Get partitions and cores

Use an rdd method to get the number of DataFrame partitions

df = spark.read.parquet(eventsPath)
df.rdd.getNumPartitions()

Access SparkContext through SparkSession to get the number of cores or slots

SparkContext is also provided in Databricks notebooks as the variable sc

print(spark.sparkContext.defaultParallelism)
# print(sc.defaultParallelism)
# return 8 

Repartition DataFrame

repartition

Returns a new DataFrame that has exactly n partitions.

repartitionedDF = df.repartition(8)
repartitionedDF.rdd.getNumPartitions()

coalesce

Returns a new DataFrame that has exactly n partitions, when the fewer partitions are requested

If a larger number of partitions is requested, it will stay at the current number of partitions

coalesceDF = df.coalesce(8)
coalesceDF.rdd.getNumPartitions()

Configure default shuffle partitions

Use SparkConf to access the spark configuration parameter for default shuffle partitions

spark.conf.get("spark.sql.shuffle.partitions")

Configure default shuffle partitions to match the number of cores

spark.conf.set("spark.sql.shuffle.partitions", "8")

Adaptive Query Execution

https://databricks.com/blog/2020/05/29/adaptive-query-execution-speeding-up-spark-sql-at-runtime.html

Spark SQL can use spark.sql.adaptive.enabled to control whether AQE is turned on/off (disabled by default)

spark.conf.get("spark.sql.adaptive.enabled")