DBMS > ClickHouse vs. Drizzle vs. Faircom EDGE vs. Microsoft Azure Data Explorer vs. YDB

System Properties Comparison ClickHouse vs. Drizzle vs. Faircom EDGE vs. Microsoft Azure Data Explorer vs. YDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded 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.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Fully managed big data interactive analytics platform

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

column oriented

Document store
Relational DBMS

Secondary database models

Time Series 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	16.20
Rank	#37	Overall
	#23	Relational DBMS

Score	0.03
Rank	#380	Overall
	#59	Key-value stores
	#157	Relational DBMS

Score	5.16
Rank	#69	Overall
	#37	Relational DBMS

Score	0.34
Rank	#285	Overall
	#43	Document stores
	#130	Relational DBMS

Website

clickhouse.com

www.faircom.com/products/faircom-edge

azure.microsoft.com/services/data-explorer

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

clickhouse.com/docs

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

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

ydb.tech/en/docs

Developer

Clickhouse Inc.

Drizzle project, originally started by Brian Aker

FairCom Corporation

Microsoft

Yandex

Initial release

2016

2008

1979

2019

Current release

v24.3.2.23-lts, April 2024

7.2.4, September 2012

V3, October 2020

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GNU GPL

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0; commercial license available

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.

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

Implementation language

C++

ANSI C, C++

Server operating systems

FreeBSD
Linux
macOS

FreeBSD
Linux
OS X

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

Fixed schema with schema-less datatypes (dynamic)

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

with proprietary extensions

yes

ANSI SQL queries

Kusto Query Language (KQL), SQL subset

SQL-like query language (YQL)

APIs and other access methods

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

JDBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

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

RESTful HTTP API (DynamoDB compatible)

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
C++
Java
PHP

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

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

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Yes, possible languages: KQL, Python, R

Triggers

hooks for callbacks inside the server can be used.

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

File partitioning

Customizable business rules for partitioning

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

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

Active-passive shard 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

Immediate Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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.

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Azure Active Directory Authentication

Access rights defined for Yandex Cloud users

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.