DBMS > Apache Pinot vs. EsgynDB vs. Google BigQuery vs. Lovefield

System Properties Comparison Apache Pinot vs. EsgynDB vs. Google BigQuery vs. Lovefield

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Editorial information provided by DB-Engines

Name

Description

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

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

Large scale data warehouse service with append-only tables

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Relational DBMS

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Website

pinot.apache.org

www.esgyn.cn

cloud.google.com/bigquery

google.github.io/lovefield

Technical documentation

docs.pinot.apache.org

cloud.google.com/bigquery/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

Apache Software Foundation and contributors

Esgyn

Google

Initial release

2015

2010

2014

Current release

1.0.0, September 2023

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++, Java

JavaScript

Server operating systems

All OS with a Java JDK11 or higher

Linux

hosted

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Data scheme

yes

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

SQL-like query language

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

Supported programming languages

Go
Java
Python

All languages supporting JDBC/ODBC/ADO.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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