mazurov/hut_performance.ipynb

## hut_performance.ipynb
{
  "cells" : [
    {
      "metadata" : {
        "trusted" : true
      },
      "cell_type" : "code",
      "source" : "from pyhiverepl import hive\nimport time\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom collections import defaultdict",
      "execution_count" : 17,
      "outputs" : [
      ]
    },
    {
      "metadata" : {
        "trusted" : true
      },
      "cell_type" : "code",
      "source" : "def mean(lst):\n    return sum(lst) / float(len(lst))",
      "execution_count" : 18,
      "outputs" : [
      ]
    },
    {
      "metadata" : {
        "trusted" : true
      },
      "cell_type" : "code",
      "source" : "cursor=hive.connect(dc='pa4').cursor()\nstart = time.time()\ncursor.execute(\"\"\"SELECT\nm.day,\nm.query_id,\nregexp_extract(m.query_name,'hut::select::hive-hiveserver2(.*?)::(.+)',2) as query_id_orig,\nregexp_extract(m.query_name,'hut::select::(hive-hiveserver2.*?)::(.+)',1) as service,\nm.current_db,\nm.query_name,\nm.username,\nm.queue,\nm.environment,\nm.datacenter,\nm.query_submit_time,\nm.query_finish_time,\nm.query_scheduling_time,\nm.query_status,\nm.num_jobs,\nm.num_maps,\nm.num_failed_maps,\nm.num_reduces,\nm.num_failed_reduces,\nm.cpu_ms,\nm.mapper_vcore_ms,\nm.reducer_vcore_ms,\nm.mapper_ms,\nm.reducer_ms,\nm.mb_ms,\nm.mapper_mb_ms,\nm.reducer_mb_ms,\nm.day_part\n FROM metrology.hive_query as m  WHERE m.query_name LIKE 'hut::select::%' AND m.query_string NOT LIKE 'SELECT_COUNT%' AND m.day_part > '2018-06-02'\nAND regexp_extract(m.query_name,'hut::select::hive-hiveserver2(.*?)::(.+)',2) NOT IN\n(-- Queries with the different number of returned rows in hiveserver2 and hiveserver2-exp\nSELECT\nh1.query_id\nFROM hut.query_result as h1, hut.query_result as h2\nWHERE\nh1.query_id=h2.query_id\nAND h2.service = 'hive-hiveserver2-exp'\nAND h1.service='hive-hiveserver2'\nAND h1.status=0\nAND h2.status=0\nAND h1.result <> h2.result)\nORDER BY query_id_orig,service\"\"\")\n# cursor.execute(\"\"\"SELECT\n# m.day,\n# m.query_id,\n# regexp_extract(m.query_name,'hut::select::hive-hiveserver2(.*?)::(.+)',2) as query_id_orig,\n# regexp_extract(m.query_name,'hut::select::(hive-hiveserver2.*?)::(.+)',1) as service,\n# m.current_db,\n# m.query_name,\n# m.username,\n# m.queue,\n# m.environment,\n# m.datacenter,\n# m.query_submit_time,\n# m.query_finish_time,\n# m.query_scheduling_time,\n# m.query_status,\n# m.num_jobs,\n# m.num_maps,\n# m.num_failed_maps,\n# m.num_reduces,\n# m.num_failed_reduces,\n# m.cpu_ms,\n# m.mapper_vcore_ms,\n# m.reducer_vcore_ms,\n# m.mapper_ms,\n# m.reducer_ms,\n# m.mb_ms,\n# m.mapper_mb_ms,\n# m.reducer_mb_ms,\n# m.day_part\n#  FROM metrology.hive_query as m  WHERE m.query_name LIKE 'hut::select::%' AND m.query_string NOT LIKE 'SELECT_COUNT%'\n# AND m.day_part='2018-06-19'\"\"\")\n\nmetrology_rows = cursor.fetchall()\nprint(\"Execution time %d (sec) \" % (time.time() - start))",
      "execution_count" : 19,
      "outputs" : [
        {
          "output_type" : "stream",
          "text" : "Execution time 804 (sec) \n",
          "name" : "stdout"
        }
      ]
    },
    {
      "metadata" : {
        "trusted" : true
      },
      "cell_type" : "code",
      "source" : "df = pandas.DataFrame(metrology_rows, columns=[\"day\", \"query_id\", \"query_id_orig\", \"service\", \"current_db\",\n                                              \"query_name\", \"username\", \"queue\", \"environment\", \"datacenter\",\n                                              \"query_submit_time\", \"query_finish_time\", \"query_scheduling_time\",\n                                              \"query_status\", \"num_jobs\", \"num_maps\", \"num_failed_maps\", \"num_reduces\",\n                                              \"num_failed_reduces\", \"cpu_ms\", \"mapper_vcore_ms\", \"reducer_vcore_ms\",\n                                              \"mapper_ms\", \"reducer_ms\", \"mb_ms\", \"mapper_mb_ms\", \"reducer_mb_ms\",\n                                              \"day_part\"])",
      "execution_count" : 20,
      "outputs" : [
      ]
    },
    {
      "metadata" : {
        "trusted" : true
      },
      "cell_type" : "code",
      "source" : "services = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))\n\nfor index, row in df.iterrows():\n    _, _, service, query_id = row[\"query_name\"].split(\"::\")\n    for key in [\"num_maps\", \"num_failed_maps\", \"num_reduces\", \"num_failed_reduces\", \"cpu_ms\", \"mapper_vcore_ms\",\n        \"reducer_vcore_ms\", \"mapper_ms\", \"reducer_ms\", \"mb_ms\", \"mapper_mb_ms\", \"reducer_mb_ms\"]:\n        if row[\"query_status\"] == 'SUCCESS' and row['num_failed_maps'] == 0 and row['num_failed_reduces'] == 0:\n            services[service][query_id][key].append(float(row[key]))\n\n",
      "execution_count" : 21,
      "outputs" : [
      ]
    },
    {
      "metadata" : {
        "trusted" : true
      },
      "cell_type" : "code",
      "source" : "hive1 = 'hive-hiveserver2'\nhive2 = 'hive-hiveserver2-exp'\n\nvalues = defaultdict(list)\nplot_keys = [\"cpu_ms\", \"mapper_vcore_ms\", \"reducer_vcore_ms\", \"mb_ms\", \"mapper_ms\",\n            \"reducer_ms\", \"mapper_mb_ms\", \"reducer_mb_ms\"]\nfor query in services[hive2]:\n    if query in services[hive1]:\n        for key in plot_keys:\n            h1 = services[hive1][query][key]\n            h2 = services[hive2][query][key]\n            m2, m1 = mean(h2), mean(h1)\n            if m1 != 0: # TODO(a.mazurov): handle this case\n                values[key].append(m2 / m1) \n\n\n\n\n",
      "execution_count" : 22,
      "outputs" : [
      ]
    },
    {
      "metadata" : {
        "trusted" : true
      },
      "cell_type" : "code",
      "source" : "plt.rcParams['figure.figsize'] = (20,50)\nfor ih, key in enumerate(values, start=1):\n    narr = np.asarray(values[key])\n    plt.subplot(len(values), 3, ih)\n    plt.hist(narr, color='c', bins=50,edgecolor='k', alpha=0.65, label=key)\n    plt.title(key)\n    plt.xlabel(\"Rate hive2/hive1\")\n    plt.ylabel(\"Entries\")\n    plt.axvline(1, color='r', linestyle='solid', linewidth=2, label=\"No difference\")\n    plt.axvline(narr.mean(), color='k', linestyle='dashed', linewidth=2, label=\"Mean\")\n    plt.legend()\n    ",
      "execution_count" : 23,
      "outputs" : [
        {
          "output_type" : "display_data",
          "data" : {
            "text/plain" : "<matplotlib.figure.Figure at 0x7fef482ddac8>",
            "image/png" : 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\n"
          },
          "metadata" : {

          }
        }
      ]
    }
  ],
  "metadata" : {
    "kernelspec" : {
      "name" : "python",
      "display_name" : "Python",
      "language" : "python"
    },
    "language_info" : {
      "nbconvert_exporter" : "python",
      "codemirror_mode" : {
        "name" : "ipython",
        "version" : 3
      },
      "pygments_lexer" : "ipython3",
      "file_extension" : ".py",
      "mimetype" : "text/x-python",
      "name" : "python",
      "version" : "3.4.5"
    }
  },
  "nbformat" : 4,
  "nbformat_minor" : 2
}