== Parsed Logical Plan ==
'GlobalLimit 5
+- 'LocalLimit 5
   +- 'Sort ['C_25 DESC NULLS LAST], true
      +- 'Project ['C_43 AS C_12#4934, 'C_4 AS C_13#4935, 'C_1 AS C_17#4936, 'C_4331 AS C_19#4937, 'C_4332 AS C_18#4938, 'C_4333 AS C_16#4939, 'C_6 AS C_15#4940, 'C_8 AS C_14#4941, 'C_9 AS C_20#4942, 'C_7 AS C_23#4943, 'C_5 AS C_22#4944, 'C_10 AS C_21#4945, 'C_11 AS C_24#4946, 'C_0 AS C_25#4947]
         +- 'SubqueryAlias C_4954424c
            +- 'Project ['C_64656661756c745f616972706f727473.id AS C_43#4918, 'C_64656661756c745f616972706f727473.type AS C_4#4919, 'C_64656661756c745f616972706f727473.name AS C_1#4920, 'C_64656661756c745f616972706f727473.lat AS C_2#4921, 'C_64656661756c745f616972706f727473.lon AS C_3#4922, 'C_64656661756c745f616972706f727473.elev AS C_0#4923, 'C_64656661756c745f616972706f727473.continent AS C_6#4924, 'C_64656661756c745f616972706f727473.country AS C_8#4925, 'C_64656661756c745f616972706f727473.region AS C_9#4926, 'C_64656661756c745f616972706f727473.city AS C_7#4927, 'C_64656661756c745f616972706f727473.iata AS C_5#4928, 'C_64656661756c745f616972706f727473.code AS C_10#4929, 'C_64656661756c745f616972706f727473.gps AS C_11#4930, ('round(('C_64656661756c745f616972706f727473.lat * 'power(10.0, 3)), 0) / 'power(10.0, 3)) AS C_4331#4931, ('round(('C_64656661756c745f616972706f727473.lon * 'power(10.0, 3)), 0) / 'power(10.0, 3)) AS C_4332#4932, ('round(('C_64656661756c745f616972706f727473.elev * 'power(10.0, 3)), 0) / 'power(10.0, 3)) AS C_4333#4933]
               +- '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_12: string, C_13: string, C_17: string, C_19: double, C_18: double, C_16: double, C_15: string, C_14: string, C_20: string, C_23: string, C_22: string, C_21: string, C_24: string, C_25: double
GlobalLimit 5
+- LocalLimit 5
   +- Sort [C_25#4947 DESC NULLS LAST], true
      +- Project [C_43#4918 AS C_12#4934, C_4#4919 AS C_13#4935, C_1#4920 AS C_17#4936, C_4331#4931 AS C_19#4937, C_4332#4932 AS C_18#4938, C_4333#4933 AS C_16#4939, C_6#4924 AS C_15#4940, C_8#4925 AS C_14#4941, C_9#4926 AS C_20#4942, C_7#4927 AS C_23#4943, C_5#4928 AS C_22#4944, C_10#4929 AS C_21#4945, C_11#4930 AS C_24#4946, C_0#4923 AS C_25#4947]
         +- SubqueryAlias C_4954424c
            +- Project [id#4948 AS C_43#4918, type#4949 AS C_4#4919, name#4950 AS C_1#4920, lat#4951 AS C_2#4921, lon#4952 AS C_3#4922, elev#4953 AS C_0#4923, continent#4954 AS C_6#4924, country#4955 AS C_8#4925, region#4956 AS C_9#4926, city#4957 AS C_7#4927, iata#4958 AS C_5#4928, code#4959 AS C_10#4929, gps#4960 AS C_11#4930, (round((lat#4951 * POWER(10.0, cast(3 as double))), 0) / POWER(10.0, cast(3 as double))) AS C_4331#4931, (round((lon#4952 * POWER(10.0, cast(3 as double))), 0) / POWER(10.0, cast(3 as double))) AS C_4332#4932, (round((elev#4953 * POWER(10.0, cast(3 as double))), 0) / POWER(10.0, cast(3 as double))) AS C_4333#4933]
               +- Filter (((lon#4952 <= -104.05) AND (lon#4952 >= -111.05)) AND ((lat#4951 >= 41.0) AND (lat#4951 <= 45.0)))
                  +- SubqueryAlias C_64656661756c745f616972706f727473
                     +- SubqueryAlias spark_catalog.default.airports
                        +- Relation spark_catalog.default.airports[id#4948,type#4949,name#4950,lat#4951,lon#4952,elev#4953,continent#4954,country#4955,region#4956,city#4957,iata#4958,code#4959,gps#4960] parquet

== Optimized Logical Plan ==
GlobalLimit 5
+- LocalLimit 5
   +- Sort [C_25#4947 DESC NULLS LAST], true
      +- Project [id#4948 AS C_12#4934, type#4949 AS C_13#4935, name#4950 AS C_17#4936, (round((lat#4951 * 1000.0), 0) / 1000.0) AS C_19#4937, (round((lon#4952 * 1000.0), 0) / 1000.0) AS C_18#4938, (round((elev#4953 * 1000.0), 0) / 1000.0) AS C_16#4939, continent#4954 AS C_15#4940, country#4955 AS C_14#4941, region#4956 AS C_20#4942, city#4957 AS C_23#4943, iata#4958 AS C_22#4944, code#4959 AS C_21#4945, gps#4960 AS C_24#4946, elev#4953 AS C_25#4947]
         +- Filter ((isnotnull(lon#4952) AND isnotnull(lat#4951)) AND (((lon#4952 <= -104.05) AND (lon#4952 >= -111.05)) AND ((lat#4951 >= 41.0) AND (lat#4951 <= 45.0))))
            +- Relation spark_catalog.default.airports[id#4948,type#4949,name#4950,lat#4951,lon#4952,elev#4953,continent#4954,country#4955,region#4956,city#4957,iata#4958,code#4959,gps#4960] parquet

== Physical Plan ==
TakeOrderedAndProject(limit=5, orderBy=[C_25#4947 DESC NULLS LAST], output=[C_12#4934,C_13#4935,C_17#4936,C_19#4937,C_18#4938,C_16#4939,C_15#4940,C_14#4941,C_20#4942,C_23#4943,C_22#4944,C_21#4945,C_24#4946,C_25#4947])
+- *(1) Project [id#4948 AS C_12#4934, type#4949 AS C_13#4935, name#4950 AS C_17#4936, (round((lat#4951 * 1000.0), 0) / 1000.0) AS C_19#4937, (round((lon#4952 * 1000.0), 0) / 1000.0) AS C_18#4938, (round((elev#4953 * 1000.0), 0) / 1000.0) AS C_16#4939, continent#4954 AS C_15#4940, country#4955 AS C_14#4941, region#4956 AS C_20#4942, city#4957 AS C_23#4943, iata#4958 AS C_22#4944, code#4959 AS C_21#4945, gps#4960 AS C_24#4946, elev#4953 AS C_25#4947]
   +- *(1) Filter (((((isnotnull(lon#4952) AND isnotnull(lat#4951)) AND (lon#4952 <= -104.05)) AND (lon#4952 >= -111.05)) AND (lat#4951 >= 41.0)) AND (lat#4951 <= 45.0))
      +- *(1) ColumnarToRow
         +- FileScan parquet spark_catalog.default.airports[id#4948,type#4949,name#4950,lat#4951,lon#4952,elev#4953,continent#4954,country#4955,region#4956,city#4957,iata#4958,code#4959,gps#4960] Batched: true, DataFilters: [isnotnull(lon#4952), isnotnull(lat#4951), (lon#4952 <= -104.05), (lon#4952 >= -111.05), (lat#495..., 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#4895, data_type#4896, comment#4897]
+- '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#4895, data_type#4896, comment#4897]

== Optimized Logical Plan ==
CommandResult [col_name#4895, data_type#4896, comment#4897], 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#4895, data_type#4896, comment#4897]

== Physical Plan ==
CommandResult [col_name#4895, data_type#4896, comment#4897]
   +- Execute DescribeTableCommand
         +- DescribeTableCommand `spark_catalog`.`default`.`airports`, false, [col_name#4895, data_type#4896, comment#4897]

== Parsed Logical Plan ==
'DescribeRelation false, [col_name#4872, data_type#4873, comment#4874]
+- '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#4872, data_type#4873, comment#4874]

== Optimized Logical Plan ==
CommandResult [col_name#4872, data_type#4873, comment#4874], 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#4872, data_type#4873, comment#4874]

== Physical Plan ==
CommandResult [col_name#4872, data_type#4873, comment#4874]
   +- Execute DescribeTableCommand
         +- DescribeTableCommand `spark_catalog`.`default`.`airports`, false, [col_name#4872, data_type#4873, comment#4874]

== 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#4847, value#4848, meaning#4849, Since version#4850], 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#4847, value#4848, meaning#4849, Since version#4850]
   +- Execute SetCommand
         +- SetCommand (-v,None)