DBMS > Prometheus vs. Snowflake vs. Spark SQL vs. TinkerGraph

System Properties Comparison Prometheus vs. Snowflake vs. Spark SQL vs. TinkerGraph

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Editorial information provided by DB-Engines

Name

Description

Open-source Time Series DBMS and monitoring system

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 lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Time Series DBMS

Relational DBMS

Graph DBMS

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

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.13
Rank	#345	Overall
	#35	Graph DBMS

Website

prometheus.io

www.snowflake.com

spark.apache.org/sql

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

prometheus.io/docs

docs.snowflake.net/manuals/index.html

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

Developer

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2015

2014

2009

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

Java

Server operating systems

Linux
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

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

yes

schema-free

Typing

predefined data types such as float or date

Numeric data only

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.

Import of XML data possible

yes

Secondary indexes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

JDBC
ODBC

TinkerPop 3

Supported programming languages

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

JavaScript (Node.js)
Python

Java
Python
R
Scala

Groovy
Java

Server-side scripts

Stored procedures

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by Federation

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

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

optional

In-memory capabilities

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

yes

User concepts

Access control

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

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