DBMS > Amazon DynamoDB vs. Hazelcast vs. Heroic vs. Percona Server for MongoDB vs. Solr

System Properties Comparison Amazon DynamoDB vs. Hazelcast vs. Heroic vs. Percona Server for MongoDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

A widely adopted in-memory data grid

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

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

Primary database model

Document store
Key-value store

Secondary database models

Document store

JSON support with IMDG 3.12

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	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/dynamodb

hazelcast.com

github.com/spotify/heroic

www.percona.com/mongodb/software/percona-server-for-mongodb

solr.apache.org

Technical documentation

docs.aws.amazon.com/dynamodb

hazelcast.org/imdg/docs

spotify.github.io/heroic

docs.percona.com/percona-distribution-for-mongodb

solr.apache.org/resources.html

Developer

Amazon

Hazelcast

Spotify

Percona

Apache Software Foundation

Initial release

2012

2008

2014

2015

2006

Current release

5.3.6, November 2023

3.4.10-2.10, November 2017

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; commercial licenses available

Open Source

Apache 2.0

Open Source

GPL Version 2

Open Source

Apache Version 2

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

Server operating systems

hosted

All OS with a Java VM

Linux

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

via Elasticsearch

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

proprietary protocol using JSON

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

.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

Event Listeners, Executor Services

JavaScript

Java plugins

Triggers

yes

by integration with AWS Lambda

yes

Events

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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 selectable by user

Raft Consensus Algorithm

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

one or two-phase-commit; repeatable reads; read commited

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

via In-Memory Engine

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

Access rights for users and roles

yes

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