DBMS > ClickHouse vs. Microsoft Azure Data Explorer vs. Sphinx vs. STSdb vs. XTDB

System Properties Comparison ClickHouse vs. Microsoft Azure Data Explorer vs. Sphinx vs. STSdb vs. XTDB

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 search engine for searching in data from different sources, e.g. relational databases

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

column oriented

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	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

clickhouse.com

azure.microsoft.com/services/data-explorer

sphinxsearch.com

github.com/STSSoft/STSdb4

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

clickhouse.com/docs

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

sphinxsearch.com/docs

www.xtdb.com/docs

Developer

Clickhouse Inc.

Microsoft

Sphinx Technologies Inc.

STS Soft SC

Juxt Ltd.

Initial release

2016

2019

2001

2011

2019

Current release

v24.4.1.2088-stable, May 2024

cloud service with continuous releases

3.5.1, February 2023

4.0.8, September 2015

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

GPL version 2, commercial licence available

Open Source

GPLv2, commercial license available

Open Source

MIT License

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++

Clojure

Server operating systems

FreeBSD
Linux
macOS

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

All OS with a Java 8 (and higher) VM
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

primitive types and user defined types (classes)

yes, extensible-data-notation format

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

full-text index on all search fields

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

Kusto Query Language (KQL), SQL subset

SQL-like query language (SphinxQL)

limited SQL, making use of Apache Calcite

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

Proprietary protocol

.NET Client API

HTTP REST
JDBC

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++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

Clojure
Java

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

Triggers

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

Implicit feature of the cloud service

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

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.

none

yes, each node contains all data

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

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