ebuildy

import { shallow, mount, configure, render } from 'enzyme';
import Adapter from 'enzyme-adapter-react-16';
import waitUntil from 'async-wait-until';

configure({ adapter: new Adapter() });

global.exceptRenderFine = async (component) => {
    // full mount the component (shallow can work and will be faster)
    const root = mount(component)

ebuildy

import org.apache.spark.sql.functions.udf
import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
import org.apache.spark.sql.Row

spark.udf.register("struct_def", (root:GenericRowWithSchema, path: String, defaultValue: String) => {
    
    var fields = path.split("\\.")
    var buffer:Row = root
    val lastItem = fields.last
    

ebuildy

private static String snakeCaseFormat(String name) {
        final StringBuilder result = new StringBuilder();

        boolean lastUppercase = false;

        for (int i = 0; i < name.length(); i++) {
            char ch = name.charAt(i);
            char lastEntry = i == 0 ? 'X' : result.charAt(result.length() - 1);
            if (ch == ' ' || ch == '_' || ch == '-' || ch == '.') {
                lastUppercase = false;

ebuildy

setup:
  # elasticsearch index template
  template:
    enabled: true
    overwrite: true
  # kibana
  dashboards.enabled: true
  kibana.host: '${KIBANA_HOST}'

http:

ebuildy

apiVersion: batch/v1
kind: Job
metadata:
  name: {{ stack_name }}-security-setup-users
spec:
  template:
    spec:
      containers:
      - name: update-credentials
        image: radial/busyboxplus

ebuildy

import org.apache.spark.sql.Column
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.functions.col

def flattenSchema(schema: StructType, prefix: String = null) : Array[Column] = {
  schema.fields.flatMap(f => {
    val colName = if (prefix == null) f.name else (prefix + "." + f.name)

    f.dataType match {
      case st: StructType => flattenSchema(st, colName)

ebuildy

val df = sqlContext.read.format("es")
   .option("es.nodes.wan.only", "true")
   .option("es.mapping.date.rich", "false")
   .option("es.read.field.exclude", "dimensions.geo_point")
   .option("es.nodes", "conso-es:9200")
  .load("clicks")

ebuildy

//create properties object
val prop = new java.util.Properties
prop.setProperty("driver", "com.mysql.jdbc.Driver")
prop.setProperty("user", "XXX")
prop.setProperty("password", "XXXX") 
 
//jdbc mysql url - destination database is named "data"
val url = "jdbc:mysql://XXXXX:3306/datahub"
  
//write data from spark dataframe to database

ebuildy

#!/usr/bin/python3

import argparse, json, requests

parser = argparse.ArgumentParser(description='Create automatically index patterns, from elasticsearch indices.')
parser.add_argument('elasticsearch_url',type=str, help='elasticsearch full URL')
parser.add_argument('kibana_url', type=str, help='kibana full URL')

args = parser.parse_args()

ebuildy

---
- hosts: recette
  become: yes
  become_user: root
  tasks:
  - name: "Get Linux uname's"
    command: "uname -{{ item }}"
    register: unames
    with_items: ["s", "m"]
  - set_fact: