DBMS > Bangdb vs. Cassandra vs. Pinecone vs. Spark SQL

System Properties Comparison Bangdb vs. Cassandra vs. Pinecone vs. Spark SQL

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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

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

A managed, cloud-native vector database

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Graph DBMS
Time Series DBMS

Wide column store

Vector DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Vector DBMS

starting with release V5

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	98.83
Rank	#12	Overall
	#1	Wide column stores

Score	3.23
Rank	#92	Overall
	#3	Vector DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

docs.bangdb.com

cassandra.apache.org/doc/latest

docs.pinecone.io/docs/overview

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Sachin Sinha, BangDB

Apache Software Foundation

Apache top level project, originally developped by Facebook

Pinecone Systems, Inc

Apache Software Foundation

Initial release

2012

2008

2019

2014

Current release

BangDB 2.0, October 2021

4.1.3, July 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

C, C++

Java

Scala

Server operating systems

Linux

BSD
Linux
OS X
Windows

hosted

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

yes

String, Number, Boolean

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

secondary, composite, nested, reverse, geospatial

restricted

only equality queries, not always the best performing solution

SQL

Support of SQL

SQL like support with command line tool

SQL-like SELECT, DML and DDL statements (CQL)

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

RESTful HTTP API

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

yes, Notifications (with Streaming only)

yes

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

no "single point of failure"

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

Representation of geographical distribution of servers is possible

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

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

User concepts

Access control

yes (enterprise version only)

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
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Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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