DBMS > DataFS vs. Rockset vs. Snowflake vs. Spark SQL vs. XTDB

System Properties Comparison DataFS vs. Rockset vs. Snowflake vs. Spark SQL vs. XTDB

Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Cloud-based data warehousing service for structured and semi-structured data

Spark SQL is a component on top of 'Spark Core' for structured data processing

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Graph DBMS

Relational DBMS
Search engine

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

dev.mobiland.com/Overview.xsp

docs.rockset.com

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

www.xtdb.com/docs

Developer

Mobiland AG

Rockset

Snowflake Computing Inc.

Apache Software Foundation

Juxt Ltd.

Initial release

2018

2019

2014

2019

Current release

1.1.263, October 2022

3.5.0 ( 2.13), September 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MIT License

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

C++

Scala

Clojure

Server operating systems

Windows

hosted

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

schema-free

Typing

predefined data types such as float or date

yes

dynamic typing

yes

yes, extensible-data-notation format

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.

ingestion from XML files supported

yes

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

Read-only SQL queries, including JOINs

yes

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

HTTP REST

CLI Client
JDBC
ODBC

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

.Net
C
C#
C++
VB.Net

Go
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Java
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

Triggers

no, except callback-events from server when changes happened

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Automatic sharding

yes

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

User concepts

Access control

Windows-Profile

Access rights for users and organizations can be defined via Rockset console

Users with fine-grained authorization concept, user roles and pluggable authentication

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