DBMS > Apache Doris vs. Google Cloud Firestore vs. GridGain vs. SAP HANA vs. TimescaleDB

System Properties Comparison Apache Doris vs. Google Cloud Firestore vs. GridGain vs. SAP HANA vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

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.

GridGain is an in-memory computing platform, built on Apache Ignite

In-memory, column based data store. Available as appliance or cloud service

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

Document store

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Relational DBMS

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

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

doris.apache.org
github.com/apache/doris

firebase.google.com/products/firestore

www.gridgain.com

www.sap.com/products/hana.html

www.timescale.com

Technical documentation

github.com/apache/doris/wiki

firebase.google.com/docs/firestore

www.gridgain.com/docs/index.html

help.sap.com/hana

docs.timescale.com

Developer

Apache Software Foundation, originally contributed from Baidu

Google

GridGain Systems, Inc.

SAP

Timescale

Initial release

2017

2007

2010

2017

Current release

1.2.2, February 2023

GridGain 8.5.1

2.0 SPS07 (April 4, 2023), April 2023

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, C++, .Net

Server operating systems

Linux

hosted

Linux
OS X
Solaris
Windows

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
MySQL client

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

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

Supported programming languages

Java

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

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

yes, Firebase Rules & Cloud Functions

yes (compute grid and cache interceptors can be used instead)

SQLScript, R

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes, with Cloud Functions

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

yes

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes (replicated cache)

yes

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Security Hooks for custom implementations

yes

fine grained access rights according to SQL-standard

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