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

Google Cloud SQL vs InfluxDB

OverviewDecisionsComparisonAlternatives

Overview

Google Cloud SQL
Google Cloud SQL
Stacks555
Followers580
Votes46
InfluxDB
InfluxDB
Stacks1.0K
Followers1.2K
Votes175

Google Cloud SQL vs InfluxDB: What are the differences?

Introduction:

When it comes to storing and managing data, Google Cloud SQL and InfluxDB are two popular choices among developers and organizations. Both offer different features and capabilities that cater to specific use cases. Understanding the key differences between Google Cloud SQL and InfluxDB can help in making an informed decision based on individual requirements.

  1. Database Type: Google Cloud SQL is a relational database service offered by Google Cloud Platform that supports popular databases like MySQL, PostgreSQL, and SQL Server. On the other hand, InfluxDB is a time-series database specifically designed for handling time-sensitive data such as monitoring, IoT, and real-time analytics.

  2. Data Model: Google Cloud SQL follows a traditional relational database model with tables, rows, and columns, making it suitable for structured data storage and complex query operations. In contrast, InfluxDB utilizes a time-series data model, optimized for storing and querying time-stamped data points efficiently with high write and query performance.

  3. Scalability: Google Cloud SQL offers vertical scaling, where resources can be increased by upgrading the instance size, but scaling out horizontally can be limited. InfluxDB, on the other hand, is designed for horizontal scalability, allowing users to distribute data across multiple nodes to handle high throughput and large volumes of time-series data effectively.

  4. Query Language: Google Cloud SQL supports standard SQL queries for accessing and manipulating data within the relational databases it supports, making it familiar to users experienced with SQL. In contrast, InfluxDB uses a specialized query language called InfluxQL tailored for time-series data operations, including functions for aggregations, downsampling, and retention policies.

  5. Use Cases: Google Cloud SQL is well-suited for traditional application development, e-commerce platforms, and business applications that require ACID compliance and a relational data model. InfluxDB, on the other hand, shines in use cases that involve storing and analyzing time-series data such as monitoring system metrics, IoT sensor data, and operational analytics for real-time insights.

  6. Ecosystem and Integration: Google Cloud SQL seamlessly integrates with other Google Cloud services like App Engine, Compute Engine, and BigQuery, offering a robust ecosystem for building cloud-native applications. InfluxDB has a strong focus on integrations with monitoring and visualization tools like Grafana, Prometheus, and Telegraf, making it a popular choice for DevOps and IoT applications.

In Summary, understanding the fundamental differences between Google Cloud SQL and InfluxDB in terms of database type, data model, scalability, query language, use cases, and ecosystem can help in choosing the right database solution based on specific requirements and use cases.

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Advice on Google Cloud SQL, InfluxDB

Anonymous
Anonymous

Apr 21, 2020

Needs advice

We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.

So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily

381k views381k
Comments
Benoit
Benoit

Principal Engineer at Sqreen

Sep 21, 2019

Decided

I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.

The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)

We are combining this with Grafana for display and Telegraf for data collection

155k views155k
Comments
pionell
pionell

Sep 16, 2020

Needs adviceonMariaDBMariaDB

I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.

159k views159k
Comments

Detailed Comparison

Google Cloud SQL
Google Cloud SQL
InfluxDB
InfluxDB

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

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.

Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed
Time-Centric Functions;Scalable Metrics; Events;Native HTTP API;Powerful Query Language;Built-in Explorer
Statistics
Stacks
555
Stacks
1.0K
Followers
580
Followers
1.2K
Votes
46
Votes
175
Pros & Cons
Pros
  • 13
    Fully managed
  • 10
    SQL
  • 10
    Backed by Google
  • 4
    Flexible
  • 3
    Automatic Software Patching
Pros
  • 59
    Time-series data analysis
  • 30
    Easy setup, no dependencies
  • 24
    Fast, scalable & open source
  • 21
    Open source
  • 20
    Real-time analytics
Cons
  • 4
    Instability
  • 1
    HA or Clustering is only in paid version
  • 1
    Proprietary query language

What are some alternatives to Google Cloud SQL, InfluxDB?

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.

SQLite

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

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.

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