DBMS > Google Cloud Bigtable vs. Lovefield vs. MySQL vs. Postgres-XL vs. RocksDB

System Properties Comparison Google Cloud Bigtable vs. Lovefield vs. MySQL vs. Postgres-XL vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Embeddable relational database for web apps written in pure JavaScript

Widely used open source RDBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Key-value store
Wide column store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

cloud.google.com/bigtable

google.github.io/lovefield

www.mysql.com

www.postgres-xl.org

rocksdb.org

Technical documentation

cloud.google.com/bigtable/docs

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

dev.mysql.com/doc

www.postgres-xl.org/documentation

github.com/facebook/rocksdb/wiki

Developer

Google

Oracle

since 2010, originally MySQL AB, then Sun

Facebook, Inc.

Initial release

2015

2014

1995

2014

since 2012, originally named StormDB

2013

Current release

2.1.12, February 2017

8.4.0, April 2024

10 R1, October 2018

9.2.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Mozilla public license

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

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Implementation language

JavaScript

C and C++

C++

Server operating systems

hosted

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

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

Linux

Data scheme

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

with proprietary extensions

yes

distributed, parallel query execution

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

ADO.NET
JDBC
ODBC
Proprietary native API

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

C++ API
Java API

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Python

JavaScript

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

none

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

Atomic single-row operations

ACID

not for MyISAM storage engine

ACID

MVCC

yes

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

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

yes

User concepts

Access control

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

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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