DBMS > Apache Phoenix vs. Badger vs. GigaSpaces vs. Google BigQuery vs. JaguarDB

System Properties Comparison Apache Phoenix vs. Badger vs. GigaSpaces vs. Google BigQuery vs. JaguarDB

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

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

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Relational DBMS

Key-value store

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Key-value store
Vector DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

phoenix.apache.org

github.com/dgraph-io/badger

www.gigaspaces.com

cloud.google.com/bigquery

www.jaguardb.com

Technical documentation

phoenix.apache.org

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

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

www.jaguardb.com/support.html

Developer

Apache Software Foundation

DGraph Labs

Gigaspaces Technologies

Google

DataJaguar, Inc.

Initial release

2014

2017

2000

2010

2015

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

15.5, September 2020

3.3 July 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

GPL V3.0

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.

Implementation language

Java

Java, C++, .Net

C++

the server part. Clients available in other languages

Server operating systems

Linux
Unix
Windows

BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Solaris
Windows

hosted

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-99 for query and DML statements

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

JDBC

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

RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

in JavaScript

Triggers

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

none

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Role-based access control

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

Google Cloud Identity & Access Management (IAM)

rights management via user accounts

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