DBMS > Bangdb vs. Infobright vs. IRONdb vs. Pinecone

System Properties Comparison Bangdb vs. Infobright vs. IRONdb vs. Pinecone

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A managed, cloud-native vector database

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	3.16
Rank	#95	Overall
	#2	Vector DBMS

Website

bangdb.com

ignitetech.com/softwarelibrary/infobrightdb

www.circonus.com/solutions/time-series-database/

www.pinecone.io

Technical documentation

docs.bangdb.com

docs.circonus.com/irondb/category/getting-started

docs.pinecone.io/docs/overview

Developer

Sachin Sinha, BangDB

Ignite Technologies Inc.; formerly InfoBright Inc.

Circonus LLC.

Pinecone Systems, Inc

Initial release

2012

2005

2017

2019

Current release

BangDB 2.0, October 2021

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

BSD 3

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++

C and C++

Server operating systems

Linux

Linux
Windows

Linux

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

text, numeric, histograms

String, Number, Boolean

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

secondary, composite, nested, reverse, geospatial

Knowledge Grid Technology used instead

SQL

Support of SQL

SQL like support with command line tool

yes

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

HTTP API

RESTful HTTP API

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Python

Server-side scripts

Stored procedures

yes, in Lua

Triggers

yes, Notifications (with Streaming only)

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

none

Automatic, metric affinity per node

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

configurable replication factor, datacenter aware

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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)

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

Application observability firm Apica buys telemetry data startup Circonus and adds more funding
21 February 2024, SiliconANGLE News

Apica Acquires Telemetry Data Management Pioneer Circonus And Lands New Funding
22 February 2024, Datanami

Apica gets $6 million in funding and buys Circonus -
21 February 2024, Enterprise Times

provided by Google News

Pinecone’s new serverless database may see few takers, analysts say
17 January 2024, InfoWorld

Pinecone Unveils Serverless Vector Database for Enhanced AI Applications
16 January 2024, Datanami

Reimagining Vector Databases for the Generative AI Era with Pinecone Serverless on AWS | Amazon Web Services
21 March 2024, AWS Blog

Seven Vector Database Startups Poised to Win in the AI Revolution
19 April 2024, Business Insider

Pinecone Brings Serverless To Vector Databases
16 January 2024, Forbes

provided by Google News

Share this page