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Posted to issues@iceberg.apache.org by "paulpaul1076 (via GitHub)" <gi...@apache.org> on 2023/10/05 06:50:08 UTC
[I] Spark fails to write into an iceberg table after updating its schema [iceberg]
paulpaul1076 opened a new issue, #8721:
URL: https://github.com/apache/iceberg/issues/8721
### Apache Iceberg version
1.3.1 (latest release)
### Query engine
Spark
### Please describe the bug 🐞
Spark fails to write the dataframe with new schema after updating the schema of a table:
```
import org.apache.iceberg.{CatalogUtil, Schema}
import org.apache.iceberg.catalog.{Catalog, TableIdentifier}
import org.apache.iceberg.types.Types
import org.apache.spark.sql.SparkSession
import java.util.Properties
//https://iceberg.apache.org/docs/latest/nessie/
object IcebergJobNessie extends App {
val spark = SparkSession.builder()
.master("local[*]")
.appName("iceberg test")
.config("spark.sql.catalog.nessie", "org.apache.iceberg.spark.SparkCatalog")
.config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions,org.projectnessie.spark.extensions.NessieSparkSessionExtensions")
.config("spark.sql.catalog.nessie.catalog-impl", "org.apache.iceberg.nessie.NessieCatalog")
.config("spark.sql.catalog.nessie.ref", "main")
.config("spark.sql.catalog.nessie.uri", "http://localhost:19120/api/v1")
.config("spark.sql.catalog.nessie.s3.endpoint", "***")
.config("spark.sql.catalog.nessie.s3.access.key", "***")
.config("spark.sql.catalog.nessie.s3.secret.key", "***")
.config("spark.sql.defaultCatalog", "nessie")
.config("spark.hadoop.fs.s3a.endpoint", "***")
.config("spark.hadoop.fs.s3a.access.key", "***")
.config("spark.hadoop.fs.s3a.secret.key", "***")
.config("spark.hadoop.fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
.config("spark.hadoop.fs.s3.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
.config("spark.sql.catalog.nessie.s3a.path-style-access ", "true")
.config("spark.sql.catalog.nessie", "org.apache.iceberg.spark.SparkCatalog")
.config("spark.sql.catalog.nessie.warehouse", "s3://hdp-temp/iceberg_catalog")
.getOrCreate()
import spark.implicits._
val options = new java.util.HashMap[String, String]()
options.put("warehouse", "s3://hdp-temp/iceberg_catalog")
options.put("ref", "main")
options.put("uri", "http://localhost:19120/api/v1")
val nessieCatalog: Catalog = CatalogUtil.loadCatalog("org.apache.iceberg.nessie.NessieCatalog", "nessie", options, spark.sparkContext.hadoopConfiguration)
// ---------------------PART 1---------------------------------------
val name = TableIdentifier.of("db_nessie", "schema_evolution15")
val schema = new Schema(
Types.NestedField.required(1, "age", Types.IntegerType.get()),
Types.NestedField.optional(2, "sibling_info",
Types.ListType.ofOptional(3, Types.StructType.of(
Types.NestedField.required(4, "age", Types.IntegerType.get()),
Types.NestedField.optional(5, "name", Types.StringType.get())
))
)
)
nessieCatalog.createTable(name, schema)
val df = List(
(1, List(
SiblingInfo(1, "John"),
SiblingInfo(2, "Sean"),
SiblingInfo(3, "Peter"))
),
(12, List(
SiblingInfo(13, "Ivan"),
SiblingInfo(11, "Sean")
)
)).toDF("age", "sibling_info")
df.writeTo("db_nessie.schema_evolution15").append()
spark.sql("select * from db_nessie.schema_evolution15").show(false)
val table = nessieCatalog.loadTable(name)
val newIcebergSchema = new Schema(
Types.NestedField.required(1, "age", Types.IntegerType.get()),
Types.NestedField.optional(2, "sibling_info",
Types.ListType.ofOptional(3, Types.StructType.of(
Types.NestedField.required(4, "age", Types.IntegerType.get()),
Types.NestedField.optional(5, "name", Types.StringType.get()),
Types.NestedField.optional(6, "lastName", Types.StringType.get())
))
)
)
table.updateSchema()
.unionByNameWith(newIcebergSchema)
.commit()
// ---------------------PART 2---------------------------------------
val df2 = List(
(1, List(
SiblingInfo2(1, "John", "Johnson"),
SiblingInfo2(2, "Sean", "Johnson"),
SiblingInfo2(3, "Peter", "Johnson"))
),
(12, List(
SiblingInfo2(13, "Ivan", "Johnson"),
SiblingInfo2(11, "Test", "Johnson")
)
)).toDF("age", "sibling_info")
df2.writeTo("db_nessie.schema_evolution15").append()
spark.sql("select * from db_nessie.schema_evolution15").show(false)
}
```
The exception is:
```
Exception in thread "main" org.apache.spark.sql.AnalysisException: Cannot write incompatible data to table 'spark_catalog1.db.schema_evolution15':
- Cannot write nullable values to non-null column 'sibling_info.x.age'.
at org.apache.spark.sql.errors.QueryCompilationErrors$.cannotWriteIncompatibleDataToTableError(QueryCompilationErrors.scala:2072)
at org.apache.spark.sql.catalyst.analysis.TableOutputResolver$.resolveOutputColumns(TableOutputResolver.scala:64)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveOutputRelation$$anonfun$apply$50.applyOrElse(Analyzer.scala:3326)
```
As you can see 1) I do an insert, then 2) update the schema by adding the field "lastName" into the element type of the field "sibling_info", then 3) I do another insert and it fails.
But if I execute these inserts (see PART 1 and PART 2 comments) separately (do 2 application runs), they work fine. What is wrong here?
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Re: [I] Spark fails to write into an iceberg table after updating its schema [iceberg]
Posted by "paulpaul1076 (via GitHub)" <gi...@apache.org>.
paulpaul1076 closed issue #8721: Spark fails to write into an iceberg table after updating its schema
URL: https://github.com/apache/iceberg/issues/8721
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Re: [I] Spark fails to write into an iceberg table after updating its schema [iceberg]
Posted by "paulpaul1076 (via GitHub)" <gi...@apache.org>.
paulpaul1076 commented on issue #8721:
URL: https://github.com/apache/iceberg/issues/8721#issuecomment-1751575139
Turns out that the problem is due to Spark caching its catalog, and since I updated the schema through iceberg's Java API instead of Spark, the cached value didn't get updated.
Russell Spitzer also told me that we can disable caching by setting spark.sql.catalog.catalog-name.cache-enabled to false.
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Re: [I] Spark fails to write into an iceberg table after updating its schema [iceberg]
Posted by "paulpaul1076 (via GitHub)" <gi...@apache.org>.
paulpaul1076 commented on issue #8721:
URL: https://github.com/apache/iceberg/issues/8721#issuecomment-1749943303
Calling this before my second insert of df2 solves the problem:
spark.catalog.refreshTable("db_nessie.schema_evolution15")
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