DBMS > Apache Pinot vs. Dragonfly vs. EventStoreDB vs. SAP HANA

System Properties Comparison Apache Pinot vs. Dragonfly vs. EventStoreDB vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Industrial-strength, open-source database solution built from the ground up for event sourcing.

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

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

pinot.apache.org

github.com/dragonflydb/dragonfly
www.dragonflydb.io

www.eventstore.com

www.sap.com/products/hana.html

Technical documentation

docs.pinot.apache.org

www.dragonflydb.io/docs

developers.eventstore.com

help.sap.com/hana

Developer

Apache Software Foundation and contributors

DragonflyDB team and community contributors

Event Store Limited

SAP

Initial release

2015

2023

2012

2010

Current release

1.0.0, September 2023

1.0, March 2023

21.2, February 2021

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSL 1.1

Open Source

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

All OS with a Java JDK11 or higher

Linux

Linux
Windows

Appliance or cloud-service

Data scheme

yes

scheme-free

yes

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

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

SQL-like query language

yes

APIs and other access methods

JDBC

Proprietary protocol

RESP - REdis Serialization Protocol

JDBC
ODBC

Supported programming languages

Go
Java
Python

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

Server-side scripts

Stored procedures

Lua

SQLScript, R

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

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

Password-based authentication

yes

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