我正在使用spark 1.3.1,我希望将数据作为ORC格式存储在配置单元中。
下面显示错误的行,看起来orc不支持spark 1.3.1中的数据源
dataframe.save("/apps/hive/warehouse/person_orc_table_5", "orc");
java.lang.RuntimeException: Failed to load class for data source: orc
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.sources.ResolvedDataSource$.lookupDataSource(ddl.scala:194)
at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:237)
at org.apache.spark.sql.DataFrame.save(DataFrame.scala:1196)
at org.apache.spark.sql.DataFrame.save(DataFrame.scala:1156)
at SparkOrcHive.main(SparkOrcHive.java:62)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:577)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:174)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:197)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:112)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Spark 1.4有..
write.format("orc").partitionBy("age").save("peoplePartitioned")
以存储为orc格式..
有没有办法在Spark 1.3.1中以ORC格式存储文件?
谢谢,
答案 0 :(得分:1)
dataframe.select(" name"," age")。save(" / apps / hive / warehouse / orc_table"," org。 apache.spark.sql.hive.orc",SaveMode.Append);
编辑:
我从hdfs获取txt文件并以orc格式将数据写入hive表。以下代码在spark 1.3.1中正常工作
java class
package com.test.spark;
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.SaveMode;
import org.apache.spark.sql.hive.HiveContext;
/**
* Created by ankit on 08/02/16.
*/
public class SparkOrcHiveInsert {
public static void main(String[] args) {
String tableName = "person_orc";
String tablePath = "/apps/hive/warehouse/" + tableName;
SparkConf conf = new SparkConf().setAppName("ORC Demo").setMaster("local");
JavaSparkContext sc = new JavaSparkContext(conf);
HiveContext hiveContext = new org.apache.spark.sql.hive.HiveContext(sc.sc());
JavaRDD<Person> people = sc.textFile("hdfs://~:8020/tmp/person.txt").map(
new Function<String, Person>() {
public Person call(String line) throws Exception {
return process(line);
}
});
DataFrame schemaPeople = hiveContext.createDataFrame(people, Person.class);
schemaPeople.select("id","name", "age").save(tablePath, "org.apache.spark.sql.hive.orc", SaveMode.Append);
}
private static Person process(String line) {
String[] parts = line.split(",");
Person person = new Person();
person.setId(Integer.parseInt(parts[0].trim()));
person.setName(parts[1]);
person.setAge(Integer.parseInt(parts[2].trim()));
return person;
}
}
Hive表脚本
create table person_orc (
id int,
name string,
age int
) stored as orc tblproperties ("orc.compress"="NONE");
Spark提交命令
~/spark/bin/spark-submit --master local --class com.test.spark.SparkOrcHiveInsert spark-orc-hive-1.0.jar