DBMS > Apache Druid vs. H2GIS vs. LeanXcale vs. Oracle NoSQL

System Properties Comparison Apache Druid vs. H2GIS vs. LeanXcale vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Spatial extension of H2

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Relational DBMS
Time Series DBMS

Spatial DBMS

Key-value store
Relational DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Relational DBMS

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

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#7	Spatial DBMS

Score	0.28
Rank	#286	Overall
	#41	Key-value stores
	#130	Relational DBMS

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Website

druid.apache.org

www.h2gis.org

www.leanxcale.com

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

druid.apache.org/docs/latest/design

www.h2gis.org/docs/home

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Apache Software Foundation and contributors

CNRS

LeanXcale

Oracle

Initial release

2012

2013

2015

2011

Current release

30.0.0, June 2024

24.1, May 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

LGPL 3.0

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
OS X
Unix

Linux
Solaris SPARC/x86

Data scheme

yes

schema-less columns are supported

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

optional

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.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

through Apache Derby

SQL-like DML and DDL statements

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Java

C
Java
Scala

C
C#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

based on H2

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

based on H2

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

off heap cache

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

yes

based on H2

Access rights for users and roles

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