DBMS > EsgynDB vs. Google Cloud Spanner vs. Pinecone vs. WakandaDB

System Properties Comparison EsgynDB vs. Google Cloud Spanner vs. Pinecone vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

A managed, cloud-native vector database

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Vector DBMS

Object oriented DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	3.23
Rank	#92	Overall
	#3	Vector DBMS

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

www.esgyn.cn

cloud.google.com/spanner

www.pinecone.io

wakanda.github.io

Technical documentation

cloud.google.com/spanner/docs

docs.pinecone.io/docs/overview

wakanda.github.io/doc

Developer

Esgyn

Google

Pinecone Systems, Inc

Wakanda SAS

Initial release

2015

2017

2019

2012

Current release

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

AGPLv3, extended commercial license 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++, Java

C++, JavaScript

Server operating systems

Linux

hosted

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

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

yes

Query statements complying to ANSI 2011

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Go
Java
JavaScript (Node.js)
Python

Python

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication with 3 replicas for regional instances.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

Transaction concepts

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

ACID

Strict serializable isolation

ACID

Concurrency

Support for concurrent manipulation of data

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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