How to use get_job_summary method in yandex-tank

Best Python code snippet using yandex-tank

indeed_scraper OLD.py

Source:indeed_scraper OLD.py

...117        website = USA_DOMAIN + '/jobs?q=' + \118            search_keywords + '&l=' + city + '%2C+' + state + '&radius=100'119    print(website)120    return website121def get_job_summary(urls):122    job_summary_df = pd.DataFrame(columns=SUMMARY_COLUMNS)123    # summary_to_csv write to the file simultaneously124    # summary_to_csv = pd.DataFrame(columns=SUMMARY_COLUMNS)125    # first_row = True126    jobcard_jks = []127    print('get_job_summary started...')128    options = Options()129    options.add_experimental_option("detach", True)130    #options.add_argument("--incognito")131    driver = webdriver.Chrome(132        options=options, executable_path=CHROME_PATH)133    driver.implicitly_wait(1)134    for url in urls:135        _flag = True136        next_url_counter = 0137        driver.implicitly_wait(1)138        while(_flag):139            time.sleep(1)140            new_site = url + '&start=' + str(next_url_counter)141            # print(new_site)142            driver.get(new_site)143            # can not have space in by_class_name. Must be one word144            jobcards = driver.find_elements_by_class_name(145                'jobsearch-SerpJobCard')146            job_check = 0147            for jobcard in jobcards:148                time.sleep(1)149                # summary_to_csv = summary_to_csv.head(0)  # Reset150                jobcard_jk = jobcard.get_attribute('data-jk')151                if jobcard_jk not in jobcard_jks:152                    jobcard_jks.append(jobcard_jk)153                    jobcard_html = BeautifulSoup(jobcard.get_attribute(154                        'innerHTML'), 'html.parser')155                    try:156                        location = jobcard_html.find(class_="location").get_text()157                        loc = location.split(',')158                        if len(loc) > 1:159                            city = loc[0]160                            state = loc[1].strip().split(' ')[0]161                        else:162                            city = 'None'163                            state = 'None'164                    except:165                        location = 'None'166                        city = 'None'167                        state = 'None'168                    try:169                        company = jobcard_html.find(class_="company").text.replace(170                            "\n", "").strip()171                    except:172                        company = 'None'173                    try:174                        salary = jobcard_html.find(class_="salary").text.replace(175                            "\n", "").strip()176                    except:177                        salary = 'None'178                    try:179                        rating = jobcard_html.find(class_="ratingsContent").text.replace(180                            "\n", "").strip()181                    except:182                        rating = 'None'183                    try:184                        remote_work = jobcard_html.find(class_="remote").text.replace(185                            "\n", "").strip()186                    except:187                        remote_work = 'None'188                    try:189                        date_posted = jobcard_html.find(class_="date").text.replace(190                            "\n", "").strip()191                    except:192                        date_posted = 'None'193                    if CANADA_DOMAIN in new_site:194                        country = 'Canada'195                    elif USA_DOMAIN in new_site:196                        country = 'USA'197                    else:198                        country = 'None'199                    job_summary_df = job_summary_df.append({"Primary_Key": jobcard_jk, 'Location': location, 'City': city, 'State': state, 'Country': country, "Company": company, "Salary": salary,200                                                            "Ratings": rating, "Remote_work": remote_work, "Date_posted": date_posted201                                                            }, ignore_index=True)202                    # summary_to_csv = summary_to_csv.append({"Primary_Key": jobcard_jk, 'Location': location, 'Country': country, "Company": company, "Salary": salary,203                    #                                         "Ratings": rating, "Remote_work": remote_work, "Date_posted": date_posted204                    #                                         }, ignore_index=True)205                    # if first_row:206                    #     summary_to_csv.to_csv('summary-'+FILE_NAME_END, mode='a',207                    #                           index=False)208                    #     first_row = False209                    # else:210                    #     summary_to_csv.to_csv('summary-'+FILE_NAME_END, mode='a',211                    #                           header=False, index=False)212                    job_check += 1213                    # print("Got these many results:", job_summary_df.shape)214            # Exit if no new jobs found215            if job_check == 0:216                _flag = False217            next_url_counter += 10218    driver.close()219    print('get_job_summary ended...')220    return jobcard_jks, job_summary_df221def get_job_description(url_jks):222    counter = 0223    job_description_df = pd.DataFrame(columns=DESCRIPTION_COLUMNS)224    # description_to_csv write to the file simultaneously225    # description_to_csv = pd.DataFrame(columns=DESCRIPTION_COLUMNS)226    # first_row = True227    print('get_job_description started...')228    for jk in url_jks:229        time.sleep(1)230        # description_to_csv = description_to_csv.head(0) # Reset231        full_link = BASE_JOB_URL + jk232        # print(full_link)233        source = requests.get(full_link).text234        job_page = BeautifulSoup(source, 'html.parser')235        # print(job_page.prettify())236        try:237            description = job_page.find(238                'div', id='jobDescriptionText').get_text("|", strip=True)239        except:240            description = 'None'241        try:242            title = job_page.find(243                'h1', class_='jobsearch-JobInfoHeader-title').get_text("|", strip=True)244            pass245        except:246            title = 'None'247        job_description_df = job_description_df.append({"Primary_Key": jk, 'Title': title,248                                                        "Full_Description": description}, ignore_index=True)249        counter += 1250        if (counter % 50 == 0):251            print("At job:", counter)252        # description_to_csv = description_to_csv.append({"Primary_Key": jk, 'Title': title,253        #                                                 "Full_Description": description}, ignore_index=True)254        # if first_row:255        #     description_to_csv.to_csv('description-'+FILE_NAME_END, mode='a',256        #                               index=False)257        #     first_row = False258        # else:259        #     description_to_csv.to_csv('description-'+FILE_NAME_END, mode='a',260        #                               header=False, index=False)261    print('get_job_description ended...')262    return job_description_df263def merge_dataframes(df1, df2):264    df1_columns = list(df1.columns.values)265    df2_columns = list(df2.columns.values)266    common_columns = list(set(df1_columns) & set(df2_columns))267    Key = common_columns[0]268    df3 = df1.merge(df2, how='inner', on=Key)269    df3 = df3[ALL_COLUMNS]270    return df3271def get_total_num_jobs(job_site):272    source = requests.get(job_site).text273    page = BeautifulSoup(source, 'html.parser')274    count_div = page.find(id='searchCountPages')275    unpack_div_string = count_div.contents[0]276    unpack_div_string = unpack_div_string.strip()277    unpack_div_list = unpack_div_string.split(" ")278    if ',' in unpack_div_list[3]:279        numbers = unpack_div_list[3].split(",")280        number = ''.join(numbers)281        num_jobs = int(number)282    elif int(unpack_div_list[3]):283        num_jobs = int(unpack_div_list[3])284    return num_jobs285def open_browser(url):286    # Check chrome version: top right corner(3dots)-> help -> about chrome287    # keep the broswer open288    chrome_options = Options()289    chrome_options.add_experimental_option("detach", True)290    driver = webdriver.Chrome(291        chrome_options=chrome_options, executable_path=CHROME_PATH)292    driver.get(url)293def create_urls():294    urls = []295    # Validate Radius296    if 'RADIUS' not in globals():297        sys.exit("Please define Radius!")298    else:299        if RADIUS not in RADIUS_OPTIONS:300            sys.exit("Please define Radius Correctly!")301    # Validate Date Posted302    if 'DATE_POSTED' not in globals():303        sys.exit("Please define DATE POSTED!")304    else:305        if DATE_POSTED not in DATE_POSTED_OPTIONS:306            sys.exit("Please define DATE POSTED Correctly!")307    # Create Canadian URLs308    for location in CANADIAN_LIST:309        website = CANADA_DOMAIN + '/jobs?q=' + JOB_TITLE + \310            '&l=' + location[0] + \311            '%2C+' + location[1] + \312            '&radius=' + RADIUS + \313            '&fromage=' + DATE_POSTED + \314            '&sort=date'315         316        urls.append(website)317    # Create American URLs318    for location in AMERICAN_LIST:319        website = USA_DOMAIN + '/jobs?q=' + JOB_TITLE + \320            '&l=' + location[0] + \321            '%2C+' + location[1] + \322            '&radius=' + RADIUS + \323            'fromage=' + DATE_POSTED + \324            '&sort=date'325        urls.append(website)326    return urls327def create_csv_upload_gcp(dataset, source_folder, filename):328    # create csv329    dataset.to_csv(source_folder + filename, index=False)330    # upload to Google Cloud Storage331    storage_client = storage.Client.from_service_account_json(SERVICE_KEY)332    # Create a Bucket Object333    bucket = storage_client.get_bucket(BUCKET_NAME)334    blob = bucket.blob(DESTINATION_BLOB_NAME)335    blob.upload_from_filename(source_folder + filename)336def main():337    start_time_main = time.time()338    firstpage_urls = create_urls()339    pprint(firstpage_urls)340    jobcard_jks, job_summary_df = get_job_summary(firstpage_urls)341    print("get_job_summary time: %s minutes" %342          ((time.time() - start_time_main)/60))343    middle_time_main = time.time()344    job_description_df = get_job_description(jobcard_jks)345    print("get_job_description time: %s minutes" %346          ((time.time() - middle_time_main)/60))347    # TODO:348    # check if file exist and only add the new rows349    final_df = merge_dataframes(job_summary_df, job_description_df)350    create_csv_upload_gcp(final_df, DATA_FOLDER, FILE_NAME)351    print('End!')352    # for testing353    # number_of_jobs = get_total_num_jobs(main_url)354    # print('Number of Jobs:', number_of_jobs)355    # open_browser(main_url)356    # job_url_jks, job_summary_df = get_job_summary(main_url)357    # job_description_df = get_job_description(job_url_jks)358    # merge_dataframes(job_summary_df, job_description_df, user_input_list)359if __name__ == "__main__":360    start_time = time.time()361    main()...

doctorprofileviews.py

Source:doctorprofileviews.py

1from annoying.decorators import render_to, ajax_request2from annoying.functions import get_object_or_None3from common.models import Profile, Job, Pic4def get_job_summary(job):5    """6    Get some kind of link text associated with the job. Just grab the first7    Pic associated with this job and get it's general description8    """9    ret = None10    pics = Pic.objects.filter(album=job.album).order_by('group')11    for pic in pics:12        if pic.description:13            ret = pic.description14            break15    return ret or "No description available"16@render_to('doctor_profile.html')17def doctor_profile(request, nickname ):18    profile = get_object_or_None(Profile, nickname=nickname)19    if not profile:20        return { 'doctor_exists' : False }21    profile_pic_url = None22    if profile.pic:23        profile_pic_url = profile.pic.get_preview_url()24    jobs = Job.objects.filter(doctor=profile).order_by('created').reverse()25    jobs = [ (j,get_job_summary(j)) for j in jobs if j.album.allow_publicly]26    return {27            'doctor_exists'     : True,28            'nickname'          : nickname,29            'profile_pic_url'   : profile_pic_url,30            'doc_profile_desc'  : profile.doc_profile_desc,31            'jobs'              : jobs,...

kube_burner.py

Source:kube_burner.py

1def get_results(benchmark, es_client):2    pod_latencies = get_pod_latency_results(benchmark, es_client)3    job_summary = get_job_summary(benchmark, es_client)4    return {**pod_latencies, **job_summary}5def get_pod_latency_results(benchmark, es_client):6    query = {7        "query":{ 8            "query_string": {9                "query": f'uuid:"{benchmark["uuid"]}" AND (metricName:"podLatencyQuantilesMeasurement")'10            }11        }12    }13    index = 'ripsaw-kube-burner'14    query_results = es_client.search(index='ripsaw-kube-burner', body=query, size=10000)15    perf_data = {doc['_source']['quantileName']: doc['_source'] for doc in query_results['hits']['hits']}16    17    return perf_data18def get_job_summary(benchmark, es_client):19    query = {20        "query": {21            "query_string": {22                "query": f'uuid:"{benchmark["uuid"]}" AND (metricName:"jobSummary")'23            }24        }25    }26    index = 'ripsaw-kube-burner'27    query_results = es_client.search(index='ripsaw-kube-burner', body=query, size=10000)28    perf_data = {"jobSummary": doc['_source'] for doc in query_results['hits']['hits']}29    ...

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