DBMS > Apache Doris vs. ClickHouse vs. HEAVY.AI vs. Lovefield vs. XTDB

System Properties Comparison Apache Doris vs. ClickHouse vs. HEAVY.AI vs. Lovefield vs. XTDB

Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

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

Embeddable relational database for web apps written in pure JavaScript

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

Primary database model

Relational DBMS

Document store

Secondary database models

Time Series DBMS

Spatial DBMS

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

doris.apache.org
github.com/apache/doris

clickhouse.com

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

google.github.io/lovefield

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

Technical documentation

github.com/apache/doris/wiki

clickhouse.com/docs

docs.heavy.ai

github.com/google/lovefield/blob/master/docs/spec_index.md

www.xtdb.com/docs

Developer

Apache Software Foundation, originally contributed from Baidu

Clickhouse Inc.

HEAVY.AI, Inc.

Google

Juxt Ltd.

Initial release

2017

2016

2014

2019

Current release

1.2.2, February 2023

v24.3.2.23-lts, April 2024

5.10, January 2022

2.1.12, February 2017

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

C++

C++ and CUDA

JavaScript

Clojure

Server operating systems

Linux

FreeBSD
Linux
macOS

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

Close to ANSI SQL (SQL/JSON + extensions)

yes

SQL-like query language

via JavaScript builder pattern

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
MySQL client

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

JDBC
ODBC
Thrift
Vega

HTTP REST
JDBC

Supported programming languages

Java

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

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

key based and custom

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Multi-source replication

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

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

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

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

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