Uso de la instrucción EXPLAIN de Spark para solucionar problemas de Spark SQL

Puede usar la instrucción EXPLAIN de Spark con Spark SQL para solucionar problemas de su código Spark. Los siguientes ejemplos de código y salida muestran este uso.

ejemplo – Instrucción SELECT de Spark

spark.sql("select * from select_taxi_table").explain(True)

Salida

Calculation started (calculation_id=20c1ebd0-1ccf-ef14-db35-7c1844876a7e) in 
(session=24c1ebcb-57a8-861e-1023-736f5ae55386). 
Checking calculation status...

Calculation completed.
== Parsed Logical Plan ==
'Project [*]
+- 'UnresolvedRelation [select_taxi_table], [], false

== Analyzed Logical Plan ==
VendorID: bigint, passenger_count: bigint, count: bigint
Project [VendorID#202L, passenger_count#203L, count#204L]
+- SubqueryAlias spark_catalog.spark_demo_database.select_taxi_table
   +- Relation spark_demo_database.select_taxi_table[VendorID#202L,
       passenger_count#203L,count#204L] csv

== Optimized Logical Plan ==
Relation spark_demo_database.select_taxi_table[VendorID#202L,
passenger_count#203L,count#204L] csv

== Physical Plan ==
FileScan csv spark_demo_database.select_taxi_table[VendorID#202L,
passenger_count#203L,count#204L] 
Batched: false, DataFilters: [], Format: CSV, 
Location: InMemoryFileIndex(1 paths)
[s3://amzn-s3-demo-bucket/select_taxi], 
PartitionFilters: [], PushedFilters: [], 
ReadSchema: struct<VendorID:bigint,passenger_count:bigint,count:bigint>

ejemplo – Marco de datos de Spark

En el siguiente ejemplo de código, se muestra cómo utilizar EXPLAIN con un marco de datos de Spark.

taxi1_df=taxi_df.groupBy("VendorID", "passenger_count").count()
taxi1_df.explain("extended")

Salida

Calculation started (calculation_id=d2c1ebd1-f9f0-db25-8477-3effc001b309) in 
(session=24c1ebcb-57a8-861e-1023-736f5ae55386). 
Checking calculation status...

Calculation completed.
== Parsed Logical Plan ==
'Aggregate ['VendorID, 'passenger_count], 
['VendorID, 'passenger_count, count(1) AS count#321L]
+- Relation [VendorID#49L,tpep_pickup_datetime#50,tpep_dropoff_datetime#51,
passenger_count#52L,trip_distance#53,RatecodeID#54L,store_and_fwd_flag#55,
PULocationID#56L,DOLocationID#57L,payment_type#58L,fare_amount#59,
extra#60,mta_tax#61,tip_amount#62,tolls_amount#63,improvement_surcharge#64,
total_amount#65,congestion_surcharge#66,airport_fee#67] parquet

== Analyzed Logical Plan ==
VendorID: bigint, passenger_count: bigint, count: bigint
Aggregate [VendorID#49L, passenger_count#52L], 
[VendorID#49L, passenger_count#52L, count(1) AS count#321L]
+- Relation [VendorID#49L,tpep_pickup_datetime#50,tpep_dropoff_datetime#51,
passenger_count#52L,trip_distance#53,RatecodeID#54L,store_and_fwd_flag#55,
PULocationID#56L,DOLocationID#57L,payment_type#58L,fare_amount#59,extra#60,
mta_tax#61,tip_amount#62,tolls_amount#63,improvement_surcharge#64,
total_amount#65,congestion_surcharge#66,airport_fee#67] parquet

== Optimized Logical Plan ==
Aggregate [VendorID#49L, passenger_count#52L], 
[VendorID#49L, passenger_count#52L, count(1) AS count#321L]
+- Project [VendorID#49L, passenger_count#52L]
   +- Relation [VendorID#49L,tpep_pickup_datetime#50,tpep_dropoff_datetime#51,
passenger_count#52L,trip_distance#53,RatecodeID#54L,store_and_fwd_flag#55,
PULocationID#56L,DOLocationID#57L,payment_type#58L,fare_amount#59,extra#60,
mta_tax#61,tip_amount#62,tolls_amount#63,improvement_surcharge#64,
total_amount#65,congestion_surcharge#66,airport_fee#67] parquet

== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[VendorID#49L, passenger_count#52L], functions=[count(1)], 
output=[VendorID#49L, passenger_count#52L, count#321L])
   +- Exchange hashpartitioning(VendorID#49L, passenger_count#52L, 1000), 
      ENSURE_REQUIREMENTS, [id=#531]
      +- HashAggregate(keys=[VendorID#49L, passenger_count#52L], 
         functions=[partial_count(1)], output=[VendorID#49L, 
         passenger_count#52L, count#326L])
         +- FileScan parquet [VendorID#49L,passenger_count#52L] Batched: true, 
            DataFilters: [], Format: Parquet, 
            Location: InMemoryFileIndex(1 paths)[s3://amzn-s3-demo-bucket/
            notebooks/yellow_tripdata_2016-01.parquet], PartitionFilters: [], 
            PushedFilters: [], 
            ReadSchema: struct<VendorID:bigint,passenger_count:bigint>