HDFS caching with SparkSQL - caching

Question

env
spark 3.1.2
hive 3.1.2
hadoop 3.2.1
problem
spark sql with hive metastore connection
for example, make table A with hdfs caching data in memory.
and make table B with non-hdfs caching data in hdfs.( but data content is same. )
I executed same query with table A and B.
I expected A table's query execution time must be faster than B table's query, but it didn't.
Actually query execution time between A and B table was almost same.
Is there anything to do enable HDFS caching with SparkSQL using hive metastore?
example code
from pyspark import SparkConf
from pyspark.context import SparkContext
from pyspark.sql import HiveContext
from pyspark.sql import SparkSession
from pyspark.sql import functions as Fd
from pyspark.sql.window import Window as W
spark_executor_cores = 8
spark_executor_memory = '3g'
spark_instances = 50
spark_shuffle_partitions = 1000
spark_default_parallelism = 1000
conf = SparkConf()
conf.setAppName("test application")
conf.set('spark.yarn.queue', 'default')
conf.set('spark.executor.memory', str(spark_executor_memory))
conf.set('spark.executor.instances', str(spark_instances))
conf.set('spark.shuffle.sort.bypassMergeThreshold', spark_instances * int(spark_executor_cores))
conf.set("spark.dynamicAllocation.enabled", "false")
conf.set("spark.sql.shuffle.partitions", str(spark_shuffle_partitions))
conf.set("spark.default.parallelism", str(spark_default_parallelism))
conf.set('spark.sql.adaptive.enabled', 'true')
conf.set('spark.sql.adaptive.coalescePartitions.enabled', 'true')
conf.set('spark.sql.adaptive.localShuffleReader.enabled', 'true')
conf.set('spark.sql.adaptive.skewJoin.enabled', 'true')
conf.set('spark.sql.execution.arrow.pyspark.enabled', 'true')
conf.set('spark.sql.execution.arrow.pyspark.fallback.enabled', 'true')
conf.set('spark.sql.warehouse.dir', metastore_dir)
conf.set('spark.hadoop.javax.jdo.option.ConnectionURL', metastore_url)
ss = SparkSession.builder.enableHiveSupport().config(conf=conf).getOrCreate()
sc = SparkContext.getOrCreate()
spark = ss
sql = spark.sql
# example query
# partition = hour, logtype
sql("""
SELECT <columns>,
...
...
FROM <table name>
WHERE hour = <hour>
AND logtype = <logtype>
group by logtype
""").show()