DBMS > Apache Drill vs. Google BigQuery vs. H2 vs. Heroic vs. HugeGraph

System Properties Comparison Apache Drill vs. Google BigQuery vs. H2 vs. Heroic vs. HugeGraph

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A fast-speed and highly-scalable Graph DBMS

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

drill.apache.org

cloud.google.com/bigquery

www.h2database.com

github.com/spotify/heroic

github.com/hugegraph
hugegraph.apache.org

Technical documentation

drill.apache.org/docs

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

spotify.github.io/heroic

hugegraph.apache.org/docs

Developer

Apache Software Foundation

Google

Thomas Mueller

Spotify

Baidu

Initial release

2012

2010

2005

2014

2018

Current release

1.20.3, January 2023

2.2.220, July 2023

0.9

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

Linux
OS X
Windows

hosted

All OS with a Java VM

Linux
macOS
Unix

Data scheme

schema-free

yes

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

via Elasticsearch

yes

also supports composite index and range index

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

C++

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

Groovy
Java
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

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

User concepts

Access control

Depending on the underlying data source

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Users, roles and permissions

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