DBMS > Apache Drill vs. HEAVY.AI vs. HyperSQL vs. Lovefield

System Properties Comparison Apache Drill vs. HEAVY.AI vs. HyperSQL vs. Lovefield

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Editorial information provided by DB-Engines

Name

Description

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Document store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Website

drill.apache.org

github.com/heavyai/heavydb
www.heavy.ai

hsqldb.org

google.github.io/lovefield

Technical documentation

drill.apache.org/docs

docs.heavy.ai

hsqldb.org/web/hsqlDocsFrame.html

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

Developer

Apache Software Foundation

HEAVY.AI, Inc.

Google

Initial release

2012

2016

2001

2014

Current release

1.20.3, January 2023

5.10, January 2022

2.7.2, June 2023

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2; enterprise edition available

Open Source

based on BSD license

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++ and CUDA

Java

JavaScript

Server operating systems

Linux
OS X
Windows

Linux

All OS with a Java VM

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

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

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

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 SELECT statement is SQL:2003 compliant

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

HTTP API

JDBC via HTTP
JDBC
ODBC

Supported programming languages

C++

All languages supporting JDBC/ODBC/Thrift
Python

All languages supporting JDBC/ODBC
Java

JavaScript

Server-side scripts

Stored procedures

user defined functions

Java, SQL

Triggers

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

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

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.

Depending on the underlying data source

yes

using MemoryDB

User concepts

Access control

Depending on the underlying data source

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

Apache Drill vs. Apache Spark — Which SQL query engine is better for you?
23 September 2019, Towards Data Science

Apache Drill case study: A tutorial on processing CSV files
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Apache Drill Poised to Crack Tough Data Challenges
19 May 2015, Datanami

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

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