sdasdasd1 opened a new issue, #10533:
URL: https://github.com/apache/iceberg/issues/10533
### Apache Iceberg version
1.5.0
### Query engine
Spark
### Please describe the bug 🐞
### What happened
I cant create or append to any table in REST nessie catalog (same actions
works correctly in classic nessie catalog).
There is an error after actions which I described below.
On createOrReplace Im getting this:
```
Py4JJavaError: An error occurred while calling o233.createOrReplace.
: java.lang.NullPointerException: Cannot read field "formatVersion" because
"x0" is null
at org.apache.iceberg.TableMetadata.access$500(TableMetadata.java:50)
at
org.apache.iceberg.TableMetadata$Builder.<init>(TableMetadata.java:936)
at
org.apache.iceberg.TableMetadata$Builder.<init>(TableMetadata.java:864)
at org.apache.iceberg.TableMetadata.buildFrom(TableMetadata.java:857)
at
org.apache.iceberg.rest.responses.LoadTableResponse.tableMetadata(LoadTableResponse.java:64)
at
org.apache.iceberg.rest.RESTSessionCatalog$Builder.createTransaction(RESTSessionCatalog.java:701)
at
org.apache.iceberg.rest.RESTSessionCatalog$Builder.createOrReplaceTransaction(RESTSessionCatalog.java:794)
at
org.apache.iceberg.CachingCatalog$CachingTableBuilder.createOrReplaceTransaction(CachingCatalog.java:299)
at
org.apache.iceberg.spark.SparkCatalog.stageCreateOrReplace(SparkCatalog.java:302)
at
org.apache.spark.sql.connector.catalog.StagingTableCatalog.stageCreateOrReplace(StagingTableCatalog.java:188)
at
org.apache.spark.sql.execution.datasources.v2.AtomicReplaceTableAsSelectExec.run(WriteToDataSourceV2Exec.scala:204)
at
org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:43)
at
org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:43)
at
org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:49)
at
org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:107)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:125)
at
org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:201)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:108)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:900)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:66)
at
org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:107)
at
org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:98)
at
org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:461)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(origin.scala:76)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:461)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:32)
at
org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
at
org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:32)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:32)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:437)
at
org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:98)
at
org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:85)
at
org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:83)
at
org.apache.spark.sql.execution.QueryExecution.assertCommandExecuted(QueryExecution.scala:142)
at
org.apache.spark.sql.DataFrameWriterV2.runCommand(DataFrameWriterV2.scala:196)
at
org.apache.spark.sql.DataFrameWriterV2.internalReplace(DataFrameWriterV2.scala:208)
at
org.apache.spark.sql.DataFrameWriterV2.createOrReplace(DataFrameWriterV2.scala:134)
at
java.base[/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0](https://jupyterhub-zdp-stage.zvq.me/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0)(Native
Method)
at
java.base[/jdk.internal.reflect.NativeMethodAccessorImpl.invoke](https://jupyterhub-zdp-stage.zvq.me/jdk.internal.reflect.NativeMethodAccessorImpl.invoke)(NativeMethodAccessorImpl.java:77)
at
java.base[/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke](https://jupyterhub-zdp-stage.zvq.me/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke)(DelegatingMethodAccessorImpl.java:43)
at
java.base[/java.lang.reflect.Method.invoke](https://jupyterhub-zdp-stage.zvq.me/java.lang.reflect.Method.invoke)(Method.java:568)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at
py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at
java.base[/java.lang.Thread.run](https://jupyterhub-zdp-stage.zvq.me/java.lang.Thread.run)(Thread.java:840)
```
Also appending to existing tables (which were created with the classic
nessie catalog) is not working. Spark doesn't throw any issues. But after that
nothing changes. No new commit in nessie UI, no new data in table.
### How to reproduce it
1. Spark 3.5.1 + settings
```
.set("spark.jars.packages",
"org.apache.iceberg:iceberg-aws:1.5.0,org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.5.0,org.apache.hadoop:hadoop-aws:3.3.4,org.projectnessie.nessie-integrations:nessie-spark-extensions-3.5_2.12:0.90.4")
.set("spark.sql.sources.partitionOverwriteMode", "dynamic")
.set("spark.sql.ansi.enabled", "true")
.set("spark.sql.storeAssignmentPolicy", "ANSI")
.set("spark.io.compression.codec", "zstd")
.set("spark.hadoop.fs.s3.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
.set("spark.sql.extensions",
"org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions,org.projectnessie.spark.extensions.NessieSparkSessionExtensions")
.set("spark.sql.debug", "true")
.set("spark.jars.packages",
"org.apache.iceberg:iceberg-aws:1.5.0,org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.5.0,org.apache.hadoop:hadoop-aws:3.3.4,org.projectnessie.nessie-integrations:nessie-spark-extensions-3.5_2.12:0.90.4")
.set("spark.driver.userClassPathFirst", "false")
.set("spark.executor.userClassPathFirst", "false")
.set("spark.sql.catalog.iceberg_rest",
"org.apache.iceberg.spark.SparkCatalog")
.set("spark.sql.catalog.iceberg_rest.uri", "https://.../iceberg/";)
.set("spark.sql.catalog.iceberg_rest.type", "rest")
.set("spark.sql.defaultCatalog", "iceberg_rest")
```
2. Nessie Rest 0.90.4 + settings:
```
...
chart:
spec:
chart: nessie
version: ${nessie_version}
sourceRef:
kind: HelmRepository
name: nessiecatalog
namespace: trino
interval: 1m
values:
logLevel: DEBUG
versionStoreType: JDBC
postgres:
jdbcUrl: ${nessie_jdbcUrl}
secret:
name: nessie-secret
username: postgres_username
password: postgres_password
advancedConfig:
nessie.catalog.default-warehouse: dwh
nessie.catalog.warehouses.dwh.location: ${s3_warehouse_bucket}
nessie.catalog.service.s3.endpoint: "https://obs....";
nessie.catalog.service.s3.region: "..."
nessie.catalog.iceberg-config-defaults.io-impl:
"org.apache.iceberg.hadoop.HadoopFileIO"
extraEnv:
- name: NESSIE_CATALOG_SERVICE_S3_ACCESS_KEY_NAME
valueFromSecret:
name: obs-s3-credentials-secret
key: accessKey
- name: NESSIE_CATALOG_SERVICE_S3_ACCESS_KEY_SECRET
valueFromSecret:
name: obs-s3-credentials-secret
key: secretKey
```
3. Try to create table with DataFrameWriterV2
```
from typing import List, NoReturn, Tuple
from pyspark.sql import Column, DataFrame, DataFrameWriterV2, SparkSession
DEFAULT_PROPERTIES = [
("write.format.default", "parquet"),
("write.parquet.compression-codec", "ZSTD"),
("write.parquet.compression-level", "6"),
("write.metadata.delete-after-commit.enabled", "true"),
("write.metadata.previous-versions-max", "100"),
("write.distribution-mode", "hash"),
("write.object-storage.enabled", "true"),
("write.data.path", "s3://obs-zdp-warehouse-stage-mz"),
("commit.retry.num-retries", "10"),
("gc.enabled", "true"),
("format-version", "2"),
]
def apply_t_properties_to_df_writer(writer: DataFrameWriterV2, properties:
List[Tuple[str, str]]) -> DataFrameWriterV2:
"""
Applies a list of table properties to a DataFrameWriterV2 instance.
:param writer: The DataFrameWriterV2 instance to apply properties to.
:param properties: A list of tuples, each containing a property name and
value.
:return: The modified DataFrameWriterV2 instance with applied properties.
"""
for property, value in properties:
writer = writer.tableProperty(property, value)
return writer
def get_writer(df: DataFrame, table_name: str) -> DataFrameWriterV2:
"""
Creates a DataFrameWriterV2 instance for writing a DataFrame to an
Iceberg table.
:param df: The DataFrame to be written.
:param table_name: The name of the Iceberg table.
:return: A DataFrameWriterV2 instance configured for the Iceberg table.
"""
return apply_t_properties_to_df_writer(df.writeTo(table_name),
DEFAULT_PROPERTIES)
df =
spark.read.table("iceberg_rest.dds.f_iceberg_maintenance_expire_snapshots_log")
# read or create any df
get_writer(df,
"iceberg_rest.dds.f_iceberg_maintenance_expire_snapshots_log_test2").createOrReplace()
```
Also I tried to remove all tbl properties and only format-version property,
but got same behavior.
### Nessie server type (docker/uber-jar/built from source) and version
0.90.4
### Client type (Ex: UI/Spark/pynessie ...) and version
Spark 3.5.1
### Additional information
_No response_
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