DBMS > etcd vs. Ingres vs. Oracle vs. Splice Machine vs. Warp 10

System Properties Comparison etcd vs. Ingres vs. Oracle vs. Splice Machine vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Well established RDBMS

Widely used RDBMS

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

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

www.actian.com/databases/ingres

www.oracle.com/database

splicemachine.com

www.warp10.io

Technical documentation

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

docs.actian.com/ingres

docs.oracle.com/en/database

splicemachine.com/how-it-works

www.warp10.io/content/02_Getting_started

Developer

Actian Corporation

Oracle

Splice Machine

SenX

Initial release

1974

originally developed at University Berkely in early 1970s

1980

2014

2015

Current release

3.4, August 2019

11.2, May 2022

23c, September 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

AGPL 3.0, commercial license available

Open Source

Apache License 2.0

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

C and C++

Java

Server operating systems

FreeBSD
Linux
Windows

experimental

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

yes

schema-free

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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

gRPC
JSON over HTTP

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
Native Spark Datasource
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

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

Server-side scripts

Stored procedures

yes

PL/SQL

also stored procedures in Java possible

yes

Java

yes

WarpScript

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding, horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

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

Ingres Replicator

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

isolation level can be parameterized

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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