I am reading changes from the cdf with availableOnce=True, processing data from checkpoint to checkpoint. During each batch, I perform transformations, but I also need to read two large tables and one small table. Does Spark read these tables from scratch for each batch, or caching internally? 

# Read
df = self.spark.readStream.format("delta").option("readChangeFeed", "true")

# Transform
def transform(batch_df):
   # Read delta table 230 GB
   big_side_df = spark.read.format("delta")...
   # Read small df 

   # Do aggregations
   batchdf.join(broadcast(small))
   batchdf.join(bigside_df)....
   return final_df

def process_each_batch(df, _):
   transformed df = transform(df)
   
   # Do Upsert
   df.upsert()


self.df.writeStream.format(DeltaUtils.FORMAT_DELTA)
            .option("checkpointLocation", self.checkpoint_loc)
            .foreachBatch(self.process_each_batch)
            .outputMode("update")
            .trigger(availableNow=True)
            .start()

Also I am pretty new and I want to optimize the jobs but I am not sure where to focus on.

First things to come my minds are

• Adding maxBytesPerTrigger
• Reading from outside of the each batch and cache?
• Doing aggregations and prepare big df's to be consumed outside of the job(with another job in the workflow)

Any suggestion would be appreciated.