DBMS > Apache IoTDB vs. Hyprcubd vs. InterSystems Caché vs. MarkLogic vs. RavenDB

System Properties Comparison Apache IoTDB vs. Hyprcubd vs. InterSystems Caché vs. MarkLogic vs. RavenDB

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Serverless Time Series DBMS

A multi-model DBMS and application server

Operational and transactional Enterprise NoSQL database

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Time Series DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store

Secondary database models

Document store

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	4.15
Rank	#70	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#8	Search engines

Score	2.68
Rank	#102	Overall
	#19	Document stores

Website

iotdb.apache.org

hyprcubd.com (offline)

www.intersystems.com/products/cache

www.progress.com/marklogic

ravendb.net

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.intersystems.com

www.progress.com/marklogic/documentation

ravendb.net/docs

Developer

Apache Software Foundation

Hyprcubd, Inc.

InterSystems

MarkLogic Corp.

Hibernating Rhinos

Initial release

2018

1997

2001

2010

Current release

1.1.0, April 2023

2018.1.4, May 2020

11.0, December 2022

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

AGPL version 3, commercial license available

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++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux
macOS
Raspberry Pi
Windows

Data scheme

yes

depending on used data model

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

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

SQL-like query language

yes

SQL92

SQL-like query language (RQL)

APIs and other access methods

JDBC
Native API

gRPC (https)

.NET Client API
JDBC
ODBC
RESTful HTTP API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
Java

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Source-replica replication

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

ACID, Cluster-wide transaction available

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

yes, with Range Indexes

User concepts

Access control

yes

token access

Access rights for users, groups and roles

Role-based access control at the document and subdocument levels

Authorization levels configured per client per database

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More resources

Conferences, events and webinars

more DBMS events

Progress MarkLogic World
Washington DC, 23-25 September 2024

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