DBMS > ArcadeDB vs. GeoMesa vs. Google Cloud Bigtable vs. PostgreSQL vs. Warp 10

System Properties Comparison ArcadeDB vs. GeoMesa vs. Google Cloud Bigtable vs. PostgreSQL vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Spatial DBMS

Key-value store
Wide column store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Time Series DBMS

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.10
Rank	#358	Overall
	#48	Document stores
	#38	Graph DBMS
	#52	Key-value stores
	#35	Time Series DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

arcadedb.com

www.geomesa.org

cloud.google.com/bigtable

www.postgresql.org

www.warp10.io

Technical documentation

docs.arcadedb.com

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

cloud.google.com/bigtable/docs

www.postgresql.org/docs

www.warp10.io/content/02_Getting_started

Developer

Arcade Data

CCRi and others

Google

PostgreSQL Global Development Group

www.postgresql.org/developer

SenX

Initial release

2021

2014

2015

1989

1989: Postgres, 1996: PostgreSQL

2015

Current release

September 2021

5.0.0, May 2024

16.3, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

commercial

Open Source

BSD

Open Source

Apache License 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

Java

Scala

Java

Server operating systems

All OS with a Java VM

hosted

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language, no joins

yes

standard with numerous extensions

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API
Jupyter
WebSocket

Supported programming languages

Java

C#
C++
Go
Java
JavaScript (Node.js)
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

partitioning by range, list and (since PostgreSQL 11) by hash

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

depending on storage layer

Internal replication in Colossus, and regional replication between two clusters in different zones

Source-replica replication

other methods possible by using 3rd party extensions

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

Atomic single-row 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

yes

User concepts

Access control

yes

depending on the DBMS used for storage

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Mandatory use of cryptographic tokens, containing fine-grained authorizations

More information provided by the system vendor

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