See it here.

This was built using reveal.js

This requires a pyenv at python 3.6.2. You need to install the requirements with:

pip install -r requirements.txt

You can use docker to run postgres and elastic search to allow easy testing.

docker run --rm --name pg -p 5432:5432 postgres

You can then connect to it from a locally installed postgres client:

psql -p 5432 -h localhost -U postgres postgres

This will allow you to easily run the sample SQL statements.

docker run --rm --name es -p 9200:9200 -e "http.host=0.0.0.0" -e "transport.host=127.0.0.1" -e "xpack.security.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:5.5.2

You can query elastic search using curl:

curl localhost:9200

The notebooks also cover connecting to elastic search using python.

An example of using this is available in the Using Postgres to Select Features.ipynb notebook. The presentation also features some sample SQL that you can run, reproduced below:

CREATE TABLE features (
    all_unique SERIAL,
    all_same INTEGER DEFAULT 1,
    five_values INTEGER,
    random_values INTEGER
);

INSERT INTO features
    (five_values, random_values)
    SELECT n, random() * 10000
        FROM generate_series(1, 5) n,
             generate_series(1, 200000);

ANALYZE features;

SELECT
    attname AS column,
    n_distinct,
    most_common_vals,
    most_common_freqs,
    histogram_bounds,
    correlation
FROM pg_stats
WHERE tablename = 'features';

You can load the pickled data using the Elastic Search Population.ipynb notebook. An example of term frequency extraction is available in Elastic Search Term Extraction.ipynb.