DBMS > Amazon DynamoDB vs. Apache Druid vs. Bangdb vs. Faircom DB

System Properties Comparison Amazon DynamoDB vs. Apache Druid vs. Bangdb vs. Faircom DB

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Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Primary database model

Document store
Key-value store

Relational DBMS
Time Series DBMS

Document store
Graph DBMS
Time Series DBMS

Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Website

aws.amazon.com/dynamodb

druid.apache.org

bangdb.com

www.faircom.com/products/faircom-db

Technical documentation

docs.aws.amazon.com/dynamodb

druid.apache.org/docs/latest/design

docs.bangdb.com

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

Developer

Amazon

Apache Software Foundation and contributors

Sachin Sinha, BangDB

FairCom Corporation

Initial release

2012

1979

Current release

29.0.1, April 2024

BangDB 2.0, October 2021

V12, November 2020

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache license v2

Open Source

BSD 3

commercial

Restricted, free version available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++

ANSI C, C++

Server operating systems

hosted

Linux
OS X
Unix

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Data scheme

schema-free

yes

schema-less columns are supported

schema-free

schema free, schema optional, schema required, partial schema,

Typing

predefined data types such as float or date

yes

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

yes, ANSI SQL Types, JSON, typed binary structures

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 for querying

SQL like support with command line tool

yes, ANSI SQL with proprietary extensions

APIs and other access methods

RESTful HTTP API

JDBC
RESTful HTTP/JSON API

Proprietary protocol
RESTful HTTP API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Java
Python

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Triggers

yes

by integration with AWS Lambda

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

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

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

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

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

tunable from ACID to Eventually Consistent

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

Yes, tunable from durable to delayed durability to in-memory

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

yes (enterprise version only)

Fine grained access rights according to SQL-standard with additional protections for files

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