DBMS > Blueflood vs. IBM Db2 Event Store vs. SurrealDB vs. Trafodion

System Properties Comparison Blueflood vs. IBM Db2 Event Store vs. SurrealDB vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Scalable TimeSeries DBMS based on Cassandra

Distributed Event Store optimized for Internet of Things use cases

A fully ACID transactional, developer-friendly, multi-model DBMS

Transactional SQL-on-Hadoop DBMS

Primary database model

Time Series DBMS

Event Store
Time Series DBMS

Document store
Graph 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	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Website

blueflood.io

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

surrealdb.com

trafodion.apache.org

Technical documentation

github.com/rax-maas/blueflood/wiki

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

surrealdb.com/docs

trafodion.apache.org/documentation.html

Developer

Rackspace

IBM

SurrealDB Ltd

Apache Software Foundation, originally developed by HP

Initial release

2013

2017

2022

2014

Current release

2.0

v1.5.0, May 2024

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free developer edition available

Open Source

Apache 2.0

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++

Rust

C++, Java

Server operating systems

Linux
OS X

Linux

Linux, macOS, Windows for the developer addition

Linux
macOS
Windows

Linux

Data scheme

predefined scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

through the embedded Spark runtime

SQL-like query language

yes

APIs and other access methods

HTTP REST

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

GraphQL
RESTful HTTP API
WebSocket

ADO.NET
JDBC
ODBC

Supported programming languages

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

Deno
Go
JavaScript (Node.js)
Rust

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

Java Stored Procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Active-active shard replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

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, based on authentication and database rules

fine grained access rights according to SQL-standard

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