SamPenrose

{"nbformat_minor": 0, "cells": [{"execution_count": 1, "cell_type": "code", "source": "import ujson as json\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport plotly.plotly as py\nimport networkx as nx\nimport collections\n\n\nfrom moztelemetry import get_pings, get_pings_properties, get_one_ping_per_client\n\n%pylab inline", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Populating the interactive namespace from numpy and matplotlib\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 3, "cell_type": "code", "source": "pings = get_pings(sc, app=\"Firefox\",\n                  channel=\"nightly\",\n                  submission_date=(\"20150507\",\"20150514\"),\n                  fraction=1,\n                  schema=\"v4\")", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 38, "cell_type": "code", "source": "def extract_sub(p):\n    return p.get('payload', {}).get('info', {}).get('subsessionId', 'NO

SamPenrose

{"nbformat_minor": 0, "cells": [{"source": "# Session Signature matching", "cell_type": "markdown", "metadata": {}}, {"execution_count": 81, "cell_type": "code", "source": "import ujson as json\nfrom operator import add\n# %pylab inline", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 82, "cell_type": "code", "source": "outBucketName = \"net-mozaws-prod-us-west-2-pipeline-analysis\"\npathToOutput = \"/bcolloran/mergedDataPerClient/nightly/2015-06-15/10009clients/\"", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 83, "cell_type": "code", "source": "# for a tiny sample, you can load one part: \"part-00000\"\n# or you can do more--\n# ten parts: part-0000*\n# or 10% of  parts: part-*0\n# or all parts: part-*\npath_to_all = \"s3n://\"+outBucketName+pathToOutput+\"part-*\"\nf = sc.sequenceFile(path_to_all)\nload_all = f.mapValues(json.loads)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 84, "ce

SamPenrose

SamPenrose

from collections import defaultdict
def get_overlap(pair, v2_extractor=None, v4_extractor=None):
    v2_blobs = pair['v2'].get('data', {}).get('days', {}) # {'YYYY-MM-DD': dict}
    v4_blobs = pair['v4'] # [{'creationDate': 'YYYY-MM-DD:...', 'k': val, ...}, ...]
    # One blob per date in v2, multiple per date in v4
    results = {'v2': {}, 'v4': defaultdict(list)}
    if not (v2_blobs and v4_blobs):
        return results

    v2_dates = v2_blobs.keys()

SamPenrose

SamPenrose

SamPenrose

SamPenrose

SamPenrose

SamPenrose