DBMS > Amazon DocumentDB vs. GeoMesa vs. Hive vs. TigerGraph

System Properties Comparison Amazon DocumentDB vs. GeoMesa vs. Hive vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

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

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

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	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

aws.amazon.com/documentdb

www.geomesa.org

hive.apache.org

www.tigergraph.com

Technical documentation

aws.amazon.com/documentdb/resources

www.geomesa.org/documentation/stable/user/index.html

cwiki.apache.org/confluence/display/Hive/Home

docs.tigergraph.com

Developer

CCRi and others

Apache Software Foundation

initially developed by Facebook

Initial release

2019

2014

2012

2017

Current release

4.0.5, February 2024

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

Open Source

Apache Version 2

commercial

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

Scala

Java

C++

Server operating systems

hosted

All OS with a Java VM

Linux

Data scheme

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

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (GSQL)

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC
Thrift

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C++
Java
PHP
Python

C++
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

depending on storage layer

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Eventual Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Relationships in graphs

Transaction concepts

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

Atomic single-document operations

ACID

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.

depending on storage layer

User concepts

Access control

Access rights for users and roles

yes

depending on the DBMS used for storage

Access rights for users, groups and roles

Role-based access control

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More resources

DB-Engines blog posts

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