twalthr commented on code in PR #28758:
URL: https://github.com/apache/flink/pull/28758#discussion_r3636549712
##########
flink-examples/flink-examples-table/src/main/java/org/apache/flink/table/examples/java/basics/WordCountSQLExample.java:
##########
@@ -34,15 +34,7 @@ public static void main(String[] args) throws Exception {
// execute a Flink SQL job and print the result locally
tableEnv.executeSql(
// define the aggregation
- "SELECT word, SUM(frequency) AS `count`\n"
- // read from an artificial fixed-size table
with rows and columns
- + "FROM (\n"
- + " VALUES ('Hello', 1), ('Ciao', 1),
('Hello', 2)\n"
- + ")\n"
- // name the table and its columns
- + "AS WordTable(word, frequency)\n"
- // group for aggregation
- + "GROUP BY word")
+ "SELECT PARSE_JSON('Infinity')")
Review Comment:
undo
##########
docs/content.zh/docs/sql/reference/data-types.md:
##########
@@ -1507,19 +1507,64 @@ close to the semantics of JSON. Compared to `ROW` and
`STRUCTURED` type, `VARIAN
flexibility to support highly nested and evolving schema.
`VARIANT` allows for deeply nested data structures, such as arrays within
arrays, maps within maps,
-or combinations of both.This capability makes `VARIANT` ideal for scenarios
where data complexity
+or combinations of both. This capability makes `VARIANT` ideal for scenarios
where data complexity
and nesting are significant.
`VARIANT` allows schema evolution, enabling the storage of data with changing
or unknown schemas
without requiring upfront schema definition. For example, if a new field is
added to the data, it
can be directly incorporated into the `VARIANT` data without modifying the
table schema. This is
particularly useful in dynamic environments where schemas may evolve over time.
+A `VARIANT` stores a single value of one of the following kinds: `NULL`,
`BOOLEAN`, `TINYINT`,
+`SMALLINT`, `INT`, `BIGINT`, `FLOAT`, `DOUBLE`, `DECIMAL` (up to precision
38), `STRING`, `DATE`,
+`TIMESTAMP`, `TIMESTAMP_LTZ`, `BYTES`, or a nested array or object.
`TIMESTAMP` and `TIMESTAMP_LTZ`
+are stored with microsecond precision and `DATE` as a day count. There is no
`TIME` kind.
Review Comment:
```suggestion
are stored with microsecond precision.
```
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