Details
-
Improvement
-
Status: Resolved
-
Major
-
Resolution: Fixed
-
4.0.0
Description
While building the DataFrame step by step, each time a new DataFrame is generated with an empty schema, which is lazily computed on access. However, if a user's code frequently accesses the schema of these new DataFrames using methods such as `df.columns`, it will result in a large number of Analyze requests to the server. Each time, the entire plan needs to be reanalyzed, leading to poor performance, especially when constructing highly complex plans.
Now, by introducing plan cache in SparkConnectPlanner, we aim to reduce the overhead of repeated analysis during this process. This is achieved by saving significant computation if the resolved logical plan of a subtree of can be cached.
A minimal example of the problem:
import pyspark.sql.functions as F df = spark.range(10) for i in range(200): if str(i) not in df.columns: # <-- The df.columns call causes a new Analyze request in every iteration df = df.withColumn(str(i), F.col("id") + i) df.show()
With this patch, the performance of the above code improved from ~110s to ~5s.