Related
I have a table with around 20-25columns in which some of the column names in the struct defined as numeric. For eg:recommendations struct<1:string> COMMENT 'from deserializer'. When I tried to do a MSCK or Alter by partition in spark, it shows the partitions but one we move the code in IntelliJ and run via Automic it fails with the below error: User class threw exception: org.apache.spark.SparkException: Cannot recognize hive type string: struct<1:string> at org.apache.spark.sql.hive.client.HiveClientImpl$.getSparkSQLDataType(HiveClientImpl.scala:895) at org.apache.spark.sql.hive.client.HiveClientImpl$.fromHiveColumn(HiveClientImpl.scala:901) at scala.collection.AbstractIterable.foreach(Iterable.scala:56) at scala.collection.TraversableLike.map(TraversableLike.scala:238) at scala.collection.TraversableLike.map$(TraversableLike.scala:231) at scala.collection.AbstractTraversable.map(Traversable.scala:108) org.apache.spark.sql.hive.HiveExternalCatalog.getRawTable(HiveExternalCatalog.scala:118) at org.apache.spark.sql.hive.HiveExternalCatalog.$anonfun$getTable$1(HiveExternalCatalog.scala:700) at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:97) at org.apache.spark.sql.hive.HiveExternalCatalog.getTable(HiveExternalCatalog.scala:700) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogWithListener.getTable(ExternalCatalogWithListener.scala:138) at org.apache.spark.sql.catalyst.catalog.SessionCatalog.getTableMetadata(SessionCatalog.scala:434) at org.apache.spark.sql.execution.command.AlterTableAddPartitionCommand.run(ddl.scala:437) at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70) at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68) at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:79) at org.apache.spark.sql.Dataset.$anonfun$logicalPlan$1(Dataset.scala:194) at org.apache.spark.sql.Dataset.$anonfun$withAction$2(Dataset.scala:3369) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:80) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127) at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:686) Caused by: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input '1' 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 7) == SQL == struct<1:string> -------^^^ 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.catalyst.parser.AbstractSqlParser.parseDataType(ParseDriver.scala:39) at org.apache.spark.sql.hive.client.HiveClientImpl$.getSparkSQLDataType(HiveClientImpl.scala:892) ... 53 more
Thanks in advance
When trying to dynamically forming the select columns to be used for selectExpr, I get this parse exception.
query_string = "'survey_id', 'response_id', 'person_name'"
column_expr = query_string.replace("'", "").split(",")
new_df = sample_df.selectExpr(column_expr, "person_name as person").show()
ParseException:
extraneous input '[' expecting {'(', '{', 'APPLY', 'CALLED', 'CHANGES', 'CLONE', 'COLLECT', 'CONTAINS', 'CONVERT', 'COPY', 'COPY_OPTIONS', 'CREDENTIAL', 'CREDENTIALS', 'DEEP', 'DEFINER', 'DELTA', 'DETERMINISTIC', 'ENCRYPTION', 'EXPECT', 'FAIL', 'FILES', 'FORMAT_OPTIONS', 'HISTORY', 'INCREMENTAL', 'INPUT', 'INVOKER', 'LANGUAGE', 'LIVE', 'MATERIALIZED', 'MODIFIES', 'OPTIMIZE', 'PATTERN', 'READS', 'RESTORE', 'RETURN', 'RETURNS', 'SAMPLE', 'SCD TYPE 1', 'SCD TYPE 2', 'SECURITY', 'SEQUENCE', 'SHALLOW', 'SNAPSHOT', 'SPECIFIC', 'SQL', 'STORAGE', 'STREAMING', 'UPDATES', 'UP_TO_DATE', 'VIOLATION', 'ZORDER', 'ADD', 'AFTER', 'ALL', 'ALTER', 'ALWAYS', 'ANALYZE', 'AND', 'ANTI', 'ANY', 'ARCHIVE', 'ARRAY', 'AS', 'ASC', 'AT', 'AUTHORIZATION', 'BETWEEN', 'BOTH', 'BUCKET', 'BUCKETS', 'BY', 'CACHE', 'CASCADE', 'CASE', 'CAST', 'CATALOG', 'CATALOGS', 'CHANGE', 'CHECK', 'CLEAR', 'CLUSTER', 'CLUSTERED', 'CODE', 'CODEGEN', 'COLLATE', 'COLLECTION', 'COLUMN', 'COLUMNS', 'COMMENT', 'COMMIT', 'COMPACT', 'COMPACTIONS', 'COMPUTE', 'CONCATENATE', 'CONSTRAINT', 'COST', 'CREATE', 'CROSS', 'CUBE', 'CURRENT', 'CURRENT_DATE', 'CURRENT_TIME', 'CURRENT_TIMESTAMP', 'CURRENT_USER', 'DAY', 'DATA', 'DATABASE', 'DATABASES', 'DATEADD', 'DATEDIFF', 'DBPROPERTIES', 'DEFAULT', 'DEFINED', 'DELETE', 'DELIMITED', 'DESC', 'DESCRIBE', 'DFS', 'DIRECTORIES', 'DIRECTORY', 'DISTINCT', 'DISTRIBUTE', 'DIV', 'DROP', 'ELSE', 'END', 'ESCAPE', 'ESCAPED', 'EXCEPT', 'EXCHANGE', 'EXISTS', 'EXPLAIN', 'EXPORT', 'EXTENDED', 'EXTERNAL', 'EXTRACT', 'FALSE', 'FETCH', 'FIELDS', 'FILTER', 'FILEFORMAT', 'FIRST', 'FN', 'FOLLOWING', 'FOR', 'FOREIGN', 'FORMAT', 'FORMATTED', 'FROM', 'FULL', 'FUNCTION', 'FUNCTIONS', 'GENERATED', 'GLOBAL', 'GRANT', 'GRANTS', 'GROUP', 'GROUPING', 'HAVING', 'HOUR', 'IDENTITY', 'IF', 'IGNORE', 'IMPORT', 'IN', 'INCREMENT', 'INDEX', 'INDEXES', 'INNER', 'INPATH', 'INPUTFORMAT', 'INSERT', 'INTERSECT', 'INTERVAL', 'INTO', 'IS', 'ITEMS', 'JOIN', 'KEY', 'KEYS', 'LAST', 'LATERAL', 'LAZY', 'LEADING', 'LEFT', 'LIKE', 'ILIKE', 'LIMIT', 'LINES', 'LIST', 'LOAD', 'LOCAL', 'LOCATION', 'LOCK', 'LOCKS', 'LOGICAL', 'MACRO', 'MAP', 'MATCHED', 'MERGE', 'MINUTE', 'MONTH', 'MSCK', 'NAMESPACE', 'NAMESPACES', 'NATURAL', 'NO', NOT, 'NULL', 'NULLS', 'OF', 'ON', 'ONLY', 'OPTION', 'OPTIONS', 'OR', 'ORDER', 'OUT', 'OUTER', 'OUTPUTFORMAT', 'OVER', 'OVERLAPS', 'OVERLAY', 'OVERWRITE', 'PARTITION', 'PARTITIONED', 'PARTITIONS', 'PERCENTILE_CONT', 'PERCENT', 'PIVOT', 'PLACING', 'POSITION', 'PRECEDING', 'PRIMARY', 'PRINCIPALS', 'PROPERTIES', 'PROVIDER', 'PROVIDERS', 'PURGE', 'QUALIFY', 'QUERY', 'RANGE', 'RECIPIENT', 'RECIPIENTS', 'RECORDREADER', 'RECORDWRITER', 'RECOVER', 'REDUCE', 'REFERENCES', 'REFRESH', 'REMOVE', 'RENAME', 'REPAIR', 'REPEATABLE', 'REPLACE', 'REPLICAS', 'RESET', 'RESPECT', 'RESTRICT', 'REVOKE', 'RIGHT', RLIKE, 'ROLE', 'ROLES', 'ROLLBACK', 'ROLLUP', 'ROW', 'ROWS', 'SECOND', 'SCHEMA', 'SCHEMAS', 'SELECT', 'SEMI', 'SEPARATED', 'SERDE', 'SERDEPROPERTIES', 'SESSION_USER', 'SET', 'MINUS', 'SETS', 'SHARE', 'SHARES', 'SHOW', 'SKEWED', 'SOME', 'SORT', 'SORTED', 'START', 'STATISTICS', 'STORED', 'STRATIFY', 'STRUCT', 'SUBSTR', 'SUBSTRING', 'SYNC', 'SYSTEM_TIME', 'SYSTEM_VERSION', 'TABLE', 'TABLES', 'TABLESAMPLE', 'TBLPROPERTIES', TEMPORARY, 'TERMINATED', 'THEN', 'TIME', 'TIMESTAMP', 'TIMESTAMPADD', 'TIMESTAMPDIFF', 'TO', 'TOUCH', 'TRAILING', 'TRANSACTION', 'TRANSACTIONS', 'TRANSFORM', 'TRIM', 'TRUE', 'TRUNCATE', 'TRY_CAST', 'TYPE', 'UNARCHIVE', 'UNBOUNDED', 'UNCACHE', 'UNION', 'UNIQUE', 'UNKNOWN', 'UNLOCK', 'UNSET', 'UPDATE', 'USE', 'USER', 'USING', 'VALUES', 'VERSION', 'VIEW', 'VIEWS', 'WHEN', 'WHERE', 'WINDOW', 'WITH', 'WITHIN', 'YEAR', 'ZONE', '+', '-', '*', '~', STRING, BIGINT_LITERAL, SMALLINT_LITERAL, TINYINT_LITERAL, INTEGER_VALUE, EXPONENT_VALUE, DECIMAL_VALUE, FLOAT_LITERAL, DOUBLE_LITERAL, BIGDECIMAL_LITERAL, IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 0)
== SQL ==
['survey_id', ' response_id', ' person_name']`
But when I use without the expression it works fine.
new_df = sample_df.selectExpr(column_expr).show()
I am not sure, how to use dynamically select columns along with using expression.
I am trying to perform array intersection in spark 2.4 using higher order functions. However, I am facing some issue. Here is the code
val my_array = Array("1","2")
spark.sql(s"""select array_intersect( array("1"), $my_array ) as intersection""").show
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input '[' 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', STRING, BIGINT_LITERAL, SMALLINT_LITERAL, TINYINT_LITERAL, INTEGER_VALUE, DECIMAL_VALUE, DOUBLE_LITERAL, BIGDECIMAL_LITERAL, IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 36)
== SQL ==
select array_intersect( array("1"), [Ljava.lang.String;#f24a38a ) as intersection
------------------------------------^^^
When I try to hard code it works fine -
scala> spark.sql(s"""select array_intersect( array("1"), array("1","2") ) as intersection""").show
+------------+
|intersection|
+------------+
| [1]|
+------------+
I got it working by defining as string then casting to array (my_array) in sql statement.
Example
val my_array ="""1,2"""
spark.sql(s"""select array_intersect(array(1),array(${my_array}))""").show()
//+--------------------------------------+
//|array_intersect(array(1), array(1, 2))|
//+--------------------------------------+
//| [1]|
#+--------------------------------------+
You need to convert your $my_array into the appropriate string representation first, e.g. (cf mkString):
val my_array_str = my_array.mkString("Array(\"", "\", \"", "\")")
then you can use
spark.sql(s"""select array_intersect( array("1"), $my_array_str ) as intersection""").show
To understand the difference, compare
scala> s"$my_array"
res5: String = [Ljava.lang.String;#128acee
with
scala> s"$my_array_str"
res6: String = Array("1", "2")
I'm trying to read the dataframe schema from an external text file and create a dataframe using it. However, I cannot understand how to cast/convert string to StructType.
I'm using Spark 2.1 and Java.
This is the code snippet.
BufferedReader br = new BufferedReader(new FileReader(new File("C:\\Users\\kt799f\\Desktop\\struct.txt")));
String struct2 = br.readLine();
session.sqlContext().read().schema(struct2).json("C:\\Users\\kt799f\\Desktop\\testJson.txt").show();
How do I cast struct2 in the above code to StructType?
struct file contains this
StructType(StructField(name,StringType,true), StructField(age,IntegerType,true))
org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input '{' expecting {'SELECT', 'FROM', 'ADD', 'AS', 'ALL', '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', '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', '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 ==
{"type":"struct","fields":[{"name":"name","type":"string","nullable":true,"metadata":{}},{"name":"age","type":"long","nullable":true,"metadata":{}}]}
^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:239)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:115)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parseTableSchema(ParseDriver.scala:64)
at org.apache.spark.sql.types.StructType$.fromDDL(StructType.scala:425)
at org.apache.spark.sql.DataFrameReader.schema(DataFrameReader.scala:84)
at com.att.sparktest.PartitionReadLocal.main(PartitionReadLocal.java:37)
mismatched input '{' expecting {'SELECT', 'FROM', 'ADD', 'AS', 'ALL', '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', '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', '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 ==
{"type":"struct","fields":[{"name":"name","type":"string","nullable":true,"metadata":{}},{"name":"age","type":"long","nullable":true,"metadata":{}}]}
^^^
How did you create the schema file in the first place? It looks like you just .toString() the schema. What I'd suggest doing is using a json library to serialize the Struct type into a string that you save to the file. Then to use the file read the file and deserialize with the same json library to get the class back.
I have a table with Danish characters in some of its column names. I can create dataframes of these tables but I cant write the column names in the where clause without getting an error. For example:
jdbcDF.where("GÆLDER_FRA_DT > '2016-12-01'")
throws an error
== SQL ==
GÆLDER_FRA_DT > '2016-12-01' and ryk_type in ('52','551','651','751')
-^^^
StackTrace: extraneous input 'Æ' expecting {<EOF>, '(', '.', '[', 'SELECT', 'FROM', 'ADD', 'AS', 'ALL', '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', 'LATERAL', 'WINDOW', 'OVER', 'PARTITION', 'RANGE', 'ROWS', 'UNBOUNDED', 'PRECEDING', 'FOLLOWING', 'CURRENT', 'FIRST', 'AFTER', 'LAST', 'ROW', 'WITH', 'VALUES', 'CREATE', 'TABLE', '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', 'IF', EQ, '<=>', '<>', '!=', '<', LTE, '>', GTE, '+', '-', '*', '/', '%', 'DIV', '&', '|', '^', 'PERCENT', 'BUCKET', 'OUT', 'OF', 'SORT', 'CLUSTER', 'DISTRIBUTE', 'OVERWRITE', 'TRANSFORM', 'REDUCE', 'USING', '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', 'CURRENT_DATE', 'CURRENT_TIMESTAMP', IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 1)
== SQL ==
GÆLDER_FRA_DT > '2016-12-01' and ryk_type in ('52','551','651','751')
-^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:217)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:114)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:48)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parseExpression(ParseDriver.scala:43)
at org.apache.spark.sql.Dataset.where(Dataset.scala:1344)
How do I handle this? Is there some way for me to say this is a unicode string?
A single quote worked fine.
jdbcDF.where("'GÆLDER_FRA_DT' > '2016-12-01'")
The backtick is probably the best way to deal with this situation though.
jdbcDF.where("`GÆLDER_FRA_DT` > '2016-12-01'")