DBMS > Apache Kylin vs. Coveo vs. Machbase Neo vs. RocksDB vs. StarRocks

System Properties Comparison Apache Kylin vs. Coveo vs. Machbase Neo vs. RocksDB vs. StarRocks

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

TimeSeries DBMS for AIoT and BigData

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

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

Forked from Apache Doris

Primary database model

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	2.11
Rank	#118	Overall
	#11	Search engines

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

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

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

Technical documentation

kylin.apache.org/docs

docs.coveo.com

machbase.com/dbms

github.com/facebook/rocksdb/wiki

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

Developer

Apache Software Foundation, originally contributed from eBay Inc

Coveo

Machbase

Facebook, Inc.

The Linux Foundation

since Feb 2023

Initial release

2015

2012

2013

2020

Current release

3.1.0, July 2020

V8.0, August 2023

9.2.1, May 2024

2.5.3, March 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free test version available

Open Source

BSD

Open Source

Apache Version 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.

Implementation language

Java

C++

C++, Java

Server operating systems

Linux

hosted

Linux
macOS
Windows

Linux

Data scheme

yes

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

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

C++ API
Java API

JDBC
MySQL protocol

Supported programming languages

C#
Java
JavaScript
Python

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C
C++
Go
Java
Perl
Python
Ruby

Java

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

horizontal partitioning

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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

granular access controls, API key management, content filters

simple password-based access control

Role based access control and fine grained access rights

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