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  1. Stackups
  2. Application & Data
  3. Relational Databases
  4. SQL Database As A Service
  5. Google Cloud SQL vs SQLite

Google Cloud SQL vs SQLite

OverviewDecisionsComparisonAlternatives

Overview

Google Cloud SQL
Google Cloud SQL
Stacks555
Followers580
Votes46
SQLite
SQLite
Stacks19.9K
Followers15.2K
Votes535

Google Cloud SQL vs SQLite: What are the differences?

Introduction

Google Cloud SQL and SQLite are both relational database management systems (RDBMS) that are widely used in web and mobile applications. However, these two database systems have several key differences that set them apart. In this article, we will explore the differences between Google Cloud SQL and SQLite.

  1. Scalability: One of the key differences between Google Cloud SQL and SQLite is in terms of scalability. Google Cloud SQL is designed to be highly scalable and can handle large amounts of data and high traffic loads. It can automatically scale up resources as needed, making it suitable for applications with growing user bases. On the other hand, SQLite is not designed to be highly scalable and is more suitable for small to medium-sized applications with low to moderate traffic loads.

  2. Deployment and Management: Another difference between Google Cloud SQL and SQLite is in terms of deployment and management. Google Cloud SQL is a fully managed database service provided by Google, which means that Google takes care of all the infrastructure and management tasks. It offers features like automated backups, high availability, and automatic software patching. On the other hand, SQLite is a self-contained, serverless database engine that is embedded within the application itself. It does not require any separate installation or management, making it easier to deploy and manage in small-scale applications.

  3. Concurrency: Concurrency is another area where Google Cloud SQL and SQLite differ. Google Cloud SQL supports high levels of concurrency and allows multiple users to access and modify the database concurrently. It uses sophisticated locking mechanisms to ensure data integrity and prevent conflicts between concurrent operations. On the other hand, SQLite has a simpler locking mechanism and is more suitable for single-user or low-concurrency applications. It locks the entire database file during write operations, which can lead to performance issues in high-concurrency scenarios.

  4. Availability: Availability is another factor that sets Google Cloud SQL and SQLite apart. Google Cloud SQL offers built-in high availability, with replication and failover mechanisms that ensure data is always accessible even in the event of hardware or network failures. It is designed to provide a highly reliable database service with minimal downtime. On the other hand, SQLite does not provide built-in high availability features. While it supports database replication, the responsibility for achieving high availability lies with the application itself.

  5. Cost: Cost is an important consideration when choosing between Google Cloud SQL and SQLite. Google Cloud SQL is a paid service and its cost depends on factors like the amount of storage, the number of CPU cores, and the amount of network traffic. It also offers different pricing tiers to cater to different application needs. On the other hand, SQLite is free and open-source, making it an attractive option for budget-sensitive projects or small-scale applications with limited financial resources.

  6. Integration with Cloud Services: Google Cloud SQL has seamless integration with other Google Cloud services, such as Google App Engine, Google Cloud Functions, and Google Kubernetes Engine. This allows developers to easily build and deploy applications that leverage the capabilities of these services. On the other hand, SQLite is a standalone database engine that does not have native integrations with cloud services. However, it can be used with other cloud platforms through custom integrations and configurations.

In summary, Google Cloud SQL and SQLite differ in terms of scalability, deployment and management, concurrency, availability, cost, and integration with cloud services. These differences make them suitable for different types of applications and use cases.

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Advice on Google Cloud SQL, SQLite

Dimelo
Dimelo

Nov 5, 2020

Needs adviceonSQLiteSQLiteMySQLMySQLPostgreSQLPostgreSQL

I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

670k views670k
Comments
Stephen
Stephen

Senior DevOps Engineer at Vital Beats

Nov 9, 2020

Review

A question you might want to think about is "What kind of experience do I want to gain, by using a DBMS?". If your aim is to have experience with SQL and any related libraries and frameworks for your language of choice (python, I think?), then it kind of doesn't matter too much which you pick so much. As others have said, SQLite would offer you the ability to very easily get started, and would give you a reasonably standard (if a little basic) SQL dialect to work with.

If your aim is actually to have a bit of "operational" experience, in terms of things like what command line tools might be available as standard for the DBMS, understanding how the DBMS handles multiple databases, when to use multiple schemas vs multiple databases, some basic privilege management etc. Then I would recommend PostgreSQL. SQLite's simplicity actually avoids most of these experiences, which is not helpful to you if that is what you hope to learn. MySQL has a few "quirks" to how it manages things like multiple databases, which may lead you to making less good decisions if you tried to take your experience over to different DBMS, especially in bigger enterprise roles. PostgreSQL is kind of a happy middle ground here, with the ability to start PostgreSQL servers via docker or docker-compose making the actual day-to-day management pretty easy, while still giving you experience of the kinds of considerations I have listed above.

At Vital Beats we make use of PostgreSQL, largely because it offers us a happy balance between good management and backup of data, and good standard command line tools, which is essential for us where we are deploying our solutions within Kubernetes / docker, and so more graphical tools are not always appropriate for us. PostgreSQL is also pretty universally supported in terms of language libraries and frameworks, without having to make compromises on how we want to store and layout our data.

316k views316k
Comments
Anonymous
Anonymous

Oct 29, 2019

Needs advice

Hi everyone! I am a high school student, starting a massive project. I'm building a system for a boarding school to be better connected to their students and be more efficient with information. In the meantime, I am developing a website and an android app. What's the best datastore I can use? I need to be able to access student data on the app from the main database and send push notifications. Also feed updates. What's the best approach? What's the best tool I can use to deploy the website and the database? One for testing and prototyping, and an official one... Thanks in advance!!!!

366k views366k
Comments

Detailed Comparison

Google Cloud SQL
Google Cloud SQL
SQLite
SQLite

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

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.

Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed
-
Statistics
Stacks
555
Stacks
19.9K
Followers
580
Followers
15.2K
Votes
46
Votes
535
Pros & Cons
Pros
  • 13
    Fully managed
  • 10
    SQL
  • 10
    Backed by Google
  • 4
    Flexible
  • 3
    Automatic Software Patching
Pros
  • 163
    Lightweight
  • 135
    Portable
  • 122
    Simple
  • 81
    Sql
  • 29
    Preinstalled on iOS and Android
Cons
  • 2
    Not for multi-process of multithreaded apps
  • 1
    Needs different binaries for each platform

What are some alternatives to Google Cloud SQL, SQLite?

MongoDB

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

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

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.

Amazon RDS

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

Cassandra

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

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

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

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

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.

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