DBMS > etcd vs. IRONdb vs. Quasardb vs. Snowflake vs. Trafodion

System Properties Comparison etcd vs. IRONdb vs. Quasardb vs. Snowflake vs. Trafodion

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

A distributed reliable key-value store

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Distributed, high-performance timeseries database

Cloud-based data warehousing service for structured and semi-structured data

Transactional SQL-on-Hadoop DBMS

Primary database model

Key-value store

Time Series DBMS

Relational DBMS

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

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

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

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

www.circonus.com/solutions/time-series-database/

quasar.ai

www.snowflake.com

trafodion.apache.org

Technical documentation

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

docs.circonus.com/irondb/category/getting-started

doc.quasar.ai/master

docs.snowflake.net/manuals/index.html

trafodion.apache.org/documentation.html

Developer

Circonus LLC.

quasardb

Snowflake Computing Inc.

Apache Software Foundation, originally developed by HP

Initial release

2017

2009

2014

Current release

3.4, August 2019

V0.10.20, January 2018

3.14.1, January 2024

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C and C++

C++

C++, Java

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux

BSD
Linux
OS X
Windows

hosted

Linux

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language

yes

APIs and other access methods

gRPC
JSON over HTTP

HTTP API

CLI Client
JDBC
ODBC

ADO.NET
JDBC
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++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes, in Lua

user defined functions

Java Stored Procedures

Triggers

yes, watching key changes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

Sharding

consistent hashing

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

configurable replication factor, datacenter aware

Source-replica replication with selectable replication factor

yes

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Cryptographically strong user authentication and audit trail

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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