Day61-spark SQL data loading and saving insider deep decryption combat

Spark SQL Load Data

Sparksql data input and output mainly Dataframe,dataframe provides some common load and save operations.

You can create a dataframe by using the load, save the Dataframe data to a file or in a specific format to indicate what format the file is to be read or what format the output data is, and directly read the specified type of file:

SqlContext Source:

Load and Save methods

@deprecated("Use Read.load (path). This is being removed in Spark 2.0. ","1.4.0")
defLoad(Path:String): DataFrame = {
Read.load (PATH)
}

/**
* Returns the dataset stored at path ASA DataFrame, using the given data source.
*
* @group genericdata
* @deprecated as of 1.4.0,replaced by`read (). Format (source). Load (path)`.
* This'll be removed in Spark 2.0.
*/
@deprecated(Useread.format (source). Load (path). This is being removed in Spark 2.0. ","1.4.0")
defLoad(Path:String,Source:String): DataFrame = {
Read.format (source). Load (path)
}

Dataframereader Source:

1.4.0 */formatString this.   this  }

* Loads input Inas a[[DataFrame]], for data sources the that ' t require a path (e.g. external
* Key-value stores).
*
* @since 1.4.0
*/
defLoad(): DataFrame = {
Valresolved=Resolveddatasource(
SqlContext,
Userspecifiedschema =Userspecifiedschema,
partitioncolumns = Array. Empty [String],
Provider =Source,
options =extraoptions. Tomap)
DataFrame (SqlContext, logicalrelation(resolved.relation))
}

Resolveddatasource Source

ObjectResolveddatasourceextendsLogging {

/** A map to maintain backward compatibility in case wemove data sources around. */
Private ValBackwardcompatibilitymap= Map(
"Org.apache.spark.sql.jdbc"-classof[jdbc. Defaultsource].getcanonicalname,
"Org.apache.spark.sql.jdbc.DefaultSource"- classof[jdbc. Defaultsource].getcanonicalname,
"Org.apache.spark.sql.json"-classof[JSON. Defaultsource].getcanonicalname,
"Org.apache.spark.sql.json.DefaultSource"- classof[JSON. Defaultsource].getcanonicalname,
"Org.apache.spark.sql.parquet"classof[parquet. Defaultsource].getcanonicalname,
"Org.apache.spark.sql.parquet.DefaultSource" classof[parquet. Defaultsource].getcanonicalname
)

Data formats can be read directly: Jdbc,parquet

defApply(
Sqlcontext:sqlcontext,
provider:String,
partitioncolumns:array[String],
Mode:savemode,
Options:Map[String,String],
data:dataframe): Resolveddatasource = {

Dataframtwriter Source:

/**
* Specifies the behavior when data ortable already exists. Options include:
*  - `Savemode.overwrite` : Overwrite the existing data.
*  - `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
*/
defMode(savemode:savemode): Dataframewriter = {
This.Mode= Savemode
This
}

Import Java.util.arraylist;import Java.util.list;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.dataframe;import Org.apache.spark.sql.row;import Org.apache.spark.sql.rowfactory;import Org.apache.spark.sql.sqlcontext;import org.apache.spark.sql.types.structfield;/** * @author author E-Mail: * @version created: May 8, 2016 7:54:28 class description */public classes Spa Rksqlloadsaveops {public    static void Main (string[] args) {        sparkconf conf = new sparkconf (). Setmaster ("local" ). Setappname ("rdd2d");        Javasparkcontext sc = new Javasparkcontext ();        SqlContext sqlcontext = new SqlContext (SC);        DataFrame PEOPLEDF = Sqlcontext.read (). Format ("JSON"). Load ("D://person.json");        Peopledf.select ("name"). Write (). Format ("JSON"). Save ("D://logs//personname.json");

File Append method: Whether to create a new file or a append append

Day61-spark SQL data loading and saving insider deep decryption combat