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{% for k, v in params.lifecycle_days.items() %}
    alter table {{ k }} drop if exists partition (ds<'{{ macros.ds_format(macros.ds_add(ds, v*-1), "%Y-%m-%d", "%Y%m%d") }}');
{% endfor %}

{% for k, v in params.lifecycle_days.items() %}
    analyze table {{ k }} partition (ds='{{ ds_nodash }}') compute statistics;
{% endfor %}

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function retry(isDone, next) {
  var current_trial = 0, max_retry = 50, interval = 10, is_timeout = false;
  var id = window.setInterval(
    function() {
      if (isDone()) {
        window.clearInterval(id);
        next(is_timeout);
      }
      if (current_trial++ > max_retry) {
        window.clearInterval(id);

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language: scala

jdk: oraclejdk8

scala:
  - "2.11.12"

# Use container-based infrastructure
sudo: false

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SELECT 
    pg_terminate_backend(pid) 
FROM 
    pg_stat_activity 
WHERE 
    -- don't kill my own connection!
    pid <> pg_backend_pid()
    -- don't kill the connections to other databases
    AND datname = 'analytics'
    ;

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-- pg equivalent of DESCRIBE TABLE

select column_name, data_type, character_maximum_length
from INFORMATION_SCHEMA.COLUMNS where table_name = 'my_table_name';

-- General Table Size Information, Performance Snippets
-- Disk usage, Works with PostgreSQL>=9.2
-- This will report size information for all tables, in both raw bytes and "pretty" form.

SELECT *, pg_size_pretty(total_bytes) AS total

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#!/usr/bin/env bash

# Path to the project directory (that should include requirements.txt),
# Files and directories within that need to be deployed.
project=../backend
contents=(module lamdba_handler.py)

# Unnecessary parts. Note that there are some inter-dependencies in SciPy,
# for example to use scipy.stats you also need scipy.linalg, scipy.integrate,
# scipy.misc, scipy.sparse, and scipy.special.

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import numpy as np
import os
import tensorflow as tf
import vgg
import vgg_preprocessing
from pycocotools.coco import COCO

slim = tf.contrib.slim

LOG_DIR = './log/fe'

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val artifactoryUrl = "http://host:port/artifactory/sbt-dev"

resolvers += "Artifactory" at artifactoryUrl

publishTo := Some("Artifactory Realm" at s"$artifactoryUrl;build.timestamp="
  + new java.util.Date().getTime)

credentials += Credentials(new File("credentials.properties"))

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# Get model size after L1 regularization
# see: https://colab.research.google.com/notebooks/mlcc/sparsity_and_l1_regularization.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=l1regularization-colab&hl=en#scrollTo=e6GfTI0CFhB8

def model_size(estimator):
  variables = estimator.get_variable_names()
  size = 0
  for variable in variables:
    if not any(x in variable 
               for x in ['global_step',
                         'centered_bias_weight',

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# Shuffle dataframe

cities.reindex(np.random.permutation(cities.index))

# Read data from Google Cloud Storage

california_housing_dataframe = pd.read_csv("https://storage.googleapis.com/mledu-datasets/california_housing_train.csv", sep=",")

# Convert pandas data into a dict of np arrays
# where `key` is column name.