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03-10-2017
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10-12-2020
04:09 PM
1 Kudo
I am trying to set ACL permissions, using the HUE interface, to a specific user in a HDFS directory. I have already installed and configured sentry to use it with Hive and HDFS. When creating the ACL i get the following error: 400 Client Error: Bad Request for url: XXX aclspec=&op=REMOVEACLENTRIES&user.name=hue&doas=admin {"RemoteException":{"exception":"IllegalArgumentException","javaClassName":"java.lang.IllegalArgumentException","message":"Required param aclspec for op: REMOVEACLENTRIES is null or empty"}}. I have tried researching this error and it seems HUE is constructing the http request wrong. Could some one help me? I have no idea what is going on Thank you in advance
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Apache Sentry
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Cloudera Hue
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HDFS
12-29-2016
05:49 AM
Even though i have already imported all the necessary libraries for using RandomForestClassifier with weightCol parameter, I still get the following error: value weightCol is not a member of org.apache.spark.ml.classification.RandomForestClassifier. I'm currently using Spark 1.6.1. Here is my code: import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{RandomForestClassificationModel, RandomForestClassifier}
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.feature.VectorAssembler
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.implicits._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.functions.{unix_timestamp, from_unixtime, to_date}
val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc)
import sqlContext.implicits._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.functions.{unix_timestamp, from_unixtime, to_date}
import org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.sql.Row
import org.apache.spark.ml.param.shared.HasWeightCol
val raw = sqlContext.sql("SELECT * FROM fraudegt.sample_cdr_train_v2")
val mod = raw.withColumn("id", raw("id").cast("string"))
val mod1 = mod.na.fill(0)
val assembler = new VectorAssembler().setInputCols(Array("hora_del_dia","dia_mes","duracion","duracion_dia","duracion_24h","avg_duracion_dia","avg_duracion_24h","avg_duracion_historica","celdas_iniciales_distintas_dia","celdas_iniciales_distintas_historico","celdas_finales_distintas_dia","celdas_finales_distintas_historico","pmc_dia","pmc_historico","imcd_dia","imcd_historico","llamadas_en_dia")).setOutputCol("features")
val df_all = assembler.transform(mod1)
val labelIndexer = new StringIndexer().setInputCol("fraude").setOutputCol("label")
val df = labelIndexer.fit(df_all).transform(df_all)
val splits = df.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))
val classifier = new RandomForestClassifier().setImpurity("gini").setMaxDepth(4).setNumTrees(100).setFeatureSubsetStrategy("auto").setSeed(5043)
val model = classifier.fit(trainingData)
val model2 = classifier.fit(trainingData, classifier.weightCol->"weight")
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Apache Spark
