DBMS > Apache Drill vs. DataFS vs. EventStoreDB vs. Pinecone vs. TerarkDB

System Properties Comparison Apache Drill vs. DataFS vs. EventStoreDB vs. Pinecone vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

A managed, cloud-native vector database

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Document store
Relational DBMS

Secondary database models

Graph DBMS

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

Score	1.90
Rank	#126	Overall
	#23	Document stores
	#60	Relational DBMS

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	1.07
Rank	#181	Overall
	#1	Event Stores

Score	3.02
Rank	#87	Overall
	#3	Vector DBMS

Score	0.00
Rank	#385	Overall
	#61	Key-value stores

Website

github.com/bytedance/terarkdb

Technical documentation

drill.apache.org/docs

dev.mobiland.com/Overview.xsp

developers.eventstore.com

docs.pinecone.io/docs/overview

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Apache Software Foundation

Mobiland AG

Event Store Limited

Pinecone Systems, Inc

ByteDance, originally Terark

Initial release

2012

2018

2012

2019

2016

Current release

1.20.3, January 2023

1.1.263, October 2022

21.2, February 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

Linux
OS X
Windows

Windows

Linux
Windows

hosted

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP API

C++ API
Java API

Supported programming languages

C++

.Net
C
C#
C++
VB.Net

Python

C++
Java

Server-side scripts

Stored procedures

user defined functions

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

Windows-Profile

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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Recent citations in the news

Apache Drill case study: A tutorial on processing CSV files
9 June 2016, TheServerSide.com

Analyse Kafka messages with SQL queries using Apache Drill
27 September 2019, Towards Data Science

MapR Advances Support for Flexible and High Performance Analytics on JSON and S3 Data with Apache Drill
30 January 2019, Business Wire

Apache Drill Adds New Data Formats
28 March 2022, iProgrammer

Apache Drill Eliminates ETL, Data Transformation for MapR Database
11 April 2016, The New Stack

provided by Google News

Pinecone serverless goes multicloud as vector database market heats up
27 August 2024, VentureBeat

Using the Pinecone vector database in .NET
12 September 2024, InfoWorld

Pinecone launches serverless vector database on Azure, GCP
27 August 2024, TechTarget

Pinecone launches its serverless vector database out of preview
21 May 2024, TechCrunch

Pinecone Makes Accurate, Fast, Scalable Generative AI Accessible to Organizations Large and Small with Launch of its Serverless Vector Database
21 May 2024, PR Newswire

provided by Google News

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