Shantanu's Blog

Database Consultant

March 26, 2016

 

Write your own slack command

You can write lambda funciton that will handle a Slack slash command and echoes the details back to the user.

Follow these steps to configure the slash command in Slack:

1. Navigate to https://.slack.com/services/new
2. Search for and select "Slash Commands".
3. Enter a name for your command and click "Add Slash Command Integration".
4. Copy the token string from the integration settings and use it in the next section.
5. After you complete this blueprint, enter the provided API endpoint URL in the URL field.
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Lambda Function

Create a new function using a blueprint called "slack-echo-command-python". The only change is to comment the encryption line and declare the variable...

#kms = boto3.client('kms')
#expected_token = kms.decrypt(CiphertextBlob = b64decode(ENCRYPTED_EXPECTED_TOKEN))['Plaintext']
expected_token = 'A9sU70Lz4isPdTet5tvGD0PB'

You will get this token when you registered a new keyword at slack.
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API Gateway

Create an AIP - LambdaMicroservice

Actions - Create Resource - getme
Actions - Create Method - post

Integration type - Lambda Function
Lambda Function: Select function name getme

Integration Request - Body Mapping Templates - Add mapping template - application/x-www-form-urlencoded

Mapping template - {"body":$input.json("$")}

Deploy API - Stage name - prod
_____

Connect API to Lambda Function and slack slash command:

Add the method name to invoke url. If the invoke URL looks like this...

https://sevbnlvu69.execute-api.us-east-1.amazonaws.com/prod

Then the actual URL to be added in "API endpoints" tab of function - getme will be:

https://sevbnlvu69.execute-api.us-east-1.amazonaws.com/prod/getme

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March 25, 2016

 

Machine Learning

In order to let the machine do predictions for us, we need to first train the computers with some data. We will download the test data from here...

https://s3.amazonaws.com/aml-sample-data/banking.csv

This data looks something like this once opened in excel.

age job marital education default housing loan contact month day_of_week duration campaign pdays previous poutcome emp_var_rate cons_price_idx cons_conf_idx euribor3m nr_employed say_yes
44 blue-collar married basic.4y unknown yes no cellular aug thu 210 1 999 0 nonexistent 1.4 93.444 -36.1 4.963 5228.1 0
53 technician married unknown no no no cellular nov fri 138 1 999 0 nonexistent -0.1 93.2 -42 4.021 5195.8 0
28 management single university.degree no yes no cellular jun thu 339 3 6 2 success -1.7 94.055 -39.8 0.729 4991.6 1

The software will learn the pattern from the above data. It will read the data like this...
Someone who is 28 years old and from Management job is likely to say yes to a bank loan. The married older people may likely say "no" (say_yes: 0).

Now when a new customer walks in, we can ask the machine first if he is going to buy the banking product or not.

32,services,divorced,basic.9y,no,unknown,yes,cellular,dec,mon,110,1,11,0,nonexistent,-1.8,94.465,-36.1,0.883,5228.1

This customer is most likely going to say "No" (Predicted label: 0) as per what machine has learned from other customers so far.

http://docs.aws.amazon.com/machine-learning/latest/dg/step-5-use-the-ml-model-to-create-batch-predictions.html

Machine learning can solve very complex problems those are very difficult to answer using standard SQL queries.

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March 09, 2016

 

Log of redshift copy command

We can get the log of copy commands those were executed successfully (aborted=0) using the table STL_query Write a function to extract data from query text column and get the results of which table was populated with which data.


drop FUNCTION f_tname(mystr VARCHAR(65000));

CREATE FUNCTION f_tname(mystr VARCHAR(65000))
RETURNS varchar(65000)
IMMUTABLE AS $$

return mystr.split()[1]+","+mystr.split()[3].split('/')[-2:-1][0]

$$ LANGUAGE plpythonu;


create table importlog as
select querytxt, starttime, endtime from STL_query where database = 'vdb' and querytxt like 'copy%' and aborted = 0

select split_part(f_tname(querytxt), ',', 1), split_part(f_tname(querytxt), ',', 1)  from importlog
 

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March 03, 2016

 

Installing adminer and ipython on a new server

Here are 3 steps to start an EC2 instance based on Amazon Linux

import easyboto
dev=easyboto.connect('XXX', 'XXX')

dev.placement='us-east-1a'
dev.key='dec15a'

dev.startEc2('ami-da4d7cb0', 't2.medium')

This will return an IP address of the newly created server and you can connect using the standard ssh command that will look something like this...

ssh -i dec15a.pem ec2-user@52.20.10.47

You should have dec15a.pem or dec15a.ppk (for putty) file in order to connect.
_____

Once you are connected to newly created server, install docker...

## install and start docker deamon

yum install docker

etc/init.d/docker start
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## python with notebook can be installed using these 3 commands:

docker run -it -p 7778:7778 --hostname conda shantanuo/miniconda_ipython /bin/bash

cd /home/
ipython notebook --ip=* --port=7778


## adminer can be installed using these 2 commands:

docker run -it -p 80:80 --hostname adminer shantanuo/adminer

sudo service apache2 start

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