DBMS > Faircom DB vs. IBM Db2 Event Store vs. RocksDB vs. YDB

System Properties Comparison Faircom DB vs. IBM Db2 Event Store vs. RocksDB vs. YDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Distributed Event Store optimized for Internet of Things use cases

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Key-value store
Relational DBMS

Event Store
Time Series DBMS

Key-value store

Document store
Relational DBMS

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

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

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

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

www.faircom.com/products/faircom-db

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

rocksdb.org

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

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

github.com/facebook/rocksdb/wiki

ydb.tech/en/docs

Developer

FairCom Corporation

IBM

Facebook, Inc.

Yandex

Initial release

1979

2017

2013

2019

Current release

V12, November 2020

2.0

9.2.1, May 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

free developer edition available

Open Source

BSD

Open Source

Apache 2.0; commercial license available

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

ANSI C, C++

C and C++

C++

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux

Linux, macOS, Windows for the developer addition

Linux

Data scheme

schema free, schema optional, schema required, partial schema,

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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, ANSI SQL with proprietary extensions

yes

through the embedded Spark runtime

SQL-like query language (YQL)

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

C++ API
Java API

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

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

C
C++
Go
Java
Perl
Python
Ruby

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Active-active shard replication

yes

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

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 with additional protections for files

fine grained access rights according to SQL-standard

Access rights defined for Yandex Cloud users

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.