DBMS > ClickHouse vs. Kinetica vs. LokiJS vs. Microsoft Access vs. Newts

System Properties Comparison ClickHouse vs. Kinetica vs. LokiJS vs. Microsoft Access vs. Newts

Editorial information provided by DB-Engines

Name

Description

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

Fully vectorized database across both GPUs and CPUs

In-memory JavaScript DBMS

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Time Series DBMS based on Cassandra

Primary database model

Secondary database models

Time Series DBMS

Spatial DBMS
Time Series DBMS

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Trend Chart

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.49
Rank	#260	Overall
	#42	Document stores

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Website

clickhouse.com

www.kinetica.com

github.com/techfort/LokiJS

www.microsoft.com/en-us/microsoft-365/access

opennms.github.io/newts

Technical documentation

clickhouse.com/docs

docs.kinetica.com

techfort.github.io/LokiJS

developer.microsoft.com/en-us/access

github.com/OpenNMS/newts/wiki

Developer

Clickhouse Inc.

Kinetica

Microsoft

OpenNMS Group

Initial release

2016

2012

2014

1992

2014

Current release

v24.4.1.2088-stable, May 2024

7.1, August 2021

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

commercial

Bundled with Microsoft Office

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
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DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

C++

C, C++

JavaScript

C++

Java

Server operating systems

FreeBSD
Linux
macOS

Linux

server-less, requires a JavaScript environment (browser, Node.js)

Windows

Not a real database server, but making use of DLLs

Linux
OS X
Windows

Data scheme

yes

schema-free

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

via views

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like DML and DDL statements

yes

but not compliant to any SQL standard

APIs and other access methods

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

JDBC
ODBC
RESTful HTTP API

JavaScript API

ADO.NET
DAO
ODBC
OLE DB

HTTP REST
Java API

Supported programming languages

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

C++
Java
JavaScript (Node.js)
Python

JavaScript

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

Java

Server-side scripts

Stored procedures

yes

user defined functions

View functions in JavaScript

yes

since Access 2010 using the ACE-engine

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

Source-replica replication

none

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a single collection possible

ACID

but no files for transaction logging

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

yes

but no files for transaction logging

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Access rights for users and roles on table level

a simple user-level security was built in till version Access 2003

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