DBMS > Machbase Neo vs. Microsoft SQL Server vs. Solr vs. StarRocks vs. Yaacomo

System Properties Comparison Machbase Neo vs. Microsoft SQL Server vs. Solr vs. StarRocks vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

TimeSeries DBMS for AIoT and BigData

Microsofts flagship relational DBMS

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

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

machbase.com

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

solr.apache.org

www.starrocks.io

yaacomo.com

Technical documentation

machbase.com/dbms

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

solr.apache.org/resources.html

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Machbase

Microsoft

Apache Software Foundation

The Linux Foundation

since Feb 2023

Q2WEB GmbH

Initial release

2013

1989

2006

2020

2009

Current release

V8.0, August 2023

SQL Server 2022, November 2022

9.6.1, May 2024

2.5.3, March 2023

License

Commercial or Open Source

commercial

free test version available

commercial

restricted free version is available

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

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

C++, Java

Server operating systems

Linux
macOS
Windows

Linux
Windows

All OS with a Java VM

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

Linux

Android
Linux
Windows

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

yes

SQL

Support of SQL

SQL-like query language

yes

Solr Parallel SQL Interface

yes

APIs and other access methods

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

Java API
RESTful HTTP/JSON API

JDBC
MySQL protocol

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
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

Java

Server-side scripts

Stored procedures

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

Java plugins

user defined functions

Triggers

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

horizontal partitioning (by range and hash)

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes, but depending on the SQL-Server Edition

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

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 capabilities

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

yes

volatile and lookup table

yes

User concepts

Access control

simple password-based access control

fine grained access rights according to SQL-standard

yes

Role based access control and fine grained access rights

fine grained access rights according to SQL-standard

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