Google Cloud Spanner vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Google Cloud Spanner

Stacks57

Followers117

Votes3

GitHub Stars2.0K

Forks1.1K

Google Cloud Spanner vs MongoDB: What are the differences?

Introduction

Google Cloud Spanner and MongoDB are both popular database management systems, but they have key differences in terms of structure, scalability, data consistency, querying capabilities, sharding, and pricing.

Structure: Google Cloud Spanner is a relational database that uses SQL-like language for querying and has a fixed schema. On the other hand, MongoDB is a NoSQL database that uses JSON-like documents and does not require a predefined schema, allowing for more flexible data models.
Scalability: Google Cloud Spanner is designed for horizontal scalability, allowing it to handle high write/read workloads across multiple regions. It provides automatic sharding and consistent performance, making it a good choice for large-scale distributed applications. MongoDB, on the other hand, offers sharding and replication capabilities, but it does not provide automatic scaling like Cloud Spanner.
Data Consistency: Google Cloud Spanner guarantees strong data consistency across its distributed nodes, ensuring that transactions operate correctly even in the face of network partitions. MongoDB, on the other hand, offers eventual consistency by default, which might lead to inconsistent data during network failures or updates.
Querying Capabilities: Google Cloud Spanner supports complex SQL queries, joins, and ACID transactions, making it suitable for applications that require strong query capabilities and data integrity. MongoDB provides a flexible document-based query language, but it does not support joins across multiple document collections, limiting its ability to perform complex queries.
Sharding: Google Cloud Spanner offers automatic sharding, distributing data across multiple nodes, and ensuring scalability. MongoDB also supports sharding, but it requires manual configuration and management, making it more complex for large-scale deployments.
Pricing: Google Cloud Spanner has a pricing model based on resource usage, including storage, read/write operations, and network egress. MongoDB provides a more traditional licensing model, with options for community edition (free) and enterprise edition (paid). The pricing for MongoDB varies based on the deployment type and features required.

In summary, Google Cloud Spanner is a scalable relational database with SQL-like querying and strong consistency, making it suitable for large-scale distributed applications. MongoDB, on the other hand, is a flexible NoSQL database with a document-based approach, providing easier scalability but with eventual data consistency.

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Advice on MongoDB, Google Cloud Spanner

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	Google Cloud Spanner
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 globally distributed database service that gives developers a production-ready storage solution. It provides key features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Global transactions; Strongly consistent reads; Automatic multi-site replication; Failover.
Statistics
GitHub Stars 27.7K	GitHub Stars 2.0K
GitHub Forks 5.7K	GitHub Forks 1.1K
Stacks 96.6K	Stacks 57
Followers 82.0K	Followers 117
Votes 4.1K	Votes 3
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 Horizontal scaling 1 Scalable 1 Strongly consistent
Integrations
No integrations available	MySQL PostgreSQL SQLite

What are some alternatives to MongoDB, Google Cloud Spanner?

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

Google Cloud Spanner vs MongoDB: What are the differences?

Introduction

Structure: Google Cloud Spanner is a relational database that uses SQL-like language for querying and has a fixed schema. On the other hand, MongoDB is a NoSQL database that uses JSON-like documents and does not require a predefined schema, allowing for more flexible data models.
Scalability: Google Cloud Spanner is designed for horizontal scalability, allowing it to handle high write/read workloads across multiple regions. It provides automatic sharding and consistent performance, making it a good choice for large-scale distributed applications. MongoDB, on the other hand, offers sharding and replication capabilities, but it does not provide automatic scaling like Cloud Spanner.
Data Consistency: Google Cloud Spanner guarantees strong data consistency across its distributed nodes, ensuring that transactions operate correctly even in the face of network partitions. MongoDB, on the other hand, offers eventual consistency by default, which might lead to inconsistent data during network failures or updates.
Querying Capabilities: Google Cloud Spanner supports complex SQL queries, joins, and ACID transactions, making it suitable for applications that require strong query capabilities and data integrity. MongoDB provides a flexible document-based query language, but it does not support joins across multiple document collections, limiting its ability to perform complex queries.
Sharding: Google Cloud Spanner offers automatic sharding, distributing data across multiple nodes, and ensuring scalability. MongoDB also supports sharding, but it requires manual configuration and management, making it more complex for large-scale deployments.
Pricing: Google Cloud Spanner has a pricing model based on resource usage, including storage, read/write operations, and network egress. MongoDB provides a more traditional licensing model, with options for community edition (free) and enterprise edition (paid). The pricing for MongoDB varies based on the deployment type and features required.

Google Cloud Spanner vs MongoDB

Overview

Google Cloud Spanner vs MongoDB: What are the differences?