DBMS > Amazon CloudSearch vs. Memgraph vs. OpenSearch vs. Sphinx vs. Teradata

System Properties Comparison Amazon CloudSearch vs. Memgraph vs. OpenSearch vs. Sphinx vs. Teradata

Editorial information provided by DB-Engines

Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

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

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

Open source search engine for searching in data from different sources, e.g. relational databases

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Secondary database models

Vector DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.85
Rank	#137	Overall
	#12	Search engines

Score	3.02
Rank	#98	Overall
	#8	Graph DBMS

Score	16.36
Rank	#37	Overall
	#4	Search engines

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

aws.amazon.com/cloudsearch

memgraph.com

github.com/opensearch-project
opensearch.org

sphinxsearch.com

www.teradata.com

Technical documentation

docs.aws.amazon.com/cloudsearch

memgraph.com/docs

opensearch.org/docs/latest

sphinxsearch.com/docs

docs.teradata.com

Social network pages

Developer

Amazon

Memgraph Ltd

Amazon Web Services

Sphinx Technologies Inc.

Teradata

Initial release

2012

2017

2021

2001

1984

Current release

2.5.0, January 2023

3.5.1, February 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

BSL 1.1; commercial license for enterprise edition available

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

commercial

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 and C++

Java

C++

Server operating systems

hosted

Linux

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

hosted
Linux

Data scheme

yes

schema-free and schema-optional

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Typing

predefined data types such as float or date

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

yes

All search fields are automatically indexed

yes

full-text index on all search fields

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like query language

SQL-like query language (SphinxQL)

yes

SQL 2016 + extensions

APIs and other access methods

HTTP API

Bolt protocol
Cypher query language

Java API
RESTful HTTP/JSON API

Proprietary protocol

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

.Net
C
C++
Elixir
Go
Haskell
Java
JavaScript
PHP
Python
Ruby
Scala

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

Sharding

dynamic graph partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

Multi-source replication using RAFT

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

yes

Transaction concepts

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

ACID

with snapshot isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

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

yes

The original contents of fields are not stored in the Sphinx index.

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

authentication via encrypted signatures

Users, roles and permissions

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Memgraph directly connects to your streaming infrastructure so you and your team...
» more

OpenSearch is a highly scalable and extensible open-source software suite for search,...
» more

Competitive advantages

Business Source License ensures a future for the Memgraph community MAGE algorithm...
» more

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
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Typical application scenarios

Graph algorithms in bioinformatics Social network analysis Cryptocurrency network...
» more

The OpenSearch platform is used across a broad range of search , analytics, and observability...
» more

Licensing and pricing models

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

The OpenSearch Project offers open source software licensed under Apache License...
» more

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