DBMS > Riak KV vs. SAP HANA vs. Solr vs. SQream DB

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

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Distributed, fault tolerant key-value store

In-memory, column based data store. Available as appliance or cloud service

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

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

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

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

www.sap.com/products/hana.html

solr.apache.org

sqream.com

Technical documentation

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

help.sap.com/hana

solr.apache.org/resources.html

docs.sqream.com

Developer

OpenSource, formerly Basho Technologies

SAP

Apache Software Foundation

SQream Technologies

Initial release

2009

2010

2006

2017

Current release

3.2.0, December 2022

2.0 SPS07 (April 4, 2023), April 2023

9.6.0, April 2024

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache version 2, commercial enterprise edition

commercial

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux
OS X

Appliance or cloud-service

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

SQL

Support of SQL

yes

Solr Parallel SQL Interface

yes

APIs and other access methods

HTTP API
Native Erlang Interface

JDBC
ODBC

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

Erlang

SQLScript, R

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"

yes

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

yes

Transaction concepts

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

ACID

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, using Riak Security

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.