DBMS > Alibaba Cloud MaxCompute vs. Amazon Redshift vs. Google Cloud Bigtable vs. Quasardb vs. Sphinx

System Properties Comparison Alibaba Cloud MaxCompute vs. Amazon Redshift vs. Google Cloud Bigtable vs. Quasardb vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

MaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousing

Large scale data warehouse service for use with business intelligence tools

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Distributed, high-performance timeseries database

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

a graph-processing framework is available with the graphs being stored in tables

Relational DBMS

Key-value store
Wide column store

Time Series DBMS

Search engine

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.alibabacloud.com/product/maxcompute

aws.amazon.com/redshift

cloud.google.com/bigtable

quasar.ai

sphinxsearch.com

Technical documentation

www.alibabacloud.com/help/en/maxcompute

docs.aws.amazon.com/redshift

cloud.google.com/bigtable/docs

doc.quasar.ai/master

sphinxsearch.com/docs

Developer

Alibaba

Amazon (based on PostgreSQL)

Google

quasardb

Sphinx Technologies Inc.

Initial release

2016

2012

2015

2009

2001

Current release

3.14.1, January 2024

3.5.1, February 2023

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

GPL version 2, commercial licence 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

C++

Server operating systems

hosted

BSD
Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

restricted

yes

with tags

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

does not fully support an SQL-standard

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

Fluentd
Flume
MaxCompute Console

JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP API

Proprietary protocol

Supported programming languages

Java

All languages supporting JDBC/ODBC

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

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Java

user defined functions

in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

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

Cryptographically strong user authentication and audit trail

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