DBMS > Google BigQuery vs. InfinityDB vs. JaguarDB vs. Stardog vs. Vitess

System Properties Comparison Google BigQuery vs. InfinityDB vs. JaguarDB vs. Stardog vs. Vitess

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A Java embedded Key-Value Store which extends the Java Map interface

Performant, highly scalable DBMS for AI and IoT applications

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Key-value store

Key-value store
Vector DBMS

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#14	Vector DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

cloud.google.com/bigquery

Technical documentation

cloud.google.com/bigquery/docs

boilerbay.com/infinitydb/manual

www.jaguardb.com/support.html

docs.stardog.com

vitess.io/docs

Developer

Google

Boiler Bay Inc.

DataJaguar, Inc.

Stardog-Union

The Linux Foundation, PlanetScale

Initial release

2010

2002

2015

2010

2013

Current release

4.0

3.3 July 2023

7.3.0, May 2020

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

Apache Version 2.0, commercial licenses 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++

the server part. Clients available in other languages

Java

Server operating systems

hosted

All OS with a Java VM

Linux

Linux
macOS
Windows

Docker
Linux
macOS

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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.

Import/export of XML data possible

Secondary indexes

manual creation possible, using inversions based on multi-value capability

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions and aggregates, HTTP Server extensions in Java

yes

proprietary syntax

Triggers

yes

via event handlers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

Multi-source replication in HA-Cluster

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Eventual Consistency

Immediate Consistency in HA-Cluster

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

relationships in graphs

yes

not for MyISAM storage engine

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Google Cloud Identity & Access Management (IAM)

rights management via user accounts

Access rights for users and roles

Users with fine-grained authorization concept

no user groups or roles

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