DBMS > etcd vs. IBM Db2 Event Store vs. JanusGraph vs. Microsoft SQL Server vs. Oracle

System Properties Comparison etcd vs. IBM Db2 Event Store vs. JanusGraph vs. Microsoft SQL Server vs. Oracle

Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

Distributed Event Store optimized for Internet of Things use cases

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Microsofts flagship relational DBMS

Widely used RDBMS

Primary database model

Key-value store

Event Store
Time Series DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Website

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

www.ibm.com/products/db2-event-store

janusgraph.org

www.microsoft.com/en-us/sql-server

www.oracle.com/database

Technical documentation

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

www.ibm.com/docs/en/db2-event-store

docs.janusgraph.org

learn.microsoft.com/en-US/sql/sql-server

docs.oracle.com/en/database

Developer

IBM

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

Oracle

Initial release

2017

1989

1980

Current release

3.4, August 2019

2.0

0.6.3, February 2023

SQL Server 2022, November 2022

23c, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free developer edition available

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

restricted free version is 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

C and C++

Java

C++

C and C++

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Unix
Windows

Linux
Windows

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

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

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

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

with proprietary extensions

APIs and other access methods

gRPC
JSON over HTTP

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

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++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Clojure
Java
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

PL/SQL

also stored procedures in Java possible

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding, horizontal 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.

Active-active shard replication

yes

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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'

User concepts

Access control

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.