DBMS > Amazon Redshift vs. Bangdb vs. OrientDB vs. Solr vs. Sqrrl

System Properties Comparison Amazon Redshift vs. Bangdb vs. OrientDB vs. Solr vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Converged and high performance database for device data, events, time series, document and graph

Multi-model DBMS (Document, Graph, Key/Value)

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

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Document store
Graph DBMS
Key-value store

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

bangdb.com

orientdb.org

solr.apache.org

sqrrl.com

Technical documentation

docs.aws.amazon.com/redshift

docs.bangdb.com

www.orientdb.com/docs/last/index.html

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Sachin Sinha, BangDB

OrientDB LTD; CallidusCloud; SAP

Apache Software Foundation

Amazon

originally Sqrrl Data, Inc.

Initial release

2012

2010

2006

2012

Current release

BangDB 2.0, October 2021

3.2.29, March 2024

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

BSD 3

Open Source

Apache version 2

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java

Server operating systems

hosted

Linux

All OS with a Java JDK (>= JDK 6)

All OS with a Java VM

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

Linux

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

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

schema-free

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL like support with command line tool

SQL-like query language, no joins

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

Proprietary protocol
RESTful HTTP API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
Python

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

user defined functions

in Python

Java, Javascript

Java plugins

Triggers

yes, Notifications (with Streaming only)

Hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication

yes

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

relationship in graphs

Transaction concepts

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

ACID

optimistic locking

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, run db with in-memory only mode

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes (enterprise version only)

Access rights for users and roles; record level security configurable

yes

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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