i have an error when I try to compile, test and run a junit test.
I want to load a local Avro file using DataFrames but I am getting an exception:
org.xerial.snappy.SnappyError: [FAILED_TO_LOAD_NATIVE_LIBRARY] null I am not using Cassandra at all, the version of involved jars are:
<properties> <!-- Generic properties --> <java.version>1.7</java.version> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding> <!-- Dependency versions --> <maven.compiler.source>1.7</maven.compiler.source> <maven.compiler.target>1.7</maven.compiler.target> <scala.version>2.10.4</scala.version> <junit.version>4.11</junit.version> <slf4j.version>1.7.12</slf4j.version> <spark.version>1.5.0-cdh5.5.2</spark.version> <databricks.version>1.5.0</databricks.version> <json4s-native.version>3.5.0</json4s-native.version> <spark-avro.version>2.0.1</spark-avro.version> </properties> and these are the dependencies:
<dependencies> <dependency> <groupId>org.json4s</groupId> <artifactId>json4s-native_2.10</artifactId> <version>${json4s-native.version}</version> </dependency> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>${junit.version}</version> <scope>test</scope> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.10</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>com.databricks</groupId> <artifactId>spark-csv_2.10</artifactId> <version>${databricks.version}</version> <exclusions> <exclusion> <groupId>org.xerial.snappy</groupId> <artifactId>snappy-java</artifactId> </exclusion> </exclusions> </dependency> <dependency> <groupId>org.xerial.snappy</groupId> <artifactId>snappy-java</artifactId> <version>1.0.4.1</version> <scope>compile</scope> </dependency> <dependency> <groupId>com.databricks</groupId> <artifactId>spark-avro_2.10</artifactId> <version>${spark-avro.version}</version> </dependency> <!-- --> <dependency> <groupId>log4j</groupId> <artifactId>log4j</artifactId> <version>1.2.17</version> </dependency> </dependencies> I have tried to compile the project with
mvn clean install -Dorg.xerial.snappy.lib.name=libsnappyjava.jnlib -Dorg.xerial.snappy.tempdir=/tmp
before copying the jar within /tmp, with no luck.
$ ls -lt /tmp/ total 1944 ...27 dic 13:01 snappy-java-1.0.4.jar This is the code:
import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Row, SQLContext, SaveMode} import org.apache.spark.{SparkConf, SparkContext} import com.databricks.spark.avro._ import java.io._ //auxiliary function def readRawData(pathToResources: String, sqlContext: SQLContext, rawFormat: String = "json"): DataFrame = { val a: DataFrame = rawFormat match { case "avro" => sqlContext.read.avro(pathToResources) case "json" => sqlContext.read.json(pathToResources) case _ => throw new Exception("Format not supported, use AVRO or JSON instead.") } val b: DataFrame = a.filter("extraData.type = 'data'") val c: DataFrame = a.select("extraData.topic", "extraData.timestamp", "extraData.sha1Hex", "extraData.filePath", "extraData.fileName", "extraData.lineNumber", "extraData.type", "message") val indexForMessage: Int = c.schema.fieldIndex("message") val result: RDD[Row] = c.rdd.filter(r => !r.anyNull match { case true => true case false => false } ).flatMap(r => { val metadata: String = r.toSeq.slice(0, indexForMessage).mkString(",") val lines = r.getString(indexForMessage).split("\n") lines.map(l => Row.fromSeq(metadata.split(",").toSeq ++ Seq(l))) }) sqlContext.createDataFrame(result, c.schema) }//readRawData def validate(rawFlumeData : String = "FlumeData.1482407196579",fileNamesToBeDigested : String = "fileNames-to-be-digested.txt", sqlContext: SQLContext,sc:SparkContext) : Boolean = { val result : Boolean = true sqlContext.sparkContext.hadoopConfiguration.set("avro.mapred.ignore.inputs.without.extension", "false") val rawDF : DataFrame = readRawData(rawFlumeData, sqlContext, rawFormat = "avro") rawDF.registerTempTable("RAW") //this line provokes the exception! cannot load snappy jar file! val arrayRows : Array[org.apache.spark.sql.Row] = sqlContext.sql("SELECT distinct fileName as filenames FROM RAW GROUP BY fileName").collect() val arrayFileNames : Array[String] = arrayRows.map(row=>row.getString(0)) val fileNamesDigested = "fileNames-AVRO-1482407196579.txt" val pw = new PrintWriter(new File(fileNamesDigested)) for (filename <-arrayFileNames) pw.write(filename + "\n") pw.close val searchListToBeDigested : org.apache.spark.rdd.RDD[String] = sc.textFile(fileNamesToBeDigested) //creo un map con valores como éstos: Map(EUR_BACK_SWVOL_SMILE_GBP_20160930.csv -> 0, UK_SC_equities_20160930.csv -> 14,... //val mapFileNamesToBeDigested: Map[String, Long] = searchListToBeDigested.zipWithUniqueId().collect().toMap val searchFilesAVRODigested = sc.textFile(fileNamesDigested) val mapFileNamesAVRODigested: Map[String, Long] = searchFilesAVRODigested.zipWithUniqueId().collect().toMap val pwResults = new PrintWriter(new File("validation-results.txt")) //Hay que guardar el resultado en un fichero de texto, en algún lado... val buffer = StringBuilder.newBuilder //Me traigo los resultados al Driver. val listFilesToBeDigested = searchListToBeDigested.map {line => val resultTemp = mapFileNamesAVRODigested.getOrElse(line,"NOT INGESTED!") var resul = "" if (resultTemp == "NOT INGESTED!"){ resul = "File " + line + " " + resultTemp + "\n" } else{ resul = "File " + line + " " + " is INGESTED!" + "\n" } resul }.collect() //añado los datos al buffer listFilesToBeDigested.foreach(buffer.append(_)) //guardo el contenido del buffer en el fichero de texto de salida. pwResults.write(buffer.toString) pwResults.close //this boolean must return false in case of a exception or error... result }// This is the unit test code:
private[validation] class ValidateInputCSVFilesTest { //AS YOU CAN SEE, I do not WANT to use snappy at all! val conf = new SparkConf() .setAppName("ValidateInputCSVFilesTest") .setMaster("local[2]") .set("spark.driver.allowMultipleContexts", "true") .set("spark.driver.host", "127.0.0.1") .set("spark.io.compression.codec", "lzf") val sc = new SparkContext(conf) val sqlContext = new org.apache.spark.sql.SQLContext(sc) val properties : Properties = new Properties() properties.setProperty("frtb.input.csv.validation.avro","./src/test/resources/avro/FlumeData.1482407196579") properties.setProperty("frtb.input.csv.validation.list.files","./src/test/resources/fileNames-to-be-digested.txt") import sqlContext.implicits._ sqlContext.sparkContext.hadoopConfiguration.set("avro.mapred.ignore.inputs.without.extension", "false") @Test def testValidateInputFiles() = { //def validate(rawFlumeData : String = "FlumeData.1482407196579",fileNamesToBeDigested : String = "fileNames-to-be-digested.txt", sqlContext: SQLContext) val rawFlumeData = properties.getProperty("frtb.input.csv.validation.avro") val fileNamesToBeDigested = properties.getProperty("frtb.input.csv.validation.list.files") println("rawFlumeData is " + rawFlumeData ) println("fileNamesToBeDigested is " + fileNamesToBeDigested ) val result : Boolean = ValidateInputCSVFiles.validate(rawFlumeData ,fileNamesToBeDigested ,sqlContext,sc) Assert.assertTrue("Must be true...",result) }//end of test method }//end of unit class I can run perfectly the same code in a local spark-shell, using this command:
$ bin/spark-shell --packages org.json4s:json4s-native_2.10:3.5.0 --packages com.databricks:spark-csv_2.10:1.5.0 --packages com.databricks:spark-avro_2.10:2.0.1 What else can I do?
Thanks in advance.
1 Answer
The problem was solved when I changed the scope of spark dependencies.
This is part of the pom.xml that solves my problem, now I can run the job with spark-submit command...
<properties> <!-- Generic properties --> <java.version>1.7</java.version> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding> <!-- Dependency versions --> <maven.compiler.source>1.7</maven.compiler.source> <maven.compiler.target>1.7</maven.compiler.target> <scala.version>2.10.4</scala.version> <junit.version>4.11</junit.version> <slf4j.version>1.7.12</slf4j.version> <spark.version>1.5.0-cdh5.5.2</spark.version> <databricks.version>1.5.0</databricks.version> <json4s-native.version>3.5.0</json4s-native.version> <spark-avro.version>2.0.1</spark-avro.version> </properties> ...
<dependencies> <dependency> <groupId>org.json4s</groupId> <artifactId>json4s-native_2.10</artifactId> <version>${json4s-native.version}</version> </dependency> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>${junit.version}</version> <scope>test</scope> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.10</artifactId> <version>${spark.version}</version> <scope>provided</scope> </dependency> <dependency> <groupId>com.databricks</groupId> <artifactId>spark-csv_2.10</artifactId> <version>${databricks.version}</version> <scope>provided</scope> <exclusions> <exclusion> <groupId>org.xerial.snappy</groupId> <artifactId>snappy-java</artifactId> </exclusion> </exclusions> </dependency> <dependency> <groupId>org.xerial.snappy</groupId> <artifactId>snappy-java</artifactId> <version>1.0.4.1</version> <scope>provided</scope> </dependency> <dependency> <groupId>com.databricks</groupId> <artifactId>spark-avro_2.10</artifactId> <version>${spark-avro.version}</version> </dependency> <!-- --> <dependency> <groupId>log4j</groupId> <artifactId>log4j</artifactId> <version>1.2.17</version> </dependency> </dependencies> ...
