DBMS > Apache Impala vs. BigchainDB vs. Google Cloud Firestore vs. H2GIS vs. Microsoft Azure Cosmos DB

System Properties Comparison Apache Impala vs. BigchainDB vs. Google Cloud Firestore vs. H2GIS vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Spatial extension of H2

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Document store

Spatial DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store

Relational DBMS

Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

impala.apache.org

www.bigchaindb.com

firebase.google.com/products/firestore

www.h2gis.org

azure.microsoft.com/services/cosmos-db

Technical documentation

impala.apache.org/impala-docs.html

bigchaindb.readthedocs.io/en/latest

firebase.google.com/docs/firestore

www.h2gis.org/docs/home

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

CNRS

Microsoft

Initial release

2013

2016

2017

2013

2014

Current release

4.1.0, June 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

AGPL v3

commercial

Open Source

LGPL 3.0

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Python

Java

Server operating systems

Linux

hosted

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

CLI Client
RESTful HTTP API

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

All languages supporting JDBC/ODBC

Go
Haskell
Java
JavaScript
Python
Ruby

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

Java

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, Firebase Rules & Cloud Functions

yes

based on H2

JavaScript

Triggers

yes, with Cloud Functions

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

yes

based on H2

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Using Cloud Dataflow

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

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, groups and roles

based on Apache Sentry and Kerberos

yes

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

yes

based on H2

Access rights can be defined down to the item level

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