DBMS > Riak KV vs. Solr vs. SQream DB

System Properties Comparison Riak KV vs. Solr vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

Distributed, fault tolerant key-value store

A widely used distributed, scalable search engine based on Apache Lucene

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Key-value store

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

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	6.52
Rank	#65	Overall
	#9	Key-value stores

Score	56.61
Rank	#20	Overall
	#3	Search engines

Score	0.58
Rank	#224	Overall
	#108	Relational DBMS

Website

riak.com/products/riak-kv

lucene.apache.org/solr

sqream.com

Technical documentation

lucene.apache.org/solr/resources.html

docs.sqream.com

Developer

Basho Technologies

Apache Software Foundation

SQream Technologies

Initial release

2009

2006

2014

Current release

2.1.0, April 2015

9.0.0, May 2022

2021.2, September 2021

License

Commercial or Open Source

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux
OS X

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes, ANSI Standard SQL Types

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.

yes

Secondary indexes

restricted

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

Solr Parallel SQL Interface

yes

APIs and other access methods

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

.Net
JDBC
ODBC

Supported programming languages

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

JavaScript and Erlang

Java plugins

user defined functions in Python

Triggers

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

horizontal and vertical partitioning

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

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate 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

optimistic locking

ACID

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

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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News

Women in Tech: “Positions and opportunities in the technology field are endless”
7 June 2022

Minimizing the carbon footprint of data analysis, maximizing sustainability for data centers
7 June 2022

SQream at Asia Tech x Singapore 2022: Big Data and AI Crucial for Customer Experience and QoS
2 June 2022

SQream Appoints Naama Saar as New COO
16 May 2022

Panoply Partners with Square to Offer Simplified Business Intelligence
16 May 2022

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