DBMS > Bangdb vs. Drizzle vs. Pinecone vs. RethinkDB vs. XTDB

System Properties Comparison Bangdb vs. Drizzle vs. Pinecone vs. RethinkDB vs. XTDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A managed, cloud-native vector database

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Vector DBMS

Document store

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	3.16
Rank	#95	Overall
	#2	Vector DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

bangdb.com

www.pinecone.io

rethinkdb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.bangdb.com

docs.pinecone.io/docs/overview

rethinkdb.com/docs

www.xtdb.com/docs

Developer

Sachin Sinha, BangDB

Drizzle project, originally started by Brian Aker

Pinecone Systems, Inc

The Linux Foundation

since July 2017

Juxt Ltd.

Initial release

2012

2008

2019

2009

2019

Current release

BangDB 2.0, October 2021

7.2.4, September 2012

2.4.1, August 2020

1.19, September 2021

License

Commercial or Open Source

Open Source

BSD 3

Open Source

GNU GPL

commercial

Open Source

Apache Version 2

Open Source

MIT License

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++

Clojure

Server operating systems

Linux

FreeBSD
Linux
OS X

hosted

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

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

yes

string, binary, float, bool, date, geometry

yes, extensible-data-notation format

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

yes

with proprietary extensions

limited SQL, making use of Apache Calcite

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC

RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

C
C#
C++
Java
Python

C
C++
Java
PHP

Python

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Clojure
Java

Server-side scripts

Stored procedures

Triggers

yes, Notifications (with Streaming only)

hooks for callbacks inside the server can be used.

Client-side triggers through changefeeds

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

range based

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

Source-replica replication

yes, each node contains all data

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

MVCC based

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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)

Pluggable authentication mechanisms

e.g. LDAP, HTTP

yes

users and table-level permissions

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