santoshsb commented on issue #5452:
URL: https://github.com/apache/hudi/issues/5452#issuecomment-1111697151
Thanks @yihua, here are the detailed spark shell commands we used
`./spark-shell --jars
'/Users/balamats/work/hudi/packaging/hudi-spark-bundle/target/hudi-spark3.2-bundle_2.12-0.12.0-SNAPSHOT.jar'
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
import org.apache.spark.sql.SaveMode
import org.apache.spark.sql.functions._
import org.apache.hudi.DataSourceWriteOptions
import org.apache.hudi.DataSourceReadOptions
import org.apache.hudi.config.HoodieWriteConfig
import org.apache.hudi.hive.MultiPartKeysValueExtractor
//Define a Patient FHIR resource, for simplicity have deleted most of the
elements and retained a few
val orgString =
"""{"resourceType":"Patient","id":"4ad86a5c-926e-439b-9352-f8ac9ab780f1","lastUpdated":"2022-03-11T15:18:18.90836+05:30","source":"4a0701fe-5c3b-482b-895d-875fcbd21481","gender":"male","birthDate":"1974-01-05","maritalStatus":{"coding":[{"system":"http://terminology.hl7.org/CodeSystem/v3-MaritalStatus","code":"M","display":"M"}],"text":"M"}}""";
//Convert to dataframe
val orgStringDf = spark.read.json(Seq(orgString).toDS)
//Specify common DataSourceWriteOptions in the single hudiOptions variable
val hudiOptions = Map[String,String](
HoodieWriteConfig.TABLE_NAME -> "patient_hudi",
DataSourceWriteOptions.TABLE_TYPE_OPT_KEY -> "COPY_ON_WRITE",
DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY -> "id",
DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY -> "source",
DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY -> "lastUpdated",
DataSourceWriteOptions.HIVE_STYLE_PARTITIONING_OPT_KEY -> "true"
)
//Write the orgStringDf to a Hudi table
orgStringDf.write
.format("org.apache.hudi")
.option(DataSourceWriteOptions.OPERATION_OPT_KEY,
DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL)
.options(hudiOptions)
.mode(SaveMode.Overwrite)
.save("/Users/balamats/work/data/updateTst/json_schema_tst/hudi")
//Read the Hudi table
val patienthudi =
spark.read.format("hudi").load("/Users/balamats/work/data/updateTst/json_schema_tst/hudi")
//Printschema
patienthudi.printSchema
root
|-- _hoodie_commit_time: string (nullable = true)
|-- _hoodie_commit_seqno: string (nullable = true)
|-- _hoodie_record_key: string (nullable = true)
|-- _hoodie_partition_path: string (nullable = true)
|-- _hoodie_file_name: string (nullable = true)
|-- birthDate: string (nullable = true)
|-- gender: string (nullable = true)
|-- id: string (nullable = true)
|-- lastUpdated: string (nullable = true)
|-- maritalStatus: struct (nullable = true)
| |-- coding: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- code: string (nullable = true)
| | | |-- display: string (nullable = true)
| | | |-- system: string (nullable = true)
| |-- text: string (nullable = true)
|-- resourceType: string (nullable = true)
|-- source: string (nullable = true)
//Select fields to verify
patienthudi.select("id","gender","maritalStatus").show(false)
+------------------------------------+------+---------------------------------------------------------------------+
|id |gender|maritalStatus
|
+------------------------------------+------+---------------------------------------------------------------------+
|4ad86a5c-926e-439b-9352-f8ac9ab780f1|male |{[{M, M,
http://terminology.hl7.org/CodeSystem/v3-MaritalStatus}], M}|
+------------------------------------+------+---------------------------------------------------------------------+
//Update: Based on our usecase add a new patient resource, this resource
might contain new columns and might not have existing columns (normal use case
with FHIR data)
val updatedString =
"""{"resourceType":"Patient","id":"596c7a94-bada-4303-85d4-7067c586999e","lastUpdated":"2022-04-20T15:18:18.90836+05:30","source":"4a0701fe-5c3b-482b-895d-875fcbd2148a","gender":"female","birthDate":"2005-08-30","multipleBirthBoolean":true}"""
//Convert the new resource string into DF
val updatedStringDf = spark.read.json(Seq(updatedString).toDS)
//Check the schema of the new resource that is being added
updatedStringDf.printSchema
root
|-- birthDate: string (nullable = true)
|-- gender: string (nullable = true)
|-- id: string (nullable = true)
|-- lastUpdated: string (nullable = true)
|-- multipleBirthBoolean: boolean (nullable = true)
|-- resourceType: string (nullable = true)
|-- source: string (nullable = true)
//Upsert the new resource
updatedStringDf.write
.format("org.apache.hudi")
.options(hudiOptions)
.option(DataSourceWriteOptions.OPERATION_OPT_KEY,
DataSourceWriteOptions.UPSERT_OPERATION_OPT_VAL)
.option(DataSourceWriteOptions.PAYLOAD_CLASS_OPT_KEY,
"org.apache.hudi.common.model.EmptyHoodieRecordPayload")
.mode(SaveMode.Append)
.save("/Users/balamats/work/data/updateTst/json_schema_tst/hudi")
//Read the Hudi table
val patienthudi =
spark.read.format("hudi").load("/Users/balamats/work/data/updateTst/json_schema_tst/hudi")
//Print the schema after adding the new record
patienthudi.printSchema
root
|-- _hoodie_commit_time: string (nullable = true)
|-- _hoodie_commit_seqno: string (nullable = true)
|-- _hoodie_record_key: string (nullable = true)
|-- _hoodie_partition_path: string (nullable = true)
|-- _hoodie_file_name: string (nullable = true)
|-- birthDate: string (nullable = true)
|-- gender: string (nullable = true)
|-- id: string (nullable = true)
|-- lastUpdated: string (nullable = true)
|-- multipleBirthBoolean: boolean (nullable = true)
|-- resourceType: string (nullable = true)
|-- source: string (nullable = true)
//Select fields to verify
patienthudi.select("id","gender","maritalStatus").show(false)
org.apache.spark.sql.AnalysisException: cannot resolve 'maritalStatus' given
input columns: [_hoodie_commit_seqno, _hoodie_commit_time, _hoodie_file_name,
_hoodie_partition_path, _hoodie_record_key, birthDate, gender, id, lastUpdated,
multipleBirthBoolean, resourceType, source];
'Project [id#130, gender#129, 'maritalStatus]
+- Relation
[_hoodie_commit_time#123,_hoodie_commit_seqno#124,_hoodie_record_key#125,_hoodie_partition_path#126,_hoodie_file_name#127,birthDate#128,gender#129,id#130,lastUpdated#131,multipleBirthBoolean#132,resourceType#133,source#134]
parquet
at
org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:54)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$$nestedInanonfun$checkAnalysis$1$2.applyOrElse(CheckAnalysis.scala:179)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$$nestedInanonfun$checkAnalysis$1$2.applyOrElse(CheckAnalysis.scala:175)
at
org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformUpWithPruning$2(TreeNode.scala:535)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:82)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformUpWithPruning(TreeNode.scala:535)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$transformExpressionsUpWithPruning$1(QueryPlan.scala:181)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$1(QueryPlan.scala:193)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:82)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:193)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.recursiveTransform$1(QueryPlan.scala:204)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$3(QueryPlan.scala:209)
at
scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at
scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at scala.collection.TraversableLike.map(TraversableLike.scala:286)
at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.recursiveTransform$1(QueryPlan.scala:209)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$4(QueryPlan.scala:214)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:323)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:214)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUpWithPruning(QueryPlan.scala:181)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:161)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis.$anonfun$checkAnalysis$1(CheckAnalysis.scala:175)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis.$anonfun$checkAnalysis$1$adapted(CheckAnalysis.scala:94)
at
org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:263)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis.checkAnalysis(CheckAnalysis.scala:94)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis.checkAnalysis$(CheckAnalysis.scala:91)
at
org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:182)
at
org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:205)
at
org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:330)
at
org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:202)
at
org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:88)
at
org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at
org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:196)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at
org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:196)
at
org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:88)
at
org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:86)
at
org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:78)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$1(Dataset.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:88)
at org.apache.spark.sql.Dataset.withPlan(Dataset.scala:3734)
at org.apache.spark.sql.Dataset.select(Dataset.scala:1454)
at org.apache.spark.sql.Dataset.select(Dataset.scala:1471)
... 49 elided`
Our expectation after adding the second row was,
1. The new column "multipleBirthBoolean" should have been added to the
schema and would be null for the previous entry.
2. The existing "maritalStatus" column present in the destination schema
added by the first entry should be present after adding the second entry and
should have been null for the second entry.
We might be missing some config or we feel that when we add a new entry it
should contain all the columns present in the destination schema regardless if
they are NULL they should be present, If we do need a uber schema we didn't
find the spark code to convert our second dataframe "updatedStringDf" to add
those columns with NULL values, basically reading the uber schema and merging
it into "updatedStringDf" with NULL values. We did try these commands while
creating the second dataframe
`val updatedStringDf =
spark.read.schema(patientHudi.schema).json(Seq(updatedString).toDS)`
But than the new schema for the updatedStringDf misses the
"multipleBirthBoolean" column present in the second entry.
`root
|-- birthDate: string (nullable = true)
|-- gender: string (nullable = true)
|-- id: string (nullable = true)
|-- lastUpdated: string (nullable = true)
|-- maritalStatus: struct (nullable = true)
| |-- coding: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- code: string (nullable = true)
| | | |-- display: string (nullable = true)
| | | |-- system: string (nullable = true)
| |-- text: string (nullable = true)
|-- resourceType: string (nullable = true)
|-- source: string (nullable = true)`
Thanks for the help.
Santosh
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