DBMS > Amazon DocumentDB vs. Heroic vs. HugeGraph vs. RocksDB

System Properties Comparison Amazon DocumentDB vs. Heroic vs. HugeGraph vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A fast-speed and highly-scalable Graph DBMS

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

aws.amazon.com/documentdb

github.com/spotify/heroic

github.com/hugegraph
hugegraph.apache.org

rocksdb.org

Technical documentation

aws.amazon.com/documentdb/resources

spotify.github.io/heroic

hugegraph.apache.org/docs

github.com/facebook/rocksdb/wiki

Developer

Spotify

Baidu

Facebook, Inc.

Initial release

2019

2014

2018

2013

Current release

0.9

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

BSD

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

Data scheme

schema-free

yes

schema-free

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

via Elasticsearch

yes

also supports composite index and range index

SQL

Support of SQL

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

C++ API
Java API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Groovy
Java
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

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

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes

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

yes

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

edges in graph

Transaction concepts

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

Atomic single-document operations

ACID

yes

Concurrency

Support for concurrent manipulation of data

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

Users, roles and permissions

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