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The following commit(s) were added to refs/heads/release-1.13 by this push:
new bf304e4 [hotfix][docs] Fix typos in Scala Table-DataStream API docs
bf304e4 is described below
commit bf304e49c4e6ea7b8d73cfb7709e87a2e4c8b52a
Author: Timo Walther <[email protected]>
AuthorDate: Thu Jun 17 13:15:23 2021 +0200
[hotfix][docs] Fix typos in Scala Table-DataStream API docs
---
docs/content.zh/docs/dev/table/data_stream_api.md | 45 +++++++++++++----------
docs/content/docs/dev/table/data_stream_api.md | 45 +++++++++++++----------
2 files changed, 50 insertions(+), 40 deletions(-)
diff --git a/docs/content.zh/docs/dev/table/data_stream_api.md
b/docs/content.zh/docs/dev/table/data_stream_api.md
index 33e43a9..a88a0be 100644
--- a/docs/content.zh/docs/dev/table/data_stream_api.md
+++ b/docs/content.zh/docs/dev/table/data_stream_api.md
@@ -1136,14 +1136,16 @@ import org.apache.flink.table.api.DataTypes
case class User(name: String, score: java.lang.Integer, event_time:
java.time.Instant)
tableEnv.executeSql(
- "CREATE TABLE GeneratedTable "
- + "("
- + " name STRING,"
- + " score INT,"
- + " event_time TIMESTAMP_LTZ(3),"
- + " WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND"
- + ")"
- + "WITH ('connector'='datagen')")
+ """
+ CREATE TABLE GeneratedTable (
+ name STRING,
+ score INT,
+ event_time TIMESTAMP_LTZ(3),
+ WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND
+ )
+ WITH ('connector'='datagen')
+ """
+)
val table = tableEnv.from("GeneratedTable")
@@ -1167,7 +1169,7 @@ val dataStream: DataStream[Row] =
tableEnv.toDataStream(table)
// since `event_time` is a single rowtime attribute, it is inserted into the
DataStream
// metadata and watermarks are propagated
-val dataStream: DataStream[User] = tableEnv.toDataStream(table, User.class)
+val dataStream: DataStream[User] = tableEnv.toDataStream(table, classOf[User])
// data types can be extracted reflectively as above or explicitly defined
@@ -1175,7 +1177,7 @@ val dataStream: DataStream[User] =
tableEnv.toDataStream(
table,
DataTypes.STRUCTURED(
- User.class,
+ classOf[User],
DataTypes.FIELD("name", DataTypes.STRING()),
DataTypes.FIELD("score", DataTypes.INT()),
DataTypes.FIELD("event_time", DataTypes.TIMESTAMP_LTZ(3))))
@@ -1185,8 +1187,9 @@ val dataStream: DataStream[User] =
Note that only non-updating tables are supported by `toDataStream`. Usually,
time-based operations
such as windows, interval joins, or the `MATCH_RECOGNIZE` clause are a good
fit for insert-only
-pipelines next to simple operations like projections and filters. Pipelines
with operations that
-produce updates can use `toChangelogStream`.
+pipelines next to simple operations like projections and filters.
+
+Pipelines with operations that produce updates can use `toChangelogStream`.
{{< top >}}
@@ -1547,14 +1550,16 @@ import java.time.Instant
// create Table with event-time
tableEnv.executeSql(
- "CREATE TABLE GeneratedTable "
- + "("
- + " name STRING,"
- + " score INT,"
- + " event_time TIMESTAMP_LTZ(3),"
- + " WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND"
- + ")"
- + "WITH ('connector'='datagen')")
+ """
+ CREATE TABLE GeneratedTable (
+ name STRING,
+ score INT,
+ event_time TIMESTAMP_LTZ(3),
+ WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND
+ )
+ WITH ('connector'='datagen')
+ """
+)
val table = tableEnv.from("GeneratedTable")
diff --git a/docs/content/docs/dev/table/data_stream_api.md
b/docs/content/docs/dev/table/data_stream_api.md
index 05b988a..5e6ceab 100644
--- a/docs/content/docs/dev/table/data_stream_api.md
+++ b/docs/content/docs/dev/table/data_stream_api.md
@@ -1135,14 +1135,16 @@ import org.apache.flink.table.api.DataTypes
case class User(name: String, score: java.lang.Integer, event_time:
java.time.Instant)
tableEnv.executeSql(
- "CREATE TABLE GeneratedTable "
- + "("
- + " name STRING,"
- + " score INT,"
- + " event_time TIMESTAMP_LTZ(3),"
- + " WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND"
- + ")"
- + "WITH ('connector'='datagen')")
+ """
+ CREATE TABLE GeneratedTable (
+ name STRING,
+ score INT,
+ event_time TIMESTAMP_LTZ(3),
+ WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND
+ )
+ WITH ('connector'='datagen')
+ """
+)
val table = tableEnv.from("GeneratedTable")
@@ -1166,7 +1168,7 @@ val dataStream: DataStream[Row] =
tableEnv.toDataStream(table)
// since `event_time` is a single rowtime attribute, it is inserted into the
DataStream
// metadata and watermarks are propagated
-val dataStream: DataStream[User] = tableEnv.toDataStream(table, User.class)
+val dataStream: DataStream[User] = tableEnv.toDataStream(table, classOf[User])
// data types can be extracted reflectively as above or explicitly defined
@@ -1174,7 +1176,7 @@ val dataStream: DataStream[User] =
tableEnv.toDataStream(
table,
DataTypes.STRUCTURED(
- User.class,
+ classOf[User],
DataTypes.FIELD("name", DataTypes.STRING()),
DataTypes.FIELD("score", DataTypes.INT()),
DataTypes.FIELD("event_time", DataTypes.TIMESTAMP_LTZ(3))))
@@ -1184,8 +1186,9 @@ val dataStream: DataStream[User] =
Note that only non-updating tables are supported by `toDataStream`. Usually,
time-based operations
such as windows, interval joins, or the `MATCH_RECOGNIZE` clause are a good
fit for insert-only
-pipelines next to simple operations like projections and filters. Pipelines
with operations that
-produce updates can use `toChangelogStream`.
+pipelines next to simple operations like projections and filters.
+
+Pipelines with operations that produce updates can use `toChangelogStream`.
{{< top >}}
@@ -1546,14 +1549,16 @@ import java.time.Instant
// create Table with event-time
tableEnv.executeSql(
- "CREATE TABLE GeneratedTable "
- + "("
- + " name STRING,"
- + " score INT,"
- + " event_time TIMESTAMP_LTZ(3),"
- + " WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND"
- + ")"
- + "WITH ('connector'='datagen')")
+ """
+ CREATE TABLE GeneratedTable (
+ name STRING,
+ score INT,
+ event_time TIMESTAMP_LTZ(3),
+ WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND
+ )
+ WITH ('connector'='datagen')
+ """
+)
val table = tableEnv.from("GeneratedTable")
