DBMS > Apache Doris vs. Apache Pinot vs. DuckDB vs. HEAVY.AI vs. RocksDB

System Properties Comparison Apache Doris vs. Apache Pinot vs. DuckDB vs. HEAVY.AI vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

An embeddable, in-process, column-oriented SQL OLAP RDBMS

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

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

pinot.apache.org

duckdb.org

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

rocksdb.org

Technical documentation

github.com/apache/doris/wiki

docs.pinot.apache.org

duckdb.org/docs

docs.heavy.ai

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation and contributors

HEAVY.AI, Inc.

Facebook, Inc.

Initial release

2017

2015

2018

2016

2013

Current release

1.2.2, February 2023

1.0.0, September 2023

1.0.0, June 2024

5.10, January 2022

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

Apache Version 2; enterprise edition available

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

C++ and CUDA

C++

Server operating systems

Linux

All OS with a Java JDK11 or higher

server-less

Linux

Data scheme

yes

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

yes

SQL-like query language

yes

APIs and other access methods

JDBC
MySQL client

JDBC

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

JDBC
ODBC
Thrift
Vega

C++ API
Java API

Supported programming languages

Java

Go
Java
Python

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

Round robin

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

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

Transaction concepts

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

ACID

yes

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

fine grained access rights according to SQL-standard

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