DBMS > Geode vs. GeoSpock vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Synapse Analytics vs. SAP Advantage Database Server

System Properties Comparison Geode vs. GeoSpock vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Synapse Analytics vs. SAP Advantage Database Server

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Spatial and temporal data processing engine for extreme data scale

Globally distributed, horizontally scalable, multi-model database service

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Low-cost RDBMS with access to ISAM and FoxPro data structures

Primary database model

Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

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

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Website

geode.apache.org

geospock.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/services/synapse-analytics

www.sap.com/products/advantage-database-server.html

Technical documentation

geode.apache.org/docs

learn.microsoft.com/azure/cosmos-db

docs.microsoft.com/azure/synapse-analytics

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

GeoSpock

Microsoft

Sybase, SAP

Initial release

2002

2014

2016

1993

Current release

1.1, February 2017

2.0, September 2019

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

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

Java, Javascript

C++

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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.

Secondary indexes

temporal, categorical

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language (OQL)

ANSI SQL for query only (using Presto)

SQL-like query language

yes

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

JDBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
ODBC

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C#
Java
PHP

Delphi
Perl
PHP

Server-side scripts

Stored procedures

user defined functions

JavaScript

Transact SQL

yes

Triggers

yes

Cache Event Listeners

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

yes

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

yes, on a single node

Multi-item ACID transactions with snapshot isolation within a partition

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

caching of (temporary) tables can be configured

User concepts

Access control

Access rights per client and object definable

Access rights for users can be defined per table

Access rights can be defined down to the item level

yes

fine grained access rights according to SQL-standard

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