DBMS > Blueflood vs. Postgres-XL vs. SAP HANA vs. SAP SQL Anywhere vs. Solr

System Properties Comparison Blueflood vs. Postgres-XL vs. SAP HANA vs. SAP SQL Anywhere vs. Solr

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

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

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

Primary database model

Time Series DBMS

Relational DBMS

Search engine

Secondary database models

Document store
Spatial DBMS

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	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

blueflood.io

www.postgres-xl.org

www.sap.com/products/hana.html

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

solr.apache.org

Technical documentation

github.com/rax-maas/blueflood/wiki

www.postgres-xl.org/documentation

help.sap.com/hana

help.sap.com/docs/SAP_SQL_Anywhere

solr.apache.org/resources.html

Developer

Rackspace

SAP

formerly Sybase

Apache Software Foundation

Initial release

2013

2014

since 2012, originally named StormDB

2010

1992

2006

Current release

10 R1, October 2018

2.0 SPS07 (April 4, 2023), April 2023

17, July 2015

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Mozilla public license

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

Java

Server operating systems

Linux
OS X

Linux
macOS

Appliance or cloud-service

AIX
HP-UX
Linux
OS X
Solaris
Windows

All OS with a Java VM

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

Data scheme

predefined 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

XML type, but no XML query functionality

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

distributed, parallel query execution

yes

Solr Parallel SQL Interface

APIs and other access methods

HTTP REST

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

JDBC
ODBC

ADO.NET
HTTP API
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

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

Server-side scripts

Stored procedures

user defined functions

SQLScript, R

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

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning

yes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

Source-replica replication

Database mirroring

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

ACID

optimistic locking

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

yes

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.