DBMS > Apache Druid vs. BaseX vs. GridDB vs. NSDb vs. RocksDB

System Properties Comparison Apache Druid vs. BaseX vs. GridDB vs. NSDb vs. RocksDB

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

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

Scalable in-memory time series database optimized for IoT and Big Data

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS
Time Series DBMS

Native XML DBMS

Time Series DBMS

Key-value store

Secondary database models

Key-value store
Relational DBMS

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

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	1.90
Rank	#136	Overall
	#4	Native XML DBMS

Score	2.02
Rank	#132	Overall
	#11	Time Series DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Website

Technical documentation

druid.apache.org/docs/latest/design

docs.basex.org

docs.griddb.net

nsdb.io/Architecture

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation and contributors

BaseX GmbH

Toshiba Corporation

Facebook, Inc.

Initial release

2012

2007

2013

2017

2013

Current release

29.0.1, April 2024

10.7, August 2023

5.1, August 2022

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

BSD license

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Apache Version 2.0

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Java, Scala

C++

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

Linux

Linux
macOS

Linux

Data scheme

yes

schema-less columns are supported

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

XQuery supports types

yes

numerical, string, blob, geometry, boolean, timestamp

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

SQL for querying

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

gRPC
HTTP REST
WebSocket

C++ API
Java API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Actionscript
C
C#
Haskell
Java
JavaScript

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

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

Java
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

via events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

multiple readers, single writer

ACID at container level

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

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

Users with fine-grained authorization concept on 4 levels

Access rights for users can be defined per database

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
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Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
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Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
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Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
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Market metrics

GitHub trending repository
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Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
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