DBMS > Amazon Redshift vs. Datomic vs. Drizzle vs. Memgraph vs. Solr

System Properties Comparison Amazon Redshift vs. Datomic vs. Drizzle vs. Memgraph vs. Solr

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

An open source graph database built for real-time streaming and compatible with Neo4j

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

Primary database model

Relational DBMS

Graph DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	3.02
Rank	#98	Overall
	#8	Graph DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

www.datomic.com

memgraph.com

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

memgraph.com/docs

solr.apache.org/resources.html

Social network pages

Developer

Amazon (based on PostgreSQL)

Cognitect

Drizzle project, originally started by Brian Aker

Memgraph Ltd

Apache Software Foundation

Initial release

2012

2008

2017

2006

Current release

1.0.6735, June 2023

7.2.4, September 2012

9.6.0, April 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

GNU GPL

Open Source

BSL 1.1; commercial license for enterprise edition available

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, Clojure

C++

C and C++

Java

Server operating systems

hosted

All OS with a Java VM

FreeBSD
Linux
OS X

Linux

All OS with a Java VM

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

Data scheme

yes

schema-free and schema-optional

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

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JDBC

Bolt protocol
Cypher query language

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

C
C++
Java
PHP

.Net
C
C++
Elixir
Go
Haskell
Java
JavaScript
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

user defined functions

in Python

yes

Transaction Functions

Java plugins

Triggers

By using transaction functions

hooks for callbacks inside the server can be used.

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

dynamic graph partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

Multi-source replication
Source-replica replication

Multi-source replication using RAFT

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

relationships in graphs

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

with snapshot isolation

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

with periodic snapshot and write-ahead logging (WAL) of changes

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Users, roles and permissions

yes

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Graph algorithms in bioinformatics Social network analysis Cryptocurrency network...
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Licensing and pricing models

You can check out our pricing model and licenses on the company website .
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