一：前置知識詳解：
Spark SQL重要是操作DataFrame，DataFrame本身提供了save和load的操作，
Load：可以建立DataFrame，
Save：把DataFrame中的資料儲存到檔案或者說與具體的格式來指明我們要讀取的檔案的型別以及與具體的格式來指出我們要輸出的檔案是什麼型別。
二：Spark SQL讀寫資料程式碼實戰：

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型別，load可以將資料讀取出來
         */
        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"
 
);

        /**
         * 直接對DataFrame進行操作
         * Json: 是一種自解釋的格式，讀取Json的時候怎麼判斷其是什麼格式？
         * 通過掃描整個Json。掃描之後才會知道元資料
         */
        //通過mode來指定輸出檔案的是append。建立新檔案來追加檔案
   peopleDF.select("name").write().mode(SaveMode.Append).save("E:\\personNames");
    }
}

讀取過程原始碼分析如下：
1. read方法返回DataFrameReader，用於讀取資料。

/**
 * :: 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
//建立DataFrameReader例項，獲得了DataFrameReader引用
def read: DataFrameReader = new DataFrameReader(this)

2.  然後再呼叫DataFrameReader類中的format，指出讀取檔案的格式。

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

3.  通過DtaFrameReader中load方法通過路徑把傳入過來的輸入變成DataFrame。

/**
 * 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，資料的讀取工作就完成了，下面就對DataFrame進行操作。
下面就是寫操作！！！
1. 呼叫DataFrame中select函式進行對列篩選

/**
 * 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.  然後通過write將結果寫入到外部儲存系統中。

/**
 * :: 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.   在保持檔案的時候mode指定追加檔案的方式

/**
 * Specifies the behavior when data or table already exists. Options include:
// Overwrite是覆蓋
 *   - `SaveMode.Overwrite`: overwrite the existing data.
//建立新的檔案，然後追加
 *   - `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.   最後，save()方法觸發action，將檔案輸出到指定檔案中。

/**
 * 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讀寫整個流程圖如下：

四：對於流程中部分函式原始碼詳解：
DataFrameReader.Load（）
1. Load（）返回DataFrame型別的資料集合，使用的資料是從預設的路徑讀取。

/**
 * 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 = {
//此時的read就是DataFrameReader
  read.load(path)
}

2.  追蹤load原始碼進去，原始碼如下：

在DataFrameReader中的方法。Load()通過路徑把輸入傳進來變成一個DataFrame。

/** 
 * 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.  追蹤load原始碼如下：

/**
 * 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 = {
//對傳入的Source進行解析
  val resolved = ResolvedDataSource(
    sqlContext,
    userSpecifiedSchema = userSpecifiedSchema,
    partitionColumns = Array.empty[String],
    provider = source,
    options = extraOptions.toMap)
  DataFrame(sqlContext, LogicalRelation(resolved.relation))
}

DataFrameReader.format()
1. Format：具體指定檔案格式，這就獲得一個巨大的啟示是：如果是Json檔案格式可以保持為Parquet等此類操作。
Spark SQL在讀取檔案的時候可以指定讀取檔案的型別。例如，Json,Parquet.

/**
 * 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. 建立DataFrameWriter例項

/**
 * :: 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)

2.  追蹤DataFrameWriter原始碼如下：

以DataFrame的方式向外部儲存系統中寫入資料。

/**
 * :: 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是覆蓋，之前寫的資料全都被覆蓋了。
Append:是追加，對於普通檔案是在一個檔案中進行追加，但是對於parquet格式的檔案則建立新的檔案進行追加。

/**
 * 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).
//預設操作
 *   - `SaveMode.ErrorIfExists`: default option, throw an exception at runtime.
 *
 * @since 1.4.0
 */
def mode(saveMode: SaveMode): DataFrameWriter = {
  this.mode = saveMode
  this
}

2.  通過模式匹配接收外部引數

/**
 * 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將結果儲存傳入的路徑。

/**
 * 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.  追蹤save方法。

/**
 * 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defaultDataSourceName

private var source: String = df.sqlContext.conf.defaultDataSourceName

其中DEFAULT_DATA_SOURCE_NAME預設引數是parquet。

// 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中部分函式詳解：
1. toDF函式是將RDD轉換成DataFrame

/**
 * 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：將結果顯示出來

/**
 * 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

追蹤showString原始碼如下：showString中觸發action收集資料。

/**
 * 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