DBMS > etcd vs. OrientDB vs. Riak TS vs. searchxml vs. Splice Machine

System Properties Comparison etcd vs. OrientDB vs. Riak TS vs. searchxml vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Multi-model DBMS (Document, Graph, Key/Value)

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

DBMS for structured and unstructured content wrapped with an application server

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

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store

Time Series DBMS

Native XML DBMS
Search engine

Relational DBMS

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

Score	7.05
Rank	#53	Overall
	#5	Key-value stores

Score	3.02
Rank	#88	Overall
	#16	Document stores
	#6	Graph DBMS
	#12	Key-value stores

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

etcd.io
github.com/etcd-io/etcd

orientdb.org

www.searchxml.net/category/products

splicemachine.com

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

www.orientdb.com/docs/last/index.html

www.tiot.jp/riak-docs/riak/ts/latest

www.searchxml.net/support/handouts

splicemachine.com/how-it-works

Developer

OrientDB LTD; CallidusCloud; SAP

Open Source, formerly Basho Technologies

informationpartners gmbh

Splice Machine

Initial release

2010

2015

2014

Current release

3.4, August 2019

3.2.29, March 2024

3.0.0, September 2022

1.0

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

Open Source

commercial

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

Java

Erlang

C++

Java

Server operating systems

FreeBSD
Linux
Windows

experimental

All OS with a Java JDK (>= JDK 6)

Linux
OS X

Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

schema-free

yes

Typing

predefined data types such as float or date

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

restricted

yes

SQL

Support of SQL

SQL-like query language, no joins

yes, limited

yes

APIs and other access methods

gRPC
JSON over HTTP

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

HTTP API
Native Erlang Interface

RESTful HTTP API
WebDAV
XQuery
XSLT

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C++

most other programming languages supported via APIs

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

Server-side scripts

Stored procedures

Java, Javascript

Erlang

yes

on the application server

yes

Java

Triggers

yes, watching key changes

Hooks

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication

selectable replication factor

yes

sychronisation to multiple collections

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

links between datasets can be stored

yes

Transaction concepts

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

ACID

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

Access rights for users and roles; record level security configurable

Domain, group and role-based access control at the document level and for application services

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

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More resources

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