DBMS > AgensGraph vs. Databricks vs. mSQL vs. XTDB

System Properties Comparison AgensGraph vs. Databricks vs. mSQL vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

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

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

mSQL (Mini SQL) is a simple and lightweight RDBMS

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Graph DBMS
Relational DBMS

Document store
Relational DBMS

Relational DBMS

Document store

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

bitnine.net/agensgraph

www.databricks.com

hughestech.com.au/products/msql

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

bitnine.net/documentation

docs.databricks.com

www.xtdb.com/docs

Developer

Bitnine Global Inc.

Databricks

Hughes Technologies

Juxt Ltd.

Initial release

2016

2013

1994

2019

Current release

2.1, December 2018

4.4, October 2021

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

free licenses can be provided

Open Source

MIT License

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

Clojure

Server operating systems

Linux
OS X
Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

depending on used data model

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

Secondary indexes

yes

SQL

Support of SQL

yes

with Databricks SQL

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

limited SQL, making use of Apache Calcite

APIs and other access methods

Cypher Query Language
JDBC

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

C
Java
JavaScript
Python

Python
R
Scala

C
C++
Delphi
Java
Perl
PHP
Tcl

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and aggregates

Triggers

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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