBM DB2 is often used in enterprise-level environments, where it provides excellent horizontal scalability. It can handle large amounts of data and scale by adding more hardware resources. MongoDB, on the other hand, offers exceptional horizontal scalability out of the box with its built-in sharding feature. Sharding allows distributing data across multiple servers, enabling high availability and increased throughput.

3. Querying Language:

IBM DB2 uses SQL (Structured Query Language) as its primary querying language. SQL is a standard language for managing relational databases and allows for complex querying and transaction management. MongoDB, however, uses its own querying language called MongoDB Query Language (MQL). MQL supports rich document queries, indexing, and aggregation operations for more flexible and efficient querying of document-oriented data.

4. Data Integrity:

As an RDBMS, IBM DB2 enforces data integrity through the use of constraints and referential integrity rules. It ensures that data remains consistent and follows predefined rules to maintain accuracy. MongoDB, on the other hand, does not provide built-in support for enforcing data integrity. While it allows defining validation rules, it's up to the application layer to enforce them. This can provide more flexibility but also requires careful implementation to maintain data consistency.

5. ACID Transactions:

IBM DB2 provides support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. ACID transactions ensure data integrity by guaranteeing that either all operations within a transaction are successfully completed or none of them are applied. MongoDB introduced multi-document ACID transactions in version 4.0, which allows developers to perform complex, multi-step operations that involve multiple documents while still maintaining data consistency.

6. Scalability in Cloud Environments:

IBM DB2 offers the flexibility to run on-premises as well as in various cloud environments. It integrates well with IBM Cloud, allowing seamless deployment and management. MongoDB, on the other hand, was designed from the ground up to support cloud environments and is often used for modern cloud-native applications. It provides extensive features for cloud integration, such as horizontal scaling and automated provisioning.

In Summary, IBM DB2 is a traditional RDBMS with a fixed schema, SQL querying language, and strong support for data integrity and ACID transactions. MongoDB, on the other hand, is a flexible document-oriented database with a JSON-like data model, MongoDB Query Language (MQL), support for horizontal scalability through sharding, and a focus on cloud-native applications.

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Advice on IBM DB2, MongoDB

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

IBM DB2	MongoDB
DB2 for Linux, UNIX, and Windows is optimized to deliver industry-leading performance across multiple workloads, while lowering administration, storage, development, and server costs.	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.
-	Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Statistics
GitHub Stars -	GitHub Stars 27.7K
GitHub Forks -	GitHub Forks 5.7K
Stacks 245	Stacks 96.6K
Followers 254	Followers 82.0K
Votes 19	Votes 4.1K
Pros & Cons
Pros 7 Rock solid and very scalable 5 BLU Analytics is amazingly fast 2 Easy 2 Secure by default 2 Native XML support	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
Integrations
Node.js JavaScript PHP Ruby Java Python C# .NET C++ Perl	No integrations available

What are some alternatives to IBM DB2, MongoDB?

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.