DBMS > Apache Drill vs. H2 vs. Splice Machine vs. YTsaurus

System Properties Comparison Apache Drill vs. H2 vs. Splice Machine vs. YTsaurus

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Editorial information provided by DB-Engines

Name

Description

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

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

YTsaurus is an open source platform for distributed storage and processing.

Primary database model

Document store
Relational DBMS

Relational DBMS

Document store
Key-value store

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	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.21
Rank	#324	Overall
	#45	Document stores
	#48	Key-value stores

Website

Technical documentation

drill.apache.org/docs

www.h2database.com/html/main.html

splicemachine.com/how-it-works

ytsaurus.tech/docs/en

Developer

Apache Software Foundation

Thomas Mueller

Splice Machine

Yandex

Initial release

2012

2005

2014

2023

Current release

1.20.3, January 2023

2.2.220, July 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

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

Open Source

AGPL 3.0, commercial license available

Open Source

Apache License, Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Linux
OS X
Solaris
Windows

Ubuntu

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

YQL, an SQL-based language, is supported

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C++

Java

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C++
Go
Java
JavaScript
Python

Server-side scripts

Stored procedures

user defined functions

Java Stored Procedures and User-Defined Functions

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

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

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

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

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

Access Control Lists

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