DBMS > Prometheus vs. Realm vs. Snowflake vs. Sqrrl vs. Vitess

System Properties Comparison Prometheus vs. Realm vs. Snowflake vs. Sqrrl vs. Vitess

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

Open-source Time Series DBMS and monitoring system

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

Adaptable, secure NoSQL built on Apache Accumulo

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Time Series DBMS

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

sqrrl.com

Technical documentation

prometheus.io/docs

realm.io/docs

docs.snowflake.net/manuals/index.html

vitess.io/docs

Developer

Realm, acquired by MongoDB in May 2019

Snowflake Computing Inc.

Amazon

originally Sqrrl Data, Inc.

The Linux Foundation, PlanetScale

Initial release

2015

2014

2012

2013

Current release

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial

Open Source

Apache Version 2.0, commercial licenses available

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
Windows

Android
Backend: server-less
iOS
Windows

hosted

Linux

Docker
Linux
macOS

Data scheme

yes

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

schema-free

yes

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

yes

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

.Net
Java

with Android only
Objective-C
React Native
Swift

JavaScript (Node.js)
Python

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

runs within the applications so server-side scripts are unnecessary

user defined functions

yes

proprietary syntax

Triggers

yes

Change Listeners

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Sharding

making use of Hadoop

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by Federation

none

yes

selectable replication factor

making use of Hadoop

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Atomic updates per row, document, or graph entity

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

In-Memory realm

yes

User concepts

Access control

yes

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

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

Users with fine-grained authorization concept

no user groups or roles

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