DBMS > etcd vs. IBM Db2 Event Store vs. Stardog vs. TerarkDB

System Properties Comparison etcd vs. IBM Db2 Event Store vs. Stardog vs. TerarkDB

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Key-value store

Event Store
Time Series DBMS

Graph DBMS
RDF store

Key-value store

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	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

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

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

www.stardog.com

github.com/bytedance/terarkdb

Technical documentation

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

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

docs.stardog.com

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

IBM

Stardog-Union

ByteDance, originally Terark

Initial release

2017

2010

2016

Current release

3.4, August 2019

2.0

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free developer edition available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

Java

C++

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux

Linux, macOS, Windows for the developer addition

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

through the embedded Spark runtime

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

gRPC
JSON over HTTP

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

C++ API
Java API

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C++
Java

Server-side scripts

Stored procedures

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes, watching key changes

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

Multi-source replication in HA-Cluster

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

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