DBMS > ClickHouse vs. Ehcache vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Tibero

System Properties Comparison ClickHouse vs. Ehcache vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Tibero

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.

A widely adopted Java cache with tiered storage options

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Fully managed big data interactive analytics platform

A secure RDBMS, designed for easy portability from Oracle

Primary database model

column oriented

Secondary database models

Time Series DBMS

Spatial DBMS

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	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

clickhouse.com

www.ehcache.org

github.com/heavyai/heavydb
www.heavy.ai

azure.microsoft.com/services/data-explorer

us.tmaxsoft.com/products/tibero

Technical documentation

clickhouse.com/docs

www.ehcache.org/documentation

docs.heavy.ai

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

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Clickhouse Inc.

Terracotta Inc, owned by Software AG

HEAVY.AI, Inc.

Microsoft

TmaxSoft

Initial release

2016

2009

2016

2019

2003

Current release

v24.4.1.2088-stable, May 2024

3.10.0, March 2022

5.10, January 2022

cloud service with continuous releases

6, April 2015

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2; enterprise edition 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.

DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.

Implementation language

C++

Java

C++ and CUDA

C and Assembler

Server operating systems

FreeBSD
Linux
macOS

All OS with a Java VM

Linux

hosted

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

yes

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

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 fields are automatically indexed

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

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

JCache

JDBC
ODBC
Thrift
Vega

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

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

Java

All languages supporting JDBC/ODBC/Thrift
Python

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

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

Persistent Stored Procedure (PSM)

Triggers

yes

Cache Event Listeners

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

by using Terracotta Server

Sharding

Round robin

Sharding

Implicit feature of the cloud service

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

by using Terracotta Server

Multi-source replication

yes

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

In-memory capabilities

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

yes

planned for next version

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.

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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