DBMS > Lovefield vs. Microsoft Azure Table Storage vs. PostgreSQL vs. Riak KV

System Properties Comparison Lovefield vs. Microsoft Azure Table Storage vs. PostgreSQL vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Embeddable relational database for web apps written in pure JavaScript

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used open source RDBMS

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

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Wide column store

Relational DBMS

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

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

google.github.io/lovefield

azure.microsoft.com/en-us/services/storage/tables

www.postgresql.org

Technical documentation

github.com/google/lovefield/blob/master/docs/spec_index.md

www.postgresql.org/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Google

Microsoft

PostgreSQL Global Development Group

www.postgresql.org/developer

OpenSource, formerly Basho Technologies

Initial release

2014

2012

1989

1989: Postgres, 1996: PostgreSQL

2009

Current release

2.1.12, February 2017

16.3, May 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

BSD

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

yes

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Implementation language

JavaScript

Erlang

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

hosted

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

Linux
OS X

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

restricted

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

standard with numerous extensions

APIs and other access methods

RESTful HTTP API

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

HTTP API
Native Erlang Interface

Supported programming languages

JavaScript

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

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

Erlang

Triggers

Using read-only observers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

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

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Source-replica replication

other methods possible by using 3rd party extensions

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

yes, using Riak Security

More information provided by the system vendor

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3rd parties

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