monitor.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from pyspark.streaming.kafka import KafkaUtils
from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.streaming import StreamingContext
from pyspark import SparkConf
import json
import msgpack
import pymysql
import pandas as pd
import time
from elasticsearch import Elasticsearch as Es
import redis
import datetime
import smtplib
import requests
from email.mime.text import MIMEText
from email.utils import formataddr
from email.mime.multipart import MIMEMultipart
from email.mime.application import MIMEApplication
import numpy as np

def get_es():
    init_args = {'sniff_on_start': False,'sniff_on_connection_fail': False,}

    new_hosts =[{'host': '172.16.31.17','port': 9000,}, {'host': '172.16.31.11','port': 9000,}, {'host': '172.16.31.13','port': 9000,}]

    new_es = Es(hosts=new_hosts, **init_args)
    return new_es
def es_index_adapt(index_prefix, doc_type, rw=None):
    """get the adapted index name
    """
    assert rw in [None, 'read', 'write']
    index = '-'.join((index_prefix, doc_type))
    if rw:
        index = '-'.join((index, rw))
    return index
def es_query(doc, body, offset, size, es=None):
    if es is None:
        es = get_es()

    index = es_index_adapt(index_prefix='gm-dbmw',doc_type=doc)
    res = es.search(index=index,timeout='10s',body=body,from_=offset,size=size)
    return res
def es_mquery(doc, body, es=None):
    if es is None:
        es = get_es()

    index = es_index_adapt(index_prefix='gm-dbmw',doc_type=doc)
    res = es.msearch(body,index=index)
    # res = es.search(index=index,timeout='10s',body=body,from_=offset,size=size)
    return res

def compute_henqiang(x):
    score = 15-x*((15-0.5)/180)
    if score>0.5:
        return score
    else:
        return 0.5
def compute_jiaoqiang(x):
    score = 12-x*(12/180)
    if score>0.5:
        return score
    else:
        return 0.5
def compute_ruoyixiang(x):
    score = 5-x*((5-0.5)/180)
    if score>0.5:
        return score
    else:
        return 0.5
def compute_validate(x):
    score = 10-x*((10-0.5)/180)
    if score>0.5:
        return score
    else:
        return 0.5
def tag_list2dict(lst,size):
    result = []
    if lst:
        for i in lst:
            tmp = dict()
            tmp["content"] = i["tag_id"]
            if isinstance(i,int):
                tmp["type"] = "tag"
            else:
                tmp["type"] = "search_word"
            tmp["score"] = i["tag_score"]
            result.append(tmp)
    return result[:size]

def query_diary(query,size,have_read_diary_list):
    url = "http://172.16.44.34:80/v1/once"
    header_dict = {'Content-Type': 'application/x-www-form-urlencoded'}
    # recall diary
    param_dict = {}
    param_dict["method"] = "doris/search/query_diary"
    param_detail = {
        "size": size,
        "query": query,
        "sort_type": 21,
        "filters": {"is_sink": False, "content_level": [5, 4, 3.5, 3], "has_cover": True},
        "have_read_diary_list": have_read_diary_list
    }
    param_dict["params"] = json.dumps(param_detail)
    results = requests.post(url=url, data=param_dict, headers=header_dict)
    diary = json.loads(results.content)
    diary_list = list()
    for items in diary['data']['hits']['hits']:
        diary_list.append(items['_id'])
    return  diary_list


def get_user_tag_score1(cur, cl_id):
    #compute and store score
    query_user_log = """select time,cl_id,score_type,tag_id,tag_referrer,action from user_new_tag_log where cl_id = '%s' """ % (cl_id)
    cur.execute(query_user_log)
    user_log = cur.fetchall()
    if user_log:
        user_log_df = pd.DataFrame(list(user_log))
        user_log_df.columns = ["time", "cl_id", "score_type","tag_id","tag_referrer","action"]
        user_log_df["tag_id"] = np.where(user_log_df["action"] == "do_search",user_log_df["tag_referrer"],user_log_df["tag_id"])
        max_time_user_log_at_difftype = user_log_df.groupby(by=["score_type","tag_id"]).apply(lambda t: t[t.time == t.time.max()]).reset_index(drop=True)
        max_time_user_log_at_difftype["days_diff_now"] = round((int(time.time())-max_time_user_log_at_difftype["time"]) / (24*60*60))
        max_time_user_log_at_difftype["tag_score"] = max_time_user_log_at_difftype.apply(
            lambda x: compute_henqiang(x.days_diff_now) if x.score_type == "henqiang" else (
                compute_jiaoqiang(x.days_diff_now) if x.score_type == "jiaoqiang" else (
                    compute_ruoyixiang(x.days_diff_now) if x.score_type == "ruoyixiang" else compute_validate(x.days_diff_now))), axis=1)
        finally_score = max_time_user_log_at_difftype.groupby("tag_id").apply(lambda x: x[x.tag_score==x.tag_score.max()]).reset_index(drop=True)[["time","cl_id","tag_id","tag_score"]].drop_duplicates()
        finally_score = finally_score.sort_values(by=["tag_score","time"],ascending=False)
        tag_id_score = dict(zip(finally_score["tag_id"],finally_score["tag_score"]))
        tag_id_list = tag_list2dict(finally_score["tag_id"].tolist()[:3])
        return tag_id_list
    else:
        return []


def get_user_tag_score(cl_id, size=3):
    db_jerry_test = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC',
                                    db='jerry_test', charset='utf8')
    cur = db_jerry_test.cursor()
    #compute and store score
    query_user_log = """select time,cl_id,score_type,tag_id,tag_referrer,action from user_new_tag_log where cl_id = '%s' """ % (cl_id)
    cur.execute(query_user_log)
    user_log = cur.fetchall()
    db_jerry_test.close()

    if user_log:
        user_log_df = pd.DataFrame(list(user_log))
        user_log_df.columns = ["time", "cl_id", "score_type","tag_id","tag_referrer","action"]
        user_log_df["tag_id"] = np.where(user_log_df["action"] == "do_search",user_log_df["tag_referrer"],user_log_df["tag_id"])
        user_log_df["days_diff_now"] = round((int(time.time())-user_log_df["time"]) / (24*60*60))
        user_log_df["tag_score"] = user_log_df.apply(
            lambda x: compute_henqiang(x.days_diff_now) if x.score_type == "henqiang" else (
                compute_jiaoqiang(x.days_diff_now) if x.score_type == "jiaoqiang" else (
                    compute_ruoyixiang(x.days_diff_now) if x.score_type == "ruoyixiang" else compute_validate(x.days_diff_now))), axis=1)
        finally_score = user_log_df.sort_values(by=["tag_score","time"],ascending=False)
        finally_score.drop_duplicates(subset="tag_id", inplace=True)
        finally_score_lst = finally_score[["tag_id","tag_score"]].to_dict('record')
        tag_id_list = tag_list2dict(finally_score_lst,size)
        return tag_id_list
    else:
        return []


def get_extra_param_id_tags(ids_lst):
    ids_tuple  = '(%s)' % ','.join([i for i in ids_lst])
    db_zhengxing = pymysql.connect(host="172.16.30.141", port=3306, user="work",
                                   password="BJQaT9VzDcuPBqkd",
                                   db="zhengxing", cursorclass=pymysql.cursors.DictCursor)
    cur_zhengxing = db_zhengxing.cursor()
    sql = "select tag_ids from category_basic_category where id in %s" % (ids_tuple)
    cur_zhengxing.execute(sql)
    tags = cur_zhengxing.fetchall()
    db_zhengxing.close()

    if tags:
        tmp = []
        for i in tags:
            tmp.extend(i['tag_ids'].split(','))
        result = []
        for i in tmp:
            tmp_dict = dict()
            tmp_dict["content"] = i
            tmp_dict["type"] = "tag"
            result.append(tmp_dict)
        return result
    else:
        send_email("get_extra_param_id_tags","on_click_button_next","id_no_tags")
        return []

def get_hot_search_word():
    db_zhengxing = pymysql.connect(host="172.16.30.141", port=3306, user="work",
                                   password="BJQaT9VzDcuPBqkd",
                                   db="zhengxing", cursorclass=pymysql.cursors.DictCursor)
    cur_zhengxing = db_zhengxing.cursor()
    sql = "select keywords from api_hot_search_words where is_delete=0 order by sorted desc limit 20"
    cur_zhengxing.execute(sql)
    hot_search_words = cur_zhengxing.fetchall()
    db_zhengxing.close()

    if hot_search_words:
        result = []
        for i in hot_search_words:
            tmp = dict()
            tmp["content"] = i['keywords']
            tmp["type"] = "search_word"
            result.append(tmp)
        return result
    else:
        send_email("get_extra_param_id_tags", "on_click_button_next", "id_no_tags")
        return []


def write_to_redis(tag_list, cl_id, action_params='device_open'):
    if tag_list:
        size = list()
        if action_params == 'device_open':
            if len(tag_list) == 1:
                size = [5]
            elif len(tag_list) == 2:
                size = [3, 2]
            elif len(tag_list) == 3:
                size = [2, 2, 1]
        elif action_params == 'new_user_interest' or 'search_word':
            size = [1 for n in range(len(tag_list))]

        have_read_diary_list = list()
        redis_client = redis.StrictRedis.from_url('redis://:ReDis!GmTx*0aN6@172.16.40.133:6379')
        diary_key = 'user_portrait_recommend_diary_queue:device_id:%s:%s' % (
        cl_id, datetime.datetime.now().strftime('%Y-%m-%d'))
        if not redis_client.exists(diary_key):
            # 过滤运营位
            db_zhengxing = pymysql.connect(host="172.16.30.143", port=3306, user="work", password="BJQaT9VzDcuPBqkd",
                                           db="zhengxing",
                                           cursorclass=pymysql.cursors.DictCursor)
            zhengxing_cursor = db_zhengxing.cursor()
            promote_sql = 'select card_id from api_feedoperatev2 where start_time <= %s  and end_time >= %s and is_online = 1 and card_type = 0' % (
                "'" + str(datetime.datetime.now()) + "'", "'" + str(datetime.datetime.now()) + "'")
            promote_num = zhengxing_cursor.execute(promote_sql)
            promote_diary_list = list()
            if promote_num > 0:
                promote_results = zhengxing_cursor.fetchall()
                for i in promote_results:
                    promote_diary_list.append(i["card_id"])
            # 过滤已读
            read_diary_key = "TS:recommend_diary_set:device_id:" + str(cl_id)
            if redis_client.exists(read_diary_key):
                p = redis_client.smembers(read_diary_key)
                have_read_diary_list = list(map(int, p))
            have_read_diary_list.extend(promote_diary_list)

            q_list = list()
            for i in range(len(tag_list)):
                if tag_list[i]['type'] == 'search_word':
                    q_list.append({})
                    q = dict()
                    query = tag_list[i]['content']
                    dsize = size[i]
                    q = {'query': {'multi_match': {'query': query,
                                                   'type': 'cross_fields',
                                                   'operator': 'and',
                                                   'fields': ['doctor.name^4',
                                                              'doctor.hospital.name^3',
                                                              'doctor.hospital.officer_name^3',
                                                              'user.last_name^2',
                                                              'service.name^1']}},
                         'size': dsize,
                         '_source': {'includes': ['id']},
                         'sort': {'recommend_score': {'order': 'desc'}},
                         'filter': {'bool': {'filter': [{'term': {'is_online': True}},
                                                        {'term': {'has_cover': True}},
                                                        {'term': {'is_sink': False}},
                                                        {'terms': {'content_level': [5, 4, 3.5, 3]}}],
                                             'must_not': {'terms': {'id': have_read_diary_list}}}}}
                    q_list.append(q)
                else:
                    q_list.append({})
                    q = dict()
                    q['query'] = {"bool": {
                        "filter": [{"term": {"closure_tag_ids": tag_list[i]['content']}}, {"term": {"is_online": True}},
                                   {"term": {"has_cover": True}}, {"term": {"is_sink": False}},
                                   {"terms": {"content_level": [5, 4, 3.5, 3]}}]}}
                    q['size'] = size[i]
                    q["_source"] = {
                        "includes": ["id"]
                    }
                    if len(have_read_diary_list) > 0:
                        q['query']['bool']['must_not'] = {"terms": {"id": have_read_diary_list}}
                    q['sort'] = {"recommend_score": {"order": "desc"}}
                    q_list.append(q)
            diary_res = es_mquery('diary', q_list)
            if diary_res:
                diary_id_list = list()
                for tags in diary_res['responses']:
                    for i in range(len(tags['hits']['hits'])):
                        diary_id_list.append(tags['hits']['hits'][i]['_source']['id'])

            # res = es_query('diary', q, 0, len(tag_list))
            # diary_id_list = list()
            # for item in res["hits"]["hits"]:
            #     diary_id_list.append(item["_source"]["id"])
            diary_key = 'user_portrait_recommend_diary_queue:device_id:%s:%s' % (
            cl_id, datetime.datetime.now().strftime('%Y-%m-%d'))
            diary_id_list_diff = list(set(diary_id_list))
            diary_id_list_diff.sort(key=diary_id_list.index)
            diary_dict = dict()
            if len(diary_id_list_diff) > 0:
                diary_dict = {
                    'diary_queue': json.dumps(diary_id_list_diff),
                    'cursor': 0,
                    'len_cursor': 0
                }
                redis_client.hmset(diary_key, diary_dict)
                redis_client.expire(diary_key, time=24 * 60 * 60)

                tag_list_log = [i["content"] for i in tag_list]
                db_jerry_test = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC',
                                                db='jerry_test', charset='utf8')
                cur_jerry_test = db_jerry_test.cursor()
                user_recsys_history_query = """insert into user_new_tag_recsys_history values(null,%d, '%s', "%s", "%s")""" % (
                    int(time.time()), cl_id, str(tag_list_log), str(diary_id_list_diff))
                cur_jerry_test.execute(user_recsys_history_query)
                db_jerry_test.commit()
                db_jerry_test.close()
            return 'save redis and history'
        else:
            return 'already recall'


def get_data(x):
    try:
        if 'type' in x[1] and 'device' in x[1]:
            data = x[1]
            if data['type'] == 'on_click_button' \
                    and data['params']['page_name'] == 'home' and data['params']['tab_name'] == '精选' \
                    and data['params']['button_name'] == 'user_feedback_type' \
                    and data['params']['extra_param'][0]["card_content_type"] == "diary":
                # 下面这一块确认一下"feedback_type" 返回的是列表还是字符串，还是两者都有
                if "1" in type(data['params']['extra_param'][0]["feedback_type"]) \
                        or "2" in type(data['params']['extra_param'][0]["feedback_type"]):
                    device_id = x[1]['device']['device_id']
                    diary_id = data['params']['extra_param'][0]["card_id"]
                    return (device_id,diary_id)
    except Exception as e:
        send_email("get_data", "get_data", e)

def Json(x):
    if b'content' in x[1]:
        data = json.loads(str(x[1][b"content"], encoding="utf-8")[:-1])
        if 'SYS' in data and 'APP' in data and 'action' in data['SYS']:
            # 首次打开APP或者重启APP
            if data["SYS"]["action"] == '/api/app/config_v2':
                return True
            else:
                return False
    elif 'type' in x[1] and 'device' in x[1]:
        data = x[1]
        #新用户选择标签或者跳过
        if data['type'] == 'on_click_button' and 'params' in data:
            if 'page_name' in data['params'] and 'button_name' in data['params'] and 'extra_param' in data['params']:
                if data['params']['page_name'] == 'page_choose_interest':
                    return True
        else:
            return False
    else:
        return False

def send_email(app,id,e):
    # 第三方 SMTP 服务
    mail_host = 'smtp.exmail.qq.com'  # 设置服务器
    mail_user = "gaoyazhe@igengmei.com"  # 用户名
    mail_pass = "VCrKTui99a7ALhiK"  # 口令

    sender = 'gaoyazhe@igengmei.com'
    receivers = ['gaoyazhe@igengmei.com']  # 接收邮件，可设置为你的QQ邮箱或者其他邮箱
    e = str(e)
    msg = MIMEMultipart()
    part = MIMEText('app_id:'+id+':fail', 'plain', 'utf-8')
    msg.attach(part)
    msg['From'] = formataddr(["gaoyazhe", sender])
    # 括号里的对应收件人邮箱昵称、收件人邮箱账号
    msg['To'] = ";".join(receivers)
    # message['Cc'] = ";".join(cc_reciver)

    msg['Subject'] = 'spark streaming:app_name:'+app
    with open('error.txt','w') as f:
         f.write(e)
         f.close()
    part = MIMEApplication(open('error.txt', 'r').read())
    part.add_header('Content-Disposition', 'attachment', filename="error.txt")
    msg.attach(part)

    try:
        smtpObj = smtplib.SMTP_SSL(mail_host, 465)
        smtpObj.login(mail_user, mail_pass)
        smtpObj.sendmail(sender, receivers, msg.as_string())
    except smtplib.SMTPException:
        print('error')
#filter lo

#rdd trans
def model(rdd):
    try:
        rdd = rdd.filter(lambda x:Json(x)).repartition(5).map(lambda x:get_data(x))
        return rdd
    except:
        print("fail")

def gbk_decoder(s):
    if not s:
        return None
    else:
        try:
            data = msgpack.loads(s, encoding='utf-8')
            return data
        except Exception as e:
            print(e)
            data = json.loads(s)
            return data
# Spark-Streaming-Kafka
sc = SparkContext(conf=SparkConf().setMaster("spark://nvwa01:7077").setAppName("new_tag_score").set("spark.io.compression.codec", "lzf"))
ssc=SQLContext(sc)
ssc = StreamingContext(sc, 0.4)
sc.setLogLevel("WARN")
kafkaParams = {"metadata.broker.list": "172.16.44.25:9092,172.16.44.31:9092,172.16.44.45:9092",
               "group.id": "new_tag_score",
               "socket.timeout.ms": "600000",
               "auto.offset.reset": "largest"}
try:
    stream = KafkaUtils.createDirectStream(ssc, ["gm-logging-prod","gm-maidian-data"], kafkaParams, keyDecoder=gbk_decoder, valueDecoder=gbk_decoder)
    transformstream = stream.transform(lambda x:model(x))
    transformstream.pprint()
    ssc.start()
    ssc.awaitTermination()
except Exception as e :
    send_email(sc.appName,sc.applicationId,e)