DBMS > BigObject vs. Cachelot.io vs. ReductStore vs. Spark SQL

System Properties Comparison BigObject vs. Cachelot.io vs. ReductStore vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

In-memory caching system

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Time Series DBMS

Relational DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

bigobject.io

cachelot.io

github.com/reductstore
www.reduct.store

spark.apache.org/sql

Technical documentation

docs.bigobject.io

www.reduct.store/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

BigObject, Inc.

ReductStore LLC

Apache Software Foundation

Initial release

2015

2023

2014

Current release

1.9, March 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free community edition available

Open Source

Simplified BSD License

Open Source

Business Source License 1.1

Open Source

Apache 2.0

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++

C++, Rust

Scala

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

Docker
Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

Memcached protocol

HTTP API

JDBC
ODBC

Supported programming languages

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

C++
JavaScript (Node.js)
Python
Rust

Java
Python
R
Scala

Server-side scripts

Stored procedures

Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

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

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