DBMS > Apache Doris vs. Firebird vs. Google Cloud Datastore vs. Postgres-XL vs. Vitess

System Properties Comparison Apache Doris vs. Firebird vs. Google Cloud Datastore vs. Postgres-XL vs. Vitess

Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Firebird is an open source RDBMS forked from Borland's InterBase

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	20.50
Rank	#30	Overall
	#18	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

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

www.firebirdsql.org

cloud.google.com/datastore

www.postgres-xl.org

vitess.io

Technical documentation

github.com/apache/doris/wiki

www.firebirdsql.org/en/reference-manuals

cloud.google.com/datastore/docs

www.postgres-xl.org/documentation

vitess.io/docs

Developer

Apache Software Foundation, originally contributed from Baidu

Firebird Foundation

Google

The Linux Foundation, PlanetScale

Initial release

2017

2000

As fork of Borland's InterBase

2008

2014

since 2012, originally named StormDB

2013

Current release

1.2.2, February 2023

5.0.0, January 2024

10 R1, October 2018

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Initial Developer's Public License

commercial

Open Source

Mozilla public license

Open Source

Apache Version 2.0, commercial licenses available

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 and C++

Server operating systems

Linux

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

hosted

Linux
macOS

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (GQL)

yes

distributed, parallel query execution

yes

with proprietary extensions

APIs and other access methods

JDBC
MySQL client

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

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

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Java

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

PSQL

using Google App Engine

user defined functions

yes

proprietary syntax

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

MVCC

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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Related products and services

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