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'use strict';

/**
 * export_dx_users.js
 *
 * Streams a DX Postgres query to a CSV file.
 * - Uses DATABASE_URL from environment (preferred)
 * - Falls back to HARDCODED_DB_URL if set
 * - Normalizes postgres:// → postgresql://
 * - Streams results to avoid memory issues

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// Jira Worklogs + Summary with Original Estimate
//
/*
Jira Worklogs + Estimates Sync

Purpose:
- Sync Jira worklogs into Postgres and build a per issue time summary with Original Estimate.

Selection:
- Processes only completed issues

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/**
 * fetch_work_item_parents.incremental.js
 *
 * Purpose: Incremental-only importer of ADO parent-child links into DX.
 *          Uses a per-project watermark based on ADO System.ChangedDate to avoid gaps and duplicates.
 *
 * Writes: batched multi-row upserts into custom.ado_work_item_links
 * Watermarks: custom.ado_wi_links_watermarks (organization_name, project_name, last_changed_at)
 *
 * Schema columns used in links table:

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/**
 * fetch_work_item_parents.js
 *
 * Backfill: WorkItemLinks WIQL, partitioned by Source.ChangedDate windows using DATE precision (YYYY-MM-DD)
 * Incremental: WorkItems WIQL by ChangedDate, then workitemsbatch expand=Relations
 * Writes: batched multi row upserts into custom.ado_work_item_links
 * Schema columns used: child_work_item_source_id, parent_work_item_source_id, relation_url
 * MIN_ID applied to both parent and child
 * ADO HTTPS via http://DX_PROXY_USER:[email protected]:80
 * Dry run writes a SQL file

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# frozen_string_literal: true

module CustomerScripts
  class accountIncidentImport
    extend Callable

    def call
      results = unprocessed_issues

      if results.empty?

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// rollup-duo-usage-autodetect.js
//
// Purpose
// -------
// Pull per-user, per-day GitLab Duo usage **without ClickHouse**.
// 1) Probe GraphQL for `group.aiUsageData.all`; if present, use it.
// 2) Else fall back to `group.aiUsageData.codeSuggestionEvents`.
// Counts are **always** computed; sizes are included when the schema exposes `suggestionSize`.
// Any non–Code-Suggestions event types from `all` are summarized into `extras` with:
//   - count (per user/day)

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"""
jira_extractor_proxy.py

DX Proxy to filter Jira Issue Types and optionally redact data before sending to the DX Datacloud API.
This Flask-based proxy acts as a middle layer between the DX Extractor and Datacloud,
giving teams control over the data they forward for ingestion.

Use Cases:
- Filter out specific Jira Issue Types (e.g., "Access Request", "Service Request")
- Redact or replace sensitive fields (e.g., issue summaries containing PII)

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// This query retrieves all merge_commit_shas from failed deployments 
// for a given repo and service that occurred *after* the last successful deployment. 
// It ensures unshipped PRs from failed attempts are rolled forward into the next successful deployment.


#!/usr/bin/env node
// dx_deployments_with_failed_sha_rollup_v4.js

import dotenv from 'dotenv';
import { Client } from 'pg';

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pipeline {
  agent any

  environment {
    DX_API_URL = 'https://yourinstance.getdx.net/api/pipelineRuns.sync'
    DX_API_TOKEN = credentials('dx-api-token') // Add this as a secret text in Jenkins credentials
    PIPELINE_SOURCE = 'Jenkins'
  }

  stages {

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apiVersion: v1
kind: ConfigMap
metadata:
  name: argocd-notifications-cm
  namespace: argocd
data:
  config.yaml: |
    templates:

      dx-cloud-pipelines: