[SPARK-24812][SQL] Last Access Time in the table description is not valid
## What changes were proposed in this pull request? Last Access Time will always displayed wrong date Thu Jan 01 05:30:00 IST 1970 when user run DESC FORMATTED table command In hive its displayed as "UNKNOWN" which makes more sense than displaying wrong date. seems to be a limitation as of now even from hive, better we can follow the hive behavior unless the limitation has been resolved from hive. spark client output ![spark_desc table](https://user-images.githubusercontent.com/12999161/42753448-ddeea66a-88a5-11e8-94aa-ef8d017f94c5.png) Hive client output ![hive_behaviour](https://user-images.githubusercontent.com/12999161/42753489-f4fd366e-88a5-11e8-83b0-0f3a53ce83dd.png) ## How was this patch tested? UT has been added which makes sure that the wrong date "Thu Jan 01 05:30:00 IST 1970 " shall not be added as value for the Last Access property Author: s71955 <sujithchacko.2010@gmail.com> Closes #21775 from sujith71955/master_hive.
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@ -1850,6 +1850,7 @@ working with timestamps in `pandas_udf`s to get the best performance, see
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## Upgrading From Spark SQL 2.3 to 2.4
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- Since Spark 2.4, Spark will display table description column Last Access value as UNKNOWN when the value was Jan 01 1970.
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- Since Spark 2.4, Spark maximizes the usage of a vectorized ORC reader for ORC files by default. To do that, `spark.sql.orc.impl` and `spark.sql.orc.filterPushdown` change their default values to `native` and `true` respectively.
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- In PySpark, when Arrow optimization is enabled, previously `toPandas` just failed when Arrow optimization is unable to be used whereas `createDataFrame` from Pandas DataFrame allowed the fallback to non-optimization. Now, both `toPandas` and `createDataFrame` from Pandas DataFrame allow the fallback by default, which can be switched off by `spark.sql.execution.arrow.fallback.enabled`.
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- Since Spark 2.4, writing an empty dataframe to a directory launches at least one write task, even if physically the dataframe has no partition. This introduces a small behavior change that for self-describing file formats like Parquet and Orc, Spark creates a metadata-only file in the target directory when writing a 0-partition dataframe, so that schema inference can still work if users read that directory later. The new behavior is more reasonable and more consistent regarding writing empty dataframe.
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@ -114,7 +114,10 @@ case class CatalogTablePartition(
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map.put("Partition Parameters", s"{${parameters.map(p => p._1 + "=" + p._2).mkString(", ")}}")
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}
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map.put("Created Time", new Date(createTime).toString)
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map.put("Last Access", new Date(lastAccessTime).toString)
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val lastAccess = {
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if (-1 == lastAccessTime) "UNKNOWN" else new Date(lastAccessTime).toString
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}
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map.put("Last Access", lastAccess)
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stats.foreach(s => map.put("Partition Statistics", s.simpleString))
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map
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}
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@ -19,6 +19,7 @@ package org.apache.spark.sql.hive.execution
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import java.io.File
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import java.net.URI
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import java.util.Date
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import scala.language.existentials
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@ -2250,6 +2251,18 @@ class HiveDDLSuite
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}
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}
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test("SPARK-24812: desc formatted table for last access verification") {
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withTable("t1") {
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sql(
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"CREATE TABLE IF NOT EXISTS t1 (c1_int INT, c2_string STRING, c3_float FLOAT)")
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val desc = sql("DESC FORMATTED t1").filter($"col_name".startsWith("Last Access"))
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.select("data_type")
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// check if the last access time doesnt have the default date of year
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// 1970 as its a wrong access time
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assert(!(desc.first.toString.contains("1970")))
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}
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}
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test("SPARK-24681 checks if nested column names do not include ',', ':', and ';'") {
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val expectedMsg = "Cannot create a table having a nested column whose name contains invalid " +
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"characters (',', ':', ';') in Hive metastore."
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