DBMS > BigObject vs. Cachelot.io vs. CrateDB vs. IRONdb

System Properties Comparison BigObject vs. Cachelot.io vs. CrateDB vs. IRONdb

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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

Analytic DBMS for real-time computations and queries

In-memory caching system

Distributed Database based on Lucene

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	0.00
Rank	#385	Overall
	#61	Key-value stores

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Website

bigobject.io

cachelot.io

cratedb.com

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

Technical documentation

docs.bigobject.io

cratedb.com/docs

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

Developer

BigObject, Inc.

Crate

Circonus LLC.

Initial release

2015

2013

2017

Current release

5.8.1, August 2024

V0.10.20, January 2018

License

Commercial or Open Source

commercial

free community edition available

Open Source

Simplified BSD License

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++

Java

C and C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

schema-free

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 DML and DDL statements

yes, but no triggers and constraints, and PostgreSQL compatibility

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

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

Memcached protocol

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

HTTP API

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

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

Server-side scripts

Stored procedures

Lua

user defined functions (Javascript)

yes, in Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Automatic, metric affinity per node

Replication methods

Methods for redundantly storing data on multiple nodes

none

Configurable replication on table/partition-level

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

none

Eventual Consistency
Read-after-write consistency on record level

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

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

rights management via user accounts

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