Skip to main content

EXP-00091

Exporting questionable statistics

Cause: Export was able to export statistics, but the statistics may not be useable. The statistics are questionable because one or more of the following happened during export:

- A row error occurred, client character set or NCHARSET does not match with the server, a query clause was specified on export,


- Only certain partitions or subpartitions were exported, or a fatal error occurred while processing a table.

Action: To export non-questionable statistics, change the client character set or NCHARSET to match the server, export with no query clause, or export complete tables. If desired, import parameters can be supplied so that only non-questionable statistics will be imported, and all questionable statistics will be recalculated.

or run the follwing query while exporting from database:-

>exp username/psswd@orclservicename tables=table1,table2 file=c:/db_dump/db.dmp statistics=none;

or

>select VALUE
2 from nls_database_parameters
3 where PARAMETER = 'NLS_CHARACTERSET';

or

>set NLS_LANG=.WE8MSWIN1252

Comments

Popular posts

Read from a hive table and write back to it using spark sql

In context to Spark 2.2 - if we read from an hive table and write to same, we get following exception- scala > dy . write . mode ( "overwrite" ). insertInto ( "incremental.test2" ) org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; 1. This error means that our process is reading from same table and writing to same table. 2. Normally, this should work as process writes to directory .hiveStaging... 3. This error occurs in case of saveAsTable method, as it overwrites entire table instead of individual partitions. 4. This error should not occur with insertInto method, as it overwrites partitions not the table. 5. A reason why this happening is because Hive table has following Spark TBLProperties in its definition. This problem will solve for insertInto met




Hive Parse JSON with Array Columns and Explode it in to Multiple rows.

 Say we have a JSON String like below -  { "billingCountry":"US" "orderItems":[       {          "itemId":1,          "product":"D1"       },   {          "itemId":2,          "product":"D2"       }    ] } And, our aim is to get output parsed like below -  itemId product 1 D1 2 D2   First, We can parse JSON as follows to get JSON String get_json_object(value, '$.orderItems.itemId') as itemId get_json_object(value, '$.orderItems.product') as product Second, Above will result String value like "[1,2]". We want to convert it to Array as follows - split(regexp_extract(get_json_object(value, '$.orderItems.itemId'),'^\\["(.*)\\"]$',1),'","') as itemId split(regexp_extract(get_json_object(value, '$.orderItems.product'),'^\\["(.*)\\"]$',1),&




Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary

Exception -  Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary at org.apache.parquet.column.Dictionary.decodeToBinary(Dictionary.java:44) at org.apache.spark.sql.execution.vectorized.ColumnVector.getUTF8String(ColumnVector.java:645) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) Analysis - This might occur because of data type mismatch between Hive Table & written Parquet file. Solution - Correct the data type to match between Hive Table & Parquet




Hadoop Distcp Error Duplicate files in input path

  One may face following error while copying data from one cluster to other, using Distcp  Command: hadoop distcp -i {src} {tgt} Error: org.apache.hadoop.toolsCopyListing$DulicateFileException: File would cause duplicates. Ideally there can't be same file names. So, what might be happening in your case is you trying to copy partitioned table from one cluster to other. And, 2 different named partitions have same file name. Your solution is to correct Source path  {src}  in your command, such that you provide path uptil partitioned sub directory, not the file. For ex - Refer below : /a/partcol=1/file1.txt /a/partcol=2/file1.txt If you use  {src}  as  "/a/*/*"  then you will get the error  "File would cause duplicates." But, if you use  {src}  as  "/a"  then you will not get error in copying.




org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.;

  Caused by: org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.; at org.apache.spark.sql.execution.command.DDLUtils$.verifyNotReadPath(ddl.scala:906) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:192) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:134) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis.apply(DataSourceStrategy.scala:134) at org.apache.spark.sql.execution.datasource