DBMS > Amazon SimpleDB vs. Badger vs. GigaSpaces vs. Google BigQuery vs. OceanBase

System Properties Comparison Amazon SimpleDB vs. Badger vs. GigaSpaces vs. Google BigQuery vs. OceanBase

Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Large scale data warehouse service with append-only tables

A distributed, high available RDBMS compatible with Oracle and MySQL

Primary database model

Key-value store

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Document store
Wide column store

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

Score	1.88
Rank	#133	Overall
	#23	Key-value stores

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.57
Rank	#149	Overall
	#69	Relational DBMS

Website

aws.amazon.com/simpledb

github.com/dgraph-io/badger

www.gigaspaces.com

cloud.google.com/bigquery

en.oceanbase.com

Technical documentation

docs.aws.amazon.com/simpledb

godoc.org/github.com/dgraph-io/badger

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

en.oceanbase.com/docs/oceanbase-database

Developer

Amazon

DGraph Labs

Gigaspaces Technologies

Google

OceanBase

previously Alibaba and Ant Group

Initial release

2007

2017

2000

2010

Current release

15.5, September 2020

4.3.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

Commercial license available

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.

Implementation language

Java, C++, .Net

C++

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Solaris
Windows

hosted

Linux

Data scheme

schema-free

yes

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

All columns are indexed automatically

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

APIs and other access methods

RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

Triggers

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

none

Sharding

none

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

none

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Role-based access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles according to SQL-standard

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