DBMS > Amazon DynamoDB vs. atoti vs. Blueflood vs. DolphinDB vs. TimescaleDB

System Properties Comparison Amazon DynamoDB vs. atoti vs. Blueflood vs. DolphinDB vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

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

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Scalable TimeSeries DBMS based on Cassandra

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store
Key-value store

Object oriented DBMS

Time Series DBMS

Time Series DBMS
Vector DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	70.06
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.45
Rank	#253	Overall
	#13	Object oriented DBMS

Score	0.06
Rank	#352	Overall
	#36	Time Series DBMS

Score	2.72
Rank	#100	Overall
	#8	Time Series DBMS
	#5	Vector DBMS

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Website

aws.amazon.com/dynamodb

Technical documentation

docs.aws.amazon.com/dynamodb

docs.atoti.io

github.com/rax-maas/blueflood/wiki

docs.dolphindb.cn/en/help200/index.html

docs.timescale.com

Developer

Amazon

ActiveViam

Rackspace

DolphinDB, Inc

Timescale

Initial release

2012

2013

2018

2017

Current release

v2.00.4, January 2022

2.15.0, May 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free versions available

Open Source

Apache 2.0

commercial

free community version available

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.

Implementation language

Java

C++

Server operating systems

hosted

Linux
OS X

Linux
Windows

Linux
OS X
Windows

Data scheme

schema-free

predefined scheme

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

RESTful HTTP API

HTTP REST

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

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

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Python

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

based on Cassandra

horizontal partitioning

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

yes

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate 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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

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

Administrators, Users, Groups

fine grained access rights according to SQL-standard

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