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Showing posts from 2017

ES Index - S3 Snapshot & Restoration:

The question is.. What brings you here? Fed up with all the searches on how to back-up and restore specific indices? 

Fear not, for your search quest ends here.!

After going through a dozens of tiny gists and manual pages, here it is.. We've done all the heavy-lifting for you.

The following tutorial was tested on elasticsearch V5.4.0

And before we proceed, remember:


Make sure that the elasticsearch version of the backed-up cluster/node <= Restoring Cluster's version.


Unless it's highly necessary;

curl -XDELETE 'http://localhost:9200/nameOfTheIndex

      - deletes a specific index

Especially not, when you are drunk!:

curl -XDELETE 'http://localhost:9200/_all

      - deletes all indexes (This is where the drunk part comes in..!!)

Step1:Install S3 plugin Support:        sudo bin/elasticsearch-plugin install repository-s3
sudo /usr/share/elasticsearch/bin/elasticsearch-plugin install repository-s3

Depends on w…

Postgres to Mongo Migrator - Batteries Included!!!

DATABASE MIGRATION ACROSS PLATFORMS - Got your goosebumps yet?      Well, long story short; cross-platform database migrations equals sleep talking, distress and long day works with coffee; and what good does it do? We will just end up writing hours and hours of scripts to conquer the end-result. However, It is of one-time-use-only, which lets you think to yourself; "All this horsepower and no room to gallop?".

Postgres to MongoDB:     Be it a platform change, or maybe, it's due to the organizational growth or perhaps bad coding, or perhaps you have got your own microservices all set in, dwelling on the JSON objects; you might have had to switch from relational to noSQL databases. Switching can be tedious, I hear you and here lies the solution to all your worries.
Behold! Enter the Pg2Mongo:
Pg2Mongo is an open source migration tool, written on pythonV3 which gives you an exclusive control over the migrations. First Steps: The initial step is to make sure you have acces…

Model Productionisation - MNIST Handwritten Digits Prediction

Yet another post about MNIST Handwritten Digits Prediction?

Nope. Not this time!!

There are about a hundred of tutorials available on-line for this cause.
Here's a quickie, to understand all the mechanics of the prediction process in tensorflow for the MNIST datasets which should get you, up and running.

Done with the basics? Head over to

and clone the project.

We are about to deploy an image prediction RESTful API, powered by Flask microframework.

The code in the repo is written in pythonV2.7, you may also use python3; should be a breeze.

Step 2, mentioned above powers up the API, serving the end-point through the port 5000. Time to test-query our API.

The project directory contains the numerals_sample, from which one may crop the required digits out. As for this demo, we shall look at numba3.jpg, numba5.jpg, numba6.jpg, numba7.jpg and numba9.jpg present in the same directory, as that of the project.

Fire up the browser, and hit the …

ELK Stack... Not!!! FEK, it is.!!! Fluentd, Elasticsearch & Kibana

If you are here, you probably know what elasticsearch is and at some point, trying to get into the mix. You were searching for the keywords "logging and elasticsearch" or perhaps, "ELK"; and probably ended up here. Well, you might have to take the following section with a pinch of salt, especially the "ELK Stack"  fam.
At least from my experience, working for start-ups teaches oneself, a lot of lessons and one of the vast challenges include minimizing the resource utilization bottlenecks. On one hand, the logging and real-time application tracking is mandatory; while on the the other hand, there's a bottle neck in the allocated system resource, which is probably an amazon EC2 instance with 4Gigs of RAM.
ELK Stack 101: Diving in, ELK => Elasticsearch, Logstash and Kibana. Hmm, That doesn't add up; don't you think? Elasticsearch stores the reformed log inputs, Logstash chops up the textual logs and transforms them to facilitate query, deriva…