DBMS > BigObject vs. Drizzle vs. SAP Advantage Database Server vs. SAP HANA vs. Solr

System Properties Comparison BigObject vs. Drizzle vs. SAP Advantage Database Server vs. SAP HANA vs. Solr

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

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

Description

Analytic DBMS for real-time computations and queries

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

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

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Search engine

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

bigobject.io

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

www.sap.com/products/hana.html

solr.apache.org

Technical documentation

docs.bigobject.io

help.sap.com/hana

solr.apache.org/resources.html

Developer

BigObject, Inc.

Drizzle project, originally started by Brian Aker

Sybase, SAP

SAP

Apache Software Foundation

Initial release

2015

2008

1993

2010

2006

Current release

7.2.4, September 2012

2.0 SPS07 (April 4, 2023), April 2023

9.6.1, May 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

GNU GPL

commercial

Open Source

Apache Version 2

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++

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

Linux
Windows

Appliance or cloud-service

All OS with a Java VM

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

Data scheme

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

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

Solr Parallel SQL Interface

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C++
Java
PHP

Delphi
Perl
PHP

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Lua

yes

SQLScript, R

Java plugins

Triggers

hooks for callbacks inside the server can be used.

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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 lock on objects (tables, trees)

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

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

yes

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