DBMS > HarperDB vs. Hawkular Metrics vs. MarkLogic vs. RDF4J vs. Splice Machine

System Properties Comparison HarperDB vs. Hawkular Metrics vs. MarkLogic vs. RDF4J vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Operational and transactional Enterprise NoSQL database

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store

Time Series DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

RDF store

Relational DBMS

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

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

Technical documentation

docs.harperdb.io/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.marklogic.com

rdf4j.org/documentation

splicemachine.com/how-it-works

Developer

HarperDB

Community supported by Red Hat

MarkLogic Corp.

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Splice Machine

Initial release

2017

2014

2001

2004

2014

Current release

3.1, August 2021

11.0, December 2022

3.1, March 2021

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Node.js

Java

C++

Java

Server operating systems

Linux
OS X

Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Solaris
Windows

Data scheme

dynamic schema

schema-free

Schema can be enforced

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

JSON data types

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 data manipulation statements

yes

SQL92

yes

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP REST

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

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Go
Java
Python
Ruby

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

Java
PHP
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes

via XQuery or JavaScript

yes

Java

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

A table resides as a whole on one (or more) nodes in a cluster

Sharding

based on Cassandra

Sharding

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

selectable replication factor

based on Cassandra

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes, using LMDB

yes

in-memory storage is supported as well

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

yes

User concepts

Access control

Access rights for users and roles

Role-based access control at the document and subdocument levels

Access rights for users, groups and roles according to SQL-standard

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