DBMS > ClickHouse vs. Hazelcast vs. Ingres vs. InterSystems Caché vs. Microsoft Azure Data Explorer

System Properties Comparison ClickHouse vs. Hazelcast vs. Ingres vs. InterSystems Caché vs. Microsoft Azure Data Explorer

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 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.

A widely adopted in-memory data grid

Well established RDBMS

A multi-model DBMS and application server

Fully managed big data interactive analytics platform

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

column oriented

Secondary database models

Time Series DBMS

Document store

JSON support with IMDG 3.12

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Website

clickhouse.com

hazelcast.com

www.actian.com/databases/ingres

www.intersystems.com/products/cache

azure.microsoft.com/services/data-explorer

Technical documentation

clickhouse.com/docs

hazelcast.org/imdg/docs

docs.actian.com/ingres

docs.intersystems.com

docs.microsoft.com/en-us/azure/data-explorer

Developer

Clickhouse Inc.

Hazelcast

Actian Corporation

InterSystems

Microsoft

Initial release

2016

2008

1974

originally developed at University Berkely in early 1970s

1997

2019

Current release

v24.4.1.2088-stable, May 2024

5.3.6, November 2023

11.2, May 2022

2018.1.4, May 2020

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available

commercial

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.

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Implementation language

C++

Java

Server operating systems

FreeBSD
Linux
macOS

All OS with a Java VM

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

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

hosted

Data scheme

yes

schema-free

yes

depending on used data model

Fixed schema with schema-less datatypes (dynamic)

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

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

the object must implement a serialization strategy

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

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

JCache
JPA
Memcached protocol
RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

.NET Client API
JDBC
ODBC
RESTful HTTP API

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP 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

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C#
C++
Java

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

Yes, possible languages: KQL, Python, R

Triggers

yes

Events

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Replicated Map

Ingres Replicator

Source-replica replication

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

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.

Role-based access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

Azure Active Directory Authentication

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