DBMS > EsgynDB vs. etcd vs. IRONdb vs. NSDb vs. XTDB

System Properties Comparison EsgynDB vs. etcd vs. IRONdb vs. NSDb vs. XTDB

Editorial information provided by DB-Engines

Name

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

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A distributed reliable key-value store

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.esgyn.cn

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

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

nsdb.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

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

nsdb.io/Architecture

www.xtdb.com/docs

Developer

Esgyn

Circonus LLC.

Juxt Ltd.

Initial release

2015

2017

2019

Current release

3.4, August 2019

V0.10.20, January 2018

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0

Open Source

MIT License

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++, Java

C and C++

Java, Scala

Clojure

Server operating systems

Linux

FreeBSD
Linux
Windows

experimental

Linux

Linux
macOS

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes: int, bigint, decimal, string

yes, extensible-data-notation format

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.

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

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

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC
JSON over HTTP

HTTP API

gRPC
HTTP REST
WebSocket

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.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

Java
Scala

Clojure
Java

Server-side scripts

Stored procedures

Java Stored Procedures

yes, in Lua

Triggers

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

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

configurable replication factor, datacenter aware

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Eventual 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

Using Apache Lucene

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

User concepts

Access control

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Monitor Amazon EKS Control Plane metrics using AWS Open Source monitoring services | Amazon Web Services
12 October 2023, AWS Blog

RBI stresses need for underlying forex exposure for rupee derivatives trades
5 April 2024, Mint

6 Cool Kubernetes Operators and How to Use Them
22 January 2024, hackernoon.com

Killing a market, softly: How an RBI communique could end India's thriving ETCD market
7 April 2024, The Economic Times

Public preview: AKS cluster control plane metrics in managed Prometheus | Azure updates
12 February 2024, Microsoft

provided by Google News

Application observability firm Apica buys telemetry data startup Circonus and adds more funding
21 February 2024, SiliconANGLE News

Apica Acquires Telemetry Data Management Pioneer Circonus And Lands New Funding
22 February 2024, Datanami

Apica gets $6 million in funding and buys Circonus -
21 February 2024, Enterprise Times

provided by Google News

Share this page