一、前置知識(shí)詳解
Spark SQL重要是操作DataFrame，DataFrame本身提供了save和load的操作，
Load：可以創(chuàng)建DataFrame，
Save：把DataFrame中的數(shù)據(jù)保存到文件或者說(shuō)與具體的格式來(lái)指明我們要讀取的文件的類型以及與具體的格式來(lái)指出我們要輸出的文件是什么類型。
二、Spark SQL讀寫數(shù)據(jù)代碼實(shí)戰(zhàn)
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類型，load可以將數(shù)據(jù)讀取出來(lái)
*/
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(duì)DataFrame進(jìn)行操作
* Json: 是一種自解釋的格式，讀取Json的時(shí)候怎么判斷其是什么格式？
* 通過(guò)掃描整個(gè)Json。掃描之后才會(huì)知道元數(shù)據(jù)
*/
//通過(guò)mode來(lái)指定輸出文件的是append。創(chuàng)建新文件來(lái)追加文件
peopleDF.select("name").write().mode(SaveMode.Append).save("E:\\personNames");
}
}
讀取過(guò)程源碼分析如下：
1. read方法返回DataFrameReader，用于讀取數(shù)據(jù)。
/**
* :: 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
//創(chuàng)建DataFrameReader實(shí)例，獲得了DataFrameReader引用
def read: DataFrameReader = new DataFrameReader(this)
2. 然后再調(diào)用DataFrameReader類中的format，指出讀取文件的格式。
/**
* Specifies the input data source format.
*
* @since 1.4.0
*/
def format(source: String): DataFrameReader = {
this.source = source
this
}
3. 通過(guò)DtaFrameReader中l(wèi)oad方法通過(guò)路徑把傳入過(guò)來(lái)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，數(shù)據(jù)的讀取工作就完成了，下面就對(duì)DataFrame進(jìn)行操作。
下面就是寫操作?。?！
1. 調(diào)用DataFrame中select函數(shù)進(jìn)行對(duì)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. 然后通過(guò)write將結(jié)果寫入到外部存儲(chǔ)系統(tǒng)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. 在保持文件的時(shí)候mode指定追加文件的方式
/**
* Specifies the behavior when data or table already exists. Options include:
// Overwrite是覆蓋
* - `SaveMode.Overwrite`: overwrite the existing data.
//創(chuàng)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()方法觸發(fā)action，將文件輸出到指定文件中。
/**
* 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(gè)流程圖如下
四、對(duì)于流程中部分函數(shù)源碼詳解
DataFrameReader.Load（）
1. Load（）返回DataFrame類型的數(shù)據(jù)集合，使用的數(shù)據(jù)是從默認(rèn)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(shí)的read就是DataFrameReader
read.load(path)
}
2. 追蹤load源碼進(jìn)去，源碼如下：
在DataFrameReader中的方法。Load()通過(guò)路徑把輸入傳進(jìn)來(lái)變成一個(gè)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源碼如下：
/**
* 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(duì)傳入的Source進(jìn)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：具體指定文件格式，這就獲得一個(gè)巨大的啟示是：如果是Json文件格式可以保持為Parquet等此類操作。
Spark SQL在讀取文件的時(shí)候可以指定讀取文件的類型。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. 創(chuàng)建DataFrameWriter實(shí)例
/**
* :: 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. 追蹤DataFrameWriter源碼如下：
以DataFrame的方式向外部存儲(chǔ)系統(tǒng)中寫入數(shù)據(jù)。
/**
* :: 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，之前寫的數(shù)據(jù)全都被覆蓋了。
Append:是追加，對(duì)于普通文件是在一個(gè)文件中進(jìn)行追加，但是對(duì)于parquet格式的文件則創(chuàng)建新的文件進(jìn)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).
//默認(rèn)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(guò)模式匹配接收外部參數(shù)
/**
* 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(jié)果保存?zhèn)魅氲穆窂健?/p>
/**
* 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默認(rèn)參數(shù)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中部分函數(shù)詳解：
1. toDF函數(shù)是將RDD轉(zhuǎn)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(jié)果顯示出來(lái)
/**
* 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源碼如下：showString中觸發(fā)action收集數(shù)據(jù)。
/**
* 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?jī)?nèi)容，希望對(duì)大家的學(xué)習(xí)有所幫助，也希望大家多多支持腳本之家。
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