[SPARK-32366][DOC] Fix doc link of datetime pattern in 3.0 migration guide

docs/sql-migration-guide.md

@@ -148,7 +148,8 @@ license: |
 
   - In Spark 3.0, Proleptic Gregorian calendar is used in parsing, formatting, and converting dates and timestamps as well as in extracting sub-components like years, days and so on. Spark 3.0 uses Java 8 API classes from the `java.time` packages that are based on [ISO chronology](https://docs.oracle.com/javase/8/docs/api/java/time/chrono/IsoChronology.html). In Spark version 2.4 and below, those operations are performed using the hybrid calendar ([Julian + Gregorian](https://docs.oracle.com/javase/7/docs/api/java/util/GregorianCalendar.html). The changes impact on the results for dates before October 15, 1582 (Gregorian) and affect on the following Spark 3.0 API:
 
-    * Parsing/formatting of timestamp/date strings. This effects on CSV/JSON datasources and on the `unix_timestamp`, `date_format`, `to_unix_timestamp`, `from_unixtime`, `to_date`, `to_timestamp` functions when patterns specified by users is used for parsing and formatting. In Spark 3.0, we define our own pattern strings in `sql-ref-datetime-pattern.md`, which is implemented via `java.time.format.DateTimeFormatter` under the hood. New implementation performs strict checking of its input. For example, the `2015-07-22 10:00:00` timestamp cannot be parse if pattern is `yyyy-MM-dd` because the parser does not consume whole input. Another example is the `31/01/2015 00:00` input cannot be parsed by the `dd/MM/yyyy hh:mm` pattern because `hh` supposes hours in the range `1-12`. In Spark version 2.4 and below, `java.text.SimpleDateFormat` is used for timestamp/date string conversions, and the supported patterns are described in [simpleDateFormat](https://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html). The old behavior can be restored by setting `spark.sql.legacy.timeParserPolicy` to `LEGACY`.
+    * Parsing/formatting of timestamp/date strings. This effects on CSV/JSON datasources and on the `unix_timestamp`, `date_format`, `to_unix_timestamp`, `from_unixtime`, `to_date`, `to_timestamp` functions when patterns specified by users is used for parsing and formatting. In Spark 3.0, we define our own pattern strings in [Datetime Patterns for Formatting and Parsing](sql-ref-datetime-pattern.html),
+     which is implemented via [DateTimeFormatter](https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html) under the hood. New implementation performs strict checking of its input. For example, the `2015-07-22 10:00:00` timestamp cannot be parse if pattern is `yyyy-MM-dd` because the parser does not consume whole input. Another example is the `31/01/2015 00:00` input cannot be parsed by the `dd/MM/yyyy hh:mm` pattern because `hh` supposes hours in the range `1-12`. In Spark version 2.4 and below, `java.text.SimpleDateFormat` is used for timestamp/date string conversions, and the supported patterns are described in [SimpleDateFormat](https://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html). The old behavior can be restored by setting `spark.sql.legacy.timeParserPolicy` to `LEGACY`.
 
     * The `weekofyear`, `weekday`, `dayofweek`, `date_trunc`, `from_utc_timestamp`, `to_utc_timestamp`, and `unix_timestamp` functions use java.time API for calculation week number of year, day number of week as well for conversion from/to TimestampType values in UTC time zone.