feat:添加告警脚本

ad/01_ad_model_update_everyday.sh

@@ -17,110 +17,109 @@ HADOOP=/opt/apps/HADOOP-COMMON/hadoop-common-current/bin/hadoop
 FM_HOME=/root/sunmingze/alphaFM
 OSS_PATH=oss://art-recommend.oss-cn-hangzhou.aliyuncs.com/ad_model/
 
-#
-## 1 判断依赖的数据表是否生产完成
-#source /root/anaconda3/bin/activate py37
-#max_hour=15
-#max_minute=00
-#while true; do
-#  python_return_code=$(python ad/ad_utils.py --excute_program check_ad_origin_hive --partition ${today_early_1} --hh ${endTime})
-#  if [ $python_return_code -eq 0 ]; then
-#    echo "Python程序返回0，退出循环。"
-#    break
-#  fi
-#  echo "Python程序返回非0值，等待五分钟后再次调用。"
-#  sleep 300
-#  current_hour=$(date +%H)
-#  current_minute=$(date +%M)
-#  if (( current_hour > max_hour || (current_hour == max_hour && current_minute >= max_minute) )); then
-#    echo "最长等待时间已到，失败:${current_hour}-${current_minute}"
-#    msg="广告特征数据校验失败，大数据分区没有数据: ${today_early_1}${endTime}"
-#    /root/anaconda3/bin/python ad/utils_monitor.py ${msg}
-#    exit 1
-#  fi
-#done
-#
-#
-## 2 原始特征生成
-#/opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
-#--class com.aliyun.odps.spark.zhp.makedata_ad.makedata_ad_31_originData_20240620 \
-#--master yarn --driver-memory 1G --executor-memory 2G --executor-cores 1 --num-executors 16 \
-#./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
-#tablePart:64 repartition:32 \
-#beginStr:${beginStr} endStr:${endStr} \
-#savePath:${originDataSavePath} \
-#table:alg_recsys_ad_sample_all_new
-#if [ $? -ne 0 ]; then
-#   echo "Spark原始样本生产任务执行失败"
-#   msg="广告特征数据生成失败，Spark原始样本生产任务执行失败"
-#   /root/anaconda3/bin/python ad/utils_monitor.py ${msg}
-#   exit 1
-#else
-#    echo "spark原始样本生产执行成功"
-#fi
-#
-#
-## 3 特征分桶
-#/opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
-#--class com.aliyun.odps.spark.zhp.makedata_ad.makedata_ad_33_bucketData_20240622 \
-#--master yarn --driver-memory 2G --executor-memory 4G --executor-cores 1 --num-executors 16 \
-#./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
-#beginStr:${today_early_1} endStr:${today_early_1} repartition:400 \
-#filterNames:XXXXX \
-#bucketFileName:20240620_ad_bucket_249_fix.txt \
-#readPath:${originDataSavePath} \
-#savePath:${bucketFeatureSavePath}
-#if [ $? -ne 0 ]; then
-#   echo "Spark特征分桶处理任务执行失败"
-#   msg="广告特征分桶失败，Spark特征分桶处理任务执行失败"
-#   /root/anaconda3/bin/python ad/utils_monitor.py ${msg}
-#   exit 1
-#else
-#   echo "spark特征分桶处理执行成功"
-#fi
-#
-#
-## 4 模型训练
-#$HADOOP fs -text ${bucketFeatureSavePath}/${today_early_1}/* | /root/sunmingze/alphaFM/bin/fm_train -m model/${model_name}_${today_early_1}.txt -dim 1,1,0 -core 8
-#if [ $? -ne 0 ]; then
-#   echo "模型训练失败"
-#   /root/anaconda3/bin/python ad/utils_monitor.py "广告模型训练失败"
-#   exit 1
-#fi
-#
-#
-## 5 对比AUC
-## 5.1 生成今天08-10的原始特征数据
-#
-#
-#
-#
-#
-#
-#
-#
-#
-#
-#
-#
-#
-#
-## 6 模型格式转换
-#cat ${MODEL_PATH}/${model_name}_${today_early_1}.txt \
-#| sed '1d' | awk -F " " '{if($2!="0") print $1"\t"$2}' \
-#> ${MODEL_PATH}/${model_name}_${today_early_1}_change.txt
 
+# 1 判断依赖的数据表是否生产完成
+source /root/anaconda3/bin/activate py37
+max_hour=15
+max_minute=00
+while true; do
+  python_return_code=$(python ad/ad_utils.py --excute_program check_ad_origin_hive --partition ${today_early_1} --hh ${endTime})
+  if [ $python_return_code -eq 0 ]; then
+    echo "Python程序返回0，退出循环。"
+    break
+  fi
+  echo "Python程序返回非0值，等待五分钟后再次调用。"
+  sleep 300
+  current_hour=$(date +%H)
+  current_minute=$(date +%M)
+  if (( current_hour > max_hour || (current_hour == max_hour && current_minute >= max_minute) )); then
+    echo "最长等待时间已到，失败:${current_hour}-${current_minute}"
+    msg="广告特征数据校验失败，大数据分区没有数据: ${today_early_1}${endTime}"
+    /root/anaconda3/bin/python ad/utils_monitor.py ${msg}
+    exit 1
+  fi
+done
 
-# 7 模型文件上传OSS
-online_model_path=${OSS_PATH}/${model_name}.txt
-$HADOOP fs -test -e ${online_model_path}
-if [ $? -eq 0 ]; then
-    echo "数据存在, 先删除。"
-    $HADOOP fs -rm -r ${online_model_path}
+
+# 2 原始特征生成
+/opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
+--class com.aliyun.odps.spark.zhp.makedata_ad.makedata_ad_31_originData_20240620 \
+--master yarn --driver-memory 1G --executor-memory 2G --executor-cores 1 --num-executors 16 \
+./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
+tablePart:64 repartition:32 \
+beginStr:${beginStr} endStr:${endStr} \
+savePath:${originDataSavePath} \
+table:alg_recsys_ad_sample_all_new
+if [ $? -ne 0 ]; then
+   echo "Spark原始样本生产任务执行失败"
+   msg="广告特征数据生成失败，Spark原始样本生产任务执行失败"
+   /root/anaconda3/bin/python ad/utils_monitor.py ${msg}
+   exit 1
 else
-    echo "数据不存在"
+    echo "spark原始样本生产执行成功"
 fi
 
+
+# 3 特征分桶
+/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.zhp.makedata_ad.makedata_ad_33_bucketData_20240622 \
+--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:400 \
+filterNames:XXXXX \
+bucketFileName:20240620_ad_bucket_249_fix.txt \
+readPath:${originDataSavePath} \
+savePath:${bucketFeatureSavePath}
+if [ $? -ne 0 ]; then
+   echo "Spark特征分桶处理任务执行失败"
+   msg="广告特征分桶失败，Spark特征分桶处理任务执行失败"
+   /root/anaconda3/bin/python ad/utils_monitor.py ${msg}
+   exit 1
+else
+   echo "spark特征分桶处理执行成功"
+fi
+
+
+# 4 模型训练
+$HADOOP fs -text ${bucketFeatureSavePath}/${today_early_1}/* | /root/sunmingze/alphaFM/bin/fm_train -m model/${model_name}_${today_early_1}.txt -dim 1,1,0 -core 8
+if [ $? -ne 0 ]; then
+   echo "模型训练失败"
+   /root/anaconda3/bin/python ad/utils_monitor.py "广告模型训练失败"
+   exit 1
+fi
+
+
+# 5 对比AUC
+# 5.1 生成今天08-10的原始特征数据
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# 6 模型格式转换
+cat ${MODEL_PATH}/${model_name}_${today_early_1}.txt \
+| sed '1d' | awk -F " " '{if($2!="0") print $1"\t"$2}' \
+> ${MODEL_PATH}/${model_name}_${today_early_1}_change.txt
+
+
+# 7 模型文件上传OSS
+online_model_path=${OSS_PATH}/${model_name}.txt
+#$HADOOP fs -test -e ${online_model_path}
+#if [ $? -eq 0 ]; then
+#    echo "数据存在, 先删除。"
+#    $HADOOP fs -rm -r ${online_model_path}
+#else
+#    echo "数据不存在"
+#fi
 $HADOOP fs -put ${MODEL_PATH}/${model_name}_${today_early_1}_change.txt ${online_model_path}
 if [ $? -eq 0 ]; then
    echo "推荐模型文件至OSS成功"