DBMS > Bangdb vs. BigObject vs. Cachelot.io vs. Pinecone

System Properties Comparison Bangdb vs. BigObject vs. Cachelot.io vs. Pinecone

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

Analytic DBMS for real-time computations and queries

In-memory caching system

A managed, cloud-native vector database

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

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	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	3.16
Rank	#95	Overall
	#2	Vector DBMS

Website

Technical documentation

docs.bangdb.com

docs.bigobject.io

docs.pinecone.io/docs/overview

Developer

Sachin Sinha, BangDB

BigObject, Inc.

Pinecone Systems, Inc

Initial release

2012

2015

2019

Current release

BangDB 2.0, October 2021

License

Commercial or Open Source

Open Source

BSD 3

commercial

free community edition available

Open Source

Simplified BSD License

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++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

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

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

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

Memcached protocol

RESTful HTTP API

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

Python

Server-side scripts

Stored procedures

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

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

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

none

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

Read/write lock on objects (tables, trees)

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)

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

Pinecone launches serverless edition of its vector database on AWS
22 May 2024, SiliconANGLE News

Pinecone launches its serverless vector database out of preview
21 May 2024, TechCrunch

Pinecone Launches Serverless Vector Database for Scalable AI Applications
21 May 2024, Datanami

How a Decades-Old Technology and a Paper From Meta Created an AI Industry Standard
21 May 2024, The Wall Street Journal

Channel Brief: Dell Explains AI Factory, Informatica AI Research, Pinecone Goes Serverless and More
22 May 2024, Channel E2E

provided by Google News

Share this page