org.apache.thrift.TApplicationException: Invalid method name: 'get_table'

### Query engine

spark 3.5.4
iceberg 1.8.1

### Question

I'm trying to use Hive 4.0.1 as the metastore service for Iceberg tables, with the metastore backed by a remote PostgreSQL database. My compute engine is Spark 3.5, and the Iceberg runtime version is 1.8.1.

When attempting to write data to an Iceberg table using the Hive catalog, I encounter the following error:
```
java.lang.RuntimeException: Failed to get table info from metastore db.sample
at org.apache.iceberg.hive.HiveTableOperations.doRefresh(HiveTableOperations.java:171)
at org.apache.iceberg.BaseMetastoreTableOperations.refresh(BaseMetastoreTableOperations.java:97)
at org.apache.iceberg.BaseMetastoreTableOperations.current(BaseMetastoreTableOperations.java:80)
at org.apache.iceberg.BaseMetastoreCatalog.loadTable(BaseMetastoreCatalog.java:47)
at org.apache.iceberg.shaded.com.github.benmanes.caffeine.cache.BoundedLocalCache.lambda$doComputeIfAbsent$14(BoundedLocalCache.java:2406)
at java.base/java.util.concurrent.ConcurrentHashMap.compute(ConcurrentHashMap.java:1908)
at org.apache.iceberg.shaded.com.github.benmanes.caffeine.cache.BoundedLocalCache.doComputeIfAbsent(BoundedLocalCache.java:2404)
at org.apache.iceberg.shaded.com.github.benmanes.caffeine.cache.BoundedLocalCache.computeIfAbsent(BoundedLocalCache.java:2387)
at org.apache.iceberg.shaded.com.github.benmanes.caffeine.cache.LocalCache.computeIfAbsent(LocalCache.java:108)
at org.apache.iceberg.shaded.com.github.benmanes.caffeine.cache.LocalManualCache.get(LocalManualCache.java:62)
at org.apache.iceberg.CachingCatalog.loadTable(CachingCatalog.java:166)
at org.apache.iceberg.spark.SparkCatalog.load(SparkCatalog.java:643)
at org.apache.iceberg.spark.SparkCatalog.loadTable(SparkCatalog.java:159)
at org.apache.spark.sql.connector.catalog.TableCatalog.tableExists(TableCatalog.java:185)
at org.apache.spark.sql.execution.datasources.v2.CreateTableExec.run(CreateTableExec.scala:42)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$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$lzycompute(QueryExecution.scala:85)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:83)
at org.apache.spark.sql.Dataset.(Dataset.scala:220)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:100)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:900)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:97)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:638)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:900)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:629)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:659)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: org.apache.thrift.TApplicationException: Invalid method name: 'get_table'
at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:79)
at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.recv_get_table(ThriftHiveMetastore.java:1514)
at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.get_table(ThriftHiveMetastore.java:1500)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.getTable(HiveMetaStoreClient.java:1346)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.invoke(RetryingMetaStoreClient.java:169)
at com.sun.proxy.$Proxy30.getTable(Unknown Source)
at org.apache.iceberg.hive.HiveTableOperations.lambda$doRefresh$0(HiveTableOperations.java:158)
at org.apache.iceberg.ClientPoolImpl.run(ClientPoolImpl.java:58)
at org.apache.iceberg.ClientPoolImpl.run(ClientPoolImpl.java:51)
at org.apache.iceberg.hive.CachedClientPool.run(CachedClientPool.java:122)
at org.apache.iceberg.hive.HiveTableOperations.doRefresh(HiveTableOperations.java:158)
... 57 more

```

Here's the code I'm using to reproduce the problem:
```
from pyspark import SparkConf
from pyspark.sql import SparkSession

conf = SparkConf().setMaster("local[6]") \
.set("spark.driver.memory", "4g") \
.set("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \
.set("spark.sql.catalog.hive_prod","org.apache.iceberg.spark.SparkCatalog") \
.set("spark.sql.catalog.hive_prod.type", "hive") \
.set("spark.sql.catalog.hive_prod.uri", "thrift://10.1.22.57:9083")

spark = SparkSession.builder.config(conf=conf).getOrCreate()

# spark.sql("CREATE DATABASE hive_prod.db")

spark.sql("""
CREATE TABLE IF NOT EXISTS hive_prod.db.sample (
id bigint NOT NULL COMMENT 'unique id',
data string)
USING iceberg
""")

spark.sql("INSERT INTO hive_prod.db.sample VALUES (1, 'a'), (2, 'b'), (3, 'c')")
spark.sql("""
SELECT count(1) as count, data
FROM hive_prod.db.sample
GROUP BY data;
""").show()

```

Start by running the provided PySpark reproduction with Spark 3.5.4, Iceberg 1.8.1, Hive 4.0.1, and the configured metastore URI. Read org.apache.iceberg.hive.HiveTableOperations.doRefresh and the HiveMetaStoreClient.getTable stack frames, then verify the metastore interaction against the remote PostgreSQL-backed service. Done means identifying the compatibility or configuration cause of the invalid get_table call and recording a verified outcome.

Written by the indexing model from the issue text.