DBMS > Blueflood vs. HarperDB vs. IRONdb vs. LeanXcale vs. SQLite

System Properties Comparison Blueflood vs. HarperDB vs. IRONdb vs. LeanXcale vs. SQLite

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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

Widely used embeddable, in-process RDBMS

Primary database model

Time Series DBMS

Document store

Time Series DBMS

Key-value store
Relational DBMS

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	0.55
Rank	#248	Overall
	#38	Document stores

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

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

blueflood.io

www.harperdb.io

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

www.leanxcale.com

www.sqlite.org

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.harperdb.io/docs

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

www.sqlite.org/docs.html

Developer

Rackspace

HarperDB

Circonus LLC.

LeanXcale

Dwayne Richard Hipp

Initial release

2013

2017

2015

2000

Current release

3.1, August 2021

V0.10.20, January 2018

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free community edition available

commercial

Open Source

Public Domain

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

Node.js

C and C++

Server operating systems

Linux
OS X

Linux

server-less

Data scheme

predefined scheme

dynamic schema

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

JSON data types

yes

text, numeric, histograms

yes

not rigid because of 'dynamic typing' concept.

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 data manipulation statements

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

yes

through Apache Derby

yes

SQL-92 is not fully supported

APIs and other access methods

HTTP REST

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

C
Java
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes, in Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

A table resides as a whole on one (or more) nodes in a cluster

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

the nodes on which a table resides can be defined

configurable replication factor, datacenter aware

none

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

yes, using LMDB

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

Access rights for users and roles

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