DBMS > InfinityDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Solr vs. ToroDB

System Properties Comparison InfinityDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Solr vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A Java embedded Key-Value Store which extends the Java Map interface

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

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

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Search engine

Document store

Secondary database models

Spatial DBMS

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

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

boilerbay.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

solr.apache.org

github.com/torodb/server

Technical documentation

boilerbay.com/infinitydb/manual

learn.microsoft.com/azure/cosmos-db

solr.apache.org/resources.html

Developer

Boiler Bay Inc.

Microsoft

Apache Software Foundation

8Kdata

Initial release

2002

2014

2012

2006

2016

Current release

4.0

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

AGPL-V3

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

Server operating systems

All OS with a Java VM

hosted

All OS with a Java VM

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

All OS with a Java 7 VM

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

JSON types

yes

supports customizable data types and automatic typing

yes

string, integer, double, boolean, date, object_id

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

manual creation possible, using inversions based on multi-value capability

yes

All properties auto-indexed by default

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

Java API
RESTful HTTP/JSON API

Supported programming languages

Java

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

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

Server-side scripts

Stored procedures

JavaScript

Java plugins

Triggers

JavaScript

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

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

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

yes

Access rights for users and roles

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