== Parsed Logical Plan ==
'GlobalLimit 5
+- 'LocalLimit 5
   +- 'Sort ['C_24 DESC NULLS LAST], true
      +- 'Project ['C_43 AS C_21#5078, 'C_0 AS C_12#5079, 'C_1 AS C_17#5080, 'C_4331 AS C_16#5081, 'C_4332 AS C_25#5082, 'C_4333 AS C_23#5083, 'C_4 AS C_19#5084, 'C_5 AS C_20#5085, 'C_7 AS C_14#5086, 'C_8 AS C_18#5087, 'C_9 AS C_22#5088, 'C_11 AS C_15#5089, 'C_10 AS C_13#5090, 'C_3 AS C_24#5091]
         +- 'SubqueryAlias C_4954424c
            +- 'Project ['C_64656661756c745f616972706f727473.id AS C_43#5062, 'C_64656661756c745f616972706f727473.type AS C_0#5063, 'C_64656661756c745f616972706f727473.name AS C_1#5064, 'C_64656661756c745f616972706f727473.lat AS C_2#5065, 'C_64656661756c745f616972706f727473.lon AS C_6#5066, 'C_64656661756c745f616972706f727473.elev AS C_3#5067, 'C_64656661756c745f616972706f727473.continent AS C_4#5068, 'C_64656661756c745f616972706f727473.country AS C_5#5069, 'C_64656661756c745f616972706f727473.region AS C_7#5070, 'C_64656661756c745f616972706f727473.city AS C_8#5071, 'C_64656661756c745f616972706f727473.iata AS C_9#5072, 'C_64656661756c745f616972706f727473.code AS C_11#5073, 'C_64656661756c745f616972706f727473.gps AS C_10#5074, ('round(('C_64656661756c745f616972706f727473.lat * 'power(10.0, 3)), 0) / 'power(10.0, 3)) AS C_4331#5075, ('round(('C_64656661756c745f616972706f727473.lon * 'power(10.0, 3)), 0) / 'power(10.0, 3)) AS C_4332#5076, ('round(('C_64656661756c745f616972706f727473.elev * 'power(10.0, 3)), 0) / 'power(10.0, 3)) AS C_4333#5077]
               +- 'Filter ((('C_64656661756c745f616972706f727473.lon <= -104.05) AND ('C_64656661756c745f616972706f727473.lon >= -111.05)) AND (('C_64656661756c745f616972706f727473.lat >= 41.0) AND ('C_64656661756c745f616972706f727473.lat <= 45.0)))
                  +- 'SubqueryAlias C_64656661756c745f616972706f727473
                     +- 'UnresolvedRelation [default, airports], [], false

== Analyzed Logical Plan ==
C_21: string, C_12: string, C_17: string, C_16: double, C_25: double, C_23: double, C_19: string, C_20: string, C_14: string, C_18: string, C_22: string, C_15: string, C_13: string, C_24: double
GlobalLimit 5
+- LocalLimit 5
   +- Sort [C_24#5091 DESC NULLS LAST], true
      +- Project [C_43#5062 AS C_21#5078, C_0#5063 AS C_12#5079, C_1#5064 AS C_17#5080, C_4331#5075 AS C_16#5081, C_4332#5076 AS C_25#5082, C_4333#5077 AS C_23#5083, C_4#5068 AS C_19#5084, C_5#5069 AS C_20#5085, C_7#5070 AS C_14#5086, C_8#5071 AS C_18#5087, C_9#5072 AS C_22#5088, C_11#5073 AS C_15#5089, C_10#5074 AS C_13#5090, C_3#5067 AS C_24#5091]
         +- SubqueryAlias C_4954424c
            +- Project [id#5092 AS C_43#5062, type#5093 AS C_0#5063, name#5094 AS C_1#5064, lat#5095 AS C_2#5065, lon#5096 AS C_6#5066, elev#5097 AS C_3#5067, continent#5098 AS C_4#5068, country#5099 AS C_5#5069, region#5100 AS C_7#5070, city#5101 AS C_8#5071, iata#5102 AS C_9#5072, code#5103 AS C_11#5073, gps#5104 AS C_10#5074, (round((lat#5095 * POWER(10.0, cast(3 as double))), 0) / POWER(10.0, cast(3 as double))) AS C_4331#5075, (round((lon#5096 * POWER(10.0, cast(3 as double))), 0) / POWER(10.0, cast(3 as double))) AS C_4332#5076, (round((elev#5097 * POWER(10.0, cast(3 as double))), 0) / POWER(10.0, cast(3 as double))) AS C_4333#5077]
               +- Filter (((lon#5096 <= -104.05) AND (lon#5096 >= -111.05)) AND ((lat#5095 >= 41.0) AND (lat#5095 <= 45.0)))
                  +- SubqueryAlias C_64656661756c745f616972706f727473
                     +- SubqueryAlias spark_catalog.default.airports
                        +- Relation spark_catalog.default.airports[id#5092,type#5093,name#5094,lat#5095,lon#5096,elev#5097,continent#5098,country#5099,region#5100,city#5101,iata#5102,code#5103,gps#5104] parquet

== Optimized Logical Plan ==
GlobalLimit 5
+- LocalLimit 5
   +- Sort [C_24#5091 DESC NULLS LAST], true
      +- Project [id#5092 AS C_21#5078, type#5093 AS C_12#5079, name#5094 AS C_17#5080, (round((lat#5095 * 1000.0), 0) / 1000.0) AS C_16#5081, (round((lon#5096 * 1000.0), 0) / 1000.0) AS C_25#5082, (round((elev#5097 * 1000.0), 0) / 1000.0) AS C_23#5083, continent#5098 AS C_19#5084, country#5099 AS C_20#5085, region#5100 AS C_14#5086, city#5101 AS C_18#5087, iata#5102 AS C_22#5088, code#5103 AS C_15#5089, gps#5104 AS C_13#5090, elev#5097 AS C_24#5091]
         +- Filter ((isnotnull(lon#5096) AND isnotnull(lat#5095)) AND (((lon#5096 <= -104.05) AND (lon#5096 >= -111.05)) AND ((lat#5095 >= 41.0) AND (lat#5095 <= 45.0))))
            +- Relation spark_catalog.default.airports[id#5092,type#5093,name#5094,lat#5095,lon#5096,elev#5097,continent#5098,country#5099,region#5100,city#5101,iata#5102,code#5103,gps#5104] parquet

== Physical Plan ==
TakeOrderedAndProject(limit=5, orderBy=[C_24#5091 DESC NULLS LAST], output=[C_21#5078,C_12#5079,C_17#5080,C_16#5081,C_25#5082,C_23#5083,C_19#5084,C_20#5085,C_14#5086,C_18#5087,C_22#5088,C_15#5089,C_13#5090,C_24#5091])
+- *(1) Project [id#5092 AS C_21#5078, type#5093 AS C_12#5079, name#5094 AS C_17#5080, (round((lat#5095 * 1000.0), 0) / 1000.0) AS C_16#5081, (round((lon#5096 * 1000.0), 0) / 1000.0) AS C_25#5082, (round((elev#5097 * 1000.0), 0) / 1000.0) AS C_23#5083, continent#5098 AS C_19#5084, country#5099 AS C_20#5085, region#5100 AS C_14#5086, city#5101 AS C_18#5087, iata#5102 AS C_22#5088, code#5103 AS C_15#5089, gps#5104 AS C_13#5090, elev#5097 AS C_24#5091]
   +- *(1) Filter (((((isnotnull(lon#5096) AND isnotnull(lat#5095)) AND (lon#5096 <= -104.05)) AND (lon#5096 >= -111.05)) AND (lat#5095 >= 41.0)) AND (lat#5095 <= 45.0))
      +- *(1) ColumnarToRow
         +- FileScan parquet spark_catalog.default.airports[id#5092,type#5093,name#5094,lat#5095,lon#5096,elev#5097,continent#5098,country#5099,region#5100,city#5101,iata#5102,code#5103,gps#5104] Batched: true, DataFilters: [isnotnull(lon#5096), isnotnull(lat#5095), (lon#5096 <= -104.05), (lon#5096 >= -111.05), (lat#509..., Format: Parquet, Location: InMemoryFileIndex(1 paths)[file:/home/acdcadmin/spark-warehouse/airports], PartitionFilters: [], PushedFilters: [IsNotNull(lon), IsNotNull(lat), LessThanOrEqual(lon,-104.05), GreaterThanOrEqual(lon,-111.05), G..., ReadSchema: struct<id:string,type:string,name:string,lat:double,lon:double,elev:double,continent:string,count...

== Parsed Logical Plan ==
'DescribeRelation false, [col_name#5039, data_type#5040, comment#5041]
+- 'UnresolvedTableOrView [default, airports], DESCRIBE TABLE, true

== Analyzed Logical Plan ==
col_name: string, data_type: string, comment: string
DescribeTableCommand `spark_catalog`.`default`.`airports`, false, [col_name#5039, data_type#5040, comment#5041]

== Optimized Logical Plan ==
CommandResult [col_name#5039, data_type#5040, comment#5041], Execute DescribeTableCommand, [[id,string,null], [type,string,null], [name,string,null], [lat,double,null], [lon,double,null], [elev,double,null], [continent,string,null], [country,string,null], [region,string,null], [city,string,null], [iata,string,null], [code,string,null], [gps,string,null]]
   +- DescribeTableCommand `spark_catalog`.`default`.`airports`, false, [col_name#5039, data_type#5040, comment#5041]

== Physical Plan ==
CommandResult [col_name#5039, data_type#5040, comment#5041]
   +- Execute DescribeTableCommand
         +- DescribeTableCommand `spark_catalog`.`default`.`airports`, false, [col_name#5039, data_type#5040, comment#5041]

== Parsed Logical Plan ==
'DescribeRelation false, [col_name#5016, data_type#5017, comment#5018]
+- 'UnresolvedTableOrView [default, airports], DESCRIBE TABLE, true

== Analyzed Logical Plan ==
col_name: string, data_type: string, comment: string
DescribeTableCommand `spark_catalog`.`default`.`airports`, false, [col_name#5016, data_type#5017, comment#5018]

== Optimized Logical Plan ==
CommandResult [col_name#5016, data_type#5017, comment#5018], Execute DescribeTableCommand, [[id,string,null], [type,string,null], [name,string,null], [lat,double,null], [lon,double,null], [elev,double,null], [continent,string,null], [country,string,null], [region,string,null], [city,string,null], [iata,string,null], [code,string,null], [gps,string,null]]
   +- DescribeTableCommand `spark_catalog`.`default`.`airports`, false, [col_name#5016, data_type#5017, comment#5018]

== Physical Plan ==
CommandResult [col_name#5016, data_type#5017, comment#5018]
   +- Execute DescribeTableCommand
         +- DescribeTableCommand `spark_catalog`.`default`.`airports`, false, [col_name#5016, data_type#5017, comment#5018]

== Parsed Logical Plan ==
SetCommand (-v,None)

== Analyzed Logical Plan ==
key: string, value: string, meaning: string, Since version: string
SetCommand (-v,None)

== Optimized Logical Plan ==
CommandResult [key#4991, value#4992, meaning#4993, Since version#4994], Execute SetCommand, [[spark.sql.adaptive.advisoryPartitionSizeInBytes,<value of spark.sql.adaptive.shuffle.targetPostShuffleInputSize>,The advisory size in bytes of the shuffle partition during adaptive optimization (when spark.sql.adaptive.enabled is true). It takes effect when Spark coalesces small shuffle partitions or splits skewed shuffle partition.,3.0.0], [spark.sql.adaptive.autoBroadcastJoinThreshold,<undefined>,Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when performing a join. By setting this value to -1 broadcasting can be disabled. The default value is same with spark.sql.autoBroadcastJoinThreshold. Note that, this config is used only in adaptive framework.,3.2.0], [spark.sql.adaptive.coalescePartitions.enabled,true,When true and 'spark.sql.adaptive.enabled' is true, Spark will coalesce contiguous shuffle partitions according to the target size (specified by 'spark.sql.adaptive.advisoryPartitionSizeInBytes'), to avoid too many small tasks.,3.0.0], [spark.sql.adaptive.coalescePartitions.initialPartitionNum,<undefined>,The initial number of shuffle partitions before coalescing. If not set, it equals to spark.sql.shuffle.partitions. This configuration only has an effect when 'spark.sql.adaptive.enabled' and 'spark.sql.adaptive.coalescePartitions.enabled' are both true.,3.0.0], [spark.sql.adaptive.coalescePartitions.minPartitionSize,1MB,The minimum size of shuffle partitions after coalescing. This is useful when the adaptively calculated target size is too small during partition coalescing.,3.2.0], [spark.sql.adaptive.coalescePartitions.parallelismFirst,true,When true, Spark does not respect the target size specified by 'spark.sql.adaptive.advisoryPartitionSizeInBytes' (default 64MB) when coalescing contiguous shuffle partitions, but adaptively calculate the target size according to the default parallelism of the Spark cluster. The calculated size is usually smaller than the configured target size. This is to maximize the parallelism and avoid performance regression when enabling adaptive query execution. It's recommended to set this config to false and respect the configured target size.,3.2.0], [spark.sql.adaptive.customCostEvaluatorClass,<undefined>,The custom cost evaluator class to be used for adaptive execution. If not being set, Spark will use its own SimpleCostEvaluator by default.,3.2.0], [spark.sql.adaptive.enabled,true,When true, enable adaptive query execution, which re-optimizes the query plan in the middle of query execution, based on accurate runtime statistics.,1.6.0], [spark.sql.adaptive.forceOptimizeSkewedJoin,false,When true, force enable OptimizeSkewedJoin even if it introduces extra shuffle.,3.3.0], [spark.sql.adaptive.localShuffleReader.enabled,true,When true and 'spark.sql.adaptive.enabled' is true, Spark tries to use local shuffle reader to read the shuffle data when the shuffle partitioning is not needed, for example, after converting sort-merge join to broadcast-hash join.,3.0.0], [spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold,0b,Configures the maximum size in bytes per partition that can be allowed to build local hash map. If this value is not smaller than spark.sql.adaptive.advisoryPartitionSizeInBytes and all the partition size are not larger than this config, join selection prefer to use shuffled hash join instead of sort merge join regardless of the value of spark.sql.join.preferSortMergeJoin.,3.2.0], [spark.sql.adaptive.optimizeSkewsInRebalancePartitions.enabled,true,When true and 'spark.sql.adaptive.enabled' is true, Spark will optimize the skewed shuffle partitions in RebalancePartitions and split them to smaller ones according to the target size (specified by 'spark.sql.adaptive.advisoryPartitionSizeInBytes'), to avoid data skew.,3.2.0], [spark.sql.adaptive.optimizer.excludedRules,<undefined>,Configures a list of rules to be disabled in the adaptive optimizer, in which the rules are specified by their rule names and separated by comma. The optimizer will log the rules that have indeed been excluded.,3.1.0], [spark.sql.adaptive.rebalancePartitionsSmallPartitionFactor,0.2,A partition will be merged during splitting if its size is small than this factor multiply spark.sql.adaptive.advisoryPartitionSizeInBytes.,3.3.0], [spark.sql.adaptive.skewJoin.enabled,true,When true and 'spark.sql.adaptive.enabled' is true, Spark dynamically handles skew in shuffled join (sort-merge and shuffled hash) by splitting (and replicating if needed) skewed partitions.,3.0.0], [spark.sql.adaptive.skewJoin.skewedPartitionFactor,5.0,A partition is considered as skewed if its size is larger than this factor multiplying the median partition size and also larger than 'spark.sql.adaptive.skewJoin.skewedPartitionThresholdInBytes',3.0.0], [spark.sql.adaptive.skewJoin.skewedPartitionThresholdInBytes,256MB,A partition is considered as skewed if its size in bytes is larger than this threshold and also larger than 'spark.sql.adaptive.skewJoin.skewedPartitionFactor' multiplying the median partition size. Ideally this config should be set larger than 'spark.sql.adaptive.advisoryPartitionSizeInBytes'.,3.0.0], [spark.sql.allowNamedFunctionArguments,true,If true, Spark will turn on support for named parameters for all functions that has it implemented.,3.5.0], [spark.sql.ansi.doubleQuotedIdentifiers,false,When true and 'spark.sql.ansi.enabled' is true, Spark SQL reads literals enclosed in double quoted (") as identifiers. When false they are read as string literals.,3.4.0], [spark.sql.ansi.enabled,false,When true, Spark SQL uses an ANSI compliant dialect instead of being Hive compliant. For example, Spark will throw an exception at runtime instead of returning null results when the inputs to a SQL operator/function are invalid.For full details of this dialect, you can find them in the section "ANSI Compliance" of Spark's documentation. Some ANSI dialect features may be not from the ANSI SQL standard directly, but their behaviors align with ANSI SQL's style,3.0.0], [spark.sql.ansi.enforceReservedKeywords,false,When true and 'spark.sql.ansi.enabled' is true, the Spark SQL parser enforces the ANSI reserved keywords and forbids SQL queries that use reserved keywords as alias names and/or identifiers for table, view, function, etc.,3.3.0], [spark.sql.ansi.relationPrecedence,false,When true and 'spark.sql.ansi.enabled' is true, JOIN takes precedence over comma when combining relation. For example, `t1, t2 JOIN t3` should result to `t1 X (t2 X t3)`. If the config is false, the result is `(t1 X t2) X t3`.,3.4.0], [spark.sql.autoBroadcastJoinThreshold,10MB,Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when performing a join.  By setting this value to -1 broadcasting can be disabled. Note that currently statistics are only supported for Hive Metastore tables where the command `ANALYZE TABLE <tableName> COMPUTE STATISTICS noscan` has been run, and file-based data source tables where the statistics are computed directly on the files of data.,1.1.0], [spark.sql.avro.compression.codec,snappy,Compression codec used in writing of AVRO files. Supported codecs: uncompressed, deflate, snappy, bzip2, xz and zstandard. Default codec is snappy.,2.4.0], ... 183 more fields]
   +- SetCommand (-v,None)

== Physical Plan ==
CommandResult [key#4991, value#4992, meaning#4993, Since version#4994]
   +- Execute SetCommand
         +- SetCommand (-v,None)