Question

平台： RHEL 7，cloudera CDH 6.2 hadoop发行，pyspark 3.7.1

我尝试过的事情：当我将表名明确提到为saveAsTable（“ tablename”）时，我可以将一个表写入配置单元仓库。但是，当我尝试从“ for循环”中的python变量获取表名时，出现以下错误。

类似于： How to save a dataframe result in hive table with different name on each iteration using pyspark

prefix_list = ["hive_table_name1","hive_table_name2", "hive_table_name3"]
list1 = ["dataframe_content_1", "dataframe_content__2", "dataframe_content_3"]

 for index, l in enumerate(list1):

    selecteddata = df.select(l)

    #Embedding table name within quotations
    tablename = '"' + prefix_list[index] + '"'

    # write the "selecteddata" dataframe to hive table
    selecteddata.write.mode("overwrite").saveAsTable(tablename)

预期：默认配置单元仓库中的3个不同的配置单元表

实际：

"ReturnMessages"
Traceback (most recent call last):
  File "/opt/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
  File "/opt/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o86.saveAsTable.
: org.apache.spark.sql.catalyst.parser.ParseException: 
mismatched input '"ReturnMessages"' expecting {'SELECT', 'FROM', 'ADD', 'AS', 'ALL', 'ANY', 'DISTINCT', 'WHERE', 'GROUP', 'BY', 'GROUPING', 'SETS', 'CUBE', 'ROLLUP', 'ORDER', 'HAVING', 'LIMIT', 'AT', 'OR', 'AND', 'IN', NOT, 'NO', 'EXISTS', 'BETWEEN', 'LIKE', RLIKE, 'IS', 'NULL', 'TRUE', 'FALSE', 'NULLS', 'ASC', 'DESC', 'FOR', 'INTERVAL', 'CASE', 'WHEN', 'THEN', 'ELSE', 'END', 'JOIN', 'CROSS', 'OUTER', 'INNER', 'LEFT', 'SEMI', 'RIGHT', 'FULL', 'NATURAL', 'ON', 'PIVOT', 'LATERAL', 'WINDOW', 'OVER', 'PARTITION', 'RANGE', 'ROWS', 'UNBOUNDED', 'PRECEDING', 'FOLLOWING', 'CURRENT', 'FIRST', 'AFTER', 'LAST', 'ROW', 'WITH', 'VALUES', 'CREATE', 'TABLE', 'DIRECTORY', 'VIEW', 'REPLACE', 'INSERT', 'DELETE', 'INTO', 'DESCRIBE', 'EXPLAIN', 'FORMAT', 'LOGICAL', 'CODEGEN', 'COST', 'CAST', 'SHOW', 'TABLES', 'COLUMNS', 'COLUMN', 'USE', 'PARTITIONS', 'FUNCTIONS', 'DROP', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', 'TO', 'TABLESAMPLE', 'STRATIFY', 'ALTER', 'RENAME', 'ARRAY', 'MAP', 'STRUCT', 'COMMENT', 'SET', 'RESET', 'DATA', 'START', 'TRANSACTION', 'COMMIT', 'ROLLBACK', 'MACRO', 'IGNORE', 'BOTH', 'LEADING', 'TRAILING', 'IF', 'POSITION', 'EXTRACT', 'DIV', 'PERCENT', 'BUCKET', 'OUT', 'OF', 'SORT', 'CLUSTER', 'DISTRIBUTE', 'OVERWRITE', 'TRANSFORM', 'REDUCE', 'SERDE', 'SERDEPROPERTIES', 'RECORDREADER', 'RECORDWRITER', 'DELIMITED', 'FIELDS', 'TERMINATED', 'COLLECTION', 'ITEMS', 'KEYS', 'ESCAPED', 'LINES', 'SEPARATED', 'FUNCTION', 'EXTENDED', 'REFRESH', 'CLEAR', 'CACHE', 'UNCACHE', 'LAZY', 'FORMATTED', 'GLOBAL', TEMPORARY, 'OPTIONS', 'UNSET', 'TBLPROPERTIES', 'DBPROPERTIES', 'BUCKETS', 'SKEWED', 'STORED', 'DIRECTORIES', 'LOCATION', 'EXCHANGE', 'ARCHIVE', 'UNARCHIVE', 'FILEFORMAT', 'TOUCH', 'COMPACT', 'CONCATENATE', 'CHANGE', 'CASCADE', 'RESTRICT', 'CLUSTERED', 'SORTED', 'PURGE', 'INPUTFORMAT', 'OUTPUTFORMAT', DATABASE, DATABASES, 'DFS', 'TRUNCATE', 'ANALYZE', 'COMPUTE', 'LIST', 'STATISTICS', 'PARTITIONED', 'EXTERNAL', 'DEFINED', 'REVOKE', 'GRANT', 'LOCK', 'UNLOCK', 'MSCK', 'REPAIR', 'RECOVER', 'EXPORT', 'IMPORT', 'LOAD', 'ROLE', 'ROLES', 'COMPACTIONS', 'PRINCIPALS', 'TRANSACTIONS', 'INDEX', 'INDEXES', 'LOCKS', 'OPTION', 'ANTI', 'LOCAL', 'INPATH', IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 0)

== SQL ==
"ReturnMessages"
^^^

    at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:241)
    at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:117)
    at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:48)
    at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parseTableIdentifier(ParseDriver.scala:49)
    at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:400)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/37Pro/files/Partnerdaten.py", line 144, in <module>
    dataframe.write.saveAsTable(filename, format="parquet", mode="overwrite")
  File "/opt/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/pyspark.zip/pyspark/sql/readwriter.py", line 775, in saveAsTable
  File "/opt/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
  File "/opt/cloudera/parcels/CDH-6.2.0-1.cdh6.2.0.p0.967373/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 73, in deco
pyspark.sql.utils.ParseException: '\nmismatched input \'"ReturnMessages"\' expecting {\'SELECT\', \'FROM\', \'ADD\', \'AS\', \'ALL\', \'ANY\', \'DISTINCT\', \'WHERE\', \'GROUP\', \'BY\', \'GROUPING\', \'SETS\', \'CUBE\', \'ROLLUP\', \'ORDER\', \'HAVING\', \'LIMIT\', \'AT\', \'OR\', \'AND\', \'IN\', NOT, \'NO\', \'EXISTS\', \'BETWEEN\', \'LIKE\', RLIKE, \'IS\', \'NULL\', \'TRUE\', \'FALSE\', \'NULLS\', \'ASC\', \'DESC\', \'FOR\', \'INTERVAL\', \'CASE\', \'WHEN\', \'THEN\', \'ELSE\', \'END\', \'JOIN\', \'CROSS\', \'OUTER\', \'INNER\', \'LEFT\', \'SEMI\', \'RIGHT\', \'FULL\', \'NATURAL\', \'ON\', \'PIVOT\', \'LATERAL\', \'WINDOW\', \'OVER\', \'PARTITION\', \'RANGE\', \'ROWS\', \'UNBOUNDED\', \'PRECEDING\', \'FOLLOWING\', \'CURRENT\', \'FIRST\', \'AFTER\', \'LAST\', \'ROW\', \'WITH\', \'VALUES\', \'CREATE\', \'TABLE\', \'DIRECTORY\', \'VIEW\', \'REPLACE\', \'INSERT\', \'DELETE\', \'INTO\', \'DESCRIBE\', \'EXPLAIN\', \'FORMAT\', \'LOGICAL\', \'CODEGEN\', \'COST\', \'CAST\', \'SHOW\', \'TABLES\', \'COLUMNS\', \'COLUMN\', \'USE\', \'PARTITIONS\', \'FUNCTIONS\', \'DROP\', \'UNION\', \'EXCEPT\', \'MINUS\', \'INTERSECT\', \'TO\', \'TABLESAMPLE\', \'STRATIFY\', \'ALTER\', \'RENAME\', \'ARRAY\', \'MAP\',

Answer 1

您没有在写语句中指定数据库的名称。这是我将要做的事情：

database_name = "my_database"
prefix_list = ["hive_table_name1","hive_table_name2", "hive_table_name3"]
list1 = ["dataframe_content_1", "dataframe_content_2", "dataframe_content_3"]


for index, l in enumerate(list1):
    selecteddata = df.select(l)

    #Embedding table name within quotations
    tablename = prefix_list[index]

    # map to the correct database and table
    db_name_and_corresponding_table = "{0}.{1}".format(database_name, tablename)

    # write the "selecteddata" dataframe to hive table
    selecteddata.write.mode("overwrite").saveAsTable(db_name_and_corresponding_table)

希望有帮助。

如何使用pyspark中的saveAsTable在每次迭代中使用不同的表名保存Spark数据帧

1 个答案: