Monday, October 22, 2012

Java to Python Cheatsheet

Null -> None array -> list hash -> dictionary or collections.Counter method -> methods (attached to classes) and functions (detached from classes) casting -> coercion overloading -> no overloading - python packages import time import sys import datetime import collections

Thursday, August 23, 2012

Grails vs. Ruby on Rails

Are you at the point when you need to decide what will be your next Rapid Application Development framework for your new website? You heard a lot about Ruby on Rails, right? It's soooo cool and sexy and stuff!

But wait, look at that Grails, Ruby on Rails:

Hugely popular in the past ROR (Ruby on Rails) is giving in to Grails. I think the reasons are the following:

  1. Performance, performance, performance. Twitter is dumping ROR because of performance. Grails, on the other hand, is compiled into Java and runs in a JVM.
  2. More and more Java developers discover Grails.
  3. I can't think of anything else right now :)

Thursday, June 21, 2012


When you search for CREATE TABLE AS SELECT in Hive you end up with documentation that simply doesn't give a simple example but rather shows complex examples that dig too dip but don't explain how to do it quickly. And here is the simple example:


It works, I've just tried it.


Later addition. It works on a limited data. If your source table is huge then it's going to be a problem. The main problem is that this query allocates only one reducer by default. So you have two choices - either increase number of reducers or do the following:

  1. Copy a sample file to your directory
  2. Create a new table based on this file

Hadoop Hive SPLIT example

For example you have a table that tracks your visitors and you want to see who was the top referers but you don't want to see anything after the "?", i.e. to remove the http parameters:

select split(referer, '\\?')[0], count(*) as cnt from event_log where referer is not null group by split(referer, '\\?')[0] order by cnt desc limit 100;

Note that we need to escape the ? because it's a special symbol in regular expressions. So if you have the following input

then the output will be             1                     1

Wednesday, June 20, 2012

NOT LIKE Function in Hadoop Hive

In SQL usually the query is the following:

select * from table_name where column_name not like '%something%';

In Hive this doesn't work. Instead do the following:

select * from table_name where not (column_name like '%something%');

Thursday, June 14, 2012

SenchaTouch is slow to load as a mobile web app

The mobile web application for Internet Polyglot (doesn't work in Firefox since it doesn't support WebKit) has been deployed to production a couple of months ago and shows very poor results in terms of user bounce rate. Too many users are dropping off almost immediately.

The application is written in SenchaTouch. There had been many thoughts about what framework to use for this app at the time. First idea was that it must be HTML5 as opposed to a native iOS or Android app - for the purpose of single code base. Then a decision was made in favor of SenchaTouch before jQueryMobile because anekdotally SenchaTouch was more powerful as a app development platform. And wrapped in PhoneGap it would serve also as a deployable app on Apple Appstore and Android Market.

But the problem is the loading speed. When I open the app in my iPhone's browser it takes too long time to show even the splash screen. I suppose it loads SenchaTouch libraries first and they are big. Maybe SenchaTouch cannot be used for mobile web apps? Maybe it's only for development of PhoneGap'd apps?

Recently the app was rewritten in SenchaTouch 2 but I don't see much of loading speed improvement.

If anybody knows how to speed up the loading of an app written in SenchaTouch - please comment.

Hadoop Summit Day 2

The second day of Hadoop Summit.

== VMWare: Apache Hadoop and Virtual Machines

The idea is to place multiple Virtual Machines on cluster's machines and place Hadoop nodes on those VMs.

You can grow and shrink a single VM to accommodate the load:

It's hard to add and remove the nodes because of losing the state - this is a problem.

And the big idea is to keep HDFS on physical hosts and place the MapReduce nodes in VMs - in this case the state is not lost and task nodes can be added/removed at will.

== Analytical Queries with Hive: SQL Windowing and Table Functions

Amazingly uninteresting session - the presenter didn't talk about high level problem statement but gave too much internal implementations. Or maybe I am just not qualified enough to understand it. Anyway there are a couple of screenshots:

== Starving for Juice (intermission)

As I mentioned before there are only few places where you can charge your laptop. And the Macbook is always hungry...

== Creating Histograms from a Data Stream via MapReduce

The problem is that the data comes as a stream, it's hard to get min and max. And the data is distributed.
Solution: Partition Incremental Discretization (PiD). Which give an approximate histogram.

== Hadoop Best Practices in the Cloud

The presenter was previously at Amazon so he presents AWS.

pic 1

Elastic MapReduce jobs run on a cluster or clusters.

pic 2

It can be provisioned to 1000 MapReduce instances in 5 minutes.
You can have lots of clusters.
Instead HDFS the data is read from S3. S3 is more durable - 11 9's of durability and 99.9% availability.
Why HDFS is faster for scanning, so it's better to use hdfs like a cache for temporary data while keeping permanent data in S3.

pic 3

Also S3 can be used for migrating HDFS clusters from older version to new version

pic 4

Steps and Bootstrap actions

Steps are MapReduce tasks or can be Hive or Pig scripts. They are stored in S3

pic 5

BootStrap actions are running in the beginning of each workflow. The are also stored in S3

pic 6

Complex Workflows can be written in scripting languages like python. Alternatively AWS Flow Framework can be used (it seems this is something like cascading)

There are development, testing and production stacks. You can develop your hadoop programs in isolation and point to different S3 storages.

pic 7

Validating Computations technique. Do computations twice using different logic to test correctness of the main logic.

pic 8

Performance tuning is achieved surprisingly by switching from one instance type to another with different memory and disk capacity:

pic 9

There are different models of payment for computing power:

  • on demand
  • spot marked
  • reserved instances

pic 10


pic 11

== Hadoop in Education

MOOCs (massive open source courses)

In Hadoop we can store:

  • Student logs
  • Student Assessments
  • Faculty interaction logs
  • Student/Faculty interaction logs
  • Processed Content Usage

  • Client-generated logs (i.e. logs from GWT/javaSCript)
  • Those logs must be provided w/o much of developers effort
  • How to feed this data to Hive
  • How to sessionize this data
Technical solution:

Technical challenges:
  • Joins of big data