DBMS > Blueflood vs. IRONdb vs. LevelDB vs. Microsoft Azure Cosmos DB vs. Sequoiadb

System Properties Comparison Blueflood vs. IRONdb vs. LevelDB vs. Microsoft Azure Cosmos DB vs. Sequoiadb

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

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Globally distributed, horizontally scalable, multi-model database service

NewSQL database with distributed OLTP and SQL

Primary database model

Time Series DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Document store
Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

blueflood.io

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

github.com/google/leveldb

azure.microsoft.com/services/cosmos-db

www.sequoiadb.com

Technical documentation

github.com/rax-maas/blueflood/wiki

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

github.com/google/leveldb/blob/main/doc/index.md

learn.microsoft.com/azure/cosmos-db

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Rackspace

Circonus LLC.

Google

Microsoft

Sequoiadb Ltd.

Initial release

2013

2017

2011

2014

2013

Current release

V0.10.20, January 2018

1.23, February 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

BSD

commercial

Open Source

Server: AGPL; Client: Apache V2

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

Java

C and C++

C++

Server operating systems

Linux
OS X

Linux

Illumos
Linux
OS X
Windows

hosted

Linux

Data scheme

predefined scheme

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

JSON types

yes

oid, date, timestamp, binary, regex

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 properties auto-indexed by default

yes

SQL

Support of SQL

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

SQL-like query language

APIs and other access methods

HTTP REST

HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

proprietary protocol using JSON

Supported programming languages

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes, in Lua

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Automatic, metric affinity per node

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

configurable replication factor, datacenter aware

none

yes

Implicit feature of the cloud service

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

User concepts

Access control

Access rights can be defined down to the item level

simple password-based access control

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