DBMS > AgensGraph vs. BaseX vs. Google BigQuery vs. NCache vs. Rockset

System Properties Comparison AgensGraph vs. BaseX vs. Google BigQuery vs. NCache vs. Rockset

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Large scale data warehouse service with append-only tables

Open-Source and Enterprise in-memory Key-Value Store

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Graph DBMS
Relational DBMS

Secondary database models

Document store
Search engine

Using distributed Lucene

Relational DBMS
Search engine

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Score	0.82
Rank	#212	Overall
	#36	Document stores

Website

bitnine.net/agensgraph

basex.org

cloud.google.com/bigquery

www.alachisoft.com/ncache

rockset.com

Technical documentation

bitnine.net/documentation

docs.basex.org

cloud.google.com/bigquery/docs

www.alachisoft.com/resources/docs

docs.rockset.com

Developer

Bitnine Global Inc.

BaseX GmbH

Google

Alachisoft

Rockset

Initial release

2016

2007

2010

2005

2019

Current release

2.1, December 2018

10.7, August 2023

5.3.3, April 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

BSD license

commercial

Open Source

Enterprise Edition available

commercial

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, .NET Core, Java

C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
Windows

hosted

Data scheme

depending on used data model

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

XQuery supports types

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

dynamic 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.

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

Read-only SQL queries, including JOINs

APIs and other access methods

Cypher Query Language
JDBC

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

RESTful HTTP/JSON API

IDistributedCache
JCache
LINQ
Proprietary native API

HTTP REST

Supported programming languages

C
Java
JavaScript
Python

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

support for stored procedures with SQL-Server CLR

Triggers

yes

via events

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

yes

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes, with selectable consistency level

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

multiple readers, single writer

Since BigQuery is designed for querying data

optimistic locking and pessimistic locking

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept on 4 levels

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

Google Cloud Identity & Access Management (IAM)

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

Access rights for users and organizations can be defined via Rockset console

More information provided by the system vendor

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