// Somehow CREATE TABLE doesn't work with five-digit year timestamps
spark.sql("select timestamp'12345-01-01 01:01:01' c").write.mode("overwrite").save("x")
spark.read.load("x").createOrReplaceTempView("t")

// date_format is run in Velox
spark.sql("select date_format(c, 'yyyy-MM') from t").explain()
// == Physical Plan ==
// VeloxColumnarToRowExec
// +- ^(14) ProjectExecTransformer [date_format(c#83, yyyy-MM, Some(Etc/UTC)) AS date_format(c, yyyy-MM)#85]
//    +- ^(14) NativeFileScan parquet [c#83] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex(1 paths)[file:/ssd/chungmin/repos/spark34/x], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<c:timestamp>

// Use collect() instead of show(), as show() makes the function run in vanilla Spark in Spark 3.5 due to the inserted ToPrettyString function.
spark.sql("select date_format(c, 'yyyy-MM') from t").collect()
// Array([12345-01])

spark.sql("create table t2 as select date_format(c, 'yyyy-MM') c from t")
spark.sql("set spark.gluten.enabled = false")
spark.sql("select unix_timestamp(c, 'yyyy-MM') from t2").collect()
// 24/04/25 02:01:01 ERROR TaskResources: Task 8 failed by error:
// org.apache.spark.SparkUpgradeException: [INCONSISTENT_BEHAVIOR_CROSS_VERSION.PARSE_DATETIME_BY_NEW_PARSER] You may get a different result due to the upgrading to Spark >= 3.0:
// Fail to parse '12345-01' in the new parser. You can set "spark.sql.legacy.timeParserPolicy" to "LEGACY" to restore the behavior before Spark 3.0, or set to "CORRECTED" and treat it as an invalid datetime string.
// ...

import java.time.{LocalDate, ZoneId}
import java.time.format.DateTimeFormatter

DateTimeFormatter.ofPattern("yyyy").withZone(ZoneId.of("Z")).format(LocalDate.of(12345, 1, 1))
// "+12345"