glom.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics_file = \"metrics/app-20201104211738-0276\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glom import glom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from collections import defaultdict\n",
    "\n",
    "evset = defaultdict(lambda: list())\n",
    "\n",
    "def dequalify_event(ev):\n",
    "    if \".\" in ev:\n",
    "        idx = ev.rfind(\".\")\n",
    "        return ev[idx+1:]\n",
    "    return ev\n",
    "\n",
    "with open(metrics_file, \"r\") as mf:\n",
    "    for line in mf:\n",
    "        js = json.loads(line)\n",
    "        event = dequalify_event(glom(json.loads(line), \"Event\"))\n",
    "        evset[event].append(js)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(evset.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "job0 = evset['SparkListenerJobStart'][0]\n",
    "job0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jobs = evset[\"SparkListenerJobStart\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(jobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec = {\n",
    "    'job_ids': ['Job ID'],\n",
    "    'stage_ids': [\n",
    "        ('Stage Infos', \n",
    "         [\n",
    "             {\n",
    "                 'stage_id': 'Stage ID', \n",
    "                 'parents': 'Parent IDs'\n",
    "             }\n",
    "         ]\n",
    "        )\n",
    "    ]\n",
    "}\n",
    "\n",
    "glom(jobs, spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec = [\n",
    "    {\n",
    "        'job_id': 'Job ID',\n",
    "        'stages': ('Stage Infos', \n",
    "         [\n",
    "             {\n",
    "                 'stage_id': 'Stage ID', \n",
    "                 'parents': 'Parent IDs'\n",
    "             }\n",
    "         ]\n",
    "        )\n",
    "    }\n",
    "]\n",
    "\n",
    "glom(jobs, spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glom import S, T, glom, Assign, Spec\n",
    "\n",
    "spec = [(\n",
    "    S(job_id=T['Job ID']),\n",
    "    {\n",
    "        'stage': ('Stage Infos', \n",
    "         [\n",
    "             {\n",
    "                 'stage_id': 'Stage ID', \n",
    "                 'job_id': S['job_id'],\n",
    "                 'parents': 'Parent IDs'\n",
    "             }\n",
    "         ]\n",
    "        )\n",
    "    }\n",
    ")]\n",
    "\n",
    "glom(jobs, spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glom import flatten\n",
    "\n",
    "spec = [(\n",
    "    S(job_id=T['Job ID']),\n",
    "    ('Stage Infos', \n",
    "         [\n",
    "             {\n",
    "                 'stage_id': 'Stage ID', \n",
    "                 'attempt_id': 'Stage Attempt ID',\n",
    "                 'name': 'Stage Name',\n",
    "                 'details': 'Details',\n",
    "                 'accumulables': 'Accumulables',\n",
    "                 'job_id': S['job_id'],\n",
    "                 'parents': 'Parent IDs'\n",
    "             }\n",
    "         ]\n",
    "    )\n",
    ")]\n",
    "\n",
    "flatten(glom(jobs, spec))[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glom import flatten, merge\n",
    "\n",
    "spec = [\n",
    "    ('Stage Infos', \n",
    "     [\n",
    "         (S(stage_id=T['Stage ID'], rdd_info=T['RDD Info']),\n",
    "          merge([{'stage_id': S['stage_id']}, {'rdd_info': S['rdd_info']}])\n",
    "         )\n",
    "     ]\n",
    "    )\n",
    "]\n",
    "glom(jobs, spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glom import flatten, merge\n",
    "\n",
    "spec = [\n",
    "    ('Stage Infos', \n",
    "     [\n",
    "         (S(stage_id=T['Stage ID'], rdd_info=T['RDD Info']),\n",
    "          [merge([{'stage_id': S['stage_id']}, rddi]) for rddi in S['rdd_info']])\n",
    "     ]\n",
    "    )\n",
    "]\n",
    "glom(jobs, spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glom import flatten, merge\n",
    "\n",
    "spec = [\n",
    "    ('Stage Infos', \n",
    "     [\n",
    "         (S(stage_id=T['Stage ID']),\n",
    "          [rddi.update({'stage_id': S['stage_id']}) for rddi in T['RDD Info']]\n",
    "         )\n",
    "         \n",
    "     ]\n",
    "    )\n",
    "]\n",
    "glom(jobs, spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glom import flatten, Merge, Iter\n",
    "\n",
    "spec = [(\n",
    "    S(job_id=T['Job ID'], properties=T['Properties']),\n",
    "    ('Properties', \n",
    "             (\n",
    "                T.items(),\n",
    "                Iter().map({'job_id': S['job_id'], 'property': T[0], 'value': T[1]}).all()\n",
    "             )\n",
    "    )\n",
    ")]\n",
    "\n",
    "flatten(glom(jobs, spec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help({'a': 1, 'b': 2}.update)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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