DBMS > Microsoft Azure Cosmos DB vs. SingleStore vs. Solr vs. TinkerGraph

System Properties Comparison Microsoft Azure Cosmos DB vs. SingleStore vs. Solr vs. TinkerGraph

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Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

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

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Search engine

Graph DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

Spatial DBMS

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

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	5.91
Rank	#61	Overall
	#34	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Score	0.12
Rank	#344	Overall
	#34	Graph DBMS

Website

azure.microsoft.com/services/cosmos-db

www.singlestore.com

solr.apache.org

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

learn.microsoft.com/azure/cosmos-db

docs.singlestore.com

solr.apache.org/resources.html

Developer

Microsoft

SingleStore Inc.

Apache Software Foundation

Initial release

2014

2013

2006

2009

Current release

8.5, January 2024

9.5.0, February 2024

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Apache Version 2

Open Source

Apache 2.0

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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

C++, Go

Java

Server operating systems

hosted

Linux

64 bit version required

All OS with a Java VM

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

Data scheme

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

JSON 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

yes

All properties auto-indexed by default

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

but no triggers and foreign keys

Solr Parallel SQL Interface

APIs and other access methods

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

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Java API
RESTful HTTP/JSON API

TinkerPop 3

Supported programming languages

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

Bash
C
C#
Java
JavaScript (Node.js)
Python

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

Groovy
Java

Server-side scripts

Stored procedures

JavaScript

yes

Java plugins

Triggers

JavaScript

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

hash partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication

stores two copies of each physical data partition on two separate nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

can define user-defined aggregate functions for map-reduce-style calculations

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

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

yes

optional

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

Fine grained access control via users, groups and roles

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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Market metrics

Customers in various industries worldwide including US and International Industry...
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Licensing and pricing models

F ree Tier and Enterprise Edition
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