Dear Spark Developers and Users,
Hyukjin and I plan to upgrade the built-in Hive from 1.2.1-spark2 <https://github.com/JoshRosen/hive/tree/release-1.2.1-spark2> to 2.3.4 <https://github.com/apache/hive/releases/tag/rel%2Frelease-2.3.4> to solve some critical issues, such as support Hadoop 3.x, solve some ORC and Parquet issues. This is the list: *Hive issues*: [SPARK-26332 <https://issues.apache.org/jira/browse/SPARK-26332>][HIVE-10790] Spark sql write orc table on viewFS throws exception [SPARK-25193 <https://issues.apache.org/jira/browse/SPARK-25193>][HIVE-12505] insert overwrite doesn't throw exception when drop old data fails [SPARK-26437 <https://issues.apache.org/jira/browse/SPARK-26437>][HIVE-13083] Decimal data becomes bigint to query, unable to query [SPARK-25919 <https://issues.apache.org/jira/browse/SPARK-25919>][HIVE-11771] Date value corrupts when tables are "ParquetHiveSerDe" formatted and target table is Partitioned [SPARK-12014 <https://issues.apache.org/jira/browse/SPARK-12014>][HIVE-11100] Spark SQL query containing semicolon is broken in Beeline *Spark issues*: [SPARK-23534 <https://issues.apache.org/jira/browse/SPARK-23534>] Spark run on Hadoop 3.0.0 [SPARK-20202 <https://issues.apache.org/jira/browse/SPARK-20202>] Remove references to org.spark-project.hive [SPARK-18673 <https://issues.apache.org/jira/browse/SPARK-18673>] Dataframes doesn't work on Hadoop 3.x; Hive rejects Hadoop version [SPARK-24766 <https://issues.apache.org/jira/browse/SPARK-24766>] CreateHiveTableAsSelect and InsertIntoHiveDir won't generate decimal column stats in parquet Since the code for the *hive-thriftserver* module has changed too much for this upgrade, I split it into two PRs for easy review. The first PR <https://github.com/apache/spark/pull/23552> does not contain the changes of hive-thriftserver. Please ignore the failed test in hive-thriftserver. The second PR <https://github.com/apache/spark/pull/23553> is complete changes. I have created a Spark distribution for Apache Hadoop 2.7, you might download it via Google Drive <https://drive.google.com/open?id=1cq2I8hUTs9F4JkFyvRfdOJ5BlxV0ujgt> or Baidu Pan <https://pan.baidu.com/s/1b090Ctuyf1CDYS7c0puBqQ>. Please help review and test. Thanks.
