//设置package包名称以及导入依赖的类
package com.jasonfeist.spark.tika

import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.sources.{BaseRelation, RelationProvider, SchemaRelationProvider}
import org.apache.spark.sql.types._


class DefaultSource
  extends RelationProvider with SchemaRelationProvider  {

    def createRelation(sqlContext: SQLContext, parameters: Map[String, String], schema: StructType): BaseRelation = {
      parameters.getOrElse("path", sys.error("No path specified."))
      new TikaMetadataRelation(
        parameters.get("path").get,
        schema,
        new MetadataExtractor(),
        new FieldDataExtractor())(sqlContext)
    }

    override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
      val struct =
        StructType(
              StructField("detectedtype", StringType, true) ::
              StructField("language", StringType, true) ::
              StructField("filename", StringType, true) ::
              StructField("author", StringType, true)  ::
              StructField("text", StringType, true)  ::
              StructField("creation-date", TimestampType, true) ::
              StructField("title", StringType, true) ::
              StructField("content-length", IntegerType, true) ::
              StructField("last-modified", DateType, true) :: Nil
        )
      createRelation(sqlContext, parameters, struct)
    }
  } 

//设置package包名称以及导入依赖的类
package com.rishabh.spark.datasource.s3

import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, RelationProvider}
import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode}

class DefaultSource extends RelationProvider with CreatableRelationProvider {

  override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]):
  BaseRelation = {
    val accessKey = parameters.getOrElse("accesskey", sys.error("accesskey is required"))
    val secretKey = parameters.getOrElse("secretkey", sys.error("secretkey is required"))
    val fileType = parameters.getOrElse("type", sys.error("filetype is required"))
    val path = parameters.getOrElse("path", sys.error("path is required"))
    val bucket = parameters.getOrElse("bucketName", sys.error("bucket is required"))
    new S3Relation(accessKey, secretKey, fileType, bucket, path, false)(sqlContext)
  }

  override def createRelation(sqlContext: SQLContext, mode: SaveMode, parameters: Map[String,
    String], data: DataFrame): BaseRelation = {
    val accesskey = parameters.getOrElse("accesskey",sys.error("accesskey is required"))
    val secretkey = parameters.getOrElse("secretkey", sys.error("secretkey is required"))
    val bucket = parameters.getOrElse("bucketName", sys.error("bucket is required"))
    val fileType = parameters.getOrElse("type", sys.error("filetype is required"))
    val path = parameters.getOrElse("path", sys.error("path is required"))

    val supported = List("json", "parquet", "csv")
    if (!supported.contains(fileType)) {
      sys.error("fileType " + fileType + " not supported.")
    }
    val hadoopConf = sqlContext.sparkContext.hadoopConfiguration
    hadoopConf.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
    hadoopConf.set("fs.s3a.access.key", accesskey)
    hadoopConf.set("fs.s3a.secret.key", secretkey)
    val s3Path = "s3a://" + bucket + path
    doSave(fileType, data, s3Path)

    new S3Relation(accesskey, secretkey, fileType, bucket, path, true)(sqlContext)
  }

  private def doSave(fileType: String, dataFrame: DataFrame, path: String) = {
    fileType match {
      case "json" =>
        dataFrame.write.json(path)
      case "parquet" =>
        dataFrame.write.parquet(path)
      case "csv" =>
        dataFrame.write.format("com.databricks.spark.csv").save(path)
    }
  }
} 

//设置package包名称以及导入依赖的类
package net.sansa_stack.inference.spark.data.loader.sql

import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.sources.{BaseRelation, RelationProvider, SchemaRelationProvider}
import org.apache.spark.sql.types.StructType


class DefaultSource extends RelationProvider with SchemaRelationProvider {
    override def createRelation(sqlContext: SQLContext, parameters: Map[String, String])
    : BaseRelation = {
      createRelation(sqlContext, parameters, null)
    }
    override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]
                                , schema: StructType)
    : BaseRelation = {
      parameters.getOrElse("path", sys.error("'path' must be specified for our data."))
      return new NTriplesRelation(parameters.get("path").get, schema)(sqlContext)
    }
  } 

//设置package包名称以及导入依赖的类
package com.github.traviscrawford.spark.dynamodb

import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.sources.BaseRelation
import org.apache.spark.sql.sources.RelationProvider
import org.apache.spark.sql.sources.SchemaRelationProvider
import org.apache.spark.sql.types.StructType

private[dynamodb] class DefaultSource
  extends RelationProvider with SchemaRelationProvider {

  override def createRelation(
      sqlContext: SQLContext,
      parameters: Map[String, String])
    : BaseRelation = getDynamoDBRelation(sqlContext, parameters)

  override def createRelation(
      sqlContext: SQLContext,
      parameters: Map[String, String],
      schema: StructType)
    : BaseRelation = getDynamoDBRelation(sqlContext, parameters, Some(schema))

  private def getDynamoDBRelation(
      sqlContext: SQLContext,
      parameters: Map[String, String],
      maybeSchema: Option[StructType] = None)
    : DynamoDBRelation = {

    val tableName = parameters.getOrElse("table",
      throw new IllegalArgumentException("Required parameter 'table' was unspecified.")
    )

    DynamoDBRelation(
      tableName = tableName,
      maybeFilterExpression = parameters.get("filter_expression"),
      maybePageSize = parameters.get("page_size"),
      maybeRegion = parameters.get("region"),
      maybeSegments = parameters.get("segments"),
      maybeRateLimit = parameters.get("rate_limit_per_segment").map(Integer.parseInt),
      maybeSchema = maybeSchema,
      maybeCredentials = parameters.get("aws_credentials_provider"),
      maybeEndpoint = parameters.get("endpoint"))(sqlContext)
  }
} 

//设置package包名称以及导入依赖的类
package com.springml.spark.workday

import com.springml.spark.workday.model.{WWSInput, XPathInput}
import com.springml.spark.workday.util.CSVUtil
import org.apache.log4j.Logger
import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, RelationProvider, SchemaRelationProvider}
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode}


class DefaultSource extends RelationProvider with SchemaRelationProvider with CreatableRelationProvider {
  @transient val logger = Logger.getLogger(classOf[DefaultSource])

  override def createRelation(sqlContext: SQLContext,
                              parameters: Map[String, String]): BaseRelation = {
    createRelation(sqlContext, parameters, null)
  }

  override def createRelation(sqlContext: SQLContext,
                              parameters: Map[String, String],
                              schema: StructType): BaseRelation = {
    val username = param(parameters, "username")
    val password = param(parameters, "password")
    val wwsEndpoint = param(parameters, "wwsEndpoint")
    val objectTag = param(parameters, "objectTagPath")
    val detailsTag = param(parameters, "detailsTagPath")
    val request = param(parameters, "request")
    val xpath = param(parameters, "xpathMap")
    val namespacePrefix = parameters.get("namespacePrefixMap")

    val wwsInput = new WWSInput(username, password, wwsEndpoint, request)
    val xPathInput = new XPathInput(objectTag, detailsTag)
    CSVUtil.populateXPathInput(xpath, xPathInput)
    xPathInput.namespaceMap = CSVUtil.readCSV(namespacePrefix.get)
    logger.debug("Namespace Map" + xPathInput.namespaceMap)

    val records = new WWSReader(wwsInput, xPathInput) read()
    new DatasetRelation(records, sqlContext, schema)
  }

  override def createRelation(sqlContext: SQLContext,
                              mode: SaveMode,
                              parameters: Map[String, String],
                              data: DataFrame): BaseRelation = {
    logger.error("Save not supported by workday connector")
    throw new UnsupportedOperationException
  }

  private def param(parameters: Map[String, String],
                    paramName: String) : String = {
    val paramValue = parameters.getOrElse(paramName,
      sys.error(s"""'$paramName' must be specified for Spark Workday package"""))

    if ("password".equals(paramName)) {
      logger.debug("Param " + paramName + " value " + paramValue)
    }

    paramValue
  }

} 

//设置package包名称以及导入依赖的类
package org.apache.spark.sql.sparkcv

import org.apache.spark.internal.Logging
import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode}
import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, RelationProvider, SchemaRelationProvider}
import org.apache.spark.sql.types.StructType
import org.bytedeco.javacpp.opencv_core.IplImage
import org.bytedeco.javacpp.opencv_imgcodecs.cvLoadImage

class DefaultSource
  extends RelationProvider
    with SchemaRelationProvider
    with CreatableRelationProvider
    with Logging {

  
  override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
    createRelation(sqlContext, parameters, new StructType())
  }

  override def createRelation(sqlContext: SQLContext, parameters: Map[String, String], schema: StructType): BaseRelation = {
    assert(parameters.get("path").isDefined, "path parameter is required")
    val image: IplImage = cvLoadImage("src/main/resources/birds-of-paradise.jpg")
    ImageRelation(sqlContext, parameters, schema)
  }

  override def createRelation(sqlContext: SQLContext, mode: SaveMode, parameters: Map[String, String], data: DataFrame): BaseRelation = {
    ImageRelation(sqlContext, parameters, data.schema)
  }
} 

//设置package包名称以及导入依赖的类
package solr

import com.lucidworks.spark.SolrRelation
import com.lucidworks.spark.util.Constants
import org.apache.spark.sql.{DataFrame, SaveMode, SQLContext}
import org.apache.spark.sql.sources.{DataSourceRegister, BaseRelation, CreatableRelationProvider, RelationProvider}

class DefaultSource extends RelationProvider with CreatableRelationProvider with DataSourceRegister {

  override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = {
    try {
      return new SolrRelation(parameters, sqlContext)
    } catch {
      case re: RuntimeException => throw re
      case e: Exception => throw new RuntimeException(e)
    }
  }

  override def createRelation(
      sqlContext: SQLContext,
      mode: SaveMode,
      parameters: Map[String, String],
      df: DataFrame): BaseRelation = {
    try {
      // TODO: What to do with the saveMode?
      val solrRelation: SolrRelation = new SolrRelation(parameters, sqlContext, Some(df))
      solrRelation.insert(df, overwrite = true)
      solrRelation
    } catch {
      case re: RuntimeException => throw re
      case e: Exception => throw new RuntimeException(e)
    }
  }

  override def shortName(): String = Constants.SOLR_FORMAT
} 

//设置package包名称以及导入依赖的类
package com.skapane.spark.xls

import org.apache.spark.sql.sources.RelationProvider
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.sources.BaseRelation

class DefaultSource extends RelationProvider {

  override def createRelation(
    sqlContext: SQLContext,
    parameters: Map[String, String]): BaseRelation = {

    val path = parameters("path")

    val normalizeNames = parameters.get("normalizeNames") match {
      case Some(e) => e.toBoolean
      case _ => true
    }

    XlsRelation(
      path,
      parameters.get("sheet"),
      normalizeNames)(sqlContext)

  }
}