DBMS > Amazon DynamoDB vs. Apache Phoenix vs. DolphinDB vs. Solr

System Properties Comparison Amazon DynamoDB vs. Apache Phoenix vs. DolphinDB vs. Solr

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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

A scale-out RDBMS with evolutionary schema built on Apache HBase

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 widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS

Search engine

Secondary database models

Relational DBMS

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	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/dynamodb

phoenix.apache.org

www.dolphindb.com

solr.apache.org

Technical documentation

docs.aws.amazon.com/dynamodb

phoenix.apache.org

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

solr.apache.org/resources.html

Developer

Amazon

Apache Software Foundation

DolphinDB, Inc

Apache Software Foundation

Initial release

2012

2014

2018

2006

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

v2.00.4, January 2022

9.6.0, April 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2.0

commercial

free community version available

Open Source

Apache Version 2

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++

Java

Server operating systems

hosted

Linux
Unix
Windows

Linux
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP API

JDBC

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

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

yes

Java plugins

Triggers

yes

by integration with AWS Lambda

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Hadoop integration

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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

yes

optimistic locking

Concurrency

Support for concurrent manipulation of data

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)

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Administrators, Users, Groups

yes

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