DBMS > Blueflood vs. Datomic vs. IRONdb vs. LeanXcale

System Properties Comparison Blueflood vs. Datomic vs. IRONdb vs. LeanXcale

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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.

Description

Scalable TimeSeries DBMS based on Cassandra

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

Key-value store
Relational DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Website

blueflood.io

www.datomic.com

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

www.leanxcale.com

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.datomic.com

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

Developer

Rackspace

Cognitect

Circonus LLC.

LeanXcale

Initial release

2013

2012

2017

2015

Current release

1.0.6735, June 2023

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

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

Java

Java, Clojure

C and C++

Server operating systems

Linux
OS X

All OS with a Java VM

Linux

Data scheme

predefined scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL

Support of SQL

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

yes

through Apache Derby

APIs and other access methods

HTTP REST

RESTful HTTP API

HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

Clojure
Java

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

C
Java
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

yes, in Lua

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

But extensive use of caching in the application peers

Automatic, metric affinity per node

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

But extensive use of caching in the application peers

configurable replication factor, datacenter aware

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

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