DBMS > Amazon Redshift vs. Atos Standard Common Repository vs. HarperDB vs. Solr vs. Yaacomo

System Properties Comparison Amazon Redshift vs. Atos Standard Common Repository vs. HarperDB vs. Solr vs. Yaacomo

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Large scale data warehouse service for use with business intelligence tools

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Document store
Key-value store

Document store

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.harperdb.io

solr.apache.org

yaacomo.com

Technical documentation

docs.aws.amazon.com/redshift

docs.harperdb.io/docs

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Atos Convergence Creators

HarperDB

Apache Software Foundation

Q2WEB GmbH

Initial release

2012

2016

2017

2006

2009

Current release

1703

3.1, August 2021

9.6.1, May 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Node.js

Java

Server operating systems

hosted

Linux

Linux
OS X

All OS with a Java VM

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

Android
Linux
Windows

Data scheme

yes

Schema and schema-less with LDAP views

dynamic schema

yes

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

yes

Typing

predefined data types such as float or date

yes

optional

yes

JSON data types

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

restricted

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like data manipulation statements

Solr Parallel SQL Interface

yes

APIs and other access methods

JDBC
ODBC

LDAP

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Java API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

Server-side scripts

Stored procedures

user defined functions

in Python

Custom Functions

since release 3.1

Java plugins

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

A table resides as a whole on one (or more) nodes in a cluster

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

fine grained access rights according to SQL-standard

LDAP bind authentication

Access rights for users and roles

yes

fine grained access rights according to SQL-standard

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