DBMS > Amazon DynamoDB vs. DuckDB vs. HugeGraph vs. Manticore Search vs. Riak KV

System Properties Comparison Amazon DynamoDB vs. DuckDB vs. HugeGraph vs. Manticore Search vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

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

An embeddable, in-process, column-oriented SQL OLAP RDBMS

A fast-speed and highly-scalable Graph DBMS

Multi-storage database for search, including full-text search.

Distributed, fault tolerant key-value store

Primary database model

Document store
Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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	4.57
Rank	#74	Overall
	#40	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

aws.amazon.com/dynamodb

duckdb.org

github.com/hugegraph
hugegraph.apache.org

manticoresearch.com

Technical documentation

docs.aws.amazon.com/dynamodb

duckdb.org/docs

hugegraph.apache.org/docs

manual.manticoresearch.com

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Amazon

Baidu

Manticore Software

OpenSource, formerly Basho Technologies

Initial release

2012

2018

2017

2009

Current release

0.10, February 2024

0.9

6.0, February 2023

3.2.0, December 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

MIT License

Open Source

Apache Version 2.0

Open Source

GPL version 2

Open Source

Apache version 2, commercial enterprise edition

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++

Java

C++

Erlang

Server operating systems

hosted

server-less

Linux
macOS
Unix

FreeBSD
Linux
macOS
Windows

Linux
OS X

Data scheme

schema-free

yes

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

yes

also supports composite index and range index

yes

full-text index on all search fields

restricted

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

HTTP API
Native Erlang Interface

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

Groovy
Java
Python

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

user defined functions

Erlang

Triggers

yes

by integration with AWS Lambda

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

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

Sharding

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

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

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

Synchronous replication based on Galera library

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

links between data sets can be stored

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

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore 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

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

Users, roles and permissions

yes, using Riak Security

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