DBMS > Amazon CloudSearch vs. Apache Doris vs. Google BigQuery vs. Vertica

System Properties Comparison Amazon CloudSearch vs. Apache Doris vs. Google BigQuery vs. Vertica

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Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

An MPP-based analytics DBMS embracing the MySQL protocol

Large scale data warehouse service with append-only tables

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Search engine

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.86
Rank	#133	Overall
	#12	Search engines

Score	0.61
Rank	#235	Overall
	#110	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

aws.amazon.com/cloudsearch

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

cloud.google.com/bigquery

www.vertica.com

Technical documentation

docs.aws.amazon.com/cloudsearch

github.com/apache/doris/wiki

cloud.google.com/bigquery/docs

vertica.com/documentation

Developer

Amazon

Apache Software Foundation, originally contributed from Baidu

Google

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2017

2010

2005

Current release

1.2.2, February 2023

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

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

hosted

Linux

hosted

Linux

Data scheme

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

all search fields are automatically indexed

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

HTTP API

JDBC
MySQL client

RESTful HTTP/JSON API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

horizontal partitioning

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

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

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.

User concepts

Access control

authentication via encrypted signatures

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)

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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