DBMS > InterSystems Caché vs. Microsoft SQL Server vs. Percona Server for MongoDB vs. RocksDB vs. Solr

System Properties Comparison InterSystems Caché vs. Microsoft SQL Server vs. Percona Server for MongoDB vs. RocksDB vs. Solr

Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

A multi-model DBMS and application server

Microsofts flagship relational DBMS

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Key-value store
Object oriented DBMS
Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.60
Rank	#246	Overall
	#39	Document stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.intersystems.com/products/cache

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

www.percona.com/mongodb/software/percona-server-for-mongodb

rocksdb.org

solr.apache.org

Technical documentation

docs.intersystems.com

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

docs.percona.com/percona-distribution-for-mongodb

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

InterSystems

Microsoft

Percona

Facebook, Inc.

Apache Software Foundation

Initial release

1997

1989

2015

2013

2006

Current release

2018.1.4, May 2020

SQL Server 2022, November 2022

3.4.10-2.10, November 2017

9.2.1, May 2024

9.6.1, May 2024

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GPL Version 2

Open Source

BSD

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java

Server operating systems

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Linux
Windows

Linux

All OS with a Java VM

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

Data scheme

depending on used data model

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

yes

Solr Parallel SQL Interface

APIs and other access methods

.NET Client API
JDBC
ODBC
RESTful HTTP API

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

proprietary protocol using JSON

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

C#
C++
Java

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

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

C
C++
Go
Java
Perl
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

yes

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

JavaScript

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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

via In-Memory Engine

yes

User concepts

Access control

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Access rights for users and roles

yes

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