DBMS > Databend vs. IBM Db2 Event Store vs. Immudb vs. RocksDB

System Properties Comparison Databend vs. IBM Db2 Event Store vs. Immudb vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Distributed Event Store optimized for Internet of Things use cases

An open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.

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

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Key-value store

Secondary database models

Relational DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.24
Rank	#305	Overall
	#43	Key-value stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

github.com/datafuselabs/databend
www.databend.com

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

github.com/codenotary/immudb
immudb.io

rocksdb.org

Technical documentation

docs.databend.com

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

docs.immudb.io

github.com/facebook/rocksdb/wiki

Developer

Databend Labs

IBM

Codenotary

Facebook, Inc.

Initial release

2021

2017

2020

2013

Current release

1.0.59, April 2023

2.0

1.2.3, April 2022

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free developer edition available

Open Source

Apache Version 2.0

Open Source

BSD

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

Rust

C and C++

C++

Server operating systems

hosted
Linux
macOS

Linux

Linux, macOS, Windows for the developer addition

BSD
Linux
macOS
Solaris
Windows
z/OS

Linux

Data scheme

yes

schema-free

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 syntax

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

gRPC protocol
PostgreSQL wire protocol

C++ API
Java API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

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

.Net
Go
Java
JavaScript (Node.js)
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Active-active shard replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

ACID

yes

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

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

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