hive load data外部表报错_生产SparkSQL如何读写本地外部数据源及排错
https://spark-packages.org/里有很多third-party数据源的package，spark把包加载进来就可以使用了
csv格式在spark2.0版本之后是内置的，2.0之前属于第三方数据源
一、读取本地外部数据源
1.直接读取一个json文件
[hadoop@hadoop000 bin]$ ./spark-shell --master local[2] --jars ~/software/mysql-connector-java-5.1.27.jar
scala> spark.read.load("file:///home/hadoop/app/spark-2.3.1-bin-2.6.0-cdh5.7.0/examples/src/main/resources/people.json").show
运行报错：
Caused by: java.lang.RuntimeException: file:/home/hadoop/app/spark-2.3.1-bin-2.6.0-cdh5.7.0/examples/src/main/resources/people.json is not a Parquet file. expected magic number at tail [80, 65, 82, 49] but found [49, 57, 125, 10]
at org.apache.parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:476)
at org.apache.parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:445)
at org.apache.parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:421)
at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$readParquetFootersInParallel$1.apply(ParquetFileFormat.scala:519)
... 32 more
查看load方法的源码：
/**
* 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
*/
def load(path: String): DataFrame = {
option("path", path).load(Seq.empty: _*) // force invocation of `load(...varargs...)`
}
---------------------------------------------------------
/**
* Loads input in as a `DataFrame`, for data sources that support multiple paths.
* Only works if the source is a HadoopFsRelationProvider.
*
* @since 1.6.0
*/
@scala.annotation.varargs
def load(paths: String*): DataFrame = {
if (source.toLowerCase(Locale.ROOT) == DDLUtils.HIVE_PROVIDER) {
throw new AnalysisException("Hive data source can only be used with tables, you can not " +
"read files of Hive data source directly.")
}
val cls = DataSource.lookupDataSource(source, sparkSession.sessionState.conf)
if (classOf[DataSourceV2].isAssignableFrom(cls)) {
val ds = cls.newInstance()
val options = new DataSourceOptions((extraOptions ++
DataSourceV2Utils.extractSessionConfigs(
ds = ds.asInstanceOf[DataSourceV2],
conf = sparkSession.sessionState.conf)).asJava)
// Streaming also uses the data source V2 API. So it may be that the data source implements
// v2, but has no v2 implementation for batch reads. In that case, we fall back to loading
// the dataframe as a v1 source.
val reader = (ds, userSpecifiedSchema) match {
case (ds: ReadSupportWithSchema, Some(schema)) =>
ds.createReader(schema, options)
case (ds: ReadSupport, None) =>
ds.createReader(options)
case (ds: ReadSupportWithSchema, None) =>
throw new AnalysisException(s"A schema needs to be specified when using $ds.")
case (ds: ReadSupport, Some(schema)) =>
val reader = ds.createReader(options)
if (reader.readSchema() != schema) {
throw new AnalysisException(s"$ds does not allow user-specified schemas.")
}
reader
case _ => null // fall back to v1
}
if (reader == null) {
loadV1Source(paths: _*)
} else {
Dataset.ofRows(sparkSession, DataSourceV2Relation(reader))
}
} else {
loadV1Source(paths: _*)
}
}
private def loadV1Source(paths: String*) = {
// Code path for data source v1.
sparkSession.baseRelationToDataFrame(
DataSource.apply(
sparkSession,
paths = paths,
userSpecifiedSchema = userSpecifiedSchema,
className = source,
options = extraOptions.toMap).resolveRelation())
}
------------------------------------------------------
private var source: String = sparkSession.sessionState.conf.defaultDataSourceName
-------------------------------------------------------
def defaultDataSourceName: String = getConf(DEFAULT_DATA_SOURCE_NAME)
--------------------------------------------------------
// This is used to set the default data source
val DEFAULT_DATA_SOURCE_NAME = buildConf("spark.sql.sources.default")
.doc("The default data source to use in input/output.")
.stringConf
.createWithDefault("parquet")
从源码中可以看出，如果不指定format，load默认读取的是parquet文件
scala> val users = spark.read.load("file:///home/hadoop/app/spark-2.3.1-bin-2.6.0-cdh5.7.0/examples/src/main/resources/users.parquet")
scala> users.show()
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
读取其他格式的文件，必须通过format指定文件格式，如下：
//windows idea环境下
val df1 = spark.read.format("json").option("timestampFormat", "yyyy/MM/dd HH:mm:ss ZZ").load("hdfs://192.168.137.141:9000/data/people.json")
df1.show()
+----+-------+
| age| name|
+----+-------+
|null|Michael|
| 30| Andy|
| 19| Justin|
+----+-------+
option("timestampFormat", "yyyy/MM/dd HH:mm:ss ZZ")必须带上，不然报错
Exception in thread "main" java.lang.IllegalArgumentException: Illegal pattern component: XXX
2.读取CSV格式文件
//源文件内容如下：
[hadoop@hadoop001 ~]$ hadoop fs -text /data/people.csv
name;age;job
Jorge;30;Developer
Bob;32;Developer
//windows idea环境下
val df2 = spark.read.format("csv")
.option("timestampFormat", "yyyy/MM/dd HH:mm:ss ZZ")
.option("sep",";")
.option("header","true") //use first line of all files as header
.option("inferSchema","true")
.load("hdfs://192.168.137.141:9000/data/people.csv")
df2.show()
df2.printSchema()
//输出结果：
+-----+---+---------+
| name|age| job|
+-----+---+---------+
|Jorge| 30|Developer|
| Bob| 32|Developer|
+-----+---+---------+
root
|-- name: string (nullable = true)
|-- age: integer (nullable = true)
|-- job: string (nullable = true)
-----------------------------------------------------------
//如果不指定option("sep",";")
+------------------+
| name;age;job|
+------------------+
|Jorge;30;Developer|
| Bob;32;Developer|
+------------------+
//如果不指定option("header","true")
+-----+---+---------+
| _c0|_c1| _c2|
+-----+---+---------+
| name|age| job|
|Jorge| 30|Developer|
| Bob| 32|Developer|
+-----+---+---------+
读取csv格式文件还可以自定义schema
val peopleschema = StructType(Array(
StructField("hlwname",StringType,true),
StructField("hlwage",IntegerType,true),
StructField("hlwjob",StringType,true)))
val df2 = spark.read.format("csv").option("timestampFormat", "yyyy/MM/dd HH:mm:ss ZZ").option("sep",";")
.option("header","true")
.schema(peopleschema)
.load("hdfs://192.168.137.141:9000/data/people.csv")
//打印测试
df2.show()
df2.printSchema()
输出结果：
+-------+------+---------+
|hlwname|hlwage| hlwjob|
+-------+------+---------+
| Jorge| 30|Developer|
| Bob| 32|Developer|
+-------+------+---------+
root
|-- hlwname: string (nullable = true)
|-- hlwage: integer (nullable = true)
|-- hlwjob: string (nullable = true)
二、将读取的文件以其他格式写出
//将上文读取的users.parquet以json格式写出
scala> users.select("name","favorite_color").write.format("json").save("file:///home/hadoop/tmp/parquet2json/")
[hadoop@hadoop000 ~]$ cd /home/hadoop/tmp/parquet2json
[hadoop@hadoop000 parquet2json]$ ll
total 4
-rw-r--r--. 1 hadoop hadoop 56 Sep 24 10:15 part-00000-dfbd9ba5-598f-4e0c-8e81-df85120333db-c000.json
-rw-r--r--. 1 hadoop hadoop 0 Sep 24 10:15 _SUCCESS
[hadoop@hadoop000 parquet2json]$ cat part-00000-dfbd9ba5-598f-4e0c-8e81-df85120333db-c000.json
{
"name":"Alyssa"}
{
"name":"Ben","favorite_color":"red"}
//将上文读取的people.json以csv格式写出
df1.write.format("csv")
.mode("overwrite")
.option("timestampFormat", "yyyy/MM/dd HH:mm:ss ZZ")
.save("hdfs://192.168.137.141:9000/data/formatconverttest/")
------------------------------------------
[hadoop@hadoop001 ~]$ hadoop fs -text /data/formatconverttest/part-00000-6fd65eff-d0d3-43e5-9549-2b11bc3ca9de-c000.csv
,Michael
30,Andy
19,Justin
//发现若没有.option("header","true")，写出的csv丢失了首行的age,name信息
//若不指定.option("sep",";")，默认逗号为分隔符
此操作的目的在于学会类型转换，生产上最开始进来的数据大多都是text，json等行式存储的文件，一般都要转成ORC，parquet列式存储的文件，加上压缩，能把文件大小减小到10%左右，大幅度减小IO和数据处理量，提高性能
此时如果再执行一次save，路径不变，则会报错：
scala> users.select("name","favorite_color").write.format("json").save("file:///home/hadoop/tmp/parquet2json/")
org.apache.spark.sql.AnalysisException: path file:/home/hadoop/tmp/parquet2json already exists.;
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:109)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
.........................................................
可以通过设置savemode来解决这个问题
默认是errorifexists
scala> users.select("name","favorite_color").write.format("json").mode("overwrite").save("file:///home/hadoop/tmp/parquet2json/")
作者：若泽数据—白面葫芦娃92
原文：https://www.jianshu.com/p/6fde69ea56bc
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