[FLINK-22782][docs] Remove legacy planner from Chinese docs

This closes apache#16018.

docs/content.zh/docs/connectors/table/hive/hive_dialect.md

@@ -69,7 +69,7 @@ Flink SQL> set table.sql-dialect=default; -- to use default dialect
 {{< tab "Java" >}}
 ```java
 
-EnvironmentSettings settings = EnvironmentSettings.newInstance().useBlinkPlanner()...build();
+EnvironmentSettings settings = EnvironmentSettings.newInstance().build();
 TableEnvironment tableEnv = TableEnvironment.create(settings);
 // to use hive dialect
 tableEnv.getConfig().setSqlDialect(SqlDialect.HIVE);
@@ -82,7 +82,7 @@ tableEnv.getConfig().setSqlDialect(SqlDialect.DEFAULT);
 ```python
 from pyflink.table import *
 
-settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 t_env = TableEnvironment.create(settings)
 
 # to use hive dialect

docs/content.zh/docs/connectors/table/hive/overview.md

@@ -312,7 +312,7 @@ export HADOOP_CLASSPATH=`hadoop classpath`
 
 ```java
 
-EnvironmentSettings settings = EnvironmentSettings.newInstance().useBlinkPlanner().build();
+EnvironmentSettings settings = EnvironmentSettings.newInstance().build();
 TableEnvironment tableEnv = TableEnvironment.create(settings);
 
 String name = "myhive";
@@ -330,7 +330,7 @@ tableEnv.useCatalog("myhive");
 
 ```scala
 
-val settings = EnvironmentSettings.newInstance().useBlinkPlanner().build()
+val settings = EnvironmentSettings.newInstance().build()
 val tableEnv = TableEnvironment.create(settings)
 
 val name = "myhive"
@@ -349,7 +349,7 @@ tableEnv.useCatalog("myhive")
 from pyflink.table import *
 from pyflink.table.catalog import HiveCatalog
 
-settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 t_env = TableEnvironment.create(settings)
 
 catalog_name = "myhive"

docs/content.zh/docs/connectors/table/jdbc.md

@@ -394,7 +394,7 @@ tableEnv.useCatalog("mypg")
 ```python
 from pyflink.table.catalog import JdbcCatalog
 
-environment_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+environment_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 t_env = TableEnvironment.create(environment_settings)
 
 name = "mypg"

docs/content.zh/docs/dev/python/dependency_management.md

@@ -294,7 +294,7 @@ import org.apache.flink.table.api.EnvironmentSettings;
 import org.apache.flink.table.api.TableEnvironment;
 
 TableEnvironment tEnv = TableEnvironment.create(
- EnvironmentSettings.newInstance().useBlinkPlanner().inBatchMode().build());
+ EnvironmentSettings.newInstance().inBatchMode().build());
 tEnv.getConfig().getConfiguration().set(CoreOptions.DEFAULT_PARALLELISM, 1);
 
 // register the Python UDF

docs/content.zh/docs/dev/python/python_config.md

@@ -47,7 +47,7 @@ The config options could be set as following in a Table API program:
 ```python
 from pyflink.table import TableEnvironment, EnvironmentSettings
 
-env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 t_env = TableEnvironment.create(env_settings)
 
 config = t_env.get_config().get_configuration()

docs/content.zh/docs/dev/python/table/intro_to_table_api.md

@@ -40,7 +40,7 @@ Python Table API 程序的基本结构
 from pyflink.table import EnvironmentSettings, TableEnvironment
 
 # 1. 创建 TableEnvironment
-env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings) 
 
 # 2. 创建 source 表
@@ -92,11 +92,11 @@ table_env.execute_sql("INSERT INTO print SELECT * FROM datagen").wait()
 from pyflink.table import EnvironmentSettings, TableEnvironment
 
 # create a blink streaming TableEnvironment
-env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 
 # or create a blink batch TableEnvironment
-env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 table_env = TableEnvironment.create(env_settings)
 ```
 
@@ -133,7 +133,7 @@ table_env = TableEnvironment.create(env_settings)
 from pyflink.table import EnvironmentSettings, TableEnvironment
 
 # 创建 blink 批 TableEnvironment
-env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 table_env = TableEnvironment.create(env_settings)
 
 table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')])
@@ -197,7 +197,7 @@ print('Now the type of the "id" column is %s.' % type)
 from pyflink.table import EnvironmentSettings, TableEnvironment
 
 # 创建 blink 流 TableEnvironment
-env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 
 table_env.execute_sql("""
@@ -277,7 +277,7 @@ new_table.to_pandas()
 from pyflink.table import EnvironmentSettings, TableEnvironment
 
 # 通过 batch table environment 来执行查询
-env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 table_env = TableEnvironment.create(env_settings)
 
 orders = table_env.from_elements([('Jack', 'FRANCE', 10), ('Rose', 'ENGLAND', 30), ('Jack', 'FRANCE', 20)],
@@ -312,7 +312,7 @@ from pyflink.table.udf import udf
 import pandas as pd
 
 # 通过 batch table environment 来执行查询
-env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 table_env = TableEnvironment.create(env_settings)
 
 orders = table_env.from_elements([('Jack', 'FRANCE', 10), ('Rose', 'ENGLAND', 30), ('Jack', 'FRANCE', 20)],
@@ -348,7 +348,7 @@ Flink 的 SQL 基于 [Apache Calcite](https://calcite.apache.org)，它实现了
 from pyflink.table import EnvironmentSettings, TableEnvironment
 
 # 通过 stream table environment 来执行查询
-env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 
 
@@ -634,7 +634,7 @@ Table API 提供了一种机制来查看 `Table` 的逻辑查询计划和优化
 # 使用流模式 TableEnvironment
 from pyflink.table import EnvironmentSettings, TableEnvironment
 
-env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 
 table1 = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data'])
@@ -687,7 +687,7 @@ Stage 136 : Data Source
 # 使用流模式 TableEnvironment
 from pyflink.table import EnvironmentSettings, TableEnvironment
 
-env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 table_env = TableEnvironment.create(environment_settings=env_settings)
 
 table1 = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data'])

docs/content.zh/docs/dev/python/table/operations/row_based_operations.md

@@ -37,7 +37,7 @@ from pyflink.table import EnvironmentSettings, TableEnvironment
 from pyflink.table.expressions import col
 from pyflink.table.types import DataTypes
 
-env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 table_env = TableEnvironment.create(env_settings)
 
 table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data'])
@@ -101,7 +101,7 @@ from pyflink.common import Row
 from pyflink.table.udf import udtf
 from pyflink.table import DataTypes, EnvironmentSettings, TableEnvironment
 
-env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 
 table = table_env.from_elements([(1, 'Hi,Flink'), (2, 'Hello')], ['id', 'data'])
@@ -181,7 +181,7 @@ agg = udaf(function,
 
 # aggregate with a python general aggregate function
 
-env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 t = table_env.from_elements([(1, 2), (2, 1), (1, 3)], ['a', 'b'])
 
@@ -196,7 +196,7 @@ result.to_pandas()
 # 1 2 1 1
 
 # aggregate with a python vectorized aggregate function
-env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+env_settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 table_env = TableEnvironment.create(env_settings)
 
 t = table_env.from_elements([(1, 2), (2, 1), (1, 3)], ['a', 'b'])
@@ -256,7 +256,7 @@ class Top2(TableAggregateFunction):
  [DataTypes.FIELD("a", DataTypes.BIGINT())])
 
 
-env_settings = EnvironmentSettings.new_instance().use_blink_planner().in_streaming_mode().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 # the result type and accumulator type can also be specified in the udtaf decorator:
 # top2 = udtaf(Top2(), result_type=DataTypes.ROW([DataTypes.FIELD("a", DataTypes.BIGINT())]), accumulator_type=DataTypes.ARRAY(DataTypes.BIGINT()))

docs/content.zh/docs/dev/python/table/python_table_api_connectors.md

@@ -83,7 +83,7 @@ t_env.sql_query("SELECT a FROM source_table") \
 from pyflink.table import TableEnvironment, EnvironmentSettings
 
 def log_processing():
- env_settings = EnvironmentSettings.new_instance().use_blink_planner().is_streaming_mode().build()
+ env_settings = EnvironmentSettings.new_instance().is_streaming_mode().build()
  t_env = TableEnvironment.create(env_settings)
  # specify connector and format jars
  t_env.get_config().get_configuration().set_string("pipeline.jars", "file:https:///my/jar/path/connector.jar;file:https:///my/jar/path/json.jar")

docs/content.zh/docs/dev/python/table/udfs/python_udfs.md

@@ -50,7 +50,7 @@ class HashCode(ScalarFunction):
  def eval(self, s):
  return hash(s) * self.factor
 
-settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 table_env = TableEnvironment.create(settings)
 
 hash_code = udf(HashCode(), result_type=DataTypes.BIGINT())
@@ -80,7 +80,7 @@ public class HashCode extends ScalarFunction {
 '''
 from pyflink.table.expressions import call
 
-settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 table_env = TableEnvironment.create(settings)
 
 # 注册 Java 函数
@@ -148,7 +148,7 @@ class Split(TableFunction):
  for s in string.split(" "):
  yield s, len(s)
 
-env_settings = EnvironmentSettings.new_instance().use_blink_planner().is_streaming_mode().build()
+env_settings = EnvironmentSettings.new_instance().is_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 my_table = ... # type: Table, table schema: [a: String]
 
@@ -186,7 +186,7 @@ public class Split extends TableFunction<Tuple2<String, Integer>> {
 '''
 from pyflink.table.expressions import call
 
-env_settings = EnvironmentSettings.new_instance().use_blink_planner().is_streaming_mode().build()
+env_settings = EnvironmentSettings.new_instance().is_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 my_table = ... # type: Table, table schema: [a: String]
 
@@ -301,7 +301,7 @@ class WeightedAvg(AggregateFunction):
  DataTypes.FIELD("f1", DataTypes.BIGINT())])
 
 
-env_settings = EnvironmentSettings.new_instance().use_blink_planner().is_streaming_mode().build()
+env_settings = EnvironmentSettings.new_instance().is_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 # the result type and accumulator type can also be specified in the udaf decorator:
 # weighted_avg = udaf(WeightedAvg(), result_type=DataTypes.BIGINT(), accumulator_type=...)
@@ -476,7 +476,7 @@ class Top2(TableAggregateFunction):
  [DataTypes.FIELD("a", DataTypes.BIGINT())])
 
 
-env_settings = EnvironmentSettings.new_instance().use_blink_planner().in_streaming_mode().build()
+env_settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
 table_env = TableEnvironment.create(env_settings)
 # the result type and accumulator type can also be specified in the udtaf decorator:
 # top2 = udtaf(Top2(), result_type=DataTypes.ROW([DataTypes.FIELD("a", DataTypes.BIGINT())]), accumulator_type=DataTypes.ARRAY(DataTypes.BIGINT()))

docs/content.zh/docs/dev/python/table/udfs/vectorized_python_udfs.md

@@ -53,7 +53,7 @@ under the License.
 def add(i, j):
  return i + j
 
-settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 table_env = TableEnvironment.create(settings)
 
 # use the vectorized Python scalar function in Python Table API
@@ -87,7 +87,7 @@ and `Over Window Aggregation` 使用它:
 def mean_udaf(v):
  return v.mean()
 
-settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 table_env = TableEnvironment.create(settings)
 
 my_table = ... # type: Table, table schema: [a: String, b: BigInt, c: BigInt]

docs/content.zh/docs/dev/python/table_api_tutorial.md

@@ -67,7 +67,7 @@ $ python -m pip install apache-flink
 编写Flink Python Table API程序的第一步是创建`TableEnvironment`。这是Python Table API作业的入口类。
 
 ```python
-settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 t_env = TableEnvironment.create(settings)
 ```
 
@@ -147,7 +147,7 @@ from pyflink.table import DataTypes, TableEnvironment, EnvironmentSettings
 from pyflink.table.descriptors import Schema, OldCsv, FileSystem
 from pyflink.table.expressions import lit
 
-settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 t_env = TableEnvironment.create(settings)
 
 # write all the data to one file

docs/content.zh/docs/dev/table/catalogs.md

@@ -240,7 +240,7 @@ from pyflink.table import *
 from pyflink.table.catalog import HiveCatalog, CatalogDatabase, ObjectPath, CatalogBaseTable
 from pyflink.table.descriptors import Kafka
 
-settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build()
+settings = EnvironmentSettings.new_instance().in_batch_mode().build()
 t_env = TableEnvironment.create(settings)
 
 # Create a HiveCatalog