Overview

Microsoft SQL Server

Stacks21.3K

Followers15.5K

Votes540

Google Cloud Spanner

Stacks57

Followers117

Votes3

GitHub Stars2.0K

Forks1.1K

Google Cloud Spanner vs Microsoft SQL Server: What are the differences?

## Introduction
In this article, we will explore the key differences between Google Cloud Spanner and Microsoft SQL Server.

1. **Architecture**: Google Cloud Spanner is a globally distributed, horizontally scalable relational database while Microsoft SQL Server is a traditional relational database system that runs on a single server or cluster of servers.
   
2. **Consistency Model**: Google Cloud Spanner uses TrueTime for external consistency, ensuring global consistency and transactions across regions, while Microsoft SQL Server follows a traditional ACID compliance model for consistency within a single server or cluster.

3. **Scalability**: Google Cloud Spanner is designed for horizontal scalability, enabling it to handle massive amounts of data and high query volumes across multiple regions, while Microsoft SQL Server has limitations in horizontal scaling.

4. **Pricing Model**: Google Cloud Spanner provides a pricing model based on the resources consumed (compute and storage), with no upfront costs, while Microsoft SQL Server has licensing fees depending on the edition and features used.

5. **Global Distribution**: Google Cloud Spanner allows users to distribute their data globally, ensuring low-latency access from any location, while Microsoft SQL Server requires additional configurations for global replication and failover setups.

6. **Integration with Cloud Services**: Google Cloud Spanner seamlessly integrates with other Google Cloud services for analytics, AI, and machine learning, providing a comprehensive cloud ecosystem, whereas Microsoft SQL Server may require additional setup and configurations for integration with cloud services.

In Summary, Google Cloud Spanner and Microsoft SQL Server differ in architecture, consistency model, scalability, pricing, global distribution capabilities, and integration with cloud services.

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Advice on Microsoft SQL Server, Google Cloud Spanner

Erin

IT Specialist

Mar 10, 2020

Needs adviceon

Microsoft SQL Server

MySQL

PostgreSQL

I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

I need to use either @{MySQL}|tool:1025| or @{PostgreSQL}|tool:1028| on a @{Linux}|tool:10483| based OS. Which would be better for this application?
I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.

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Detailed Comparison

Microsoft SQL Server	Google Cloud Spanner
Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.	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.
-	Global transactions; Strongly consistent reads; Automatic multi-site replication; Failover.
Statistics
GitHub Stars -	GitHub Stars 2.0K
GitHub Forks -	GitHub Forks 1.1K
Stacks 21.3K	Stacks 57
Followers 15.5K	Followers 117
Votes 540	Votes 3
Pros & Cons
Pros 139 Reliable and easy to use 101 High performance 95 Great with .net 65 Works well with .net 56 Easy to maintain Cons 4 Expensive Licensing 2 Microsoft 1 The maximum number of connections is only 14000 connect 1 Replication can loose the data 1 Allwayon can loose data in asycronious mode	Pros 1 Strongly consistent 1 Scalable 1 Horizontal scaling
Integrations
No integrations available	MySQL PostgreSQL MongoDB SQLite

What are some alternatives to Microsoft SQL Server, Google Cloud Spanner?

MongoDB

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.

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.

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.