Using Hive tables can indeed improve Spark read performance, especially when leveraging optimizations like predicate pushdown, column pruning, and partition pruning.

Example:

Create Sample Data and Hive Table

import time

# Create a sample DataFrame
data = [(i, f"name_{i}") for i in range(1000000)]
columns = ["id", "name"]
df = spark.createDataFrame(data, columns)

# Save DataFrame as a Hive table
df.write.mode("overwrite").saveAsTable("mydb.sample_hive_table")

Measure Read Performance without Hive Table

# Measure time to read DataFrame
start_time = time.time()
df = spark.read.format("parquet").load("/data/mydb.db/sample_hive_table")
df.count()
end_time = time.time()
print(f"Time taken to read DataFrame: {end_time - start_time} seconds")

Time taken to read DataFrame: 1.2805027961730957 seconds

Measure Read Performance with Hive Table

# Measure time to read Hive table
# Measure time to read Hive table
start_time = time.time()
hive_df = spark.sql("SELECT * FROM mydb.sample_hive_table")
hive_df.count()
end_time = time.time()

print(f"Time taken to read Hive table: {end_time - start_time} seconds")

Time taken to read Hive table: 0.5941483974456787 seconds

By comparing the time taken to read data directly from a DataFrame versus a Hive table, you can observe the performance improvements (1.3s vs. 0.6s). Typically, Hive tables offer better performance due to these optimizations:

Predicate Pushdown - When using Hive tables, Spark can push down filters to the storage layer, reducing the amount of data read. Column Pruning - Only the necessary columns are read, reducing I/O. Partition Pruning - Only relevant partitions are scanned, improving read performance1.