DBMS > Google Cloud Datastore vs. PostgreSQL vs. YugabyteDB

System Properties Comparison Google Cloud Datastore vs. PostgreSQL vs. YugabyteDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.

Primary database model

Document store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Relational DBMS

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

Document store
Wide column store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	3.62
Rank	#71	Overall
	#12	Document stores

Score	674.32
Rank	#4	Overall
	#4	Relational DBMS

Score	2.20
Rank	#108	Overall
	#53	Relational DBMS

Website

cloud.google.com/datastore

www.postgresql.org

www.yugabyte.com

Technical documentation

cloud.google.com/datastore/docs

www.postgresql.org/docs

docs.yugabyte.com
github.com/yugabyte/yugabyte-db

Developer

Google

PostgreSQL Global Development Group

www.postgresql.org/developer

Yugabyte Inc.

Initial release

2008

1989

1989: Postgres, 1996: PostgreSQL

2017

Current release

16.4, August 2024

2024.2, December 2024

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

PostgreSQL Flex @ STACKIT offers managed PostgreSQL Instances with adjustable CPU, RAM, storage amount and speed and several extensions available, in enterprise grade to perfectly match all application requirements. All 100% GDPR-compliant.

Implementation language

C and C++

Server operating systems

hosted

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
OS X

Data scheme

schema-free

yes

depending on used data model

Typing

predefined data types such as float or date

yes, details here

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

standard with numerous extensions

yes, PostgreSQL compatible

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
Scala

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

sql, plpgsql, C

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

partitioning by range, list and (since PostgreSQL 11) by hash

Hash and Range Sharding, row-level geo-partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

other methods possible by using 3rd party extensions

Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Strong consistency on writes and tunable consistency on reads

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Distributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

based on RocksDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.