DBMS > Google BigQuery vs. JanusGraph vs. jBASE vs. Postgres-XL vs. Solr

System Properties Comparison Google BigQuery vs. JanusGraph vs. jBASE vs. Postgres-XL vs. Solr

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

A robust multi-value DBMS comprising development tools and middleware

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

cloud.google.com/bigquery

janusgraph.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.postgres-xl.org

solr.apache.org

Technical documentation

cloud.google.com/bigquery/docs

docs.janusgraph.org

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

www.postgres-xl.org/documentation

solr.apache.org/resources.html

Developer

Google

Linux Foundation; originally developed as Titan by Aurelius

Rocket Software (formerly Zumasys)

Apache Software Foundation

Initial release

2010

2017

1991

2014

since 2012, originally named StormDB

2006

Current release

0.6.3, February 2023

5.7

10 R1, October 2018

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Mozilla public license

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux
OS X
Unix
Windows

AIX
Linux
Windows

Linux
macOS

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

optional

yes

supports customizable data types and automatic typing

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

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

Embedded SQL for jBASE in BASIC

yes

distributed, parallel query execution

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

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

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Clojure
Java
Python

.Net
Basic
Jabbascript
Java

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

MVCC

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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)

User authentification and security via Rexster Graph Server

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

yes

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