rmetzger commented on code in PR #721:
URL: https://github.com/apache/flink-web/pull/721#discussion_r1521014951
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docs/content/posts/2024-03-xx-release-1.19.0.md:
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+---
+authors:
+- LincolnLee:
+ name: "Lincoln Lee"
+ twitter: lincoln_86xy
+
+date: "2024-03-xxT22:00:00Z"
+subtitle: ""
+title: Announcing the Release of Apache Flink 1.19
+aliases:
+- /news/2024/03/xx/release-1.19.0.html
+---
+
+The Apache Flink PMC is pleased to announce the release of Apache Flink
1.19.0. As usual, we are
+looking at a packed release with a wide variety of improvements and new
features. Overall, 162
+people contributed to this release completing 33 FLIPs and 600+ issues. Thank
you!
+
+Let's dive into the highlights.
+
+# Flink SQL Improvements
+
+## Custom Parallelism for Table/SQL Sources
+
+Now in Flink 1.19, you can set a custom parallelism for performance tuning via
the `scan.parallelism`
+option. The first available connector is DataGen (Kafka connector is on the
way). Here is an example
+using SQL Client:
+
+```sql
+-- set parallelism within the ddl
+CREATE TABLE Orders (
+ order_number BIGINT,
+ price DECIMAL(32,2),
+ buyer ROW<first_name STRING, last_name STRING>,
+ order_time TIMESTAMP(3)
+) WITH (
+ 'connector' = 'datagen',
+ 'scan.parallelism' = '4'
+);
+
+-- or set parallelism via dynamic table option
+SELECT * FROM Orders /*+ OPTIONS('scan.parallelism'='4') */;
+```
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/dev/table/sourcessinks/#scan-table-source)
+* [FLIP-367: Support Setting Parallelism for Table/SQL
Sources](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=263429150)
+
+
+## Configurable SQL Gateway Java Options
+
+A new option `env.java.opts.sql-gateway` for specifying the Java options is
introduced in Flink 1.19,
+so you can fine-tune the memory settings, garbage collection behavior, and
other relevant Java
+parameters for SQL Gateway.
+
+**More Information**
+* [FLINK-33203](https://issues.apache.org/jira/browse/FLINK-33203)
+
+
+## Configure Different State TTLs using SQL Hint
+
+Starting from Flink 1.18, Table API and SQL users can set state time-to-live
(TTL) individually for
+stateful operators via the SQL compiled plan. In Flink 1.19, users have a more
flexible way to
+specify custom TTL values for regular joins and group aggregations directly
within their queries by [utilizing the STATE_TTL
hint](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/dev/table/sql/queries/hints/#state-ttl-hints).
+This improvement means that you no longer need to alter your compiled plan to
set specific TTLs for
+these frequently used operators. With the introduction of `STATE_TTL` hints,
you can streamline your workflow and
+dynamically adjust the TTL based on your operational requirements.
+
+Here is an example:
+```sql
+-- set state ttl for join
+SELECT /*+ STATE_TTL('Orders'= '1d', 'Customers' = '20d') */ *
+FROM Orders LEFT OUTER JOIN Customers
+ ON Orders.o_custkey = Customers.c_custkey;
+
+-- set state ttl for aggregation
+SELECT /*+ STATE_TTL('o' = '1d') */ o_orderkey, SUM(o_totalprice) AS revenue
+FROM Orders AS o
+GROUP BY o_orderkey;
+```
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/dev/table/sql/queries/hints/#state-ttl-hints)
+* [FLIP-373: Support Configuring Different State TTLs using SQL
Hint](https://cwiki.apache.org/confluence/display/FLINK/FLIP-373%3A+Support+Configuring+Different+State+TTLs+using+SQL+Hint)
+
+
+## Named Parameters for Functions and Procedures
+
+Named parameters now can be used when calling a function or stored procedure.
With named parameters,
+users do not need to strictly specify the parameter position, just specify the
parameter name and its
+corresponding value. At the same time, if non-essential parameters are not
specified, they will default to being filled with null.
+
+Here's an example of defining a function with one mandatory parameter and two
optional parameters using named parameters:
+```java
+public static class NamedArgumentsTableFunction extends TableFunction<Object> {
+
+ @FunctionHint(
+ output = @DataTypeHint("STRING"),
+ arguments = {
+ @ArgumentHint(name = "in1", isOptional
= false, type = @DataTypeHint("STRING")),
+ @ArgumentHint(name = "in2", isOptional
= true, type = @DataTypeHint("STRING")),
+ @ArgumentHint(name = "in3", isOptional
= true, type = @DataTypeHint("STRING"))})
+ public void eval(String arg1, String arg2, String arg3) {
+ collect(arg1 + ", " + arg2 + "," + arg3);
+ }
+
+}
+```
+When calling the function in SQL, parameters can be specified by name, for
example:
+```sql
+SELECT * FROM TABLE(myFunction(in1 => 'v1', in3 => 'v3', in2 => 'v2'))
+```
+Also the optional parameters can be omitted:
+```sql
+SELECT * FROM TABLE(myFunction(in1 => 'v1'))
+```
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/dev/table/functions/udfs/#named-parameters)
+* [FLIP-387: Support named parameters for functions and call
procedures](https://cwiki.apache.org/confluence/display/FLINK/FLIP-387%3A+Support+named+parameters+for+functions+and+call+procedures)
+
+## Window TVF Aggregation Features
+
+* Supports SESSION Window TVF in Streaming Mode<br />
+Now users can use SESSION Window TVF in streaming mode. A simple example is as
follows:
+```sql
+-- session window with partition keys
+SELECT * FROM TABLE(
+ SESSION(TABLE Bid PARTITION BY item, DESCRIPTOR(bidtime), INTERVAL '5'
MINUTES));
+
+-- apply aggregation on the session windowed table with partition keys
+SELECT window_start, window_end, item, SUM(price) AS total_price
+FROM TABLE(
+ SESSION(TABLE Bid PARTITION BY item, DESCRIPTOR(bidtime), INTERVAL '5'
MINUTES))
+GROUP BY item, window_start, window_end;
+```
+* Supports Changelog Inputs For Window TVF Aggregation<br />
+ Window aggregation operators (generated based on Window TVF Function) can
now handle changelog
+ streams (e.g., CDC data sources, etc.). Users are recommended to migrate
from legacy window
+ aggregation to the new syntax for more complete feature support.
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/dev/table/sql/queries/window-tvf/#session)
+
+## New UDF Type: AsyncScalarFunction
+
+The common UDF type `ScalarFunction` works well for CPU-intensive operations,
but less well for IO
+bound or otherwise long-running computations. In Flink 1.19, we have a new
`AsyncScalarFunction`
+which is a user-defined asynchronous `ScalarFunction` allows for issuing
concurrent function calls
+asynchronously.
+
+**More Information**
+* [FLIP-400: AsyncScalarFunction for asynchronous scalar function
support](https://cwiki.apache.org/confluence/display/FLINK/FLIP-400%3A+AsyncScalarFunction+for+asynchronous+scalar+function+support)
+
+## Tuning: MiniBatch Optimization for Regular Joins
+
+The record amplification is a pain point when performing cascading joins in
Flink, now in Flink 1.19,
+the new mini-batch optimization can be used for regular join to reduce
intermediate result in such
+cascading join scenarios.
+
+<div style="text-align: center;">
+<img src="/img/blog/2024-03-xx-release-1.19.0/minibatch_join.png"
style="width:90%;margin:15px">
+</div>
+
+**More Information**
+*
[minibatch-regular-joins](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/dev/table/tuning/#minibatch-regular-joins).
+* [FLIP-415: Introduce a new join operator to support
minibatch](https://cwiki.apache.org/confluence/display/FLINK/FLIP-415%3A+Introduce+a+new+join+operator+to+support+minibatch)
+
+# Runtime & Coordination Improvements
+
+## Dynamic Source Parallelism Inference for Batch Jobs
+
+In Flink 1.19, we have supported dynamic source parallelism inference for
batch jobs, which allows
+source connectors to dynamically infer the parallelism based on the actual
amount of data to consume.
+This feature is a significant improvement over previous versions, which only
assigned a fixed default
+parallelism to source vertices.
+Source connectors need to implement the inference interface to enable dynamic
parallelism inference.
+Currently, the FileSource connector has already been developed with this
functionality in place.
+Additionally, the configuration
`execution.batch.adaptive.auto-parallelism.default-source-parallelism`
+will be used as the upper bound of source parallelism inference. And now it
will not default to 1.
+Instead, if it is not set, the upper bound of allowed parallelism set via
+`execution.batch.adaptive.auto-parallelism.max-parallelism` will be used. If
that configuration is
+also not set, the default parallelism set via `parallelism.default` or
`StreamExecutionEnvironment#setParallelism()`
+will be used instead.
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/deployment/elastic_scaling/#enable-dynamic-parallelism-inference-support-for-sources).
+* [FLIP-379: Support dynamic source parallelism inference for batch
jobs](https://cwiki.apache.org/confluence/display/FLINK/FLIP-379%3A+Dynamic+source+parallelism+inference+for+batch+jobs)
+
+## Standard YAML for FLINK Configuration
Review Comment:
```suggestion
## Standard YAML for Flink Configuration
```
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