[jira] [Updated] (HUDI-3214) Optimize auto partition in spark

["Zhaojing Yu (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/HUDI-3214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Zhaojing Yu updated HUDI-3214:
------------------------------
    Fix Version/s: 0.13.0
                       (was: 0.12.1)

> Optimize auto partition in spark
> --------------------------------
>
>                 Key: HUDI-3214
>                 URL: https://issues.apache.org/jira/browse/HUDI-3214
>             Project: Apache Hudi
>          Issue Type: Improvement
>          Components: spark, writer-core
>            Reporter: Yann Byron
>            Assignee: Yann Byron
>            Priority: Critical
>              Labels: pull-request-available
>             Fix For: 0.13.0
>
>   Original Estimate: 2h
>  Remaining Estimate: 2h
>
> recently, if partition's value has the format like "pt1=xxxx/pt2=yyyy/pt3=zzzz" which split by slash, Hudi will partition automatically. The directory of this table will have multi partition structure.
> I think it's unpredictable. So create this umbrella task to optimize auto partition in order to make the behavior more reasonable.
> Also, in hudi 0.8, schama will hold `pt1`, `pt2`, `pt3`, but not in 0.9+.
> There are a few of sub tasks:
>  * add a flag to control whether enable auto-partition, to make the default behavior reasonable..
>  * achieve a new key generator designed specifically for this scenario.
>  * solve the bug about the different schema when enable *hoodie.file.index.enable* or not in this case.
>  
> Test Codes: 
> {code:java}
> import org.apache.hudi.QuickstartUtils._
> import scala.collection.JavaConversions._
> import org.apache.spark.sql.SaveMode._
> import org.apache.hudi.DataSourceReadOptions._
> import org.apache.hudi.DataSourceWriteOptions._
> import org.apache.hudi.config.HoodieWriteConfig._
> val tableName = "hudi_trips_cow"
> val basePath = "file:///tmp/hudi_trips_cow"
> val dataGen = new DataGenerator
> val inserts = convertToStringList(dataGen.generateInserts(10))
> val df = spark.read.json(spark.sparkContext.parallelize(inserts, 2))
> val newDf = df.withColumn("partitionpath", regexp_replace($"partitionpath", "(.*)(\\/){1}(.*)(\\/){1}", "continent=$1$2country=$3$4city="))
> newDf.write.format("hudi").
> options(getQuickstartWriteConfigs).
> option(PRECOMBINE_FIELD_OPT_KEY, "ts").
> option(RECORDKEY_FIELD_OPT_KEY, "uuid").
> option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
> option(TABLE_NAME, tableName).
> mode(Overwrite).
> save(basePath) {code}



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