DBMS > Amazon DynamoDB vs. Kyligence Enterprise vs. MarkLogic vs. Solr vs. SwayDB

System Properties Comparison Amazon DynamoDB vs. Kyligence Enterprise vs. MarkLogic vs. Solr vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

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

A distributed analytics engine for big data, built on top of Apache Kylin

Operational and transactional Enterprise NoSQL database

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Key-value store

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Search engine

Key-value store

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	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

aws.amazon.com/dynamodb

kyligence.io/kyligence-enterprise

www.marklogic.com

solr.apache.org

swaydb.simer.au

Technical documentation

docs.aws.amazon.com/dynamodb

docs.marklogic.com

solr.apache.org/resources.html

Developer

Amazon

Kyligence, Inc.

MarkLogic Corp.

Apache Software Foundation

Simer Plaha

Initial release

2012

2016

2001

2006

2018

Current release

11.0, December 2022

9.6.0, April 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

restricted free version is available

Open Source

Apache Version 2

Open Source

GNU Affero GPL V3.0

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++

Java

Scala

Server operating systems

hosted

Linux

Linux
OS X
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

schema-free

Schema can be enforced

yes

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

schema-free

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

ANSI SQL for queries (using Apache Calcite)

yes

SQL92

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

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

Immediate 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

can act as a resource manager in an XA/JTA transaction

optimistic locking

Atomic execution of operations

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, with Range Indexes

yes

User concepts

Access control

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

Role-based access control at the document and subdocument levels

yes

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