DBMS > ClickHouse vs. Microsoft Azure Data Explorer vs. Splice Machine vs. SWC-DB

System Properties Comparison ClickHouse vs. Microsoft Azure Data Explorer vs. Splice Machine vs. SWC-DB

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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 managed big data interactive analytics platform

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

column oriented

Relational DBMS

Wide column store

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

Time Series DBMS

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

clickhouse.com

azure.microsoft.com/services/data-explorer

splicemachine.com

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

clickhouse.com/docs

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

splicemachine.com/how-it-works

Developer

Clickhouse Inc.

Microsoft

Splice Machine

Alex Kashirin

Initial release

2016

2019

2014

2020

Current release

v24.4.1.2088-stable, May 2024

cloud service with continuous releases

3.1, March 2021

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

GPL V3

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.
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.

Implementation language

C++

Java

C++

Server operating systems

FreeBSD
Linux
macOS

hosted

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

schema-free

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)

Kusto Query Language (KQL), SQL subset

yes

SQL-like query language

APIs and other access methods

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

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

JDBC
Native Spark Datasource
ODBC

Proprietary protocol
Thrift

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
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C++

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

yes

Java

Triggers

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

Implicit feature of the cloud service

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

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

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (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.

Azure Active Directory Authentication

Access rights for users, groups and roles according to SQL-standard

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