DBMS > Databend vs. DolphinDB vs. EventStoreDB vs. Heroic

System Properties Comparison Databend vs. DolphinDB vs. EventStoreDB vs. Heroic

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Relational DBMS

Time Series DBMS
Vector DBMS

Event Store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	0.28
Rank	#287	Overall
	#131	Relational DBMS

Score	2.72
Rank	#100	Overall
	#8	Time Series DBMS
	#5	Vector DBMS

Score	1.07
Rank	#181	Overall
	#1	Event Stores

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Website

github.com/datafuselabs/databend
www.databend.com

www.dolphindb.com

www.eventstore.com

github.com/spotify/heroic

Technical documentation

docs.databend.com

docs.dolphindb.cn/en/help200/index.html

developers.eventstore.com

spotify.github.io/heroic

Developer

Databend Labs

DolphinDB, Inc

Event Store Limited

Spotify

Initial release

2021

2018

2012

2014

Current release

1.0.59, April 2023

v2.00.4, January 2022

21.2, February 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community version available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Rust

C++

Java

Server operating systems

hosted
Linux
macOS

Linux
Windows

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

via Elasticsearch

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

yes

Concurrency

Support for concurrent manipulation of data

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

Users with fine-grained authorization concept, user roles

Administrators, Users, Groups

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