51zero / 51zero/eel-sdk

Need an example of creating DDL for a Hive Parquet table with EEL

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Beschreibung

- CSVSource to HiveSink
```scala
val schema = AvroSchemaFns.fromAvroSchema(new Schema.Parser().parse(new File("user.avsc")))
CsvSource(path)
.withSchema(schema)
.to(HiveSink("mydatabase", "myTable"))
```
- Table field: **fname**, **lname**, **age**, **salary**
- 2 partition keys of **country** and **city**

```scala
object EelCreateTableExample extends App {
val crateTableCommand = HiveDDL.showDDL(
tableName = "mydatabase.mytable",
partitions = Seq(
PartitionColumn("country", StringType),
PartitionColumn("city", StringType)
),
fields = Seq(
Field("fname", StringType),
Field("lname", StringType),
Field("age", IntType.Signed),
Field("salary", DecimalType(38, 5))
),
tableType = TableType.EXTERNAL_TABLE,
location = Some("hdfs://nameservice1/blah/mytable_location"),
serde = "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe",
inputFormat = "org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat",
outputFormat = "org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat",
props = Map.empty,
tableComment = Some("my lovely table"),
ifNotExists = true
)
println(crateTableCommand)
}
```

- Ouput:
```
CREATE EXTERNAL TABLE IF NOT EXISTS `mydatabase.mytable` (
`fname` string,
`lname` string,
`age` int,
`salary` decimal(38,5))
PARTITIONED BY (
`country` string,
`city` string)
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://nameservice1/blah/mytable_location'
```

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Rechercherichtung

The example is already provided in the issue body. Look for the HiveDDL class in the codebase to understand how showDDL is implemented. Verify the output matches the expected CREATE EXTERNAL TABLE statement. Add this example to the project's documentation, likely in a examples or docs directory. Run the example to ensure it compiles and prints the correct DDL.

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Bewertung

Tech-Stack
hadoop, scala
Bereich
data-engineering, databases
Issue-Typ
Dokumentation
Schwierigkeit
2/5
Geschätzter Aufwand
1-3 Stunden
Aktivitätsstatus
Veraltet
Klarheit
Klar beschrieben
Anfängerfreundlichkeit
75/100

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