This document provides an overview of the RApache software architecture. RApache allows R to be used as a statistical engine for web applications by embedding the R interpreter into the Apache web server using a module. This allows R functions to be called from Apache to dynamically generate web content. Examples shown include a "Hello World" example using a simple R handler function, generating plots from R code and returning them to the client, and several RApache demonstrations of applications with different capabilities. Ongoing work is focused on improving performance and interfaces.
Apache Scoop - Import with Append mode and Last Modified mode Rupak Roy
Familiar with scoop advanced functions like import with append and last modified mode.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Installing Apache Hive, internal and external table, import-export Rupak Roy
Perform Hive installation with internal and external table import-export and much more
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Automate the complete big data process from import to export data from HDFS to RDBMS like sql with apache sqoop
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Get acquainted with the differences in scoop, the added advantages with hands-on implementation
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Apache Scoop - Import with Append mode and Last Modified mode Rupak Roy
Familiar with scoop advanced functions like import with append and last modified mode.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Installing Apache Hive, internal and external table, import-export Rupak Roy
Perform Hive installation with internal and external table import-export and much more
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Automate the complete big data process from import to export data from HDFS to RDBMS like sql with apache sqoop
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Get acquainted with the differences in scoop, the added advantages with hands-on implementation
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Introduction to MapReduce - Hadoop Streaming | Big Data Hadoop Spark Tutorial...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sh5b3E
This CloudxLab Hadoop Streaming tutorial helps you to understand Hadoop Streaming in detail. Below are the topics covered in this tutorial:
1) Hadoop Streaming and Why Do We Need it?
2) Writing Streaming Jobs
3) Testing Streaming jobs and Hands-on on CloudxLab
Spencer Christensen
There are many aspects to managing an RDBMS. Some of these are handled by an experienced DBA, but there are a good many things that any sys admin should be able to take care of if they know what to look for.
This presentation will cover basics of managing Postgres, including creating database clusters, overview of configuration, and logging. We will also look at tools to help monitor Postgres and keep an eye on what is going on. Some of the tools we will review are:
* pgtop
* pg_top
* pgfouine
* check_postgres.pl.
Check_postgres.pl is a great tool that can plug into your Nagios or Cacti monitoring systems, giving you even better visibility into your databases.
Monitoring a Kubernetes-backed microservice architecture with PrometheusFabian Reinartz
As many startups of the last decade, SoundCloud’s architecture started as a Ruby-on-Rails monolith, which later had to be broken into microservices to cope with the growing size and complexity of the site. The microservices initially ran on an in-house container management and deployment platform. Recently, the company has started to migrate to Kubernetes.
With the introduction of microservices, the existing conventional monitoring setup failed both conceptually and in terms of scalability. Thus, starting in 2012, SoundCloud invested heavily into the development of the open-source monitoring system Prometheus, which was designed for large-scale highly dynamic service-oriented architectures.
Migrating to Kubernetes, it became apparent that Prometheus and Kubernetes are a match made in open-source heaven. The talk will demonstrate the current Prometheus setup at SoundCloud, monitoring a large-scale Kubernetes cluster.
If you want to extend Apache Spark and think that you will need to maintain a separate code base in your own fork, you’re wrong. You can customize different components of the framework, like file commit protocols or state and checkpoint stores.
In this video you are going to learn what is an operator in Apache Airflow. There are multiple kinds of operator such as Action Operator, Sensor Operator and Transfer Operator and it's important to know why and when to use one over another.
If you want to access to the entire course and support my work go to
https://www.udemy.com/the-complete-hands-on-course-to-master-apache-airflow/?couponCode=YOUTUBE-AIRFLOW
Thank you very much and have a good learning day :)
SF Big Analytics 2019112: Uncovering performance regressions in the TCP SACK...Chester Chen
Uncovering performance regressions in the TCP SACKs vulnerability fixes
In early July 2019, Databricks noticed some Apache Spark workloads regressing by as much as 6x. In this talk, we'll discuss how we traced these regressions back to the Linux kernel and the fixes for the TCP SACKs vulnerabilities. We will explain the symptoms we were seeing, walk through how we debugged the TCP connections, and dive into the Linux source to uncover the root cause.
Speaker: Chris Stevens (Databricks)
Chris Stevens is a software engineer at Databricks where he works on the reliability, scalability, and security of Apache Spark clusters. His work focuses on auto-scaling compute, auto-scaling storage, node initialization performance, and node health monitoring. Prior to Databricks, Chris founded the Minoca OS project, where he built a POSIX compliant, general purpose OS - from scratch - to run on resource constrained device. He got his start at Microsoft working on the Windows kernel team, porting the Windows boot environment from BIOS to UEFI.
This ppt provide information about:
1. Database basics,
2. Indexes,
3. PHP MyAdmin Connect & Pconnect,
4. MySQL Create,
5. MySQL Insert,
6. MySQL Select,
7. MySQL Update,
8. MySQL Delete,
9. MySQL Truncate,
10. MySQL Drop
Introduction to MapReduce - Hadoop Streaming | Big Data Hadoop Spark Tutorial...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sh5b3E
This CloudxLab Hadoop Streaming tutorial helps you to understand Hadoop Streaming in detail. Below are the topics covered in this tutorial:
1) Hadoop Streaming and Why Do We Need it?
2) Writing Streaming Jobs
3) Testing Streaming jobs and Hands-on on CloudxLab
Spencer Christensen
There are many aspects to managing an RDBMS. Some of these are handled by an experienced DBA, but there are a good many things that any sys admin should be able to take care of if they know what to look for.
This presentation will cover basics of managing Postgres, including creating database clusters, overview of configuration, and logging. We will also look at tools to help monitor Postgres and keep an eye on what is going on. Some of the tools we will review are:
* pgtop
* pg_top
* pgfouine
* check_postgres.pl.
Check_postgres.pl is a great tool that can plug into your Nagios or Cacti monitoring systems, giving you even better visibility into your databases.
Monitoring a Kubernetes-backed microservice architecture with PrometheusFabian Reinartz
As many startups of the last decade, SoundCloud’s architecture started as a Ruby-on-Rails monolith, which later had to be broken into microservices to cope with the growing size and complexity of the site. The microservices initially ran on an in-house container management and deployment platform. Recently, the company has started to migrate to Kubernetes.
With the introduction of microservices, the existing conventional monitoring setup failed both conceptually and in terms of scalability. Thus, starting in 2012, SoundCloud invested heavily into the development of the open-source monitoring system Prometheus, which was designed for large-scale highly dynamic service-oriented architectures.
Migrating to Kubernetes, it became apparent that Prometheus and Kubernetes are a match made in open-source heaven. The talk will demonstrate the current Prometheus setup at SoundCloud, monitoring a large-scale Kubernetes cluster.
If you want to extend Apache Spark and think that you will need to maintain a separate code base in your own fork, you’re wrong. You can customize different components of the framework, like file commit protocols or state and checkpoint stores.
In this video you are going to learn what is an operator in Apache Airflow. There are multiple kinds of operator such as Action Operator, Sensor Operator and Transfer Operator and it's important to know why and when to use one over another.
If you want to access to the entire course and support my work go to
https://www.udemy.com/the-complete-hands-on-course-to-master-apache-airflow/?couponCode=YOUTUBE-AIRFLOW
Thank you very much and have a good learning day :)
SF Big Analytics 2019112: Uncovering performance regressions in the TCP SACK...Chester Chen
Uncovering performance regressions in the TCP SACKs vulnerability fixes
In early July 2019, Databricks noticed some Apache Spark workloads regressing by as much as 6x. In this talk, we'll discuss how we traced these regressions back to the Linux kernel and the fixes for the TCP SACKs vulnerabilities. We will explain the symptoms we were seeing, walk through how we debugged the TCP connections, and dive into the Linux source to uncover the root cause.
Speaker: Chris Stevens (Databricks)
Chris Stevens is a software engineer at Databricks where he works on the reliability, scalability, and security of Apache Spark clusters. His work focuses on auto-scaling compute, auto-scaling storage, node initialization performance, and node health monitoring. Prior to Databricks, Chris founded the Minoca OS project, where he built a POSIX compliant, general purpose OS - from scratch - to run on resource constrained device. He got his start at Microsoft working on the Windows kernel team, porting the Windows boot environment from BIOS to UEFI.
This ppt provide information about:
1. Database basics,
2. Indexes,
3. PHP MyAdmin Connect & Pconnect,
4. MySQL Create,
5. MySQL Insert,
6. MySQL Select,
7. MySQL Update,
8. MySQL Delete,
9. MySQL Truncate,
10. MySQL Drop
Intro to big data analytics using microsoft machine learning server with sparkAlex Zeltov
Alex Zeltov - Intro to Big Data Analytics using Microsoft Machine Learning Server with Spark
By combining enterprise-scale R analytics software with the power of Apache Hadoop and Apache Spark, Microsoft R Server for HDP or HDInsight gives you the scale and performance you need. Multi-threaded math libraries and transparent parallelization in R Server handle up to 1000x more data and up to 50x faster speeds than open-source R, which helps you to train more accurate models for better predictions. R Server works with the open-source R language, so all of your R scripts run without changes.
Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business with full support for Python and R. Machine Learning Server meets the needs of all constituents of the process – from data engineers and data scientists to line-of-business programmers and IT professionals. It offers a choice of languages and features algorithmic innovation that brings the best of open source and proprietary worlds together.
R support is built on a legacy of Microsoft R Server 9.x and Revolution R Enterprise products. Significant machine learning and AI capabilities enhancements have been made in every release. In 9.2.1, Machine Learning Server adds support for the full data science lifecycle of your Python-based analytics.
This meetup will NOT be a data science intro or R intro to programming. It is about working with data and big data on MLS .
- How to Scale R
- Work with R and Hadoop + Spark
-Demo of MLS on HDP/HDInsight server with RStudio
- How to operationalize deploying models using MLS Webservice operationalization features on MLS Server or on the cloud Azure ML (PaaS) offering. Speaker Bio:
Alex Zeltov is Big Data Solutions Architect / Software Engineer / Programmer Analyst / Data Scientist with over 19 years of industry experience in Information Technology and most recently in Big Data and Predictive Analytics. He currently works as Global black belt Technical Specialist in Microsoft where he concentrates on Big Data and Advanced Analytics use cases. Previously to joining Microsoft he worked as a Sr. Solutions Engineer at Hortonworks where he specialized in HDP and HDF platforms.
These are the slides that were presented at Red Hat's workshop: Achieving True Integration Agility with Containers, Microservices and APIs. Seattle, WA, October 26, 2017
Can you teach coding to kids in a mobile game app in local languages. Do you need to be good in English to learn coding in R or Python?
How young can we train people in coding-
something we worked on for six months but now we are giving up due to lack of funds is this idea.
Feel free to use it, it is licensed cc-by-sa
1. RApache Software Architecture
Using R/Apache as the Statistical Engine for
Web Applications
http://biostat.mc.vanderbilt.edu/RApacheProject
Jeffrey Horner
Vanderbilt University
Department of Biostatistics
June 15, 2006
Apache 2.0 Multi-Processing Models Hello World Example: http://locahost/test/hello
-----------http.conf---------------+----------test.R---------------------------
|
LoadModule R_module mod_R.so | handler <- function(r){
Prefork Perchild | apache.write(r,"<h1>Hello World!</h1>")
Parent Parent <Location /test/hello> | OK
SetHandler r-handler | }
Children Children Rsource /var/www/html/test.R |
RreqHandler handler |
</Location> |
|
R R R -----------------------------------+--------------------------------------------
Worker WinNT
Parent Parent
Children
Child
2. GDD/NRart Example GDD/NRart Example Output
library(’GDD’)
library(’NRart’)
r2 <- function(r)
{
apache.set_content_type(r,"image/png")
GDD(ctx=apache.gdlib_ioctx(r),w=500,h=500,type="png")
nr.image(x^3 + .28 * tan(x + t) + cos(x + 2*t)*.3i - 0.7,
steps=3, points=400, col=rainbow(256), zlim=c(-pi, pi))
dev.off()
OK
}
RApache Demonstrations Ongoing Work
Full Feature Test Handler
Demonstrates all the capabilities of RApache include CGI,
Rmemcache
manipulating HTTP headers, and file uploading.
Implements client API to memcached, a possibly distributed
EVT-Web cache (not a database) of small objects. R objects are
Implements a web interface to a protein searching stored in memcache server via serialize().
algorithm using Extreme Value Theory. The web interface
udbMySQL
takes a group of related proteins as input from the user and
generates a profile via an external program (MEME). Then Uses the "User Defined Database" interface to store R
a model is built to predict which other proteins from a large objects of arbitrary size and structure in a MySQL table.
database (MySQL) are also related to the group of proteins Getting the next release out the door
that the user specified. Testing against Apache 2.2, R-2.3.1. Looking into getting
Polyart rid of some of the apache.* methods in favor of a more
Demonstrates the use of RApache as a web service to transparent interface, especially apache.write().
explore images created through the process of finding roots
of functions, i.e. using the NRart package.