#!/usr/bin/env python
# encoding: utf-8

"""
File: harvestResults_postscript.py
Author: <NAME>
Email: <EMAIL>
Github: https://github.com/iross
Description:
    Modifying the cron'ed harvestResults script to run as a postscript on the htcondor jobs
    TODO: changes needed:
        Don't need to loop over dirs
        Read pickle from output dir (it should have been copied over)
        Write RESULT with 0 or 1 based on PATTERN input
        Better handling of --update junk
        no args (or at least fewer)
        Overall cleanup
"""


import cPickle as pickle
import pymongo
import ConfigParser
import glob
import os,sys
import argparse
from datetime import datetime
import time
import pprint
import re
import pdb

DEBUG=False
VERBOSE=False

STRIP_MAP = { "ocr": "_out",
              "nlp": "_out_NLP",
              "cuneiform": "_out",
              "fonttype": "_out_FontType",
              }

PATTERN_MAP = {"ocr": "*.html",
                "nlp": "*.text",
                "cuneiform": "*.html",
                "fonttype": "*.text",
                }


BASE = "/home/iaross/DeepDive/deepdive/"

def patternMatch(pattern, dir='./'):
    """

    :pattern: A file pattern to match the desired output. Input to a glob, so use traditional unix wildcarding.
    :dir: The directory to search.
    :returns: list of matching files in the target directory

    """
    files = []
    files = glob.glob(dir+pattern)
    return files

def cleanPath(path):
    """
    Cleans up raw path of pdf to just the name of the PDF (no extension or directories)

    :path: path to pdf
    :returns: The PDF name with all directory/extensions stripped

    """
    filename = path.split("/")[-1]
    filename = filename.split(".")[0]
    return filename

def getMatch(jobid, collection, filepath_map):
    """
    Finds the document in the collection that matches the specified jobpath

    :jobid: ID of job output directory (i.e. job000001)
    :collection: MongoDB collection to look over
    :returns: dict of matching document

    """
    # Elsevier-specific stuff for matching
    pii = cleanPath(filepath_map[jobid])
    url = "http://api.elsevier.com/content/article/pii/" + pii
    match = collection.find_one({"URL": url})
    return match

def parseTime(line):
    """
    Parse the time of submission/termination/execution from a line
    Assumes: the log is from the current year

    :line: Line from the process.log file.
        expected input is of the type:
            "001 (132663.000.000) 12/29 15:16:50 Job executing on host: <172.16.31.10:35219>"
    :returns: Date object

    """
    now = datetime.now()
    line = line.split(" ")
    timeString = line[2] + " " + line[3]
    date_object = datetime.strptime(timeString, '%m/%d %H:%M:%S')
    date_object = date_object.replace(year=now.year) # ASSUMES the log is from the current year.
    return date_object

def parseResources(chunk):
    """
    Takes a chunk of log text that includes the "Partitionable Resources" table
    and returns a parsed dict of {"diskUsage": xxx, "memUsage": yyy }

    :chunk: The text block containing the Partitional Resources table from standard HTCondor output.
    :returns: Dictionary of the form usage = { memUsage: (memory usage in MB), diskUsage: (disk usage in KB) }

    """
    usage = {}
    # break the chunk up by \n
    chunk = chunk.split("\n")
    for line in chunk:
        if "Disk" in line:
            numbers = [int(s) for s in line.split() if s.isdigit()]
            usage["diskUsage"] = numbers[0]
        if "Memory" in line:
            numbers = [int(s) for s in line.split() if s.isdigit()]
            usage["memUsage"] = numbers[0]
    return usage

def readLog(jobpath):
    """
    Reads log to determine disk/mem usage, runtime
    For processing time, it will only grab the last execution/evict/terminated times.
    And runTime supercedes evictTime (eg. an exec->evict combination will not be written if
    a later exec-termination combination exists in the log)

    To be appended to processing database, so that structure is:
    ocr_processing["tag"]["jobs"] = [ {startTime: xxx, execTime: yyy, ... }, {reports from other jobs...} ]

    :jobid: id of the job within the submit/output directories
    :basedir: base directory for job output
    :returns: If successful, returns dict of the form
        jobReport = { subTime: (time of submission),
                        execTime: (start time of latest execution),
                        evictTime: (time of job eviction, if any),
                        termTime: (time of job termination, if any),
                        runTime: (time between execution start and termination/eviction time),
                        usage: { usage dictionary from above},
                    }

    """
    try:
        with open(jobpath + "/process.log") as file:
            chunk = ""
            subTime = None
            execTime = None
            evictTime = None
            termTime = None
            runTime = None
            jobReport = {}
            jobReport["path"] = jobpath
            for line in file:
                if line.startswith("..."):
                    if chunk.startswith("000"): # submitted
                        jobReport["subTime"] = parseTime(chunk.split('\n')[0])
                    elif chunk.startswith("001"): # executing
                        jobReport["execTime"] = parseTime(chunk.split('\n')[0])
                    elif chunk.startswith("004"): # evicted, has partitionable table
                        jobReport["evictTime"] = parseTime(chunk.split('\n')[0])
                        runTime = (jobReport["evictTime"] - jobReport["execTime"])
                        jobReport["runTime"] = runTime.days * 86400 + runTime.seconds
                        jobReport["usage"] = parseResources(chunk)
                    elif chunk.startswith("005"): # termination, has partitionable table
                        jobReport["termTime"] = parseTime(chunk.split('\n')[0])
                        runTime = (jobReport["termTime"] - jobReport["execTime"])
                        jobReport["runTime"] = runTime.days * 86400 + runTime.seconds
                        jobReport["usage"] = parseResources(chunk)
                    elif chunk.startswith("006"):
                        pass
                    elif chunk.startswith("009"):
                        pass
                    else:
                        if DEBUG:
                            print "UNKNOWN CODE"
                            print chunk
                    chunk=""
                else:
                    chunk+=line
        return jobReport
    except IOError:
        print "Couldn't find file at %s/process.log" % jobpath
        return None

def updateArticle(match, jobpath, tag, proctype, articlesColl, filepath_map, file_pattern, dryrun=False, update=False):
    """
    Take an article matched in the DB, its associated job path, and update the relevant article collections with files found + harvested
    """
    files = patternMatch(file_pattern, jobpath)
    if files == []: # no matching output. May be overall fail, may have just not run this proctype
        return 1,False

    try:  # if this article + tag have already been harvested, then skip
        if match["%s_processing" % proctype][tag]["harvested"]:
            if not update: # if UPDATE flag isn't used, move to the next job
                return 2,False
    except KeyError:
        pass

    # get all files associated with the job
    temp = {}
    temp["filename"] = files
    temp["contents"] = []

    temp["harvested"] = True #indicate that this article+tag combination has been handled
    temp["success"] = True # we found files, so it's a success!
    match["%s_processing" % proctype][tag] = temp

    if dryrun:
        print "This would be the new article document: "
        ppr.pprint(match)
    else:
        articlesColl.update( { "_id" : match["_id"] }, {"$set": match}, upsert = False )

    return 0, temp["success"]

def updateProcessing(collection, tag, pubname, report, success):
    """
    Update the process DB with this job's information. Add to this publication's success/failure tally, appending its runtime.
    NOTE: This will run for each attempt at running the condor job. So if a job fails 10 times, 10 entries will appear in the processing database.

    :collection: MongoDB collection to update
    :tag: Processing tag
    :pubname: Name of the publication to update
    :report: The processing report obtained from readLog function

    """
    if success:
        collection.update({ "tag": tag }, {'$inc': {"pub_totals.%s.success" % pubname : 1 } }, upsert=True )
        collection.update({ "tag": tag }, {'$inc': {"pub_totals.%s.cpusuccess" % pubname : report["runTime"]} }, upsert = True )
    else:
        collection.update({ "tag": tag },
                {'$inc': {"pub_totals.%s.failure" % pubname : 1 } }, upsert = True)
        collection.update({ "tag": tag },
                {'$inc': {"pub_totals.%s.cpufailure" % pubname : report["runTime"]} } , upsert = True)
    return 0

def exit(success):
    """
    Exit. If success, write 0 to RESULT and quite with sys.exist(0). Otherwise, write 1 and use non-zero exit.

    :success: TODO
    :returns: TODO

    """
    with open("RESULT","w") as fout:
        if success:
            fout.write("0\n")
            sys.exit(0)
        else:
            fout.write("1\n")
            sys.exit(1)

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Harvest some condor output')
    parser.add_argument('tag', type=str, default="", help="Tag of this processing batch")
    parser.add_argument('--basedir', type=str, default=os.getcwd(), help="The base directory")
    parser.add_argument('--dryrun', type=bool, required=False, default=False, help="Don't actually write to the databases--only show\
            what would have been added/updated.")
    parser.add_argument('--update', type=bool, required=False, default=True, help="Force update to the database.")
    args = parser.parse_args()

    config = ConfigParser.RawConfigParser()
    config.read(BASE+'db_conn.cfg')
    harvest_user = config.get('database','harvest_user')
    harvest_password = config.get('database','harvest_password')
    uri = "mongodb://%s:%s@<EMAIL>:27017/?authMechanism=MONGODB-CR" % (harvest_user, harvest_password)
    client = pymongo.MongoClient(uri)
    articlesdb = client.articles_harvesting_overhaul
    articles = articlesdb.articles

    procdb = client.processing_dev

    #technically, we can grab this from the path, based on how I'm structuring my job submission
    tag = args.tag

    processingsColl = procdb[tag]

    # Should be getting run in output/job0000001
    # get filepath_mapping.pickle from output
    try:
        filepath_map = pickle.load(open("../filepath_mapping.pickle"))
    except IOError:
        with open("post.log","w") as fout:
            fout.write("No filepath mapping found! Cannot determine what article this job belongs to! Exiting!")
        exit(False)
    jobpath = os.getcwd()

    # match against the articles collection in the DB
    if not jobpath.endswith("/"): #ensure we have a trailing /
        jobpath = jobpath + "/"
    jobid = jobpath.split("/")[-2]

    match = getMatch(jobid, articles, filepath_map)

    if match is None:
        with open("post.log","w") as fout:
            fout.write("No match for the article found! Job id: %s\nfilepath_map: %s"% \
                ( jobid, filepath_map[jobid] ) )
        exit(False)

    check = None
    jobSuccess = None
    job_config = ConfigParser.RawConfigParser()
    job_config.read(BASE+'jobs.cfg')
    overall_success = False
    with open("post.log","w") as fout:
        fout.write('TAG: %s\n' % tag)
        for proctype in job_config.sections():
            pattern = job_config.get(proctype, 'pattern')

            check,jobSuccess = updateArticle(match, jobpath, args.tag, proctype, articles, filepath_map, pattern, args.dryrun, args.update)
            fout.write(proctype+"\n")
            fout.write('\t%s -- %s\n' % (check, jobSuccess))
            if jobSuccess:
                overall_success = True

    # read log file
    tempReport = readLog(jobpath)
    if tempReport is None or "runTime" not in tempReport:
        exit(False) # couldn't read file log

    pubname = match["pubname"]

    updateProcessing(processingsColl, tag, pubname, tempReport, overall_success)
    if overall_success:
        exit(True)
    else:
        exit(False)

#!/usr/bin/env python
# encoding: utf-8

"""
File: harvestResults_postscript.py
Author: <NAME>
Email: <EMAIL>
Github: https://github.com/iross
Description:
    Modifying the cron'ed harvestResults script to run as a postscript on the htcondor jobs
    TODO: changes needed:
        Don't need to loop over dirs
        Read pickle from output dir (it should have been copied over)
        Write RESULT with 0 or 1 based on PATTERN input
        Better handling of --update junk
        no args (or at least fewer)
        Overall cleanup
"""


import cPickle as pickle
import pymongo
import ConfigParser
import glob
import os,sys
import argparse
from datetime import datetime
import time
import pprint
import re
import pdb

DEBUG=False
VERBOSE=False

STRIP_MAP = { "ocr": "_out",
              "nlp": "_out_NLP",
              "cuneiform": "_out",
              "fonttype": "_out_FontType",
              }

PATTERN_MAP = {"ocr": "*.html",
                "nlp": "*.text",
                "cuneiform": "*.html",
                "fonttype": "*.text",
                }


BASE = "/home/iaross/DeepDive/deepdive/"

def patternMatch(pattern, dir='./'):
    """

    :pattern: A file pattern to match the desired output. Input to a glob, so use traditional unix wildcarding.
    :dir: The directory to search.
    :returns: list of matching files in the target directory

    """
    files = []
    files = glob.glob(dir+pattern)
    return files

def cleanPath(path):
    """
    Cleans up raw path of pdf to just the name of the PDF (no extension or directories)

    :path: path to pdf
    :returns: The PDF name with all directory/extensions stripped

    """
    filename = path.split("/")[-1]
    filename = filename.split(".")[0]
    return filename

def getMatch(jobid, collection, filepath_map):
    """
    Finds the document in the collection that matches the specified jobpath

    :jobid: ID of job output directory (i.e. job000001)
    :collection: MongoDB collection to look over
    :returns: dict of matching document

    """
    # Elsevier-specific stuff for matching
    pii = cleanPath(filepath_map[jobid])
    url = "http://api.elsevier.com/content/article/pii/" + pii
    match = collection.find_one({"URL": url})
    return match

def parseTime(line):
    """
    Parse the time of submission/termination/execution from a line
    Assumes: the log is from the current year

    :line: Line from the process.log file.
        expected input is of the type:
            "001 (132663.000.000) 12/29 15:16:50 Job executing on host: <172.16.31.10:35219>"
    :returns: Date object

    """
    now = datetime.now()
    line = line.split(" ")
    timeString = line[2] + " " + line[3]
    date_object = datetime.strptime(timeString, '%m/%d %H:%M:%S')
    date_object = date_object.replace(year=now.year) # ASSUMES the log is from the current year.
    return date_object

def parseResources(chunk):
    """
    Takes a chunk of log text that includes the "Partitionable Resources" table
    and returns a parsed dict of {"diskUsage": xxx, "memUsage": yyy }

    :chunk: The text block containing the Partitional Resources table from standard HTCondor output.
    :returns: Dictionary of the form usage = { memUsage: (memory usage in MB), diskUsage: (disk usage in KB) }

    """
    usage = {}
    # break the chunk up by \n
    chunk = chunk.split("\n")
    for line in chunk:
        if "Disk" in line:
            numbers = [int(s) for s in line.split() if s.isdigit()]
            usage["diskUsage"] = numbers[0]
        if "Memory" in line:
            numbers = [int(s) for s in line.split() if s.isdigit()]
            usage["memUsage"] = numbers[0]
    return usage

def readLog(jobpath):
    """
    Reads log to determine disk/mem usage, runtime
    For processing time, it will only grab the last execution/evict/terminated times.
    And runTime supercedes evictTime (eg. an exec->evict combination will not be written if
    a later exec-termination combination exists in the log)

    To be appended to processing database, so that structure is:
    ocr_processing["tag"]["jobs"] = [ {startTime: xxx, execTime: yyy, ... }, {reports from other jobs...} ]

    :jobid: id of the job within the submit/output directories
    :basedir: base directory for job output
    :returns: If successful, returns dict of the form
        jobReport = { subTime: (time of submission),
                        execTime: (start time of latest execution),
                        evictTime: (time of job eviction, if any),
                        termTime: (time of job termination, if any),
                        runTime: (time between execution start and termination/eviction time),
                        usage: { usage dictionary from above},
                    }

    """
    try:
        with open(jobpath + "/process.log") as file:
            chunk = ""
            subTime = None
            execTime = None
            evictTime = None
            termTime = None
            runTime = None
            jobReport = {}
            jobReport["path"] = jobpath
            for line in file:
                if line.startswith("..."):
                    if chunk.startswith("000"): # submitted
                        jobReport["subTime"] = parseTime(chunk.split('\n')[0])
                    elif chunk.startswith("001"): # executing
                        jobReport["execTime"] = parseTime(chunk.split('\n')[0])
                    elif chunk.startswith("004"): # evicted, has partitionable table
                        jobReport["evictTime"] = parseTime(chunk.split('\n')[0])
                        runTime = (jobReport["evictTime"] - jobReport["execTime"])
                        jobReport["runTime"] = runTime.days * 86400 + runTime.seconds
                        jobReport["usage"] = parseResources(chunk)
                    elif chunk.startswith("005"): # termination, has partitionable table
                        jobReport["termTime"] = parseTime(chunk.split('\n')[0])
                        runTime = (jobReport["termTime"] - jobReport["execTime"])
                        jobReport["runTime"] = runTime.days * 86400 + runTime.seconds
                        jobReport["usage"] = parseResources(chunk)
                    elif chunk.startswith("006"):
                        pass
                    elif chunk.startswith("009"):
                        pass
                    else:
                        if DEBUG:
                            print "UNKNOWN CODE"
                            print chunk
                    chunk=""
                else:
                    chunk+=line
        return jobReport
    except IOError:
        print "Couldn't find file at %s/process.log" % jobpath
        return None

def updateArticle(match, jobpath, tag, proctype, articlesColl, filepath_map, file_pattern, dryrun=False, update=False):
    """
    Take an article matched in the DB, its associated job path, and update the relevant article collections with files found + harvested
    """
    files = patternMatch(file_pattern, jobpath)
    if files == []: # no matching output. May be overall fail, may have just not run this proctype
        return 1,False

    try:  # if this article + tag have already been harvested, then skip
        if match["%s_processing" % proctype][tag]["harvested"]:
            if not update: # if UPDATE flag isn't used, move to the next job
                return 2,False
    except KeyError:
        pass

    # get all files associated with the job
    temp = {}
    temp["filename"] = files
    temp["contents"] = []

    temp["harvested"] = True #indicate that this article+tag combination has been handled
    temp["success"] = True # we found files, so it's a success!
    match["%s_processing" % proctype][tag] = temp

    if dryrun:
        print "This would be the new article document: "
        ppr.pprint(match)
    else:
        articlesColl.update( { "_id" : match["_id"] }, {"$set": match}, upsert = False )

    return 0, temp["success"]

def updateProcessing(collection, tag, pubname, report, success):
    """
    Update the process DB with this job's information. Add to this publication's success/failure tally, appending its runtime.
    NOTE: This will run for each attempt at running the condor job. So if a job fails 10 times, 10 entries will appear in the processing database.

    :collection: MongoDB collection to update
    :tag: Processing tag
    :pubname: Name of the publication to update
    :report: The processing report obtained from readLog function

    """
    if success:
        collection.update({ "tag": tag }, {'$inc': {"pub_totals.%s.success" % pubname : 1 } }, upsert=True )
        collection.update({ "tag": tag }, {'$inc': {"pub_totals.%s.cpusuccess" % pubname : report["runTime"]} }, upsert = True )
    else:
        collection.update({ "tag": tag },
                {'$inc': {"pub_totals.%s.failure" % pubname : 1 } }, upsert = True)
        collection.update({ "tag": tag },
                {'$inc': {"pub_totals.%s.cpufailure" % pubname : report["runTime"]} } , upsert = True)
    return 0

def exit(success):
    """
    Exit. If success, write 0 to RESULT and quite with sys.exist(0). Otherwise, write 1 and use non-zero exit.

    :success: TODO
    :returns: TODO

    """
    with open("RESULT","w") as fout:
        if success:
            fout.write("0\n")
            sys.exit(0)
        else:
            fout.write("1\n")
            sys.exit(1)

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Harvest some condor output')
    parser.add_argument('tag', type=str, default="", help="Tag of this processing batch")
    parser.add_argument('--basedir', type=str, default=os.getcwd(), help="The base directory")
    parser.add_argument('--dryrun', type=bool, required=False, default=False, help="Don't actually write to the databases--only show\
            what would have been added/updated.")
    parser.add_argument('--update', type=bool, required=False, default=True, help="Force update to the database.")
    args = parser.parse_args()

    config = ConfigParser.RawConfigParser()
    config.read(BASE+'db_conn.cfg')
    harvest_user = config.get('database','harvest_user')
    harvest_password = config.get('database','harvest_password')
    uri = "mongodb://%s:%s@<EMAIL>:27017/?authMechanism=MONGODB-CR" % (harvest_user, harvest_password)
    client = pymongo.MongoClient(uri)
    articlesdb = client.articles_harvesting_overhaul
    articles = articlesdb.articles

    procdb = client.processing_dev

    #technically, we can grab this from the path, based on how I'm structuring my job submission
    tag = args.tag

    processingsColl = procdb[tag]

    # Should be getting run in output/job0000001
    # get filepath_mapping.pickle from output
    try:
        filepath_map = pickle.load(open("../filepath_mapping.pickle"))
    except IOError:
        with open("post.log","w") as fout:
            fout.write("No filepath mapping found! Cannot determine what article this job belongs to! Exiting!")
        exit(False)
    jobpath = os.getcwd()

    # match against the articles collection in the DB
    if not jobpath.endswith("/"): #ensure we have a trailing /
        jobpath = jobpath + "/"
    jobid = jobpath.split("/")[-2]

    match = getMatch(jobid, articles, filepath_map)

    if match is None:
        with open("post.log","w") as fout:
            fout.write("No match for the article found! Job id: %s\nfilepath_map: %s"% \
                ( jobid, filepath_map[jobid] ) )
        exit(False)

    check = None
    jobSuccess = None
    job_config = ConfigParser.RawConfigParser()
    job_config.read(BASE+'jobs.cfg')
    overall_success = False
    with open("post.log","w") as fout:
        fout.write('TAG: %s\n' % tag)
        for proctype in job_config.sections():
            pattern = job_config.get(proctype, 'pattern')

            check,jobSuccess = updateArticle(match, jobpath, args.tag, proctype, articles, filepath_map, pattern, args.dryrun, args.update)
            fout.write(proctype+"\n")
            fout.write('\t%s -- %s\n' % (check, jobSuccess))
            if jobSuccess:
                overall_success = True

    # read log file
    tempReport = readLog(jobpath)
    if tempReport is None or "runTime" not in tempReport:
        exit(False) # couldn't read file log

    pubname = match["pubname"]

    updateProcessing(processingsColl, tag, pubname, tempReport, overall_success)
    if overall_success:
        exit(True)
    else:
        exit(False)

#!/usr/bin/env python # encoding: utf-8 File: harvestResults_postscript.py Author: <NAME> Email: <EMAIL> Github: https://github.com/iross Description:     Modifying the cron'ed harvestResults script to run as a postscript on the htcondor jobs     TODO: changes needed:         Don't need to loop over dirs         Read pickle from output dir (it should have been copied over)         Write RESULT with 0 or 1 based on PATTERN input         Better handling of --update junk         no args (or at least fewer)         Overall cleanup :pattern: A file pattern to match the desired output. Input to a glob, so use traditional unix wildcarding.     :dir: The directory to search.     :returns: list of matching files in the target directory Cleans up raw path of pdf to just the name of the PDF (no extension or directories)      :path: path to pdf     :returns: The PDF name with all directory/extensions stripped Finds the document in the collection that matches the specified jobpath      :jobid: ID of job output directory (i.e. job000001)     :collection: MongoDB collection to look over     :returns: dict of matching document # Elsevier-specific stuff for matching Parse the time of submission/termination/execution from a line     Assumes: the log is from the current year      :line: Line from the process.log file.         expected input is of the type:             "001 (132663.000.000) 12/29 15:16:50 Job executing on host: <172.16.31.10:35219>"     :returns: Date object # ASSUMES the log is from the current year. Takes a chunk of log text that includes the "Partitionable Resources" table     and returns a parsed dict of {"diskUsage": xxx, "memUsage": yyy }      :chunk: The text block containing the Partitional Resources table from standard HTCondor output.     :returns: Dictionary of the form usage = { memUsage: (memory usage in MB), diskUsage: (disk usage in KB) } # break the chunk up by \n Reads log to determine disk/mem usage, runtime     For processing time, it will only grab the last execution/evict/terminated times.     And runTime supercedes evictTime (eg. an exec->evict combination will not be written if     a later exec-termination combination exists in the log)      To be appended to processing database, so that structure is:     ocr_processing["tag"]["jobs"] = [ {startTime: xxx, execTime: yyy, ... }, {reports from other jobs...} ]      :jobid: id of the job within the submit/output directories     :basedir: base directory for job output     :returns: If successful, returns dict of the form         jobReport = { subTime: (time of submission),                         execTime: (start time of latest execution),                         evictTime: (time of job eviction, if any),                         termTime: (time of job termination, if any),                         runTime: (time between execution start and termination/eviction time),                         usage: { usage dictionary from above},                     } # submitted # executing # evicted, has partitionable table # termination, has partitionable table Take an article matched in the DB, its associated job path, and update the relevant article collections with files found + harvested # no matching output. May be overall fail, may have just not run this proctype # if this article + tag have already been harvested, then skip # if UPDATE flag isn't used, move to the next job # get all files associated with the job #indicate that this article+tag combination has been handled # we found files, so it's a success! Update the process DB with this job's information. Add to this publication's success/failure tally, appending its runtime.     NOTE: This will run for each attempt at running the condor job. So if a job fails 10 times, 10 entries will appear in the processing database.      :collection: MongoDB collection to update     :tag: Processing tag     :pubname: Name of the publication to update     :report: The processing report obtained from readLog function Exit. If success, write 0 to RESULT and quite with sys.exist(0). Otherwise, write 1 and use non-zero exit.      :success: TODO     :returns: TODO #technically, we can grab this from the path, based on how I'm structuring my job submission # Should be getting run in output/job0000001 # get filepath_mapping.pickle from output # match against the articles collection in the DB #ensure we have a trailing / # read log file # couldn't read file log