DBMS > ReductStore vs. Riak KV vs. RocksDB vs. Spark SQL

System Properties Comparison ReductStore vs. Riak KV vs. RocksDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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.

Distributed, fault tolerant key-value store

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Key-value store

Relational DBMS

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

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/reductstore
www.reduct.store

rocksdb.org

spark.apache.org/sql

Technical documentation

www.reduct.store/docs

www.tiot.jp/riak-docs/riak/kv/latest

github.com/facebook/rocksdb/wiki

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

Developer

ReductStore LLC

OpenSource, formerly Basho Technologies

Facebook, Inc.

Apache Software Foundation

Initial release

2023

2009

2013

2014

Current release

1.9, March 2024

3.2.0, December 2022

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Business Source License 1.1

Open Source

Apache version 2, commercial enterprise edition

Open Source

BSD

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Rust

Erlang

C++

Scala

Server operating systems

Docker
Linux
macOS
Windows

Linux
OS X

Linux

Linux
OS X
Windows

Data scheme

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

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

HTTP API
Native Erlang Interface

C++ API
Java API

JDBC
ODBC

Supported programming languages

C++
JavaScript (Node.js)
Python
Rust

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

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

yes, using Riak Security

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