DBMS > Apache Impala vs. EJDB vs. Pinecone vs. Transwarp KunDB

System Properties Comparison Apache Impala vs. EJDB vs. Pinecone vs. Transwarp KunDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

A managed, cloud-native vector database

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

Primary database model

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	3.23
Rank	#92	Overall
	#2	Vector DBMS

Score	0.14
Rank	#341	Overall
	#149	Relational DBMS

Website

impala.apache.org

github.com/Softmotions/ejdb

www.pinecone.io

www.transwarp.cn/en/product/kundb

Technical documentation

impala.apache.org/impala-docs.html

github.com/Softmotions/ejdb/blob/master/README.md

docs.pinecone.io/docs/overview

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Softmotions

Pinecone Systems, Inc

Transwarp

Initial release

2013

2012

2019

Current release

4.1.0, June 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPLv2

commercial

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

server-less

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

String, Number, Boolean

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 DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

in-process shared library

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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