Hydrosphere Mist: Serverless Proxy for Spark Cluster For Scala

Hydrosphere Mist

Hydrosphere Mist is a serverless proxy for Spark cluster. Mist provides a new functional programming framework and deployment model for Spark applications.

Please see our quick start guide and documentation

Features:

Spark Function as a Service. Deploy Spark functions rather than notebooks or scripts.
Spark Cluster and Session management. Fully managed Spark sessions backed by on-demand EMR, Hortonworks, Cloudera, DC/OS and vanilla Spark clusters.
Typesafe programming framework that clearly defines inputs and outputs of every Spark job.
REST HTTP & Messaging (MQTT, Kafka) API for Scala & Python Spark jobs.
Multi-cluster mode: Seamless Spark cluster on-demand provisioning, autoscaling and termination(pending)

It creates a unified API layer for building enterprise solutions and microservices on top of a Spark functions.

Mist use cases

High Level Architecture

Contact

Please report bugs/problems to: https://github.com/Hydrospheredata/mist/issues.

http://hydrosphere.io/

Facebook

Twitter

Author: Hydrospheredata
Source code: https://github.com/Hydrospheredata/mist
License: Apache-2.0 license

#scala #apache-spark #python

Hydrosphere Mist: Serverless Proxy for Spark Cluster For Scala

Hydrosphere Mist

Hydrosphere Mist is a serverless proxy for Spark cluster. Mist provides a new functional programming framework and deployment model for Spark applications.

Please see our quick start guide and documentation

Features:

Spark Function as a Service. Deploy Spark functions rather than notebooks or scripts.
Spark Cluster and Session management. Fully managed Spark sessions backed by on-demand EMR, Hortonworks, Cloudera, DC/OS and vanilla Spark clusters.
Typesafe programming framework that clearly defines inputs and outputs of every Spark job.
REST HTTP & Messaging (MQTT, Kafka) API for Scala & Python Spark jobs.
Multi-cluster mode: Seamless Spark cluster on-demand provisioning, autoscaling and termination(pending)

It creates a unified API layer for building enterprise solutions and microservices on top of a Spark functions.

Mist use cases

High Level Architecture

Contact

Please report bugs/problems to: https://github.com/Hydrospheredata/mist/issues.

http://hydrosphere.io/

Facebook

Twitter

Author: Hydrospheredata
Source code: https://github.com/Hydrospheredata/mist
License: Apache-2.0 license

#scala #apache-spark #python

TopDevelopers Co

1606890999

Top Spark Development Companies | Best Spark Developers - TopDevelopers.co

An extensively researched list of top Apache spark developers with ratings & reviews to help find the best spark development Companies around the world.

Our thorough research on the ace qualities of the best Big Data Spark consulting and development service providers bring this list of companies. To predict and analyze businesses and in the scenarios where prompt and fast data processing is required, Spark application will greatly be effective for various industry-specific management needs. The companies listed here have been skillfully boosting businesses through effective Spark consulting and customized Big Data solutions.

Check out this list of Best Spark Development Companies with Best Spark Developers.

#spark development service providers #top spark development companies #best big data spark development #spark consulting #spark developers #spark application

Hermann Frami

1655426640

Serverless Plugin for Microservice Code Management and Deployment

Serverless M

Serverless M (or Serverless Modular) is a plugin for the serverless framework. This plugins helps you in managing multiple serverless projects with a single serverless.yml file. This plugin gives you a super charged CLI options that you can use to create new features, build them in a single file and deploy them all in parallel

Currently this plugin is tested for the below stack only

AWS
NodeJS λ
Rest API (You can use other events as well)

Prerequisites

Make sure you have the serverless CLI installed

# Install serverless globally
$ npm install serverless -g

Getting Started

To start the serverless modular project locally you can either start with es5 or es6 templates or add it as a plugin

ES6 Template install

# Step 1. Download the template
$ sls create --template-url https://github.com/aa2kb/serverless-modular/tree/master/template/modular-es6 --path myModularService

# Step 2. Change directory
$ cd myModularService

# Step 3. Create a package.json file
$ npm init

# Step 3. Install dependencies
$ npm i serverless-modular serverless-webpack webpack --save-dev

ES5 Template install

# Step 1. Download the template
$ sls create --template-url https://github.com/aa2kb/serverless-modular/tree/master/template/modular-es5 --path myModularService

# Step 2. Change directory
$ cd myModularService

# Step 3. Create a package.json file
$ npm init

# Step 3. Install dependencies
$ npm i serverless-modular --save-dev

If you dont want to use the templates above you can just add in your existing project

Adding it as plugin

plugins:
  - serverless-modular

Now you are all done to start building your serverless modular functions

API Reference

The serverless CLI can be accessed by

# Serverless Modular CLI
$ serverless modular

# shorthand
$ sls m

Serverless Modular CLI is based on 4 main commands

sls m init
sls m feature
sls m function
sls m build
sls m deploy

init command

sls m init

The serverless init command helps in creating a basic .gitignore that is useful for serverless modular.

The basic .gitignore for serverless modular looks like this

#node_modules
node_modules

#sm main functions
sm.functions.yml

#serverless file generated by build
src/**/serverless.yml

#main serverless directories generated for sls deploy
.serverless

#feature serverless directories generated sls deploy
src/**/.serverless

#serverless logs file generated for main sls deploy
.sm.log

#serverless logs file generated for feature sls deploy
src/**/.sm.log

#Webpack config copied in each feature
src/**/webpack.config.js

feature command

The feature command helps in building new features for your project

options (feature Command)

This command comes with three options

--name: Specify the name you want for your feature

--remove: set value to true if you want to remove the feature

--basePath: Specify the basepath you want for your feature, this base path should be unique for all features. helps in running offline with offline plugin and for API Gateway

options	shortcut	required	values	default value
--name	-n	✅	string	N/A
--remove	-r	❎	true, false	false
--basePath	-p	❎	string	same as name

Examples (feature Command)

Creating a basic feature

# Creating a jedi feature
$ sls m feature -n jedi

Creating a feature with different base path

# A feature with different base path
$ sls m feature -n jedi -p tatooine

Deleting a feature

# Anakin is going to delete the jedi feature
$ sls m feature -n jedi -r true

function command

The function command helps in adding new function to a feature

options (function Command)

This command comes with four options

--name: Specify the name you want for your function

--feature: Specify the name of the existing feature

--path: Specify the path for HTTP endpoint helps in running offline with offline plugin and for API Gateway

--method: Specify the path for HTTP method helps in running offline with offline plugin and for API Gateway

options	shortcut	required	values	default value
--name	-n	✅	string	N/A
--feature	-f	✅	string	N/A
--path	-p	❎	string	same as name
--method	-m	❎	string	'GET'

Examples (function Command)

Creating a basic function

# Creating a cloak function for jedi feature
$ sls m function -n cloak -f jedi

Creating a basic function with different path and method

# Creating a cloak function for jedi feature with custom path and HTTP method
$ sls m function -n cloak -f jedi -p powers -m POST

build command

The build command helps in building the project for local or global scope

options (build Command)

This command comes with four options

--scope: Specify the scope of the build, use this with "--feature" tag

--feature: Specify the name of the existing feature you want to build

options	shortcut	required	values	default value
--scope	-s	❎	string	local
--feature	-f	❎	string	N/A

Saving build Config in serverless.yml

You can also save config in serverless.yml file

custom:
  smConfig:
    build:
      scope: local

Examples (build Command)

all feature build (local scope)

# Building all local features
$ sls m build

Single feature build (local scope)

# Building a single feature
$ sls m build -f jedi -s local

All features build global scope

# Building all features with global scope
$ sls m build -s global

deploy command

The deploy command helps in deploying serverless projects to AWS (it uses sls deploy command)

options (deploy Command)

This command comes with four options

--sm-parallel: Specify if you want to deploy parallel (will only run in parallel when doing multiple deployments)

--sm-scope: Specify if you want to deploy local features or global

--sm-features: Specify the local features you want to deploy (comma separated if multiple)

options	shortcut	required	values	default value
--sm-parallel	❎	❎	true, false	true
--sm-scope	❎	❎	local, global	local
--sm-features	❎	❎	string	N/A
--sm-ignore-build	❎	❎	string	false

Saving deploy Config in serverless.yml

You can also save config in serverless.yml file

custom:
  smConfig:
    deploy:
      scope: local
      parallel: true
      ignoreBuild: true

Examples (deploy Command)

Deploy all features locally

# deploy all local features
$ sls m deploy

Deploy all features globally

# deploy all global features
$ sls m deploy --sm-scope global

Deploy single feature

# deploy all global features
$ sls m deploy --sm-features jedi

Deploy Multiple features

# deploy all global features
$ sls m deploy --sm-features jedi,sith,dark_side

Deploy Multiple features in sequence

# deploy all global features
$ sls m deploy  --sm-features jedi,sith,dark_side --sm-parallel false

Author: aa2kb
Source Code: https://github.com/aa2kb/serverless-modular
License: MIT license

#serverless #aws #node #lambda

Darius Greenholt

1591631526

Dynamic Partition Pruning in Spark 3.0

Dynamic Partition Pruning in Spark 3.0

With the release of Spark 3.0, big improvements were implemented to enable Spark to execute faster and there came many new features along with it. Among them, dynamic partition pruning is one. Before diving into the features which are new in Dynamic Partition Pruning let us understand what is Partition Pruning.

Partition Pruning in Spark

In standard database pruning means that the optimizer will avoid reading files that cannot contain the data that you are looking for

#apache spark #scala #partitioning in apache spark #spark #spark sql

Roberta Ward

1595344320

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Hydrosphere Mist: Serverless Proxy for Spark Cluster For Scala

Hydrosphere Mist

High Level Architecture

Contact

What is GEEK

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Hydrosphere Mist: Serverless Proxy for Spark Cluster For Scala

Hydrosphere Mist

High Level Architecture

Contact

Top Spark Development Companies | Best Spark Developers - TopDevelopers.co

Serverless Plugin for Microservice Code Management and Deployment

Serverless M

Prerequisites

Getting Started

ES6 Template install

ES5 Template install

Adding it as plugin

API Reference

init command

feature command

options (feature Command)

Examples (feature Command)

function command

options (function Command)

Examples (function Command)

build command

options (build Command)

Examples (build Command)

deploy command

options (deploy Command)

Examples (deploy Command)

Dynamic Partition Pruning in Spark 3.0

Wondering how to upgrade your skills in the pandemic? Here's a simple way you can do it.

How to upgrade your skills?

Confused with which technology to start with?