DBMS > Bangdb vs. JaguarDB vs. Solr vs. StarRocks

System Properties Comparison Bangdb vs. JaguarDB vs. Solr vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

Performant, highly scalable DBMS for AI and IoT applications

A widely used distributed, scalable search engine based on Apache Lucene

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

Document store
Graph DBMS
Time Series DBMS

Key-value store
Vector DBMS

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#15	Vector DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

Technical documentation

docs.bangdb.com

www.jaguardb.com/support.html

solr.apache.org/resources.html

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

Developer

Sachin Sinha, BangDB

DataJaguar, Inc.

Apache Software Foundation

The Linux Foundation

since Feb 2023

Initial release

2012

2015

2006

2020

Current release

BangDB 2.0, October 2021

3.3 July 2023

9.6.1, May 2024

3.3, June 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

GPL V3.0

Open Source

Apache Version 2

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++

C++

the server part. Clients available in other languages

Java

C++, Java

Server operating systems

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL like support with command line tool

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

Solr Parallel SQL Interface

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC

Java API
RESTful HTTP/JSON API

JDBC
MySQL protocol

Supported programming languages

C
C#
C++
Java
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Java

Server-side scripts

Stored procedures

Java plugins

user defined functions

Triggers

yes, Notifications (with Streaming only)

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

rights management via user accounts

yes

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

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4 February 2013, Paul Andlinger

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