Project: Flink (App Definition and Development / Streaming & Messaging)
File: common.md

title: "Concepts & Common API" weight: 2 type: docs aliases: /dev/table/common.html <!-- 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. --> The Table API and SQL are integrated in a joint API. The central concept of this API is a `Table` which serves as input and output of queries. This document shows the common structure of programs with Table API and SQL queries, how to register a `Table`, how to query a `Table`, and how to emit a `Table`. Structure of Table API and SQL Programs The following code example shows the common structure of Table API and SQL programs. {{< hint warning >}} All Flink Scala APIs are deprecated and will be removed in a future Flink version. You can still build your application in Scala, but you should move to the Java version of either the DataStream and/or Table API. See <a href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-265+Deprecate+and+remove+Scala+API+support">FLIP-265 Deprecate and remove Scala API support</a> {{< /hint >}} {{< tabs "0727d1e7-3f22-4eba-a25f-6a554b6a1359" >}} {{< tab "Java" >}} ```java import org.apache.flink.table.api.*; import org.apache.flink.connector.datagen.table.DataGenConnectorOptions; // Create a TableEnvironment for batch or streaming execution. // See the "Create a TableEnvironment" section for details. TableEnvironment tableEnv = TableEnvironment.create(//); // Create a source table tableEnv.createTemporaryTable("SourceTable", TableDescriptor.forConnector("datagen") .schema(Schema.newBuilder() .column("f0", DataTypes.STRING()) .build()) .option(DataGenConnectorOptions.ROWSPERSECOND, 100L) .build()); // Create a sink table (using SQL DDL) tableEnv.executeSql("CREATE TEMPORARY TABLE SinkTable WITH ('connector' = 'blackhole') LIKE SourceTable (EXCLUDING OPTIONS) "); // Create a Table object from a Table API query Table table1 = tableEnv.from("SourceTable"); // Create a Table object from a SQL query Table table2 = tableEnv.sqlQuery("SELECT * FROM SourceTable"); // Emit a Table API result Table to a TableSink, same for SQL result TableResult tableResult = table1.insertInto("SinkTable").execute(); ``` {{< /tab >}} {{< tab "Scala" >}} ```scala import org.apache.flink.table.api._ import org.apache.flink.connector.datagen.table.DataGenConnectorOptions // Create a TableEnvironment for batch or streaming execution. // See the "Create a TableEnvironment" section for details. val tableEnv = TableEnvironment.create(//) // Create a source table tableEnv.createTemporaryTable("SourceTable", TableDescriptor.forConnector("datagen") .schema(Schema.newBuilder() .column("f0", DataTypes.STRING()) .build()) .option(DataGenConnectorOptions.ROWSPERSECOND, 100L) .build()) // Create a sink table (using SQL DDL) tableEnv.executeSql("CREATE TEMPORARY TABLE SinkTable WITH ('connector' = 'blackhole') LIKE SourceTable (EXCLUDING OPTIONS) ") // Create a Table object from a Table API query val table1 = tableEnv.from("SourceTable") // Create a Table object from a SQL query val table2 = tableEnv.sqlQuery("SELECT * FROM SourceTable") // Emit a Table API result Table to a TableSink, same for SQL result val tableResult = table1.insertInto("SinkTable").execute() ``` {{< /tab >}} {{< tab "Python" >}} ```python from pyflink.table import * table_env = ... # see "Create a TableEnvironment" section table_env.executeSql("""CREATE TEMPORARY TABLE SourceTable ( f0 STRING ) WITH ( 'connector' = 'datagen', 'rows-per-second' = '100' ) """) table_env.executeSql("CREATE TEMPORARY TABLE SinkTable WITH ('connector' = 'blackhole') LIKE SourceTable (EXCLUDING OPTIONS) ") table1 = tableenv.frompath("SourceTable").select(...) table2 = tableenv.sqlquery("SELECT ... FROM SourceTable ...") tableresult = table1.executeinsert("SinkTable") ``` {{< /tab >}} {{< /tabs >}} {{< hint info >}} Table API and SQL queries can be easily integrated with and embedded into DataStream

Have a look at the page to learn how DataStreams can be converted into Tables and vice versa. {{< /hint >}} {{< top >}} Create a TableEnvironment The `TableEnvironment` is the entrypoint for Table API and SQL integration and is responsible for: Registering a `Table` in the internal catalog Registering catalogs Loading pluggable modules Executing SQL queries Registering a user-defined (scalar, table, or aggregation) function Converting between `DataStream` and `Table` (in case of `StreamTableEnvironment`) A `Table` is always bound to a specific `TableEnvironment`. It is not possible to combine tables of different TableEnvironments in the same query, e.g., to join or union them. A `TableEnvironment` is created by calling the static `TableEnvironment.create()` method. {{< tabs "e013e0d9-f0d0-4280-ac0f-bb984caffa4c" >}} {{< tab "Java" >}} ```java import org.apache.flink.table.api.EnvironmentSettings; import org.apache.flink.table.api.TableEnvironment; EnvironmentSettings settings = EnvironmentSettings .newInstance() .inStreamingMode() //.inBatchMode() .build(); TableEnvironment tEnv = TableEnvironment.create(settings); ``` {{< /tab >}} {{< tab "Scala" >}} ```scala import org.apache.flink.table.api.{EnvironmentSettings, TableEnvironment} val settings = EnvironmentSettings .newInstance() .inStreamingMode() //.inBatchMode() .build() val tEnv = TableEnvironment.create(settings) ``` {{< /tab >}} {{< tab "Python" >}} ```python from pyflink.table import EnvironmentSettings, TableEnvironment envsettings = EnvironmentSettings.instreaming_mode() tableenv = TableEnvironment.create(envsettings) envsettings = EnvironmentSettings.inbatch_mode() tableenv = TableEnvironment.create(envsettings) ``` {{< /tab >}} {{< /tabs >}} Alternatively, users can create a `StreamTableEnvironment` from an existing `StreamExecutionEnvironment` to interoperate with the `DataStream` API. {{< tabs "c91b91ec-7197-4305-827d-9f91dedadce5" >}} {{< tab "Java" >}} ```java import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.table.api.EnvironmentSettings; import org.apache.flink.table.api.bridge.java.StreamTableEnvironment; StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); ``` {{< /tab >}} {{< tab "Scala" >}} ```scala import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment import org.apache.flink.table.api.EnvironmentSettings import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment val env = StreamExecutionEnvironment.getExecutionEnvironment val tEnv = StreamTableEnvironment.create(env) ``` {{< /tab >}} {{< tab "Python" >}} ```python from pyflink.datastream import StreamExecutionEnvironment from pyflink.table import StreamTableEnvironment senv = StreamExecutionEnvironment.getexecution_environment() tenv = StreamTableEnvironment.create(senv) ``` {{< /tab >}} {{< /tabs >}} {{< top >}} Create Tables in the Catalog A `TableEnvironment` maintains a map of catalogs of tables which are created with an identifier. Each identifier consists of 3 parts: catalog name, database name and object name. If a catalog or database is not specified, the current default value will be used (see examples in the section). Tables can be either virtual (`VIEWS`) or regular (`TABLES`). `VIEWS` can be created from an existing `Table` object, usually the result of a Table API or SQL query. `TABLES` describe external data, such as a file, database table, or message queue. Tables may either be temporary, and tied to the lifecycle of a single Flink session, or permanent, and visible across multiple Flink sessions and clusters. Permanent tables require a (such as Hive Metastore) to maintain metadata about the table. Once a permanent table is created, it is visible to any Flink session that is connected to the catalog and will continue to exist until the table is explicitly dropped. On the other hand, temporary tables are always stored in memory and only exist for the duration of the Flink session they are created within. These tables are not visible to other sessions. They are not bound to any catalog or database but can be created in the namespace of one. Temporary tables are not dropped if their corresponding database is removed. It is possible to register a temporary table with the same identifier as an existing permanent table. The temporary table shadows the permanent one and makes the permanent table inaccessible as long as the temporary one exists. All queries with that identifier will be executed against the temporary table. This might be useful for experimentation. It allows running exactly the same query first against a temporary table that e.g. has just a subset of data, or the data is

Once verified that the query is correct it can be run against the real production table. A `Table` API object corresponds to a `VIEW` (virtual table) in SQL terms. It encapsulates a logical query plan. It can be created in a catalog as follows: {{< tabs "180b1bfe-5749-4b96-a120-e186fd361c8d" >}} {{< tab "Java" >}} ```java // get a TableEnvironment TableEnvironment tableEnv = ...; // see "Create a TableEnvironment" section // table is the result of a simple projection query Table projTable = tableEnv.from("X").select(...); // register the Table projTable as table "projectedTable" tableEnv.createTemporaryView("projectedTable", projTable); ``` {{< /tab >}} {{< tab "Scala" >}} ```scala // get a TableEnvironment val tableEnv = ... // see "Create a TableEnvironment" section // table is the result of a simple projection query val projTable: Table = tableEnv.from("X").select(...) // register the Table projTable as table "projectedTable" tableEnv.createTemporaryView("projectedTable", projTable) ``` {{< /tab >}} {{< tab "Python" >}} ```python table_env = ... # see "Create a TableEnvironment" section projtable = tableenv.from_path("X").select(...) tableenv.registertable("projectedTable", proj_table) ``` {{< /tab >}} {{< /tabs >}} Note: `Table` objects are similar to `VIEW`'s from relational database systems, i.e., the query that defines the `Table` is not optimized but will be inlined when another query references the registered `Table`. If multiple queries reference the same registered `Table`, it will be inlined for each referencing query and executed multiple times, i.e., the result of the registered `Table` will not be shared. {{< top >}} It is also possible to create a `TABLE` as known from relational databases from a declaration. The connector describes the external system that stores the data of a table. Storage systems such as Apache Kafka or a regular file system can be declared here. Such tables can either be created using the Table API directly, or by switching to SQL DDL. {{< tabs "059e9a56-282c-5e78-98d3-85be5abd04a2" >}} {{< tab "Java" >}} ```java // Using table descriptors final TableDescriptor sourceDescriptor = TableDescriptor.forConnector("datagen") .schema(Schema.newBuilder() .column("f0", DataTypes.STRING()) .build()) .option(DataGenConnectorOptions.ROWSPERSECOND, 100L) .build(); tableEnv.createTable("SourceTableA", sourceDescriptor); tableEnv.createTemporaryTable("SourceTableB", sourceDescriptor); // Using SQL DDL tableEnv.executeSql("CREATE [TEMPORARY] TABLE MyTable (...) WITH (...)"); ``` {{< /tab >}} {{< tab "Python" >}} ```python sourcedescriptor = TableDescriptor.forconnector("datagen") \ .schema(Schema.new_builder() .column("f0", DataTypes.STRING()) .build()) \ .option("rows-per-second", "100") \ .build() tenv.createtable("SourceTableA", source_descriptor) tenv.createtemporarytable("SourceTableB", sourcedescriptor) tenv.executesql("CREATE [TEMPORARY] TABLE MyTable (...) WITH (...)") ``` {{< /tab >}} {{< /tabs >}} Tables are always registered with a 3-part identifier consisting of catalog, database, and table name. Users can set one catalog and one database inside it to be the current catalog and current database. With them, the first two parts in the 3-parts identifier mentioned above can be optional - if they are not provided, the current catalog and current database will be referred. Users can switch the current catalog and current database via table API or SQL. Identifiers follow SQL requirements which means that they can be escaped with a backtick character (`` ` ``). {{< tabs "059e9a56-282c-4e69-98d3-85be9abd06a3" >}} {{< tab "Java" >}} ```java TableEnvironment tEnv = ...; tEnv.useCatalog("custom_catalog"); tEnv.useDatabase("custom_database"); Table table = ...; // register the view named 'exampleView' in the catalog named 'custom_catalog' // in the database named 'custom_database' tableEnv.createTemporaryView("exampleView", table); // register the view named 'exampleView' in the catalog named 'custom_catalog' // in the database named 'other_database' tableEnv.createTemporaryView("other_database.exampleView", table); // register the view named 'example.View' in the catalog named 'custom_catalog' // in the database named 'custom_database' tableEnv.createTemporaryView("`example.View`", table); // register the view named 'exampleView' in the catalog named 'other_catalog' // in the database named 'other_database' tableEnv.createTemporaryView("othercatalog.otherdatabase.exampleView", table); ``` {{< /tab >}} {{< tab "Scala" >}} ```scala // get a TableEnvironment val tEnv: TableEnvironment = ... tEnv.useCatalog("custom_catalog") tEnv.useDatabase("custom_database") val table: Table = ... // register the view named 'exampleView' in the catalog named 'custom_catalog' // in the database named 'custom_database'

table) // register the view named 'exampleView' in the catalog named 'custom_catalog' // in the database named 'other_database' tableEnv.createTemporaryView("other_database.exampleView", table) // register the view named 'example.View' in the catalog named 'custom_catalog' // in the database named 'custom_database' tableEnv.createTemporaryView("`example.View`", table) // register the view named 'exampleView' in the catalog named 'other_catalog' // in the database named 'other_database' tableEnv.createTemporaryView("othercatalog.otherdatabase.exampleView", table) ``` {{< /tab >}} {{< tab "Python" >}} ```python t_env = TableEnvironment.create(...) tenv.usecatalog("custom_catalog") tenv.usedatabase("custom_database") table = ... tenv.createtemporaryview("otherdatabase.exampleView", table) tenv.createtemporary_view("`example.View`", table) tenv.createtemporaryview("othercatalog.other_database.exampleView", table) ``` {{< /tab >}} {{< /tabs >}} Query a Table The Table API is a language-integrated query API for Scala and Java. In contrast to SQL, queries are not specified as Strings but are composed step-by-step in the host language. The API is based on the `Table` class which represents a table (streaming or batch) and offers methods to apply relational operations. These methods return a new `Table` object, which represents the result of applying the relational operation on the input `Table`. Some relational operations are composed of multiple method calls such as `table.groupBy(...).select()`, where `groupBy(...)` specifies a grouping of `table`, and `select(...)` the projection on the grouping of `table`. The document describes all Table API operations that are supported on streaming and batch tables. The following example shows a simple Table API aggregation query: {{< tabs "53400a89-4d54-4c67-a731-f3ca25aaf1f4" >}} {{< tab "Java" >}} ```java // get a TableEnvironment TableEnvironment tableEnv = ...; // see "Create a TableEnvironment" section // register Orders table // scan registered Orders table Table orders = tableEnv.from("Orders"); // compute revenue for all customers from France Table revenue = orders .filter($("cCountry").isEqual("FRANCE")) .groupBy($("cID"), $("cName")) .select($("cID"), $("cName"), $("revenue").sum().as("revSum")); // emit or convert Table // execute query ``` {{< /tab >}} {{< tab "Scala" >}} ```scala // get a TableEnvironment val tableEnv = ... // see "Create a TableEnvironment" section // register Orders table // scan registered Orders table val orders = tableEnv.from("Orders") // compute revenue for all customers from France val revenue = orders .filter($"cCountry" === "FRANCE") .groupBy($"cID", $"cName") .select($"cID", $"cName", $"revenue".sum AS "revSum") // emit or convert Table // execute query ``` Note: The Scala Table API uses Scala String interpolation that starts with a dollar sign (`$`) to reference the attributes of a `Table`. The Table API uses Scala implicits. Make sure to import `org.apache.flink.table.api._` - for implicit expression conversions `org.apache.flink.api.scala.` and `org.apache.flink.table.api.bridge.scala.` if you want to convert from/to DataStream. {{< /tab >}} {{< tab "Python" >}} ```python table_env = # see "Create a TableEnvironment" section orders = tableenv.frompath("Orders") revenue = orders \ .filter(col('cCountry') == 'FRANCE') \ .group_by(col('cID'), col('cName')) \ .select(col('cID'), col('cName'), col('revenue').sum.alias('revSum')) ``` {{< /tab >}} {{< /tabs >}} {{< top >}} Flink's SQL integration is based on , which implements the SQL standard. SQL queries are specified as regular Strings. The document describes Flink's SQL support for streaming and batch tables. The following example shows how to specify a query and return the result as a `Table`. {{< tabs "8ae3f8c2-315a-4941-a8a7-bffa677b4404" >}} {{< tab "Java" >}} ```java // get a TableEnvironment TableEnvironment tableEnv = ...; // see "Create a TableEnvironment" section // register Orders table // compute revenue for all customers from France Table revenue = tableEnv.sqlQuery( "SELECT cID, cName, SUM(revenue) AS revSum " + "FROM Orders " + "WHERE cCountry = 'FRANCE' " + "GROUP BY cID, cName" ); // emit or convert Table // execute query ``` {{< /tab >}} {{< tab "Scala" >}} ```scala // get a TableEnvironment val tableEnv = ... // see "Create a TableEnvironment" section // register Orders table // compute revenue for all customers from France val revenue =

|SELECT cID, cName, SUM(revenue) AS revSum |FROM Orders |WHERE cCountry = 'FRANCE' |GROUP BY cID, cName """.stripMargin) // emit or convert Table // execute query ``` {{< /tab >}} {{< tab "Python" >}} ```python table_env = ... # see "Create a TableEnvironment" section revenue = tableenv.sqlquery( "SELECT cID, cName, SUM(revenue) AS revSum " "FROM Orders " "WHERE cCountry = 'FRANCE' " "GROUP BY cID, cName" ) ``` {{< /tab >}} {{< /tabs >}} The following example shows how to specify an update query that inserts its result into a registered table. {{< tabs "3dad7016-6707-4218-98f2-785635c88cde" >}} {{< tab "Java" >}} ```java // get a TableEnvironment TableEnvironment tableEnv = ...; // see "Create a TableEnvironment" section // register "Orders" table // register "RevenueFrance" output table // compute revenue for all customers from France and emit to "RevenueFrance" tableEnv.executeSql( "INSERT INTO RevenueFrance " + "SELECT cID, cName, SUM(revenue) AS revSum " + "FROM Orders " + "WHERE cCountry = 'FRANCE' " + "GROUP BY cID, cName" ); ``` {{< /tab >}} {{< tab "Scala" >}} ```scala // get a TableEnvironment val tableEnv = ... // see "Create a TableEnvironment" section // register "Orders" table // register "RevenueFrance" output table // compute revenue for all customers from France and emit to "RevenueFrance" tableEnv.executeSql(""" |INSERT INTO RevenueFrance |SELECT cID, cName, SUM(revenue) AS revSum |FROM Orders |WHERE cCountry = 'FRANCE' |GROUP BY cID, cName """.stripMargin) ``` {{< /tab >}} {{< tab "Python" >}} ```python table_env = ... # see "Create a TableEnvironment" section tableenv.executesql( "INSERT INTO RevenueFrance " "SELECT cID, cName, SUM(revenue) AS revSum " "FROM Orders " "WHERE cCountry = 'FRANCE' " "GROUP BY cID, cName" ) ``` {{< /tab >}} {{< /tabs >}} {{< top >}} Table API and SQL queries can be easily mixed because both return `Table` objects: A Table API query can be defined on the `Table` object returned by a SQL query. A SQL query can be defined on the result of a Table API query by in the `TableEnvironment` and referencing it in the `FROM` clause of the SQL query. {{< top >}} Emit a Table A `Table` is emitted by writing it to a `TableSink`. A `TableSink` is a generic interface to support a wide variety of file formats (e.g. CSV, Apache Parquet, Apache Avro), storage systems (e.g., JDBC, Apache HBase, Apache Cassandra, Elasticsearch), or messaging systems (e.g., Apache Kafka, RabbitMQ). A batch `Table` can only be written to a `BatchTableSink`, while a streaming `Table` requires either an `AppendStreamTableSink`, a `RetractStreamTableSink`, or an `UpsertStreamTableSink`. Please see the documentation about for details about available sinks and instructions for how to implement a custom `DynamicTableSink`. The `Table.insertInto(String tableName)` method defines a complete end-to-end pipeline emitting the source table to a registered sink table. The method looks up the table sink from the catalog by the name and validates that the schema of the `Table` is identical to the schema of the sink. A pipeline can be explained with `TablePipeline.explain()` and executed invoking `TablePipeline.execute()`. The following examples shows how to emit a `Table`: {{< tabs "08af6f8e-246c-451f-939b-96dc9b886b37" >}} {{< tab "Java" >}} ```java // get a TableEnvironment TableEnvironment tableEnv = ...; // see "Create a TableEnvironment" section // create an output Table final Schema schema = Schema.newBuilder() .column("a", DataTypes.INT()) .column("b", DataTypes.STRING()) .column("c", DataTypes.BIGINT()) .build(); tableEnv.createTemporaryTable("CsvSinkTable", TableDescriptor.forConnector("filesystem") .schema(schema) .option("path", "/path/to/file") .format(FormatDescriptor.forFormat("csv") .option("field-delimiter", "|") .build()) .build()); // compute a result Table using Table API operators and/or SQL queries Table result = ...; // Prepare the insert into pipeline TablePipeline pipeline = result.insertInto("CsvSinkTable"); // Print explain details pipeline.printExplain(); // emit the result Table to the registered TableSink

``` {{< /tab >}} {{< tab "Scala" >}} ```scala // get a TableEnvironment val tableEnv = ... // see "Create a TableEnvironment" section // create an output Table val schema = Schema.newBuilder() .column("a", DataTypes.INT()) .column("b", DataTypes.STRING()) .column("c", DataTypes.BIGINT()) .build() tableEnv.createTemporaryTable("CsvSinkTable", TableDescriptor.forConnector("filesystem") .schema(schema) .option("path", "/path/to/file") .format(FormatDescriptor.forFormat("csv") .option("field-delimiter", "|") .build()) .build()) // compute a result Table using Table API operators and/or SQL queries val result: Table = ... // Prepare the insert into pipeline val pipeline = result.insertInto("CsvSinkTable") // Print explain details pipeline.printExplain() // emit the result Table to the registered TableSink pipeline.execute() ``` {{< /tab >}} {{< tab "Python" >}} ```python table_env = ... # see "Create a TableEnvironment" section schema = Schema.new_builder() .column("a", DataTypes.INT()) .column("b", DataTypes.STRING()) .column("c", DataTypes.BIGINT()) .build() tableenv.createtemporarytable("CsvSinkTable", TableDescriptor.forconnector("filesystem") .schema(schema) .option("path", "/path/to/file") .format(FormatDescriptor.for_format("csv") .option("field-delimiter", "|") .build()) .build()) result = ... result.execute_insert("CsvSinkTable") ``` {{< /tab >}} {{< /tabs >}} {{< top >}} Translate and Execute a Query -- Table API and SQL queries are translated into programs whether their input is streaming or batch. A query is internally represented as a logical query plan and is translated in two phases: Optimization of the logical plan, Translation into a DataStream program. A Table API or SQL query is translated when: `TableEnvironment.executeSql()` is called. This method is used for executing a given statement, and the sql query is translated immediately once this method is called. `TablePipeline.execute()` is called. This method is used for executing a source-to-sink pipeline, and the Table API program is translated immediately once this method is called. `Table.execute()` is called. This method is used for collecting the table content to the local client, and the Table API is translated immediately once this method is called. `StatementSet.execute()` is called. A `TablePipeline` (emitted to a sink through `StatementSet.add()`) or an INSERT statement (specified through `StatementSet.addInsertSql()`) will be buffered in `StatementSet` first. They are transformed once `StatementSet.execute()` is called. All sinks will be optimized into one DAG. A `Table` is translated when it is converted into a `DataStream` (see ). Once translated, it's a regular DataStream program and is executed when `StreamExecutionEnvironment.execute()` is called. {{< top >}} Query Optimization Apache Flink leverages and extends Apache Calcite to perform sophisticated query optimization. This includes a series of rule and cost-based optimizations such as: Subquery decorrelation based on Apache Calcite Project pruning Partition pruning Filter push-down Sub-plan deduplication to avoid duplicate computation Special subquery rewriting, including two parts: Converts IN and EXISTS into left semi-joins Converts NOT IN and NOT EXISTS into left anti-join Optional join reordering Enabled via `table.optimizer.join-reorder-enabled` Note: IN/EXISTS/NOT IN/NOT EXISTS are currently only supported in conjunctive conditions in subquery rewriting. The optimizer makes intelligent decisions, based not only on the plan but also rich statistics available from the data sources and fine-grain costs for each operator such as io, cpu, network, and memory. Advanced users may provide custom optimizations via a `CalciteConfig` object that can be provided to the table environment by calling `TableEnvironment#getConfig#setPlannerConfig`. Explaining a Table The Table API provides a mechanism to explain the logical and optimized query plans to compute a `Table`. This is done through the `Table.explain()` method or `StatementSet.explain()` method. `Table.explain()`returns the plan of a `Table`. `StatementSet.explain()` returns the plan of multiple sinks. It returns a String describing three plans: the Abstract Syntax Tree of the relational query, i.e., the unoptimized logical query plan, the optimized logical query plan, and the physical execution plan. `TableEnvironment.explainSql()` and `TableEnvironment.executeSql()` support execute a `EXPLAIN` statement to get the plans, Please refer to page. The following code shows an example and the corresponding output for given `Table` using `Table.explain()` method: {{< tabs "152e6feb-1fa8-42b4-9c2f-993442487a5c" >}} {{< tab "Java" >}} ```java StreamExecutionEnvironment env =

StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); DataStream<Tuple2<Integer, String>> stream1 = env.fromElements(new Tuple2<>(1, "hello")); DataStream<Tuple2<Integer, String>> stream2 = env.fromElements(new Tuple2<>(1, "hello")); // explain Table API Table table1 = tEnv.fromDataStream(stream1, $("count"), $("word")); Table table2 = tEnv.fromDataStream(stream2, $("count"), $("word")); Table table = table1 .where($("word").like("F%")) .unionAll(table2); System.out.println(table.explain()); ``` {{< /tab >}} {{< tab "Scala" >}} ```scala val env = StreamExecutionEnvironment.getExecutionEnvironment val tEnv = StreamTableEnvironment.create(env) val table1 = env.fromElements((1, "hello")).toTable(tEnv, $"count", $"word") val table2 = env.fromElements((1, "hello")).toTable(tEnv, $"count", $"word") val table = table1 .where($"word".like("F%")) .unionAll(table2) println(table.explain()) ``` {{< /tab >}} {{< tab "Python" >}} ```python env = StreamExecutionEnvironment.getexecutionenvironment() t_env = StreamTableEnvironment.create(env) table1 = tenv.fromelements([(1, "hello")], ["count", "word"]) table2 = tenv.fromelements([(1, "hello")], ["count", "word"]) table = table1 \ .where(col('word').like('F%')) \ .union_all(table2) print(table.explain()) ``` {{< /tab >}} {{< /tabs >}} The result of the above example is {{< details "Explain" >}} ```text == Abstract Syntax Tree == LogicalUnion(all=[true]) :- LogicalFilter(condition=[LIKE($1, _UTF-16LE'F%')]) : +- LogicalTableScan(table=[[UnregisteredDataStream1]]) +- LogicalTableScan(table=[[UnregisteredDataStream2]]) == Optimized Physical Plan == Union(all=[true], union=[count, word]) :- Calc(select=[count, word], where=[LIKE(word, _UTF-16LE'F%')]) : +- DataStreamScan(table=[[UnregisteredDataStream1]], fields=[count, word]) +- DataStreamScan(table=[[UnregisteredDataStream2]], fields=[count, word]) == Optimized Execution Plan == Union(all=[true], union=[count, word]) :- Calc(select=[count, word], where=[LIKE(word, _UTF-16LE'F%')]) : +- DataStreamScan(table=[[UnregisteredDataStream1]], fields=[count, word]) +- DataStreamScan(table=[[UnregisteredDataStream2]], fields=[count, word]) ``` {{< /details >}} The following code shows an example and the corresponding output for multiple-sinks plan using `StatementSet.explain()` method: {{< tabs "46971f29-7db6-46d2-b9a1-53ee0918752f" >}} {{< tab "Java" >}} ```java EnvironmentSettings settings = EnvironmentSettings.inStreamingMode(); TableEnvironment tEnv = TableEnvironment.create(settings); final Schema schema = Schema.newBuilder() .column("count", DataTypes.INT()) .column("word", DataTypes.STRING()) .build(); tEnv.createTemporaryTable("MySource1", TableDescriptor.forConnector("filesystem") .schema(schema) .option("path", "/source/path1") .format("csv") .build()); tEnv.createTemporaryTable("MySource2", TableDescriptor.forConnector("filesystem") .schema(schema) .option("path", "/source/path2") .format("csv") .build()); tEnv.createTemporaryTable("MySink1", TableDescriptor.forConnector("filesystem") .schema(schema) .option("path", "/sink/path1") .format("csv") .build()); tEnv.createTemporaryTable("MySink2", TableDescriptor.forConnector("filesystem") .schema(schema) .option("path", "/sink/path2") .format("csv") .build()); StatementSet stmtSet = tEnv.createStatementSet(); Table table1 = tEnv.from("MySource1").where($("word").like("F%")); stmtSet.add(table1.insertInto("MySink1")); Table table2 = table1.unionAll(tEnv.from("MySource2")); stmtSet.add(table2.insertInto("MySink2")); String explanation = stmtSet.explain(); System.out.println(explanation); ``` {{< /tab >}} {{< tab "Scala" >}} ```scala val settings = EnvironmentSettings.inStreamingMode() val tEnv = TableEnvironment.create(settings) val schema = Schema.newBuilder() .column("count", DataTypes.INT()) .column("word", DataTypes.STRING()) .build() tEnv.createTemporaryTable("MySource1", TableDescriptor.forConnector("filesystem") .schema(schema) .option("path", "/source/path1") .format("csv") .build()) tEnv.createTemporaryTable("MySource2", TableDescriptor.forConnector("filesystem") .schema(schema) .option("path", "/source/path2") .format("csv") .build()) tEnv.createTemporaryTable("MySink1", TableDescriptor.forConnector("filesystem") .schema(schema) .option("path", "/sink/path1") .format("csv") .build()) tEnv.createTemporaryTable("MySink2", TableDescriptor.forConnector("filesystem") .schema(schema) .option("path", "/sink/path2") .format("csv") .build()) val stmtSet = tEnv.createStatementSet() val table1 = tEnv.from("MySource1").where($"word".like("F%")) stmtSet.add(table1.insertInto("MySink1")) val table2 = table1.unionAll(tEnv.from("MySource2")) stmtSet.add(table2.insertInto("MySink2")) val explanation = stmtSet.explain() println(explanation) ``` {{< /tab >}} {{< tab "Python" >}} ```python settings = EnvironmentSettings.instreamingmode() tenv = TableEnvironment.create(environmentsettings=settings) schema = Schema.new_builder() .column("count", DataTypes.INT()) .column("word", DataTypes.STRING()) .build() tenv.createtemporarytable("MySource1", TableDescriptor.forconnector("filesystem") .schema(schema) .option("path", "/source/path1") .format("csv") .build()) tenv.createtemporarytable("MySource2", TableDescriptor.forconnector("filesystem") .schema(schema) .option("path", "/source/path2") .format("csv") .build()) tenv.createtemporarytable("MySink1", TableDescriptor.forconnector("filesystem") .schema(schema) .option("path", "/sink/path1") .format("csv") .build()) tenv.createtemporarytable("MySink2", TableDescriptor.forconnector("filesystem") .schema(schema) .option("path", "/sink/path2") .format("csv") .build()) stmtset = tenv.createstatementset() table1 = tenv.frompath("MySource1").where(col('word').like('F%')) stmtset.addinsert("MySink1", table1) table2 = table1.unionall(tenv.from_path("MySource2")) stmtset.addinsert("MySink2", table2) explanation = stmt_set.explain() print(explanation) ``` {{< /tab >}} {{< /tabs >}} the result of multiple-sinks plan is {{< details "MultiTable Explain" >}} ```text == Abstract Syntax Tree == LogicalLegacySink(name=[`defaultcatalog`.`defaultdatabase`.`MySink1`], fields=[count, word]) +- LogicalFilter(condition=[LIKE($1, _UTF-16LE'F%')]) +- LogicalTableScan(table=[[defaultcatalog, defaultdatabase, MySource1, source: [CsvTableSource(read fields: count, word)]]]) LogicalLegacySink(name=[`defaultcatalog`.`defaultdatabase`.`MySink2`], fields=[count, word]) +- LogicalUnion(all=[true]) :- LogicalFilter(condition=[LIKE($1, _UTF-16LE'F%')]) : +- LogicalTableScan(table=[[defaultcatalog, defaultdatabase, MySource1, source: [CsvTableSource(read fields: count, word)]]]) +- LogicalTableScan(table=[[defaultcatalog, defaultdatabase, MySource2, source: [CsvTableSource(read fields: count, word)]]]) == Optimized Physical Plan == LegacySink(name=[`defaultcatalog`.`defaultdatabase`.`MySink1`], fields=[count, word]) +- Calc(select=[count, word], where=[LIKE(word, _UTF-16LE'F%')]) +- LegacyTableSourceScan(table=[[defaultcatalog, defaultdatabase, MySource1, source: [CsvTableSource(read fields: count, word)]]], fields=[count, word]) LegacySink(name=[`defaultcatalog`.`defaultdatabase`.`MySink2`], fields=[count, word]) +- Union(all=[true], union=[count, word]) :- Calc(select=[count, word], where=[LIKE(word, _UTF-16LE'F%')]) : +- LegacyTableSourceScan(table=[[defaultcatalog, defaultdatabase, MySource1, source: [CsvTableSource(read fields: count, word)]]], fields=[count, word]) +- LegacyTableSourceScan(table=[[defaultcatalog, defaultdatabase, MySource2, source: [CsvTableSource(read fields: count, word)]]], fields=[count, word]) == Optimized Execution Plan == Calc(select=[count, word], where=[LIKE(word, UTF-16LE'F%')])(reuseid=[1]) +- LegacyTableSourceScan(table=[[defaultcatalog, defaultdatabase, MySource1, source: [CsvTableSource(read fields: count, word)]]], fields=[count, word]) LegacySink(name=[`defaultcatalog`.`defaultdatabase`.`MySink1`], fields=[count, word]) +- Reused(reference_id=[1]) LegacySink(name=[`defaultcatalog`.`defaultdatabase`.`MySink2`], fields=[count, word]) +- Union(all=[true], union=[count, word]) :- Reused(reference_id=[1]) +- LegacyTableSourceScan(table=[[defaultcatalog, defaultdatabase, MySource2, source: [CsvTableSource(read fields: count, word)]]], fields=[count, word]) ``` {{< /details >}}