DBMS > Alibaba Cloud MaxCompute vs. Kinetica vs. ObjectBox vs. RocksDB vs. Solr

System Properties Comparison Alibaba Cloud MaxCompute vs. Kinetica vs. ObjectBox vs. RocksDB vs. Solr

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

Fully vectorized database across both GPUs and CPUs

Extremely fast embedded database for small devices, IoT and Mobile

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Secondary database models

Spatial DBMS
Time Series DBMS

Time Series DBMS

Spatial DBMS

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

Score	0.84
Rank	#209	Overall
	#98	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.alibabacloud.com/product/maxcompute

Technical documentation

www.alibabacloud.com/help/en/maxcompute

docs.kinetica.com

docs.objectbox.io

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

Alibaba

Kinetica

ObjectBox Limited

Facebook, Inc.

Apache Software Foundation

Initial release

2016

2012

2017

2013

2006

Current release

7.1, August 2021

9.2.1, May 2024

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

Open Source

BSD

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++

C and C++

C++

Java

Server operating systems

hosted

Linux

Android
iOS
Linux
macOS
Windows

Linux

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-like DML and DDL statements

Solr Parallel SQL Interface

APIs and other access methods

Fluentd
Flume
MaxCompute Console

JDBC
ODBC
RESTful HTTP API

Proprietary native API

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

Java

C++
Java
JavaScript (Node.js)
Python

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C
C++
Go
Java
Perl
Python
Ruby

.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

user defined functions

Java plugins

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

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

Sharding

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication

online/offline synchronization between client and server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles

Access rights for users and roles on table level

yes

More information provided by the system vendor

News

The on-device Vector Database for Android and Java
29 May 2024

Vector search: making sense of search queries
29 May 2024

Python on-device Vector and Object Database for Local AI
28 May 2024

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 May 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.