读取使用Spark用CTE(带有子句)创建的Hive视图

Question

我对使用CTE创建的Hive（带有子句）的观点，即联合两个表，然后计算以显示每个ID的最新记录。在我的env中，我有一个用于浏览Hive数据库的工具（DBEAVER，非DataCalake Dev的强制性浏览数据）。

查看代码

    CREATE VIEW IF NOT EXISTS db.test_cte_view AS
      with cte as (select * from db.test_cte union select * from db.test_cte_2),
       tmp as (SELECT id, idate, ROW_NUMBER() over(PARTITION BY id ORDER BY idate desc ) AS row_num from cte)
      SELECT cte.* from cte
         join (SELECT * from tmp where tmp.row_num =1) tmp_2
         on cte.id = tmp_2.id
         and cte.idate = tmp_2.idate

问题是：

当通过Beeline创建视图

时

（这是我们在Hive中创建表和视图的主要方式）
我可以轻松地浏览dbeaver，但是，当运行火花过程以从中读取它时，它会失败：

    ##pyspark
    spark.sql("select * from db.test_cte_view").show()
    'Table or view not found: cte; line 3 pos 56'
    Traceback (most recent call last):
    File "DATA/fs3/hadoop/yarn/local/usercache/ingouagn/appcache/application_1552132357519_15102/container_e378_1552132357519_15102_01_000001/pyspark.zip/pyspark/sql/session.py", line 545, in sql
      return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
    File "/DATA/fs3/hadoop/yarn/local/usercache/ingouagn/appcache/application_1552132357519_15102/container_e378_1552132357519_15102_01_000001/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
      answer, self.gateway_client, self.target_id, self.name)
    File "/DATA/fs3/hadoop/yarn/local/usercache/ingouagn/appcache/application_1552132357519_15102/container_e378_1552132357519_15102_01_000001/pyspark.zip/pyspark/sql/utils.py", line 69, in deco
      raise AnalysisException(s.split(': ', 1)[1], stackTrace)
    pyspark.sql.utils.AnalysisException: 'Table or view not found: cte; line 3 pos 56'

使用sparl.sql创建视图（创建视图为...＆quot;）

我可以用

阅读它

    ##pyspark
    spark.sql("select * from db.test_cte_view").show()

但是，当试图与Dbeaver浏览时，它会失败：

Query execution failed
Reason:
SQL Error [40000] [42000]: Error while compiling statement: FAILED: SemanticException line 1:330 Failed to recognize predicate 'UNION'. Failed rule: 'identifier' in subquery source in definition of VIEW test_cte_view [
SELECT `gen_attr_0` AS `id`, `gen_attr_1` AS `status`, `gen_attr_2` AS `idate` FROM (SELECT `gen_attr_0`, `gen_attr_1`, `gen_attr_2` FROM ((SELECT `gen_attr_0`, `gen_attr_1`, `gen_attr_2` FROM (SELECT `id` AS `gen_attr_0`, `status` AS `gen_attr_1`, `idate` AS `gen_attr_2` FROM `db`.`test_cte`) AS gen_subquery_0) UNION DISTINCT (SELECT `gen_attr_5`, `gen_attr_6`, `gen_attr_7` FROM (SELECT `id` AS `gen_attr_5`, `status` AS `gen_attr_6`, `idate` AS `gen_attr_7` FROM `db`.`test_cte_2`) AS gen_subquery_1)) AS cte INNER JOIN (SELECT `gen_attr_3`, `gen_attr_4`, `gen_attr_8` FROM (SELECT `gen_attr_3`, `gen_attr_4`, `gen_attr_8` FROM (SELECT gen_subquery_4.`gen_attr_3`, gen_subquery_4.`gen_attr_4`, row_number() OVER (PARTITION BY `gen_attr_3` ORDER BY `gen_attr_4` DESC NULLS LAST ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS `gen_attr_8` FROM (SELECT `gen_attr_3`, `gen_attr_4` FROM ((SELECT `gen_attr_3`, `gen_attr_9`, `gen_attr_4` FROM (SELECT `id` AS `gen_attr_3`, `status` AS `gen_attr_9`, `idate` AS `gen_attr_4` FROM `db`.`test_cte`) AS gen_subquery_2) UNION DISTINCT (SELECT `gen_attr_5`, `gen_attr_6`, `gen_attr_7` FROM (SELECT `id` AS `gen_attr_5`, `status` AS `gen_attr_6`, `idate` AS `gen_attr_7` FROM `db`.`test_cte_2`) AS gen_subquery_3)) AS cte) AS gen_subquery_4) AS gen_subquery_5) AS tmp WHERE (`gen_attr_8` = 1)) AS tmp_2 ON ((`gen_attr_0` = `gen_attr_3`) AND (`gen_attr_2` = `gen_attr_4`))) AS cte
] used as test_cte_view at Line 1:14

似乎生成的代码在创建视图的一种方式之间是不同的。

有没有办法制作第一个方案（通过Beeline创建视图并通过Spark SQL访问）工作？

谢谢。

spark：2.1.1 ， hive：1.2.1

表

     CREATE TABLE db.test_cte(
      id int, 
      status string, 
      idate date )
  
     CREATE TABLE db.test_cte_2(
      id int, 
      status string, 
      idate date )

填充：

      insert into db.test_cte values
      (1,"green","2019-03-08"),
      (2,"green","2019-03-08"),
      (3,"green","2019-03-08"),
      (1,"red","2019-03-09"),
      (1,"yellow","2019-03-10"),
      (2,"gray","2019-03-09")
  
      insert into db.test_cte_2 values
      (10,"green","2019-03-08"),
      (20,"green","2019-03-08"),
      (30,"green","2019-03-08"),
      (10,"red","2019-03-09"),
      (10,"yellow","2019-03-10"),
      (20,"gray","2019-03-09")

编辑：
对于任何有兴趣的人，我在Spark Jira上创建了一个问题：
https://issues.apache.org/jira/browse/spark-27203

Answer 1

我在Spark2.1.1.2.6.1.0-129中遇到了同样的问题。升级到Spark2.4解决了问题。

如果不是升级，那么此解决方法在2.1上为我带来了技巧：

spark.table("db.my_view_with_ctes").registerTempTable("tmp")
spark.sql("select * from tmp")

这要比通过Spark2.4中的Spark-SQL阅读视图的时间要长得多（我用例的运行时超过10倍），但起作用。