ebongzzang

#!/usr/bin/env bash
set -e

SOURCE_KV=$1
TARGET_KV=$2
results=()
list_secrets() {
    local path=$1
    # List secrets at the current path
    secrets=$(vault kv list -format=json "${SOURCE_KV}/${path}" 2>/dev/null)

ebongzzang

    private val tableEnv: StreamTableEnvironment by lazy {
        val tableEnvConfigMap = mapOf(
            "lookup.cache" to "PARTIAL",
            "lookup.partial-cache.max-rows" to "50000",
            "lokup.partial-cache.expire-after-access" to "60min"
        )
        val tableEnvSetting = EnvironmentSettings.Builder()
            .withConfiguration(Configuration.fromMap(tableEnvConfigMap))
            .build()

ebongzzang

#!/bin/bash

startdate=$1
enddate=$2

function print_formatted() {
    res=`date -j -f %Y-%m-%d $1 +%Y-%m-%d`
    echo $res

}

ebongzzang

local csv = require("csv")
local f = csv.open("device-table2.csv", {['header'] = true})
local columns = {"id", "free_storage_in_byte", "used_storage_in_byte"}
local parameters = {}
local counter = 0

for fields in f:lines() do
    perRowCondition = {}    
    for index, value in pairs(fields) do
        for _,v in pairs(columns) do

ebongzzang

import json
import re
from io import BytesIO, StringIO
from typing import List

import requests
import bs4
import boto3

s3 = boto3.client('s3')

ebongzzang

# Register your models here.
import inspect
import sys

from cytoolz import *
from django.contrib import admin
from guardian.admin import GuardedModelAdmin

from .models import *

ebongzzang

from itertools import chain

import numpy as np
from collections import Counter
from multiprocessing import cpu_count, Pool


def a(weights):
    w_sum = np.sum(weights)
    value = np.random.randint(w_sum, size=1)[0]

ebongzzang

@ContextConfiguration
@DataJpaTest
class DbunitWithGenesisTest extends Specification {

    private static Logger log = LoggerFactory.getLogger(DbunitWithGenesisTest.class)

    def hi = 1L..10L
    // testEntityManager는 persistence context 테스트 목적으로 사용된다.
    // 즉, 실제 데이터베이스에 적용할수는 없다
    @Autowired

ebongzzang

import numpy as np
import pandas as pd
import pyarrow as pa
from pyarrow import plasma


def insert_df_to_plasma(client, df: pd.DataFrame, key=plasma.ObjectID(np.random.bytes(20))) -> plasma.ObjectID:
    record_batch = pa.RecordBatch.from_pandas(df)
    object_id = key

ebongzzang

# before apply below script, run command
# kubectl create clusterrolebinding ecr-cluster-rule --clusterrole=cluster-admin --serviceaccount=default:default

apiVersion: batch/v1beta1
kind: CronJob  # Tell kuber' that this is a cronjob
metadata:
  name: ecr-puller  # Name of the job ,can be anything
  namespace: default
spec:
  concurrencyPolicy: Allow