locactus

Motivation

Integrate JMH (Java Microbenchmarking Harness) with Spring (Boot) and make developing and running benchmarks as easy and convinent as writing tests.

Idea

Wrap the necessary JMH boilerplate code within JUnit to benefit from all the existing test infrastructure Spring (Boot) provides. It should be as easy and convinent to write benchmarks as it is to write tests.

TL;DR;

locactus

package com.blazemeter;

import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
import io.kubernetes.client.ApiClient;
import io.kubernetes.client.ApiException;
import io.kubernetes.client.Configuration;
import io.kubernetes.client.apis.AppsV1beta1Api;
import io.kubernetes.client.models.AppsV1beta1Deployment;

locactus

public class PasswordValidator {
    public static boolean validate(String password) {
        if (password.matches("((?=.*\\d)(?=.*[a-z])(?=.*[A-Z])(?=.*[@#$%]).{8,20})"))
            return true;

        return false;
    }

    public static boolean symbol(String password) {
        if (password.matches(".*[@#$%].*"))

locactus

- General
  [ ] The code works
  [ ] The code is easy to understand
  [ ] Follows coding conventions
  [ ] Names are simple and if possible short
  [ ] Names are spelt correctly
  [ ] Names contain units where applicable
  [ ] Enums are used instead of int constants where applicable
  [ ] There are no usages of 'magic numbers'
  [ ] All variables are in the smallest scope possible

locactus

#!/bin/bash
 
MONGO_DATABASE="your_db_name"
APP_NAME="your_app_name"

MONGO_HOST="127.0.0.1"
MONGO_PORT="27017"
TIMESTAMP=`date +%F-%H%M`
MONGODUMP_PATH="/usr/bin/mongodump"
BACKUPS_DIR="/home/username/backups/$APP_NAME"

locactus

Currently, there is an explosion of tools that aim to manage secrets for automated, cloud native infrastructure management. Daniel Somerfield did some work classifying the various approaches, but (as far as I know) no one has made a recent effort to summarize the various tools.

This is an attempt to give a quick overview of what can be found out there. The list is alphabetical. There will be tools that are missing, and some of the facts might be wrong--I welcome your corrections. For the purpose, I can be reached via @maxvt on Twitter, or just leave me a comment here.

There is a companion feature matrix of various tools. Comments are welcome in the same manner.

locactus

/*
Problem Description:
  Time series forecast for a single securities minor reversal points.  
  A "minor reversal point" is defined as either a period (day) with a high price greater than both the previous and next high prices, or a period with a low value lower than both the previous and next low prices.  
  This is a time series regression problem with 8 input features and 2 regression output dimensions

Input Features: 
 - period return r = (p2 - p1) / p1, descr: a linear return value, typically [-0.05,0.05]
 - period volume, descr: standardized, z = (x - Mean) / SD
 - High-Low spread, descr: standardized, z = (x - Mean) / SD

locactus

/*
 * See LICENSE for licensing and NOTICE for copyright.
 */
package edu.vt.middleware.app;

import java.io.File;
import java.security.*;
import java.util.ArrayList;
import java.util.List;
import java.util.function.Predicate;