DBMS > Blueflood vs. Heroic vs. IBM Db2 Event Store vs. NSDb vs. SAP Advantage Database Server

System Properties Comparison Blueflood vs. Heroic vs. IBM Db2 Event Store vs. NSDb vs. SAP Advantage Database Server

Editorial information provided by DB-Engines

Name

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

Description

Scalable TimeSeries DBMS based on Cassandra

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Distributed Event Store optimized for Internet of Things use cases

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

Primary database model

Time Series DBMS

Event Store
Time Series DBMS

Time Series DBMS

Relational DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

blueflood.io

github.com/spotify/heroic

www.ibm.com/products/db2-event-store

nsdb.io

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

Technical documentation

github.com/rax-maas/blueflood/wiki

spotify.github.io/heroic

www.ibm.com/docs/en/db2-event-store

nsdb.io/Architecture

Developer

Rackspace

Spotify

IBM

Sybase, SAP

Initial release

2013

2014

2017

1993

Current release

2.0

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

free developer edition available

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C and C++

Java, Scala

Server operating systems

Linux
OS X

Linux

Linux, macOS, Windows for the developer addition

Linux
macOS

Linux
Windows

Data scheme

predefined scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

yes

via Elasticsearch

all fields are automatically indexed

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

SQL-like query language

yes

APIs and other access methods

HTTP REST

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Java
Scala

Delphi
Perl
PHP

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

Active-active shard replication

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

Using Apache Lucene

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

fine grained access rights according to SQL-standard

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