DBMS > BigObject vs. DolphinDB vs. EsgynDB vs. StarRocks

System Properties Comparison BigObject vs. DolphinDB vs. EsgynDB vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

Technical documentation

docs.bigobject.io

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

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

BigObject, Inc.

DolphinDB, Inc

Esgyn

The Linux Foundation

since Feb 2023

Initial release

2015

2018

2015

2020

Current release

v2.00.4, January 2022

3.3, June 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

free community version available

commercial

Open Source

Apache Version 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

C++

C++, Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
Windows

Linux

Data scheme

yes

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

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

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

ADO.NET
JDBC
ODBC

JDBC
MySQL protocol

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

Java

Server-side scripts

Stored procedures

Lua

yes

Java Stored Procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Multi-source replication between multi datacenters

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Administrators, Users, Groups

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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