DBMS > Apache Kylin vs. Coveo vs. Graph Engine vs. Machbase Neo vs. RocksDB

System Properties Comparison Apache Kylin vs. Coveo vs. Graph Engine vs. Machbase Neo vs. RocksDB

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

TimeSeries DBMS for AIoT and BigData

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

Primary database model

Relational DBMS

Search engine

Graph DBMS
Key-value store

Time Series DBMS

Key-value store

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	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

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

Website

Technical documentation

kylin.apache.org/docs

docs.coveo.com

www.graphengine.io/docs/manual

machbase.com/dbms

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation, originally contributed from eBay Inc

Coveo

Microsoft

Machbase

Facebook, Inc.

Initial release

2015

2012

2010

2013

Current release

3.1.0, July 2020

V8.0, August 2023

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

MIT License

commercial

free test version available

Open Source

BSD

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

.NET and C

C++

Server operating systems

Linux

hosted

.NET

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

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

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

Supported programming languages

C#
Java
JavaScript
Python

C#
C++
F#
Visual Basic

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

horizontal partitioning

Sharding

horizontal partitioning

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

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