I have a file which contains below details : file.txt
+----------------------------------------------------+
| createtab_stmt |
+----------------------------------------------------+
| CREATE EXTERNAL TABLE `dv.par_kst`( |
| `col1` string, |
| `col2` string, |
| `col3` int, |
| `col4` int, |
| `col5` string, |
| `col6` float, |
| `col7` int, |
| `col8` string, |
| `col9` string, |
| `col10` int, |
| `col11` int, |
| `col12` string, |
| `col13` float, |
| `col14` string, |
| `col15` string) |
| PARTITIONED BY ( |
| `part_col1` int, |
| `part_col2` int) |
| ROW FORMAT SERDE |
| 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' |
| STORED AS INPUTFORMAT |
| 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' |
| OUTPUTFORMAT |
| 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' |
| LOCATION |
| 'hdfs://nameservicets1/dv/hdfsdata/par_kst' |
| TBLPROPERTIES ( |
| 'spark.sql.create.version'='2.2 or prior', |
| 'spark.sql.sources.schema.numPartCols'='2', |
| 'spark.sql.sources.schema.numParts'='1', |
| 'spark.sql.sources.schema.part.0'='{"type":"struct","fields":[{"name":"col1","type":"string","nullable":true,"metadata":{}},{"name":"col2","type":"string","nullable":true,"metadata":{}},{"name":"col3","type":"integer","nullable":true,"metadata":{}},{"name":"col4","type":"integer","nullable":true,"metadata":{}},{"name":"col5","type":"string","nullable":true,"metadata":{}},{"name":"col6","type":"float","nullable":true,"metadata":{}},{"name":"col7","type":"integer","nullable":true,"metadata":{}},{"name":"col8","type":"string","nullable":true,"metadata":{}},{"name":"col9","type":"string","nullable":true,"metadata":{}},{"name":"col10","type":"integer","nullable":true,"metadata":{}},{"name":"col11","type":"integer","nullable":true,"metadata":{}},{"name":"col12","type":"string","nullable":true,"metadata":{}},{"name":"col13","type":"float","nullable":true,"metadata":{}},{"name":"col14","type":"string","nullable":true,"metadata":{}},{"name":"col15","type":"string","nullable":true,"metadata":{}},{"name":"part_col1","type":"integer","nullable":true,"metadata":{}},{"name":"part_col2","type":"integer","nullable":true,"metadata":{}}]}', |
| 'spark.sql.sources.schema.partCol.0'='part_col1', |
| 'spark.sql.sources.schema.partCol.1'='part_col2', |
| 'transient_lastDdlTime'='1587487456') |
+----------------------------------------------------+
from above file I want to extract PARTITIONED BY details.
Desired output :
part_col1 , part_col2
and these PARTITIONED BY is not fixed , means for some other file it might contains 3 or more , so I want extract all the PARTITIONED BY.
All the values between PARTITIONED BY and ROW FORMAT SERDE , removing spaces "`" and data types!
Could you please help me with this ?