## What changes were proposed in this pull request?

As of Spark 2.1, Spark SQL assumes the machine timezone for datetime manipulation, which is bad if users are not in the same timezones as the machines, or if different users have different timezones.

We should introduce a session local timezone setting that is used for execution.

An explicit non-goal is locale handling.

### Semantics

Setting the session local timezone means that the timezone-aware expressions listed below should use the timezone to evaluate values, and also it should be used to convert (cast) between string and timestamp or between timestamp and date.

- `CurrentDate`
- `CurrentBatchTimestamp`
- `Hour`
- `Minute`
- `Second`
- `DateFormatClass`
- `ToUnixTimestamp`
- `UnixTimestamp`
- `FromUnixTime`

and below are implicitly timezone-aware through cast from timestamp to date:

- `DayOfYear`
- `Year`
- `Quarter`
- `Month`
- `DayOfMonth`
- `WeekOfYear`
- `LastDay`
- `NextDay`
- `TruncDate`

For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT`, the values evaluated by some of timezone-aware expressions are:

```scala
scala> val df = Seq(new java.sql.Timestamp(1451606400000L)).toDF("ts")
df: org.apache.spark.sql.DataFrame = [ts: timestamp]

scala> df.selectExpr("cast(ts as string)", "year(ts)", "month(ts)", "dayofmonth(ts)", "hour(ts)", "minute(ts)", "second(ts)").show(truncate = false)
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|ts                 |year(CAST(ts AS DATE))|month(CAST(ts AS DATE))|dayofmonth(CAST(ts AS DATE))|hour(ts)|minute(ts)|second(ts)|
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|2016-01-01 00:00:00|2016                  |1                      |1                           |0       |0         |0         |
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
```

whereas setting the session local timezone to `"PST"`, they are:

```scala
scala> spark.conf.set("spark.sql.session.timeZone", "PST")

scala> df.selectExpr("cast(ts as string)", "year(ts)", "month(ts)", "dayofmonth(ts)", "hour(ts)", "minute(ts)", "second(ts)").show(truncate = false)
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|ts                 |year(CAST(ts AS DATE))|month(CAST(ts AS DATE))|dayofmonth(CAST(ts AS DATE))|hour(ts)|minute(ts)|second(ts)|
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|2015-12-31 16:00:00|2015                  |12                     |31                          |16      |0         |0         |
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
```

Notice that even if you set the session local timezone, it affects only in `DataFrame` operations, neither in `Dataset` operations, `RDD` operations nor in `ScalaUDF`s. You need to properly handle timezone by yourself.

### Design of the fix

I introduced an analyzer to pass session local timezone to timezone-aware expressions and modified DateTimeUtils to take the timezone argument.

## How was this patch tested?

Existing tests and added tests for timezone aware expressions.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #16308 from ueshin/issues/SPARK-18350.