recommend-emr-dataprocess/ad/01_ad_model_update.sh

#!/bin/sh
set -x

source /root/anaconda3/bin/activate py37
sh_path=$(dirname $0)
source ${sh_path}/00_common.sh

export PATH=$SPARK_HOME/bin:$PATH
export HADOOP_CONF_DIR=/etc/taihao-apps/hadoop-conf
export JAVA_HOME=/usr/lib/jvm/java-1.8.0

# 全局常量
HADOOP=/opt/apps/HADOOP-COMMON/hadoop-common-current/bin/hadoop

TRAIN_PATH=/dw/recommend/model/31_ad_sample_data_v4_test
BUCKET_FEATURE_PATH=/dw/recommend/model/33_ad_train_data_v4_test
MODEL_PATH=/dw/recommend/model/35_ad_model_test
PREDICT_RESULT_SAVE_PATH=/dw/recommend/model/34_ad_predict_data_test
TABLE=alg_recsys_ad_sample_all

# 特征文件名
feature_file=20240703_ad_feature_name.txt

# 模型OSS保存路径，测试时修改为其他路径，避免影响线上
MODEL_OSS_PATH=oss://art-recommend.oss-cn-hangzhou.aliyuncs.com/model/
# 线上模型名，测试时修改为其他模型名，避免影响线上
model_name=model_xgb_351_1000_v2_test

# 本地保存HDFS模型路径文件
model_path_file=/root/zhaohp/XGB/online_model_path.txt

today_early_1="$(date -d '1 days ago' +%Y%m%d)"
# 训练用的数据路径
train_data_path=""
# 评估用的数据路径
predict_date_path=""
#评估结果保存路径
new_model_predict_result_path=""
# 模型保存路径
model_save_path=""
# 模型本地临时保存路径
model_local_path=/root/zhaohp/XGB
# 任务开始时间
start_time=$(date +%s)
# 线上模型在HDFS中的路径
online_model_path=`cat ${model_path_file}`

# 校验命令的退出码
check_run_status() {
    local status=$1
    local step_start_time=$2
    local step_name=$3

    local step_end_time=$(date +%s)
    local step_elapsed=$(($step_end_time - $step_start_time))

    if [ $status -ne 0 ]; then
        echo "$LOG_PREFIX -- ${step_name}失败: 耗时 $step_elapsed"
        local elapsed=$(($step_end_time - $start_time))
        /root/anaconda3/bin/python ${sh_path}/ad_monitor_util.py --level error --msg "$msg" --start "$start_time" --elapsed "$elapsed"
        exit 1
    else
        echo "$LOG_PREFIX -- ${step_name}成功: 耗时 $step_elapsed"
    fi
}

init() {
  
  declare -a date_keys=()
  local count=1
  local current_data="$(date -d '2 days ago' +%Y%m%d)"
  # 循环获取前 n 天的非节日日期
  while [[ $count -lt 7 ]]; do
    date_key=$(date -d "$current_data" +%Y%m%d)
    # 判断是否是节日，并拼接训练数据路径
    if [ $(is_not_holidays $date_key) -eq 1 ]; then

      # 将 date_key 放入数组
      date_keys+=("$date_key")

      if [[ -z ${train_data_path} ]]; then
        train_data_path="${BUCKET_FEATURE_PATH}/${date_key}"
      else
        train_data_path="${BUCKET_FEATURE_PATH}/${date_key},${train_data_path}"
      fi 
      count=$((count + 1))
    else
      echo "日期: ${date_key}是节日，跳过"
    fi
    current_data=$(date -d "$current_data -1 day" +%Y%m%d)
  done

  last_index=$((${#date_keys[@]} - 1))
  train_first_day=${date_keys[$last_index]}
  train_last_day=${date_keys[0]}

  model_save_path=${MODEL_PATH}/${model_name}_${train_first_day: -4}_${train_last_day: -4}
  predict_date_path=${BUCKET_FEATURE_PATH}/${today_early_1}
  new_model_predict_result_path=${PREDICT_RESULT_SAVE_PATH}/${today_early_1}_351_1000_${train_first_day: -4}_${train_last_day: -4}
  online_model_predict_result_path=${PREDICT_RESULT_SAVE_PATH}/${today_early_1}_351_1000_${online_model_path: -9}

  echo "init param train_data_path: ${train_data_path}"
  echo "init param predict_date_path: ${predict_date_path}"
  echo "init param new_model_predict_result_path: ${new_model_predict_result_path}"
  echo "init param online_model_predict_result_path: ${online_model_predict_result_path}"
  echo "init param model_save_path: ${model_save_path}"
  echo "init param online_model_path: ${online_model_path}"
  echo "init param feature_file: ${feature_file}"
  echo "init param model_name: ${model_name}"
  echo "init param model_local_path: ${model_local_path}"
  echo "init param model_oss_path: ${MODEL_OSS_PATH}"
}

# 校验大数据任务是否执行完成
check_ad_hive() {
    local step_start_time=$(date +%s)
    local max_hour=05
    local max_minute=30
    local elapsed=0
    while true; do
        local python_return_code=$(python ${sh_path}/ad_utils.py --excute_program check_ad_origin_hive --partition ${today_early_1} --hh 23)

        elapsed=$(($(date +%s) - $step_start_time))
        if [ "$python_return_code" -eq 0 ]; then
            break
        fi
        echo "Python程序返回非0值，等待五分钟后再次调用。"
        sleep 300
        local current_hour=$(date +%H)
        local current_minute=$(date +%M)
        if (( current_hour > max_hour || (current_hour == max_hour && current_minute >= max_minute) )); then
            local msg="大数据数据生产校验失败, 分区: ${today_early_1}"
            echo -e "$LOG_PREFIX -- 大数据数据生产校验 -- ${msg}: 耗时 $elapsed"
            /root/anaconda3/bin/python ${sh_path}/ad_monitor_util.py --level error --msg "$msg" --start "$start_time" --elapsed "$elapsed"
            exit 1
        fi
    done
    echo "$LOG_PREFIX -- 大数据数据生产校验 -- 大数据数据生产校验通过: 耗时 $elapsed"

}

make_origin_data() {
  
  local step_start_time=$(date +%s)

  /opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
  --class com.aliyun.odps.spark.examples.makedata_ad.v20240718.makedata_ad_31_originData_20240718 \
  --master yarn --driver-memory 1G --executor-memory 2G --executor-cores 1 --num-executors 16 \
  ./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
  tablePart:64 repartition:32 \
  beginStr:${today_early_1}00 endStr:${today_early_1}12 \
  savePath:${TRAIN_PATH} \
  table:${TABLE} \
  filterHours:00,01,02,03,04,05,06,07 \
  idDefaultValue:0.1 &

  /opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
  --class com.aliyun.odps.spark.examples.makedata_ad.v20240718.makedata_ad_31_originData_20240718 \
  --master yarn --driver-memory 1G --executor-memory 2G --executor-cores 1 --num-executors 16 \
  ./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
  tablePart:64 repartition:32 \
  beginStr:${today_early_1}13 endStr:${today_early_1}18 \
  savePath:${TRAIN_PATH} \
  table:${TABLE} \
  filterHours:00,01,02,03,04,05,06,07 \
  idDefaultValue:0.1 &

  /opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
  --class com.aliyun.odps.spark.examples.makedata_ad.v20240718.makedata_ad_31_originData_20240718 \
  --master yarn --driver-memory 1G --executor-memory 2G --executor-cores 1 --num-executors 16 \
  ./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
  tablePart:64 repartition:32 \
  beginStr:${today_early_1}19 endStr:${today_early_1}23 \
  savePath:${TRAIN_PATH} \
  table:${TABLE} \
  filterHours:00,01,02,03,04,05,06,07 \
  idDefaultValue:0.1 &

  wait

  local return_code=$?
  check_run_status $return_code $step_start_time "spark原始样本生产任务"
}

make_bucket_feature() {

  local step_start_time=$(date +%s)
  
  /opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
  --class com.aliyun.odps.spark.examples.makedata_ad.v20240718.makedata_ad_33_bucketData_20240718 \
  --master yarn --driver-memory 2G --executor-memory 4G --executor-cores 1 --num-executors 16 \
  ./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
  beginStr:${today_early_1} endStr:${today_early_1} repartition:100 \
  filterNames:_4h_,_5h_,adid_,targeting_conversion_ \
  readPath:${TRAIN_PATH} \
  savePath:${BUCKET_FEATURE_PATH}

  local return_code=$?
  check_run_status $return_code $step_start_time "spark特征分桶任务"
}

xgb_train() {
  local step_start_time=$(date +%s)

  /opt/apps/SPARK3/spark-3.3.1-hadoop3.2-1.0.5/bin/spark-class org.apache.spark.deploy.SparkSubmit \
  --class com.tzld.piaoquan.recommend.model.train_01_xgb_ad_20240808 \
  --master yarn --driver-memory 6G --executor-memory 9G --executor-cores 1 --num-executors 31 \
  --conf spark.yarn.executor.memoryoverhead=1000 \
  --conf spark.shuffle.service.enabled=true \
  --conf spark.shuffle.service.port=7337 \
  --conf spark.shuffle.consolidateFiles=true \
  --conf spark.shuffle.manager=sort \
  --conf spark.storage.memoryFraction=0.4 \
  --conf spark.shuffle.memoryFraction=0.5 \
  --conf spark.default.parallelism=200 \
  /root/zhangbo/recommend-model/recommend-model-produce/target/recommend-model-produce-jar-with-dependencies.jar \
  featureFile:20240703_ad_feature_name.txt \
  trainPath:${train_data_path} \
  testPath:${predict_date_path} \
  savePath:${new_model_predict_result_path} \
  modelPath:${model_save_path} \
  eta:0.01 gamma:0.0 max_depth:5 num_round:1000 num_worker:30 repartition:20

  local return_code=$?
  check_run_status $return_code $step_start_time "XGB模型训练任务"
}

model_predict() {

  # 线上模型评估最新的数据
  local step_start_time=$(date +%s)
  /opt/apps/SPARK3/spark-3.3.1-hadoop3.2-1.0.5/bin/spark-class org.apache.spark.deploy.SparkSubmit \
  --class com.tzld.piaoquan.recommend.model.pred_01_xgb_ad_hdfsfile_20240813 \
  --master yarn --driver-memory 1G --executor-memory 1G --executor-cores 1 --num-executors 30 \
  --conf spark.yarn.executor.memoryoverhead=1024 \
  --conf spark.shuffle.service.enabled=true \
  --conf spark.shuffle.service.port=7337 \
  --conf spark.shuffle.consolidateFiles=true \
  --conf spark.shuffle.manager=sort \
  --conf spark.storage.memoryFraction=0.4 \
  --conf spark.shuffle.memoryFraction=0.5 \
  --conf spark.default.parallelism=200 \
  /root/zhangbo/recommend-model/recommend-model-produce/target/recommend-model-produce-jar-with-dependencies.jar \
  featureFile:20240703_ad_feature_name.txt \
  testPath:${predict_date_path} \
  savePath:${online_model_predict_result_path} \
  modelPath:${online_model_path}

  local return_code=$?
  check_run_status $return_code $step_start_time "线上模型评估${predict_date_path: -8}的数据"

  local mean_abs_diff=$(/root/anaconda3/bin/python ${sh_path}/model_predict_analyse.py -p ${online_model_predict_result_path} ${new_model_predict_result_path})
  if (( $(echo "${mean_abs_diff} > 0.000400" | bc -l ) ));then
    check_run_status 1 $step_start_time "线上模型评估${predict_date_path: -8}的数据，绝对误差大于0.000400，请检查"
    echo "线上模型评估${predict_date_path: -8}的数据，绝对误差大于0.000400，请检查"
    exit 1
  fi 
}

model_upload_oss() {
  cd ${model_local_path}
  $hadoop fs -get ${model_save_path} ./${model_name}

  if [ ! -d ./${model_name} ]; then
    echo "从HDFS下载模型失败"
    check_run_status 1 $step_start_time "XGB模型训练任务" 
    exit 1 
  fi

  tar -czvf ${model_name}.tar.gz -C ${model_name} .

  rm -rf .${model_name}.tar.gz.crc

  $hadoop fs -rm -r -skipTrash ${MODEL_OSS_PATH}/${model_name}.tar.gz
  
  $hadoop fs -put ${model_name}.tar.gz ${MODEL_OSS_PATH}
  check_run_status $return_code $step_start_time "模型上传OSS"

  echo ${model_save_path} > ${model_path_file}
}

# 主方法
main() {
  init

  check_ad_hive

  make_origin_data

  make_bucket_feature

  xgb_train

  model_predict

  # model_upload_oss

}


main