DBMS > EJDB vs. GridGain vs. SAP IQ vs. Snowflake vs. Solr

System Properties Comparison EJDB vs. GridGain vs. SAP IQ vs. Snowflake vs. Solr

Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Columnar RDBMS optimized for Big Data analytics

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Document store

Key-value store
Relational DBMS

Relational DBMS

column-oriented

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

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

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

github.com/Softmotions/ejdb

www.gridgain.com

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

www.snowflake.com

solr.apache.org

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

www.gridgain.com/docs/index.html

help.sap.com/docs/SAP_IQ

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Softmotions

GridGain Systems, Inc.

SAP, formerly Sybase

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2012

2007

1994

2014

2006

Current release

GridGain 8.5.1

16.1 SPS04, April 2019

9.6.0, April 2024

License

Commercial or Open Source

Open Source

GPLv2

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

Java, C++, .Net

Java

Server operating systems

server-less

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Windows

hosted

All OS with a Java VM

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

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

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

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

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

yes

Solr Parallel SQL Interface

APIs and other access methods

in-process shared library

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

ADO.NET
JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Java
Perl
PHP
Python
Ruby

JavaScript (Node.js)
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

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

yes

user defined functions

Java plugins

Triggers

yes (cache interceptors and events)

yes

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes (replicated cache)

SAP/Sybase Replication Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

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