Google Cloud Bigtable vs PostgreSQL

Overview

PostgreSQL

Stacks103.1K

Followers83.9K

Votes3.6K

GitHub Stars19.0K

Forks5.2K

Google Cloud Bigtable

Stacks173

Followers363

Votes25

Google Cloud Bigtable vs PostgreSQL: What are the differences?

Introduction

Google Cloud Bigtable and PostgreSQL are both popular database management systems used in various applications. However, they have several key differences that make them suitable for different use cases. In this Markdown code, I will highlight six specific differences between Google Cloud Bigtable and PostgreSQL.

1. Data Model:

Google Cloud Bigtable is a NoSQL database that follows a wide-column store data model. It is schema-less and allows storing unstructured and semi-structured data, making it suitable for handling large-scale analytical and time series data. On the other hand, PostgreSQL is a relational database management system that follows a tabular data model. It requires a predefined schema and enforces data consistency through relationships and constraints.

2. Scalability and Performance:

Google Cloud Bigtable is designed for massive scalability, capable of handling petabytes of data and providing low latency for read and write operations. It is horizontally scalable and can automatically distribute data across multiple nodes. PostgreSQL, while also scalable, may require additional configuration and replication setup to achieve similar scalability and performance levels as Bigtable.

3. Data Integrity and ACID Compliance:

PostgreSQL prioritizes data integrity and fully supports ACID (Atomicity, Consistency, Isolation, Durability) properties. It offers advanced concurrency control mechanisms and transaction management, ensuring data consistency even in complex scenarios. Bigtable, being a NoSQL database, sacrifices some of the ACID properties for scalability, allowing eventual consistency with a focus on high throughput.

4. Querying Flexibility and SQL Support:

PostgreSQL has comprehensive SQL support and allows complex querying capabilities, including JOINs, subqueries, and user-defined functions. It provides a rich set of built-in functions and supports indexes for optimizing queries. Bigtable, being a NoSQL database, does not support SQL directly and relies on a limited set of query operations. It primarily uses a key-value access pattern and requires denormalization and pre-aggregation for efficient querying.

5. Cost Model:

Google Cloud Bigtable is a fully managed service offered by Google Cloud Platform, which means that it handles infrastructure management, updates, and scalability. However, the usage of Bigtable is billed based on various factors, including the storage used, operations performed, and network egress. On the other hand, PostgreSQL can be self-managed or hosted on various cloud providers. The cost is usually based on the chosen infrastructure and additional services used, making it a more flexible choice in terms of cost optimization.

6. Community and Ecosystem:

PostgreSQL has a large and active open-source community, which results in continuous development, bug fixes, and feature enhancements. It offers a wide range of tools, libraries, and extensions developed by the community, providing additional functionalities and integrations. Bigtable, being a proprietary service, has a more limited community and ecosystem. While it is backed by Google's expertise, the availability of third-party tools and libraries may be comparatively limited.

In summary, Google Cloud Bigtable is a NoSQL database with a wide-column data model, designed for massive scalability and high throughput but sacrificing some ACID properties. PostgreSQL, a relational database management system, offers full ACID compliance, advanced querying capabilities, and a vibrant open-source community. The choice between the two depends on the specific requirements of the application, emphasizing scalability and performance or data integrity and querying flexibility.

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Advice on PostgreSQL, Google Cloud Bigtable

Kyle

Web Application Developer at Redacted DevWorks

Dec 3, 2019

Decidedon

PostGIS

While there's been some very clever techniques that has allowed non-natively supported geo querying to be performed, it is incredibly slow in the long game and error prone at best.

MySQL finally introduced it's own GEO functions and special indexing operations for GIS type data. I prototyped with this, as MySQL is the most familiar database to me. But no matter what I did with it, how much tuning i'd give it, how much I played with it, the results would come back inconsistent.

It was very disappointing.

I figured, at this point, that SQL Server, being an enterprise solution authored by one of the biggest worldwide software developers in the world, Microsoft, might contain some decent GIS in it.

I was very disappointed.

Postgres is a Database solution i'm still getting familiar with, but I noticed it had no built in support for GIS. So I hilariously didn't pay it too much attention. That was until I stumbled upon PostGIS and my world changed forever.

449k views449k

Comments

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

Navraj

CEO at SuPragma

Apr 16, 2020

Needs adviceon

MySQL

PostgreSQL

I asked my last question incorrectly. Rephrasing it here.

I am looking for the most secure open source database for my project I'm starting: https://github.com/SuPragma/SuPragma/wiki

Which database is more secure? MySQL or PostgreSQL? Are there others I should be considering? Is it possible to change the encryption keys dynamically?

Thanks,

Raj

401k views401k

Comments

Detailed Comparison

PostgreSQL	Google Cloud Bigtable
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.	Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
-	Unmatched Performance: Single-digit millisecond latency and over 2X the performance per dollar of unmanaged NoSQL alternatives.;Open Source Interface: Because Cloud Bigtable is accessed through the HBase API, it is natively integrated with much of the existing big data and Hadoop ecosystem and supports Google’s big data products. Additionally, data can be imported from or exported to existing HBase clusters through simple bulk ingestion tools using industry-standard formats.;Low Cost: By providing a fully managed service and exceptional efficiency, Cloud Bigtable’s total cost of ownership is less than half the cost of its direct competition.;Security: Cloud Bigtable is built with a replicated storage strategy, and all data is encrypted both in-flight and at rest.;Simplicity: Creating or reconfiguring a Cloud Bigtable cluster is done through a simple user interface and can be completed in less than 10 seconds. As data is put into Cloud Bigtable the backing storage scales automatically, so there’s no need to do complicated estimates of capacity requirements.;Maturity: Over the past 10+ years, Bigtable has driven Google’s most critical applications. In addition, the HBase API is a industry-standard interface for combined operational and analytical workloads.
Statistics
GitHub Stars 19.0K	GitHub Stars -
GitHub Forks 5.2K	GitHub Forks -
Stacks 103.1K	Stacks 173
Followers 83.9K	Followers 363
Votes 3.6K	Votes 25
Pros & Cons
Pros 765 Relational database 511 High availability 439 Enterprise class database 383 Sql 304 Sql + nosql Cons 10 Table/index bloatings	Pros 11 High performance 9 Fully managed 5 High scalability
Integrations
No integrations available	Heroic Hadoop Apache Spark

What are some alternatives to PostgreSQL, Google Cloud Bigtable?

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.

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.

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

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.

Related Comparisons

Google Cloud Bigtable vs PostgreSQL

Overview

Google Cloud Bigtable vs PostgreSQL: What are the differences?

Introduction

1. Data Model:

2. Scalability and Performance:

3. Data Integrity and ACID Compliance:

4. Querying Flexibility and SQL Support:

5. Cost Model: