DBMS > Alibaba Cloud MaxCompute vs. Couchbase vs. Hive vs. Solr

System Properties Comparison Alibaba Cloud MaxCompute vs. Couchbase vs. Hive vs. Solr

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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

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

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

Document store

Relational DBMS

Search engine

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.alibabacloud.com/product/maxcompute

www.couchbase.com

hive.apache.org

solr.apache.org

Technical documentation

www.alibabacloud.com/help/en/maxcompute

docs.couchbase.com

cwiki.apache.org/confluence/display/Hive/Home

solr.apache.org/resources.html

Developer

Alibaba

Couchbase, Inc.

Apache Software Foundation

initially developed by Facebook

Apache Software Foundation

Initial release

2016

2011

2012

2006

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

3.1.3, April 2022

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache Version 2

Open Source

Apache Version 2

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, C++, Go and Erlang

Java

Server operating systems

hosted

Linux
OS X
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like DML and DDL statements

Solr Parallel SQL Interface

APIs and other access methods

Fluentd
Flume
MaxCompute Console

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

JDBC
ODBC
Thrift

Java API
RESTful HTTP/JSON API

Supported programming languages

Java

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C++
Java
PHP
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in Java

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

user defined functions and integration of map-reduce

Java plugins

Triggers

yes

via the TAP protocol

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Automatic Sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

optimistic locking

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

Ephemeral buckets

yes

User concepts

Access control

Access rights for users and roles

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Access rights for users, groups and roles

yes

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