DBMS > Apache Doris vs. etcd vs. Google BigQuery vs. Sadas Engine

System Properties Comparison Apache Doris vs. etcd vs. Google BigQuery vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

A distributed reliable key-value store

Large scale data warehouse service with append-only tables

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

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

etcd.io
github.com/etcd-io/etcd

cloud.google.com/bigquery

www.sadasengine.com

Technical documentation

github.com/apache/doris/wiki

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

cloud.google.com/bigquery/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Apache Software Foundation, originally contributed from Baidu

Google

SADAS s.r.l.

Initial release

2017

2010

2006

Current release

1.2.2, February 2023

3.4, August 2019

8.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

free trial version 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++

Server operating systems

Linux

FreeBSD
Linux
Windows

experimental

hosted

AIX
Linux
Windows

Data scheme

yes

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

yes

APIs and other access methods

JDBC
MySQL client

gRPC
JSON over HTTP

RESTful HTTP/JSON API

JDBC
ODBC
Proprietary protocol

Supported programming languages

Java

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Since BigQuery is designed for querying data

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

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles according to SQL-standard

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