DBMS > Ignite vs. Microsoft SQL Server vs. Solr vs. TerminusDB

System Properties Comparison Ignite vs. Microsoft SQL Server vs. Solr vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Microsofts flagship relational DBMS

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Search engine

Graph DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

Document store
RDF store

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

Score	3.64
Rank	#89	Overall
	#13	Key-value stores
	#48	Relational DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Score	0.22
Rank	#323	Overall
	#28	Graph DBMS

Website

ignite.apache.org

www.microsoft.com/en-us/sql-server

solr.apache.org

terminusdb.com

Technical documentation

apacheignite.readme.io/docs

learn.microsoft.com/en-US/sql/sql-server

solr.apache.org/resources.html

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

Microsoft

Apache Software Foundation

DataChemist Ltd.

Initial release

2015

1989

2006

2018

Current release

Apache Ignite 2.6

SQL Server 2022, November 2022

9.5.0, February 2024

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

Apache Version 2

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java, .Net

C++

Java

Prolog, Rust

Server operating systems

Linux
OS X
Solaris
Windows

Linux
Windows

All OS with a Java VM

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

Linux

Data scheme

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

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 search fields are automatically indexed

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

Solr Parallel SQL Interface

SQL-like query language (WOQL)

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Java API
RESTful HTTP/JSON API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

JavaScript
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Java plugins

yes

Triggers

yes (cache interceptors and events)

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes, but depending on the SQL-Server Edition

yes

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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 journaling

In-memory capabilities

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

yes

User concepts

Access control

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

yes

Role-based access control

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