SQL Server Spark SQL数据加载和保存的解决办法

这篇文章主要为大家详细介绍了SQL Server Spark SQL数据加载和保存的简单示例，具有一定的参考价值，可以用来参考一下。

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一、前置知识详解

二、Spark SQL读写数据代码实战

代码如下：

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.sql.*;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

import java.util.ArrayList;
import java.util.List;

public class SparkSQLLoadSaveOps {
 public static void main(String[] args) {
  SparkConf conf = new SparkConf().setMaster("local").setAppName("SparkSQLLoadSaveOps");
  JavaSparkContext sc = new JavaSparkContext(conf);
  SQLContext = new SQLContext(sc);
  /**
   * read()是DataFrameReader类型，load可以将数据读取出来
   */
  DataFrame peopleDF = sqlContext.read().format("json").load("E:\\Spark\\Sparkinstanll_package\\Big_Data_Software\\spark-1.6.0-bin-hadoop2.6\\examples\\src\\main\\resources\\people.json");

  /**
   * 直接对DataFrame进行操作
   * Json: 是一种自解释的格式，读取Json的时候怎么判断其是什么格式？
   * 通过扫描整个Json。扫描之后才会知道元数据
   */
  //通过mode来指定输出文件的是append。创建新文件来追加文件
 peopleDF.select("name").write().mode(SaveMode.Append).save("E:\\personNames");
 }
}

读取过程源码分析如下：1. read方法返回DataFrameReader，用于读取数据。

代码如下：

/**
 * :: Experimental ::
 * Returns a [[DataFrameReader]] that can be used to read data in as a [[DataFrame]].
 * {{{
 *  sqlContext.read.parquet("/path/to/file.parquet")
 *  sqlContext.read.schema(schema).json("/path/to/file.json")
 * }}}
 *
 * @group genericdata
 * @since 1.4.0
 */
@Experimental
//创建DataFrameReader实例，获得了DataFrameReader引用
def read: DataFrameReader = new DataFrameReader(this)

2. 然后再调用DataFrameReader类中的format，指出读取文件的格式。

代码如下：

/**
 * Specifies the input data source format.
 *
 * @since 1.4.0
 */
def format(source: String): DataFrameReader = {
 this.source = source
 this
}

3. 通过DtaFrameReader中load方法通过路径把传入过来的输入变成DataFrame。

代码如下：

/**
 * Loads input in as a [[DataFrame]], for data sources that require a path (e.g. data backed by
 * a local or distributed file system).
 *
 * @since 1.4.0
 */
// TODO: Remove this one in Spark 2.0.
def load(path: String): DataFrame = {
 option("path", path).load()
}

至此，数据的读取工作就完成了，下面就对DataFrame进行操作。下面就是写操作！！！

1. 调用DataFrame中select函数进行对列筛选

代码如下：

/**
 * Selects a set of columns. This is a variant of `select` that can only select
 * existing columns using column names (i.e. cannot construct expressions).
 *
 * {{{
 *  // The following two are equivalent:
 *  df.select("colA", "colB")
 *  df.select($"colA", $"colB")
 * }}}
 * @group dfops
 * @since 1.3.0
 */
@scala.annotation.varargs
def select(col: String, cols: String*): DataFrame = select((col +: cols).map(Column(_)) : _*)

2. 然后通过write将结果写入到外部存储系统中。

代码如下：

/**
 * :: Experimental ::
 * Interface for saving the content of the [[DataFrame]] out into external storage.
 *
 * @group output
 * @since 1.4.0
 */
@Experimental
def write: DataFrameWriter = new DataFrameWriter(this)

3. 在保持文件的时候mode指定追加文件的方式

代码如下：

/**
 * Specifies the behavior when data or table already exists. Options include:
// Overwrite是覆盖
 *  - `SaveMode.Overwrite`: overwrite the existing data.
//创建新的文件，然后追加
 *  - `SaveMode.Append`: append the data.
 *  - `SaveMode.Ignore`: ignore the operation (i.e. no-op).
 *  - `SaveMode.ErrorIfExists`: default option, throw an exception at runtime.
 *
 * @since 1.4.0
 */
def mode(saveMode: SaveMode): DataFrameWriter = {
 this.mode = saveMode
 this
}

4. 最后，save()方法触发action，将文件输出到指定文件中。

代码如下：

/**
 * Saves the content of the [[DataFrame]] at the specified path.
 *
 * @since 1.4.0
 */
def save(path: String): Unit = {
 this.extraOptions += ("path" -> path)
 save()
}

三、Spark SQL读写整个流程图如下

【图片暂缺】

四、对于流程中部分函数源码详解

DataFrameReader.Load（）

1. Load（）返回DataFrame类型的数据集合，使用的数据是从默认的路径读取。

代码如下：

/**
 * Returns the dataset stored at path as a DataFrame,
 * using the default data source configured by spark.sql.sources.default.
 *
 * @group genericdata
 * @deprecated As of 1.4.0, replaced by `read().load(path)`. This will be removed in Spark 2.0.
 */
@deprecated("Use read.load(path). This will be removed in Spark 2.0.", "1.4.0")
def load(path: String): DataFrame = {
//此时的read就是DataFrameReader
 read.load(path)
}

2. 追踪load源码进去，源码如下：在DataFrameReader中的方法。Load()通过路径把输入传进来变成一个DataFrame。

代码如下：

/** 
 * Loads input in as a [[DataFrame]], for data sources that require a path (e.g. data backed by
 * a local or distributed file system).
 *
 * @since 1.4.0
 */
// TODO: Remove this one in Spark 2.0.
def load(path: String): DataFrame = {
 option("path", path).load()
}

3. 追踪load源码如下：

代码如下：

/**
 * Loads input in as a [[DataFrame]], for data sources that don't require a path (e.g. external
 * key-value stores).
 *
 * @since 1.4.0
 */
def load(): DataFrame = {
//对传入的Source进行解析
 val resolved = ResolvedDataSource(
  sqlContext,
  userSpecifiedSchema = userSpecifiedSchema,
  partitionColumns = Array.empty[String],
  provider = source,
  options = extraOptions.toMap)
 DataFrame(sqlContext, LogicalRelation(resolved.relation))
}

DataFrameReader.format()

1. Format：具体指定文件格式，这就获得一个巨大的启示是：如果是Json文件格式可以保持为Parquet等此类操作。Spark SQL在读取文件的时候可以指定读取文件的类型。例如，Json,Parquet.

代码如下：

/**
 * Specifies the input data source format.Built-in options include “parquet”,”json”,etc.
 *
 * @since 1.4.0
 */
def format(source: String): DataFrameReader = {
 this.source = source //FileType
 this
}

DataFrame.write()

1. 创建DataFrameWriter实例

代码如下：

/**
 * :: Experimental ::
 * Interface for saving the content of the [[DataFrame]] out into external storage.
 *
 * @group output
 * @since 1.4.0
 */
@Experimental
def write: DataFrameWriter = new DataFrameWriter(this)
1

2. 追踪DataFrameWriter源码如下：以DataFrame的方式向外部存储系统中写入数据。

代码如下：

/**
 * :: Experimental ::
 * Interface used to write a [[DataFrame]] to external storage systems (e.g. file systems,
 * key-value stores, etc). Use [[DataFrame.write]] to access this.
 *
 * @since 1.4.0
 */
@Experimental
final class DataFrameWriter private[sql](df: DataFrame) {

DataFrameWriter.mode()

1. Overwrite是覆盖，之前写的数据全都被覆盖了。Append:是追加，对于普通文件是在一个文件中进行追加，但是对于parquet格式的文件则创建新的文件进行追加。

代码如下：

/**
 * Specifies the behavior when data or table already exists. Options include:
 *  - `SaveMode.Overwrite`: overwrite the existing data.
 *  - `SaveMode.Append`: append the data.
 *  - `SaveMode.Ignore`: ignore the operation (i.e. no-op).
//默认操作
 *  - `SaveMode.ErrorIfExists`: default option, throw an exception at runtime.
 *
 * @since 1.4.0
 */
def mode(saveMode: SaveMode): DataFrameWriter = {
 this.mode = saveMode
 this
}

2. 通过模式匹配接收外部参数

代码如下：

/**
 * Specifies the behavior when data or table already exists. Options include:
 *  - `overwrite`: overwrite the existing data.
 *  - `append`: append the data.
 *  - `ignore`: ignore the operation (i.e. no-op).
 *  - `error`: default option, throw an exception at runtime.
 *
 * @since 1.4.0
 */
def mode(saveMode: String): DataFrameWriter = {
 this.mode = saveMode.toLowerCase match {
  case "overwrite" => SaveMode.Overwrite
  case "append" => SaveMode.Append
  case "ignore" => SaveMode.Ignore
  case "error" | "default" => SaveMode.ErrorIfExists
  case _ => throw new IllegalArgumentException(s"Unknown save mode: $saveMode. " +
   "Accepted modes are 'overwrite', 'append', 'ignore', 'error'.")
 }
 this
}

DataFrameWriter.save()

1. save将结果保存传入的路径。

代码如下：

/**
 * Saves the content of the [[DataFrame]] at the specified path.
 *
 * @since 1.4.0
 */
def save(path: String): Unit = {
 this.extraOptions += ("path" -> path)
 save()
}

2. 追踪save方法。

代码如下：

/**
 * Saves the content of the [[DataFrame]] as the specified table.
 *
 * @since 1.4.0
 */
def save(): Unit = {
 ResolvedDataSource(
  df.sqlContext,
  source,
  partitioningColumns.map(_.toArray).getOrElse(Array.empty[String]),
  mode,
  extraOptions.toMap,
  df)
}

3. 其中source是SQLConf的defaultDataSourceNameprivate var source: String = df.sqlContext.conf.defaultDataSourceName其中DEFAULT_DATA_SOURCE_NAME默认参数是parquet。

代码如下：

// This is used to set the default data source
val DEFAULT_DATA_SOURCE_NAME = stringConf("spark.sql.sources.default",
 defaultValue = Some("org.apache.spark.sql.parquet"),
 doc = "The default data source to use in input/output.")

DataFrame.scala中部分函数详解：

1. toDF函数是将RDD转换成DataFrame

代码如下：

/**
 * Returns the object itself.
 * @group basic
 * @since 1.3.0
 */
// This is declared with parentheses to prevent the Scala compiler from treating
// `rdd.toDF("1")` as invoking this toDF and then apply on the returned DataFrame.
def toDF(): DataFrame = this

2. show()方法：将结果显示出来

代码如下：

/**
 * Displays the [[DataFrame]] in a tabular form. For example:
 * {{{
 *  year month AVG('Adj Close) MAX('Adj Close)
 *  1980 12  0.503218    0.595103
 *  1981 01  0.523289    0.570307
 *  1982 02  0.436504    0.475256
 *  1983 03  0.410516    0.442194
 *  1984 04  0.450090    0.483521
 * }}}
 * @param numRows Number of rows to show
 * @param truncate Whether truncate long strings. If true, strings more than 20 characters will
 *       be truncated and all cells will be aligned right
 *
 * @group action
 * @since 1.5.0
 */
// scalastyle:off println
def show(numRows: Int, truncate: Boolean): Unit = println(showString(numRows, truncate))
// scalastyle:on println

追踪showString源码如下：showString中触发action收集数据。

代码如下：

/**
 * Compose the string representing rows for output
 * @param _numRows Number of rows to show
 * @param truncate Whether truncate long strings and align cells right
 */
private[sql] def showString(_numRows: Int, truncate: Boolean = true): String = {
 val numRows = _numRows.max(0)
 val sb = new StringBuilder
 val takeResult = take(numRows + 1)
 val hasMoreData = takeResult.length > numRows
 val data = takeResult.take(numRows)
 val numCols = schema.fieldNames.length

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持512笔记。

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