Question

我对使用CTE创建的Hive（WITH子句）有一个看法，将两个表合并起来，然后进行计算以仅显示每个ID的最新记录。在我的环境中，我有一个浏览配置单元数据库的工具（DBeaver，非datalake开发人员浏览数据所必需）。

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：

通过直线创建视图时

（这是我们在Hive中创建表和视图的主要方式）
我可以轻松地在DBeaver上浏览，但是，当运行spark进程以读取它时，它将失败，并显示以下内容：

##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创建视图时（“ CREATE VIEW AS ...”）

我可以很好地阅读

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

但是，当尝试使用DBeaver浏览时，它失败，并显示类似以下内容：

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

在一种创建视图的方法与另一种创建方法之间，似乎生成的代码是不同的。

有没有办法使第一种情况（通过beeline创建视图并通过spark sql访问它）起作用？

谢谢。

火花：2.1.1 ，配置单元：1.2.1

表格

 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")

修改：
对于有兴趣的人，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中遇到了相同的问题。升级到Spark2.4解决了该问题。

如果无法升级，则此变通办法在2.1上对我有用：

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

这比在Spark2.4中通过spark-sql读取视图的运行时间长得多（对于我的用例而言，运行时间是其运行时间的10倍以上），但是它可以工作。

从spark

通过直线创建视图时

使用sparl.sql创建视图时（“ CREATE VIEW AS ...”）

表格

1 个答案: