DBMS > IRONdb vs. NSDb vs. Riak TS vs. Sqrrl

System Properties Comparison IRONdb vs. NSDb vs. Riak TS vs. Sqrrl

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Editorial information provided by DB-Engines

Name

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

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

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

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

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

nsdb.io

sqrrl.com

Technical documentation

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

nsdb.io/Architecture

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Circonus LLC.

Open Source, formerly Basho Technologies

Amazon

originally Sqrrl Data, Inc.

Initial release

2017

2015

2012

Current release

V0.10.20, January 2018

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

commercial

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, Scala

Erlang

Java

Server operating systems

Linux

Linux
macOS

Linux
OS X

Linux

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes: int, bigint, decimal, string

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.

Secondary indexes

all fields are automatically indexed

restricted

yes

SQL

Support of SQL

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

SQL-like query language

yes, limited

APIs and other access methods

HTTP API

gRPC
HTTP REST
WebSocket

HTTP API
Native Erlang Interface

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

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

Java
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

yes, in Lua

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

User concepts

Access control

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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More resources

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