DBMS > Apache Pinot vs. Google Cloud Datastore vs. HyperSQL vs. STSdb

System Properties Comparison Apache Pinot vs. Google Cloud Datastore vs. HyperSQL vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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Trend Chart

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

pinot.apache.org

cloud.google.com/datastore

hsqldb.org

github.com/STSSoft/STSdb4

Technical documentation

docs.pinot.apache.org

cloud.google.com/datastore/docs

hsqldb.org/web/hsqlDocsFrame.html

Developer

Apache Software Foundation and contributors

Google

STS Soft SC

Initial release

2015

2008

2001

2011

Current release

1.0.0, September 2023

2.7.2, June 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

based on BSD license

Open Source

GPLv2, 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

Java

Server operating systems

All OS with a Java JDK11 or higher

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

primitive types and user defined types (classes)

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

SQL-like query language (GQL)

yes

APIs and other access methods

JDBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP API

JDBC via HTTP
JDBC
ODBC

.NET Client API

Supported programming languages

Go
Java
Python

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

All languages supporting JDBC/ODBC
Java

C#
Java

Server-side scripts

Stored procedures

using Google App Engine

Java, SQL

Triggers

Callbacks using the Google Apps Engine

yes

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 using Paxos

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 or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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.

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

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