DBMS > Amazon DynamoDB vs. HugeGraph vs. LMDB vs. Machbase Neo vs. Sphinx

System Properties Comparison Amazon DynamoDB vs. HugeGraph vs. LMDB vs. Machbase Neo vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

A high performant, light-weight, embedded key-value database library

TimeSeries DBMS for AIoT and BigData

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

Primary database model

Document store
Key-value store

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	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

aws.amazon.com/dynamodb

github.com/hugegraph
hugegraph.apache.org

www.symas.com/symas-embedded-database-lmdb

machbase.com

sphinxsearch.com

Technical documentation

docs.aws.amazon.com/dynamodb

hugegraph.apache.org/docs

www.lmdb.tech/doc

machbase.com/dbms

sphinxsearch.com/docs

Developer

Amazon

Baidu

Symas

Machbase

Sphinx Technologies Inc.

Initial release

2012

2018

2011

2013

2001

Current release

0.9

0.9.32, January 2024

V8.0, August 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2.0

Open Source

commercial

free test version available

Open Source

GPL version 2, commercial licence available

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++

Server operating systems

hosted

Linux
macOS
Unix

Linux
Unix
Windows

Linux
macOS
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

also supports composite index and range index

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Proprietary protocol

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Groovy
Java
Python

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

Triggers

yes

by integration with AWS Lambda

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

none

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

In-memory capabilities

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

yes

volatile and lookup table

User concepts

Access control

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

Users, roles and permissions

simple password-based access control

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