DBMS > Amazon DynamoDB vs. DolphinDB vs. GridGain vs. Solr

System Properties Comparison Amazon DynamoDB vs. DolphinDB vs. GridGain 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

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.

GridGain is an in-memory computing platform, built on Apache Ignite

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Key-value store

Time Series DBMS

Key-value store
Relational 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	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/dynamodb

www.dolphindb.com

www.gridgain.com

solr.apache.org

Technical documentation

docs.aws.amazon.com/dynamodb

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

www.gridgain.com/docs/index.html

solr.apache.org/resources.html

Developer

Amazon

DolphinDB, Inc

GridGain Systems, Inc.

Apache Software Foundation

Initial release

2012

2018

2007

2006

Current release

v2.00.4, January 2022

GridGain 8.5.1

9.6.0, April 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free community version available

commercial

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

C++

Java, C++, .Net

Java

Server operating systems

hosted

Linux
Windows

Linux
OS X
Solaris
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

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

SQL-like query language

ANSI-99 for query and DML statements, subset of DDL

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP API

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

C#
C++
Java
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

Java plugins

Triggers

yes

by integration with AWS Lambda

yes (cache interceptors and events)

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

yes (replicated cache)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

yes (compute grid and hadoop accelerator)

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

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

yes

ACID

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)

Administrators, Users, Groups

Security Hooks for custom implementations

yes

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