DBMS > Alibaba Cloud MaxCompute vs. Brytlyt vs. RocksDB vs. SAP IQ vs. Solr

System Properties Comparison Alibaba Cloud MaxCompute vs. Brytlyt vs. RocksDB vs. SAP IQ 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

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Columnar RDBMS optimized for Big Data analytics

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

column-oriented

Secondary database models

Spatial DBMS

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

Score	0.84
Rank	#209	Overall
	#98	Relational DBMS

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

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

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.alibabacloud.com/product/maxcompute

brytlyt.io

rocksdb.org

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

solr.apache.org

Technical documentation

www.alibabacloud.com/help/en/maxcompute

docs.brytlyt.io

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_IQ

solr.apache.org/resources.html

Developer

Alibaba

Brytlyt

Facebook, Inc.

SAP, formerly Sybase

Apache Software Foundation

Initial release

2016

2013

1994

2006

Current release

5.0, August 2023

9.2.1, May 2024

16.1 SPS04, April 2019

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

BSD

commercial

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++ and CUDA

C++

Java

Server operating systems

hosted

Linux
OS X
Windows

Linux

AIX
HP-UX
Linux
Solaris
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

specific XML-type available, but no XML query functionality.

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

Solr Parallel SQL Interface

APIs and other access methods

Fluentd
Flume
MaxCompute Console

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

C++ API
Java API

ADO.NET
JDBC
ODBC
RESTful HTTP API

Java API
RESTful HTTP/JSON API

Supported programming languages

Java

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Java
Perl
PHP
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

in PL/pgSQL

yes

Java plugins

Triggers

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

horizontal partitioning

shared disk or shared nothing architectures with SAP IQ Multiplexer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication

yes

SAP/Sybase Replication Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

yes

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