DBMS > Amazon DynamoDB vs. AnzoGraph DB vs. NCache vs. Solr vs. Vertica

System Properties Comparison Amazon DynamoDB vs. AnzoGraph DB vs. NCache vs. Solr vs. Vertica

Editorial information provided by DB-Engines

Name

Description

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

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Open-Source and Enterprise in-memory Key-Value Store

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store
Key-value store

Column oriented

Secondary database models

Document store
Search engine

Using distributed Lucene

Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

aws.amazon.com/dynamodb

cambridgesemantics.com/anzograph

www.alachisoft.com/ncache

solr.apache.org

www.vertica.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

www.alachisoft.com/resources/docs

solr.apache.org/resources.html

vertica.com/documentation

Developer

Amazon

Cambridge Semantics

Alachisoft

Apache Software Foundation

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2018

2005

2006

2005

Current release

2.3, January 2021

5.3.3, April 2024

9.6.1, May 2024

12.0.3, January 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free trial version available

Open Source

Enterprise Edition available

Open Source

Apache Version 2

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C#, .NET, .NET Core, Java

Java

C++

Server operating systems

hosted

Linux

Linux
Windows

All OS with a Java VM

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

Linux

Data scheme

schema-free

Schema-free and OWL/RDFS-schema support

schema-free

yes

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

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

yes

supports customizable data types and automatic typing

yes

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

Solr Parallel SQL Interface

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

RESTful HTTP API

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

IDistributedCache
JCache
LINQ
Proprietary native API

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C++
Java
Python

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

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

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

Server-side scripts

Stored procedures

user defined functions and aggregates

support for stored procedures with SQL-Server CLR

Java plugins

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

by integration with AWS Lambda

yes

Notifications

yes

User configurable commands triggered on index changes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

yes

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

yes, with selectable consistency level

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Kerberos/HDFS data loading

yes

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

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency in MPP-Cluster

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

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

ACID

optimistic locking and pessimistic locking

optimistic locking

ACID

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 rights for users and roles

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

yes

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

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