select * from bucketing_table tablesample(bucket n out of y on clustered_criteria_column);

哪里 bucketing_table 是你的bucket表名吗

n => nth bucket
y => total no. of buckets

您可以通过不同的方式实现这一点：
方法一：拿到table stored location 从 desc formatted <db>.<tab_name> 然后阅读 9th bucket 直接从文件 HDFS filesystem .
（或）
方法2：使用输入文件名（）
然后只过滤 9th bucket 使用文件名的数据 Example: Approach-1: Scala: ```
val df = spark.sql("desc formatted .<tab_name>")

//get table location in hdfs path
val loc_hdfs = df.filter('col_name === "Location").select("data_type").collect.map(x => x(0)).mkString

//based on your table format change the read format
val ninth_buk = spark.read.orc(s"${loc_hdfs}/000008_0*")

//display the data
ninth_buk.show()
`Pyspark:`
from pyspark.sql.functions import *

df = spark.sql("desc formatted .<tab_name>")

loc_hdfs = df.filter(col("col_name") == "Location").select("data_type").collect()[0].getattr("data_type")

ninth_buk = spark.read.orc(loc_hdfs + "/000008_0*")

ninth_buk.show()

Approach-2: `Scala:` ```
val df = spark.read.table("<db>.<tab_name>")

//add input_file_name 
 val df1 = df.withColumn("filename",input_file_name())

# filter only the 9th bucket filename and select only required columns

val ninth_buk = df1.filter('filename.contains("000008_0")).select(df.columns.head,df.columns.tail:_*)

ninth_buk.show()
``` `pyspark:` ```
from pyspark.sql.functions import *

 df = spark.read.table("<db>.<tab_name>")

df1 = df.withColumn("filename",input_file_name())

ninth_buk = df1.filter(col("filename").contains("000008_0")).select(*df.columns)

ninth_buk.show()

如果您有大量数据，我们不建议使用方法-2，因为我们需要过滤整个Dataframe。。！！ In Hive: ```
set hive.support.quoted.identifiers=none;
select (fn)?+.+ from (
select *,input__file__name fn from table_name)e
where e.fn like '%000008_0%';

如果是兽人的table

SELECT * FROM orc.<bucket_HDFS_path>