DBMS > Amazon DocumentDB vs. Brytlyt vs. GridGain vs. MySQL vs. SAP SQL Anywhere

System Properties Comparison Amazon DocumentDB vs. Brytlyt vs. GridGain vs. MySQL vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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

GridGain is an in-memory computing platform, built on Apache Ignite

Widely used open source RDBMS

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Document store

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

aws.amazon.com/documentdb

brytlyt.io

www.gridgain.com

www.mysql.com

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

aws.amazon.com/documentdb/resources

docs.brytlyt.io

www.gridgain.com/docs/index.html

dev.mysql.com/doc

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Brytlyt

GridGain Systems, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

SAP

formerly Sybase

Initial release

2019

2016

2007

1995

1992

Current release

5.0, August 2023

GridGain 8.5.1

8.4.0, April 2024

17, July 2015

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C, C++ and CUDA

Java, C++, .Net

C and C++

Server operating systems

hosted

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

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.

yes

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

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

yes

with proprietary extensions

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C#
C++
Java
PHP
Python
Ruby
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

yes (compute grid and cache interceptors can be used instead)

yes

proprietary syntax

yes, in C/C++, Java, .Net or Perl

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Source-replica replication

yes (replicated cache)

Multi-source replication
Source-replica replication

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

not for MyISAM storage engine

yes

Transaction concepts

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

Atomic single-document operations

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Security Hooks for custom implementations

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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