DBMS > ClickHouse vs. Microsoft Azure Table Storage vs. QuestDB vs. TinkerGraph

System Properties Comparison ClickHouse vs. Microsoft Azure Table Storage vs. QuestDB vs. TinkerGraph

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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.

A Wide Column Store for rapid development using massive semi-structured datasets

A high performance open source SQL database for time series data

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Secondary database models

Time Series DBMS

Relational DBMS

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

Score	15.88
Rank	#37	Overall
	#22	Relational DBMS

Score	3.55
Rank	#80	Overall
	#6	Wide column stores

Score	2.81
Rank	#98	Overall
	#7	Time Series DBMS

Score	0.08
Rank	#345	Overall
	#34	Graph DBMS

Website

clickhouse.com

azure.microsoft.com/en-us/services/storage/tables

questdb.io

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

clickhouse.com/docs

questdb.io/docs

Developer

Clickhouse Inc.

Microsoft

QuestDB Technology Inc

Initial release

2016

2012

2014

2009

Current release

v24.6.2.17-stable, July 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

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.

Implementation language

C++

Java (Zero-GC), C++, Rust

Java

Server operating systems

FreeBSD
Linux
macOS

hosted

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free via InfluxDB Line Protocol

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL with time-series extensions

APIs and other access methods

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

RESTful HTTP API

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

TinkerPop 3

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++
Java
JavaScript (Node.js)
PHP
Python
Ruby

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

Groovy
Java

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

Implicit feature of the cloud service

horizontal partitioning (by timestamps)

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.

Source-replica replication with eventual consistency

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

optimistic locking

ACID for single-table writes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional

In-memory capabilities

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

yes

through memory mapped files

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.

Access rights based on private key authentication or shared access signatures

More information provided by the system vendor

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