DBMS > Amazon DynamoDB vs. Bangdb vs. Citus vs. Datomic vs. VictoriaMetrics

System Properties Comparison Amazon DynamoDB vs. Bangdb vs. Citus vs. Datomic vs. VictoriaMetrics

Editorial information provided by DB-Engines

Name

Description

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

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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Document store
Key-value store

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Document store

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

aws.amazon.com/dynamodb

Technical documentation

docs.aws.amazon.com/dynamodb

docs.bangdb.com

docs.citusdata.com

docs.datomic.com

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Amazon

Sachin Sinha, BangDB

Cognitect

VictoriaMetrics

Initial release

2012

2010

2012

2018

Current release

BangDB 2.0, October 2021

8.1, December 2018

1.0.6735, June 2023

v1.91, May 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

BSD 3

Open Source

AGPL, commercial license also available

commercial

limited edition free

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java, Clojure

Server operating systems

hosted

Linux

All OS with a Java VM

FreeBSD
Linux
macOS
OpenBSD

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

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

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

standard, with numerous extensions

APIs and other access methods

RESTful HTTP API

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Java
Python

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

Transaction Functions

Triggers

yes

by integration with AWS Lambda

yes, Notifications (with Streaming only)

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

none

But extensive use of caching in the application peers

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Source-replica replication

other methods possible by using 3rd party extensions

none

But extensive use of caching in the application peers

Synchronous replication

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

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Eventual Consistency

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

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

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

recommended only for testing and development

User concepts

Access control

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

yes (enterprise version only)

fine grained access rights according to SQL-standard

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