DBMS > Brytlyt vs. GridGain vs. RavenDB vs. SAP HANA vs. Spark SQL

System Properties Comparison Brytlyt vs. GridGain vs. RavenDB vs. SAP HANA vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

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

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

In-memory, column based data store. Available as appliance or cloud service

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store

Relational DBMS

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

brytlyt.io

www.gridgain.com

ravendb.net

www.sap.com/products/hana.html

spark.apache.org/sql

Technical documentation

docs.brytlyt.io

www.gridgain.com/docs/index.html

ravendb.net/docs

help.sap.com/hana

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Brytlyt

GridGain Systems, Inc.

Hibernating Rhinos

SAP

Apache Software Foundation

Initial release

2016

2007

2010

2014

Current release

5.0, August 2023

GridGain 8.5.1

5.4, July 2022

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

AGPL version 3, commercial license available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++ and CUDA

Java, C++, .Net

Scala

Server operating systems

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux
macOS
Raspberry Pi
Windows

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

yes

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

SQL-like query language (RQL)

yes

SQL-like DML and DDL statements

APIs and other access methods

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

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

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

yes

SQLScript, R

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes (replicated cache)

Multi-source replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

ACID

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

fine grained access rights according to SQL-standard

Security Hooks for custom implementations

Authorization levels configured per client per database

yes

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