Spark EXPLAIN 문을 사용하여 Spark SQL 문제 해결 - Amazon Athena

Spark EXPLAIN 문을 사용하여 Spark SQL 문제 해결

Spark EXPLAIN 문을 Spark SQL과 함께 사용하여 Spark 코드 문제를 해결할 수 있습니다. 다음 코드 및 출력 예는 이 사용법을 보여줍니다.

예 – Spark SELECT 문
spark.sql("select * from select_taxi_table").explain(True)

출력

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>
예 – Spark 데이터 프레임

다음 코드 예는 Spark 데이터 프레임과 함께 EXPLAIN을 사용하는 방법을 보여줍니다.

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

출력

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>