DBMS > Amazon DynamoDB vs. DuckDB vs. HugeGraph vs. Riak KV vs. Spark SQL

System Properties Comparison Amazon DynamoDB vs. DuckDB vs. HugeGraph vs. Riak KV vs. Spark SQL

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

Distributed, fault tolerant key-value store

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Key-value store

Relational DBMS

Graph DBMS

Key-value store

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

Relational DBMS

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	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/dynamodb

duckdb.org

github.com/hugegraph
hugegraph.apache.org

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/dynamodb

duckdb.org/docs

hugegraph.apache.org/docs

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

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon

Baidu

OpenSource, formerly Basho Technologies

Apache Software Foundation

Initial release

2012

2018

2009

2014

Current release

0.10, February 2024

0.9

3.2.0, December 2022

3.5.0 ( 2.13), September 2023

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

Apache version 2, commercial enterprise edition

Open Source

Apache 2.0

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

Erlang

Scala

Server operating systems

hosted

server-less

Linux
macOS
Unix

Linux
OS X

Linux
OS X
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

restricted

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API
Native Erlang Interface

JDBC
ODBC

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

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

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

no "single point of failure"

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

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

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

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

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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)

Users, roles and permissions

yes, using Riak Security

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