dianfu commented on a change in pull request #13273: URL: https://github.com/apache/flink/pull/13273#discussion_r479984221
########## File path: docs/dev/python/user-guide/table/10_minutes_to_table_api.zh.md ########## @@ -0,0 +1,719 @@ +--- +title: "10分钟入门Table API" +nav-parent_id: python_tableapi +nav-pos: 25 +--- +<!-- +Licensed to the Apache Software Foundation (ASF) under one +or more contributor license agreements. See the NOTICE file +distributed with this work for additional information +regarding copyright ownership. The ASF licenses this file +to you under the Apache License, Version 2.0 (the +"License"); you may not use this file except in compliance +with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, +software distributed under the License is distributed on an +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, either express or implied. See the License for the +specific language governing permissions and limitations +under the License. +--> + +This document is a short introduction to PyFlink Table API, which is used to help novice users quickly understand the basic usage of PyFlink Table API. +For advanced usage, please refer to other documents in this User Guide. + +* This will be replaced by the TOC +{:toc} + +Common Structure of Python Table API Program +-------------------------------------------- + +All Table API and SQL programs, both batch and streaming, follow the same pattern. The following code example shows the common structure of Table API and SQL programs. + +{% highlight python %} + +from pyflink.table import EnvironmentSettings, StreamTableEnvironment + +# 1. create a TableEnvironment +env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build() +table_env = StreamTableEnvironment.create(environment_settings=env_settings) + +# 2. create source Table +table_env.execute_sql(""" + CREATE TABLE datagen ( + id INT, + data STRING + ) WITH ( + 'connector' = 'datagen', + 'fields.id.kind' = 'sequence', + 'fields.id.start' = '1', + 'fields.id.end' = '10' + ) +""") + +# 3. create sink Table +table_env.execute_sql(""" + CREATE TABLE print ( + id INT, + data STRING + ) WITH ( + 'connector' = 'print' + ) +""") + +# 4. query from source table and perform caculations +# create a Table from a Table API query: +source_table = table_env.from_path("datagen") +# or create a Table from a SQL query: +source_table = table_env.sql_query("SELECT * FROM datagen") + +result_table = source_table.select("id + 1, data") + +# 5. emit query result to sink table +# emit a Table API result Table to a sink table: +result_table.execute_insert("print").get_job_client().get_job_execution_result().result() +# or emit results via SQL query: +table_env.execute_sql("INSERT INTO print SELECT * FROM datagen").get_job_client().get_job_execution_result().result() + +{% endhighlight %} + +{% top %} + +Create a TableEnvironment +------------------------- + +The `TableEnvironment` is a central concept of the Table API and SQL integration. The following code example shows how to create a TableEnvironment: + +{% highlight python %} + +from pyflink.table import EnvironmentSettings, StreamTableEnvironment, BatchTableEnvironment + +# create a blink streaming TableEnvironment +env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build() +table_env = StreamTableEnvironment.create(environment_settings=env_settings) + +# create a blink batch TableEnvironment +env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build() +table_env = BatchTableEnvironment.create(environment_settings=env_settings) + +# create a flink streaming TableEnvironment +env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_old_planner().build() +table_env = StreamTableEnvironment.create(environment_settings=env_settings) + +# create a flink batch TableEnvironment +env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_old_planner().build() +table_env = BatchTableEnvironment.create(environment_settings=env_settings) + +{% endhighlight %} + +The `TableEnvironment` is responsible for: + +* Creating `Table`s +* Registering `Table`s as a temporary view +* Executing SQL queries +* Registering user-defined (scalar, table, or aggregation) functions, see [General User-defined Functions]({% link dev/python/user-guide/table/udfs/python_udfs.zh.md %}) and [Vectorized User-defined Functions]({% link dev/python/user-guide/table/udfs/vectorized_python_udfs.zh.md %}) +* Configuring the job +* Managing Python dependencies, see [Dependency Management]({% link dev/python/user-guide/table/dependency_management.zh.md %}) +* Submitting the jobs for execution + +Currently there are 2 planners available: flink planner and blink planner. + +You should explicitly set which planner to use in the current program. +We recommend using the blink planner as much as possible. + +{% top %} + +Creating Tables +------------- + +`Table` is a core component of the Python Table API. A `Table` is a logical representation of the intermediate result of a Table API Job. + +A `Table` is always bound to a specific `TableEnvironment`. It is not possible to combine tables from different TableEnvironments in same query, e.g., to join or union them. + +### Create using a List Object + +You can create a Table from a list object: + +{% highlight python %} + +# create a blink batch TableEnvironment +from pyflink.table import EnvironmentSettings, BatchTableEnvironment +env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build() +table_env = BatchTableEnvironment.create(environment_settings=env_settings) + +table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')]) +table.to_pandas() + +{% endhighlight %} + +The result is: + +{% highlight text %} + _1 _2 +0 1 Hi +1 2 Hello +{% endhighlight %} + +You can also create the Table with column names: + +{% highlight python %} + +table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data']) +table.to_pandas() + +{% endhighlight %} + +The result is: + +{% highlight text %} + id data +0 1 Hi +1 2 Hello +{% endhighlight %} + +By default the table schema is extracted from the data automatically. + +If the table schema is not as your wish, you can specify it manually: + +{% highlight python %} + +table_without_schema = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data']) +# by default the type of the "id" column is 64 bit int +default_type = table_without_schema.to_pandas()["id"].dtype +print('By default the type of the "id" column is %s.' % default_type) + +from pyflink.table import DataTypes +table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], + DataTypes.ROW([DataTypes.FIELD("id", DataTypes.TINYINT()), + DataTypes.FIELD("data", DataTypes.STRING())])) +# now the type of the "id" column is 8 bit int +type = table.to_pandas()["id"].dtype +print('Now the type of the "id" column is %s.' % type) + +{% endhighlight %} + +The result is: + +{% highlight text %} +By default the type of the "id" column is int64. +Now the type of the "id" column is int8. +{% endhighlight %} + +### Create using a Connector + +You can create a Table using connector DDL: + +{% highlight python %} + +table_env.execute_sql(""" + CREATE TABLE random_source ( + id BIGINT, + data TINYINT + ) WITH ( + 'connector' = 'datagen', + 'fields.id.kind'='sequence', + 'fields.id.start'='1', + 'fields.id.end'='3', + 'fields.data.kind'='sequence', + 'fields.data.start'='4', + 'fields.data.end'='6' + ) +""") +table = table_env.from_path("random_source") +table.to_pandas() + +{% endhighlight %} + +The result is: + +{% highlight text %} + id data +0 2 5 +1 1 4 +2 3 6 +{% endhighlight %} + +### Create using a Catalog + +A TableEnvironment maintains a map of catalogs of tables which are created with an identifier. + +The tables in a catalog may either be temporary, and tied to the lifecycle of a single Flink session, or permanent, and visible across multiple Flink sessions and clusters. + +The tables and views created via SQL DDL, e.g. "create table ..." and "create view ..." are also stored in a catalog. + +You can directly access the tables in a catalog via SQL. + +If you want to use tables from a catalog with the Table API, you can use the "from_path" method to create the Table API objects: + +{% highlight python %} + +# prepare the catalog +# register Table API tables in the catalog +table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data']) +table_env.create_temporary_view('source_table', table) + +# create Table API table from catalog +new_table = table_env.from_path('source_table') +new_table.to_pandas() + +{% endhighlight %} + +The result is: + +{% highlight text %} + id data +0 1 Hi +1 2 Hello +{% endhighlight %} + +{% top %} + +Write Queries +------------- + +### Writing Table API Queries + +The `Table` object offers many methods for applying relational operations. +These methods return new `Table` objects representing the result of applying the relational operations on the input `Table`. +These relational operations may be composed of multiple method calls, such as `table.group_by(...).select(...)`. + +The [Table API]({{ site.baseurl }}/zh/dev/table/tableApi.html?code_tab=python) documentation describes all Table API operations that are supported on streaming and batch tables. + +The following example shows a simple Table API aggregation query: + +{% highlight python %} + +# using batch table environment to execute the queries +from pyflink.table import EnvironmentSettings, BatchTableEnvironment +env_settings = EnvironmentSettings.new_instance().in_batch_mode().use_blink_planner().build() +table_env = BatchTableEnvironment.create(environment_settings=env_settings) + +orders = table_env.from_elements([('Jack', 'FRANCE', 10), ('Rose', 'ENGLAND', 30), ('Jack', 'FRANCE', 20)], + ['name', 'country', 'revenue']) +# compute revenue for all customers from France +revenue = orders \ + .select("name, country, revenue") \ + .where("country === 'FRANCE'") \ + .group_by("name") \ + .select("name, revenue.sum AS rev_sum") + +revenue.to_pandas() + +{% endhighlight %} + +The result is: + +{% highlight text %} + name rev_sum +0 Jack 30 +{% endhighlight %} + +### Write SQL Queries + +Flink's SQL integration is based on [Apache Calcite](https://calcite.apache.org), which implements the SQL standard. SQL queries are specified as Strings. + +The [SQL]({{ site.baseurl }}/zh/dev/table/sql/index.html) documentation describes Flink's SQL support for streaming and batch tables. + +The following example shows a simple SQL aggregation query: + +{% highlight python %} + +# use a StreamTableEnvironment to execute the queries +from pyflink.table import EnvironmentSettings, StreamTableEnvironment +env_settings = EnvironmentSettings.new_instance().in_streaming_mode().use_blink_planner().build() +table_env = StreamTableEnvironment.create(environment_settings=env_settings) + + +table_env.execute_sql(""" + CREATE TABLE random_source ( + id BIGINT, + data TINYINT + ) WITH ( + 'connector' = 'datagen', + 'fields.id.kind'='sequence', + 'fields.id.start'='1', + 'fields.id.end'='8', + 'fields.data.kind'='sequence', + 'fields.data.start'='4', + 'fields.data.end'='11' + ) +""") + +table_env.execute_sql(""" + CREATE TABLE print_sink ( + id BIGINT, + data_sum TINYINT + ) WITH ( + 'connector' = 'print' + ) +""") + +table_env.execute_sql(""" + INSERT INTO print_sink + SELECT id, sum(data) as data_sum FROM + (SELECT id / 2 as id, data FROM random_source) + WHERE id > 1 + GROUP BY id +""").get_job_client().get_job_execution_result().result() + +{% endhighlight %} + +The result is: + +{% highlight text %} +2> +I(4,11) +6> +I(2,8) +8> +I(3,10) +6> -U(2,8) +8> -U(3,10) +6> +U(2,15) +8> +U(3,19) +{% endhighlight %} + +In fact, this shows the change logs received by the print sink. +The output format of a change log is: +{% highlight python %} +{subtask id}> {message type}{string format of the value} +{% endhighlight %} +For example, "2> +I(4,11)" means this message comes from the 2nd subtask, and "+I" means it is an insert message. "(4, 11)" is the content of the message. +In addition, "-U" means a retract record (i.e. update-before), which means this message should be deleted or retracted from the sink. +"+U" means this is an update record (i.e. update-after), which means this message should be updated or inserted by the sink. + +So, we get this result from the change logs above: + +{% highlight text %} +(4, 11) +(2, 15) +(3, 19) +{% endhighlight %} + +### Mixing the Table API and SQL + +The `Table` objects used in Table API and the tables used in SQL can be freely converted to each other. + +The following example shows how to use a `Table` object in SQL: + +{% highlight python %} + +# create a sink table to emit results +table_env.execute_sql(""" + CREATE TABLE table_sink ( + id BIGINT, + data VARCHAR + ) WITH ( + 'connector' = 'print' + ) +""") + +# convert the Table API table to a SQL view +table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data']) +table_env.create_temporary_view('table_api_table', table) + +# emit the Table API table +table_env.execute_sql("INSERT INTO table_sink SELECT * FROM table_api_table") \ + .get_job_client().get_job_execution_result().result() + +{% endhighlight %} + +The result is: + +{% highlight text %} +6> +I(1,Hi) +6> +I(2,Hello) +{% endhighlight %} + +And the following example shows how to use SQL tables in the Table API: + +{% highlight python %} + +# create a sql source table +table_env.execute_sql(""" + CREATE TABLE sql_source ( + id BIGINT, + data TINYINT + ) WITH ( + 'connector' = 'datagen', + 'fields.id.kind'='sequence', + 'fields.id.start'='1', + 'fields.id.end'='4', + 'fields.data.kind'='sequence', + 'fields.data.start'='4', + 'fields.data.end'='7' + ) +""") + +# convert the sql table to Table API table +table = table_env.from_path("sql_source") + +# or create the table from a sql query +table = table_env.sql_query("SELECT * FROM sql_source") + +# emit the table +table.to_pandas() + +{% endhighlight %} + +{% top %} + +Emit Results +------------ + +### Capturing Data in a Variable + +You can call the "to_pandas" method to emit the data from a `Table` object to a pandas DataFrame: + +{% highlight python %} + +table = table_env.from_elements([(1, 'Hi'), (2, 'Hello')], ['id', 'data']) +table.to_pandas() + +{% endhighlight %} + +The result is: + +{% highlight text %} + id data +0 1 Hi +1 2 Hello +{% endhighlight %} + +Note that "to_pandas" is not supported by the flink planner, and not all data types can be emitted to pandas DataFrames. + +### Emitting Results to One Sink Table Review comment: @alpinegizmo Thanks a lot for the explanation. I'm OK with "Emit". Just propose this suggestion as I noticed that there is no **ing** suffix in the following doc: https://ci.apache.org/projects/flink/flink-docs-master/dev/table/common.html. However, both cases exist throughout the Flink documentation. I'm OK with both of them as long as we make sure that the styles keep consistent. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
