SparkSql讀取csv實現統計功能

前面已經介紹過有關sparksql讀取json文件取得DataSet的功能，但實際開發中除了json外還可以使用csv、數據庫等作爲sparksql的數據源，因爲csv日常開發也用的很多所以藉此機會把我的學習代碼分享給大家

一 關於csv的schema

sparksql讀取csv可以根據csv文件的第一行作爲header自動推導出列名或schema,也可以通過手動的方式指定schema,自動推導讀取csv時需要指定option參數，看下官方的文檔

You can set the following CSV-specific options to deal with CSV files:

• sep (default ,): sets a single character as a separator for each field and value.
• encoding (default UTF-8): decodes the CSV files by the given encoding type.
• quote (default "): sets a single character used for escaping quoted values where the separator can be part of the value. If you would like to turn off quotations, you need to set not null but an empty string. This behaviour is different from com.databricks.spark.csv.
• escape (default \): sets a single character used for escaping quotes inside an already quoted value.
• charToEscapeQuoteEscaping (default escape or \0): sets a single character used for escaping the escape for the quote character. The default value is escape character when escape and quote characters are different, \0 otherwise.
• comment (default empty string): sets a single character used for skipping lines beginning with this character. By default, it is disabled.
• header (default false): uses the first line as names of columns.
• enforceSchema (default true): If it is set to true, the specified or inferred schema will be forcibly applied to datasource files, and headers in CSV files will be ignored. If the option is set to false, the schema will be validated against all headers in CSV files in the case when the header option is set to true. Field names in the schema and column names in CSV headers are checked by their positions taking into account spark.sql.caseSensitive. Though the default value is true, it is recommended to disable the enforceSchema option to avoid incorrect results.
• inferSchema (default false): infers the input schema automatically from data. It requires one extra pass over the data.
• samplingRatio (default is 1.0): defines fraction of rows used for schema inferring.
• ignoreLeadingWhiteSpace (default false): a flag indicating whether or not leading whitespaces from values being read should be skipped.
• ignoreTrailingWhiteSpace (default false): a flag indicating whether or not trailing whitespaces from values being read should be skipped.
• nullValue (default empty string): sets the string representation of a null value. Since 2.0.1, this applies to all supported types including the string type.
• emptyValue (default empty string): sets the string representation of an empty value.
• nanValue (default NaN): sets the string representation of a non-number" value.
• positiveInf (default Inf): sets the string representation of a positive infinity value.
• negativeInf (default -Inf): sets the string representation of a negative infinity value.
• dateFormat (default yyyy-MM-dd): sets the string that indicates a date format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to date type.
• timestampFormat (default yyyy-MM-dd'T'HH:mm:ss.SSSXXX): sets the string that indicates a timestamp format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to timestamp type.
• maxColumns (default 20480): defines a hard limit of how many columns a record can have.
• maxCharsPerColumn (default -1): defines the maximum number of characters allowed for any given value being read. By default, it is -1 meaning unlimited length
• mode (default PERMISSIVE): allows a mode for dealing with corrupt records during parsing. It supports the following case-insensitive modes.
  • PERMISSIVE : when it meets a corrupted record, puts the malformed string into a field configured by columnNameOfCorruptRecord, and sets other fields to null. To keep corrupt records, an user can set a string type field named columnNameOfCorruptRecord in an user-defined schema. If a schema does not have the field, it drops corrupt records during parsing. A record with less/more tokens than schema is not a corrupted record to CSV. When it meets a record having fewer tokens than the length of the schema, sets null to extra fields. When the record has more tokens than the length of the schema, it drops extra tokens.
  • DROPMALFORMED : ignores the whole corrupted records.
  • FAILFAST : throws an exception when it meets corrupted records.
• columnNameOfCorruptRecord (default is the value specified in spark.sql.columnNameOfCorruptRecord): allows renaming the new field having malformed string created by PERMISSIVE mode. This overrides spark.sql.columnNameOfCorruptRecord.
• multiLine (default false): parse one record, which may span multiple lines.

參數看起來特別多，但是大多都有默認值，實際讀取的時候只需指定很少的就行了，如下所示

Dataset<Row> ds=spark.read()
	  //自動推斷列類型
	   .option("inferSchema", "true")
	   //指定一個指示空值的字符串
	   .option("nullvalue", "?")
	   //當設置爲 true 時，第一行文件將被用來命名列，而不包含在數據中
	   .option("header", "true")
	   .csv("/home/cry/myStudyData/userList.csv");

如果不喜歡這種方式也可以選擇手動方式指定schema

List<StructField> fs=new ArrayList<StructField>();
StructField f1=DataTypes.createStructField("id", DataTypes.IntegerType, true);
StructField f2=DataTypes.createStructField("name", DataTypes.StringType, true);
StructField f3=DataTypes.createStructField("age", DataTypes.IntegerType, true);
        
fs.add(f1);
fs.add(f2);
fs.add(f3);
		
StructType schema=DataTypes.createStructType(fs);
		
     
Dataset<Row> ds=spark.read().schema(schema).csv("/home/cry/myStudyData");

二  完整的代碼

package com.debug;

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

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

public class ReadCsv {

	public static void main(String[] args) {
		SparkSession spark = SparkSession.builder().appName("讀取csv做統計").master("local[*]").getOrCreate();
	    List<StructField> fs=new ArrayList<StructField>();
        StructField f1=DataTypes.createStructField("id", DataTypes.IntegerType, true);
        StructField f2=DataTypes.createStructField("name", DataTypes.StringType, true);
        StructField f3=DataTypes.createStructField("age", DataTypes.IntegerType, true);
        
        fs.add(f1);
        fs.add(f2);
        fs.add(f3);
		
        StructType schema=DataTypes.createStructType(fs);
		
		/*Dataset<Row> ds=spark.read()
		  //自動推斷列類型
		  .option("inferSchema", "true")
		  //指定一個指示空值的字符串
		  .option("nullvalue", "?")
		  //當設置爲 true 時，第一行文件將被用來命名列，而不包含在數據中
		  .option("header", "true")
		  .csv("/home/cry/myStudyData/userList.csv");*/
     
        Dataset<Row> ds=spark.read().schema(schema).csv("/home/cry/myStudyData");
		ds.createOrReplaceTempView("user");
		Dataset<Row> res=spark.sql("select * from user where age>25");
		res.show();
		
		spark.stop();
	}

}

其中的一個csv內容如下