| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You under the Apache License, Version 2.0 |
| * (the "License"); you may not use this file except in compliance with |
| * the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package org.apache.ambari.metrics.adservice.spark.prototype |
| |
| import org.apache.ambari.metrics.adservice.prototype.methods.ema.EmaTechnique |
| import org.apache.hadoop.metrics2.sink.timeline.TimelineMetric |
| import org.apache.spark.sql.SQLContext |
| import org.apache.spark.{SparkConf, SparkContext} |
| |
| object SparkPhoenixReader { |
| |
| def main(args: Array[String]) { |
| |
| // if (args.length < 6) { |
| // System.err.println("Usage: SparkPhoenixReader <metric_name> <appId> <hostname> <weight> <timessdev> <phoenixConnectionString> <model_dir>") |
| // System.exit(1) |
| // } |
| // |
| // var metricName = args(0) |
| // var appId = args(1) |
| // var hostname = args(2) |
| // var weight = args(3).toDouble |
| // var timessdev = args(4).toInt |
| // var phoenixConnectionString = args(5) //avijayan-ams-3.openstacklocal:61181:/ams-hbase-unsecure |
| // var modelDir = args(6) |
| // |
| // val conf = new SparkConf() |
| // conf.set("spark.app.name", "AMSAnomalyModelBuilder") |
| // //conf.set("spark.master", "spark://avijayan-ams-2.openstacklocal:7077") |
| // |
| // var sc = new SparkContext(conf) |
| // val sqlContext = new SQLContext(sc) |
| // |
| // val currentTime = System.currentTimeMillis() |
| // val oneDayBack = currentTime - 24*60*60*1000 |
| // |
| // val df = sqlContext.load("org.apache.phoenix.spark", Map("table" -> "METRIC_RECORD", "zkUrl" -> phoenixConnectionString)) |
| // df.registerTempTable("METRIC_RECORD") |
| // val result = sqlContext.sql("SELECT METRIC_NAME, HOSTNAME, APP_ID, SERVER_TIME, METRIC_SUM, METRIC_COUNT FROM METRIC_RECORD " + |
| // "WHERE METRIC_NAME = '" + metricName + "' AND HOSTNAME = '" + hostname + "' AND APP_ID = '" + appId + "' AND SERVER_TIME < " + currentTime + " AND SERVER_TIME > " + oneDayBack) |
| // |
| // var metricValues = new java.util.TreeMap[java.lang.Long, java.lang.Double] |
| // result.collect().foreach( |
| // t => metricValues.put(t.getLong(3), t.getDouble(4) / t.getInt(5)) |
| // ) |
| // |
| // //val seriesName = result.head().getString(0) |
| // //val hostname = result.head().getString(1) |
| // //val appId = result.head().getString(2) |
| // |
| // val timelineMetric = new TimelineMetric() |
| // timelineMetric.setMetricName(metricName) |
| // timelineMetric.setAppId(appId) |
| // timelineMetric.setHostName(hostname) |
| // timelineMetric.setMetricValues(metricValues) |
| // |
| // var emaModel = new EmaTechnique(weight, timessdev) |
| // emaModel.test(timelineMetric) |
| // emaModel.save(sc, modelDir) |
| |
| } |
| |
| } |
