[FLINK-6746] [table] [docs] Updated Table API / SQL docs: Common API This closes #4012.
Project: http://git-wip-us.apache.org/repos/asf/flink/repo Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/fffce090 Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/fffce090 Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/fffce090 Branch: refs/heads/master Commit: fffce0904521d3e600799ba846d73d15757b24b1 Parents: 4088409 Author: Fabian Hueske <fhue...@apache.org> Authored: Sun May 28 15:35:09 2017 +0200 Committer: Fabian Hueske <fhue...@apache.org> Committed: Thu Jun 15 11:42:19 2017 +0200 ---------------------------------------------------------------------- docs/dev/table/common.md | 800 +++++++++++++++++++++++++++++++----------- 1 file changed, 593 insertions(+), 207 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/flink/blob/fffce090/docs/dev/table/common.md ---------------------------------------------------------------------- diff --git a/docs/dev/table/common.md b/docs/dev/table/common.md index d0d4914..fed2b6d 100644 --- a/docs/dev/table/common.md +++ b/docs/dev/table/common.md @@ -22,9 +22,7 @@ specific language governing permissions and limitations under the License. --> -The Table API and SQL are integrated API and share many concepts and much of their API. - -**TODO: Extend** +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`. * This will be replaced by the TOC {:toc} @@ -32,440 +30,828 @@ The Table API and SQL are integrated API and share many concepts and much of the Structure of Table API and SQL Programs --------------------------------------- -All Table API and SQL programs for batch and streaming have the same structure. +All Table API and SQL programs for batch and streaming follow the same pattern. The following code example shows the common structure of Table API and SQL programs. <div class="codetabs" markdown="1"> <div data-lang="java" markdown="1"> {% highlight java %} +// for batch programs use ExecutionEnvironment instead of StreamExecutionEnvironment StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); -// Create a TableEnvironment +// create a TableEnvironment +// for batch programs use BatchTableEnvironment instead of StreamTableEnvironment StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); -// Register a Table -tableEnv.registerTable("yourTable", ...) // or -tableEnv.registerTableSource("yourTableSrc", ...); // or -tableEnv.registerDataStream("yourTableStream", ...); // or -tableEnv.registerDataSet("yourTableSet", ...); // or -tableEnv.registerExternalCatalog("yourCatalog", ...); +// register a Table +tableEnv.registerTable("table1", ...) // or +tableEnv.registerTableSource("table2", ...); // or +tableEnv.registerExternalCatalog("extCat", ...); -// Create a table from a Table API query -Table tapiResult = tableEnv.scan("yourTableSrc").select(...); -// Or create a table from a SQL query -Table sqlResult = tableEnv.sql("SELECT ... FROM yourTableSrc ... "); +// create a Table from a Table API query +Table tapiResult = tableEnv.scan("table1").select(...); +// create a Table from a SQL query +Table sqlResult = tableEnv.sql("SELECT ... FROM table2 ... "); -// Emit a Table to a TableSink / DataStream / DataSet -resultTable.writeToSink(...); // or -resultTable.toAppendStream(...); // or -resultTable.toRetractStream(...); // or -resultTable.toDataSet(...); +// emit a Table API result Table to a TableSink, same for SQL result +tapiResult.writeToSink(...); -// Execute -env.execute("Your Query"); +// execute +env.execute(); {% endhighlight %} </div> <div data-lang="scala" markdown="1"> {% highlight scala %} -val env = ExecutionEnvironment.getExecutionEnvironment +// for batch programs use ExecutionEnvironment instead of StreamExecutionEnvironment +val env = StreamExecutionEnvironment.getExecutionEnvironment -// Create a TableEnvironment +// create a TableEnvironment val tableEnv = TableEnvironment.getTableEnvironment(env) -// Register a Table -tableEnv.registerTable("yourTable", ...) // or -tableEnv.registerTableSource("yourTableSrc", ...) // or -tableEnv.registerDataStream("yourTableStream", ...) // or -tableEnv.registerDataSet("yourTableSet", ...) // or -tableEnv.registerExternalCatalog("yourCatalog", ...) +// register a Table +tableEnv.registerTable("table1", ...) // or +tableEnv.registerTableSource("table2", ...) // or +tableEnv.registerExternalCatalog("extCat", ...) -// Create a table from a Table API query -val tapiResult = tableEnv.scan("yourTableSrc").select(...) -// Or create a table from a SQL query -val sqlResult = tableEnv.sql("SELECT ... FROM yourTableSrc ...") +// create a Table from a Table API query +val tapiResult = tableEnv.scan("table1").select(...) +// Create a Table from a SQL query +val sqlResult = tableEnv.sql("SELECT ... FROM table2 ...") -// Emit a Table -resultTable.writeToSink(...) // or -resultTable.toAppendStream(...) // or -resultTable.toRetractStream(...) // or -resultTable.toDataSet(...) +// emit a Table API result Table to a TableSink, same for SQL result +tapiResult.writeToSink(...) -// Execute -env.execute("Your Query") +// execute +env.execute() {% endhighlight %} </div> </div> +**Note:** Table API and SQL queries can be easily integrated with and embedded into DataStream or DataSet programs. Have a look at the [Integration with DataStream and DataSet API](#integration-with-datastream-and-dataset-api) section to learn how DataStreams and DataSets can be converted into Tables and vice versa. + {% top %} Create a TableEnvironment ------------------------- -A `Table` is always bound to a specific `TableEnvironment`. It is not possible to combine Tables of different TableEnvironments. +The `TableEnvironment` is a central concept of the Table API and SQL integration. It is responsible for: -**TODO: Extend** +* Registering a `Table` in the internal catalog +* Registering an external catalog +* Executing SQL queries +* Registering a user-defined (scalar, table, or aggregation) function +* Converting a `DataStream` or `DataSet` into a `Table` +* Holding a reference to an `ExecutionEnvironment` or `StreamExecutionEnvironment` + +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.getTableEnvironment()` method with a `StreamExecutionEnvironment` or an `ExecutionEnvironment` and an optional `TableConfig`. The `TableConfig` can be used to configure the `TableEnvironment` or to customize the query optimization and translation process (see [Query Optimization](#query-optimization)). + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +// *************** +// STREAMING QUERY +// *************** +StreamExecutionEnvironment sEnv = StreamExecutionEnvironment.getExecutionEnvironment(); +// create a TableEnvironment for streaming queries +StreamTableEnvironment sTableEnv = TableEnvironment.getTableEnvironment(sEnv); + +// *********** +// BATCH QUERY +// *********** +ExecutionEnvironment bEnv = ExecutionEnvironment.getExecutionEnvironment(); +// create a TableEnvironment for batch queries +BatchTableEnvironment bTableEnv = TableEnvironment.getTableEnvironment(bEnv); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +// *************** +// STREAMING QUERY +// *************** +val sEnv = StreamExecutionEnvironment.getExecutionEnvironment +// create a TableEnvironment for streaming queries +val sTableEnv = TableEnvironment.getTableEnvironment(sEnv) + +// *********** +// BATCH QUERY +// *********** +val bEnv = ExecutionEnvironment.getExecutionEnvironment +// create a TableEnvironment for batch queries +val bTableEnv = TableEnvironment.getTableEnvironment(bEnv) +{% endhighlight %} +</div> +</div> {% top %} Register a Table in the Catalog ------------------------------- -`TableEnvironment`s have an internal table catalog to which tables can be registered with a unique name. After registration, a table can be accessed from the `TableEnvironment` by its name. +A `TableEnvironment` has an internal catalog of tables, organized by table name. Table API or SQL queries can access tables which are registered in the catalog, by referencing them by name. + +A `TableEnvironment` allows you to register a table from various sources: + +* an existing `Table` object, usually the result of a Table API or SQL query. +* a `TableSource`, which accesses external data, such as a file, database, or messaging system. +* a `DataStream` or `DataSet` from a DataStream or DataSet program. -*Note: `DataSet`s or `DataStream`s can be directly converted into `Table`s without registering them in the `TableEnvironment`. See [Create a Table from a DataStream or DataSet](#tbd) for details. +Registering a `DataStream` or `DataSet` as a table is discussed in the [Integration with DataStream and DataSet API](#integration-with-datastream-and-dataset-api) section. ### Register a Table -A `Table` that originates from a Table API operation or a SQL query is registered in a `TableEnvironment` as follows: +A `Table` is registered in a `TableEnvironment` as follows: <div class="codetabs" markdown="1"> <div data-lang="java" markdown="1"> {% highlight java %} -// works for StreamExecutionEnvironment identically -ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); -BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); +// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); -// convert a DataSet into a Table -Table custT = tableEnv - .toTable(custDs, "name, zipcode") - .where("zipcode = '12345'") - .select("name"); +// Table is the result of a simple projection query +Table projTable = tableEnv.scan("X").project(...); -// register the Table custT as table "custNames" -tableEnv.registerTable("custNames", custT); +// register the Table projTable as table "projectedX" +tableEnv.registerTable("projectedTable", projTable); {% endhighlight %} </div> <div data-lang="scala" markdown="1"> {% highlight scala %} -// works for StreamExecutionEnvironment identically -val env = ExecutionEnvironment.getExecutionEnvironment +// get a TableEnvironment val tableEnv = TableEnvironment.getTableEnvironment(env) -// convert a DataSet into a Table -val custT = custDs - .toTable(tableEnv, 'name, 'zipcode) - .where('zipcode === "12345") - .select('name) +// Table is the result of a simple projection query +val projTable: Table = tableEnv.scan("X").project(...) -// register the Table custT as table "custNames" -tableEnv.registerTable("custNames", custT) +// register the Table projTable as table "projectedX" +tableEnv.registerTable("projectedTable", projTable) {% endhighlight %} </div> </div> -A registered `Table` that originates from a Table API operation or SQL query is treated similarly as a view as known from relational DBMS, i.e., it can be inlined when optimizing the query. +**Note:** A registered `Table` is treated similarly to a `VIEW` as known 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 %} -### Register a DataSet +### Register a TableSource + +A `TableSource` provides access to external data which is stored in a storage systems such as a database (MySQL, HBase, ...), a file with specific encoding (CSV, Apache \[Parquet, Avro, ORC\], ...), or a messaging system (Apache Kafka, RabbitMQ, ...). -A `DataSet` is registered as a `Table` in a `BatchTableEnvironment` as follows: +Flink aims to provide TableSources for common data formats and storage systems. Please have a look at the [Table Sources and Sinks]({{ site.baseurl }}/dev/table/sourceSinks.html) page for a list of supported TableSources and instructions for how to build a custom `TableSource`. + +A `TableSource` is registered in a `TableEnvironment` as follows: <div class="codetabs" markdown="1"> <div data-lang="java" markdown="1"> {% highlight java %} -ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); -BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); +// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); -// register the DataSet cust as table "Customers" with fields derived from the dataset -tableEnv.registerDataSet("Customers", cust); +// create a TableSource +TableSource csvSource = new CsvTableSource("/path/to/file", ...); -// register the DataSet ord as table "Orders" with fields user, product, and amount -tableEnv.registerDataSet("Orders", ord, "user, product, amount"); +// register the TableSource as table "CsvTable" +tableEnv.registerTableSource("CsvTable", csvSource); {% endhighlight %} </div> <div data-lang="scala" markdown="1"> {% highlight scala %} -val env = ExecutionEnvironment.getExecutionEnvironment +// get a TableEnvironment val tableEnv = TableEnvironment.getTableEnvironment(env) -// register the DataSet cust as table "Customers" with fields derived from the dataset -tableEnv.registerDataSet("Customers", cust) +// create a TableSource +val csvSource: TableSource = new CsvTableSource("/path/to/file", ...) -// register the DataSet ord as table "Orders" with fields user, product, and amount -tableEnv.registerDataSet("Orders", ord, 'user, 'product, 'amount) +// register the TableSource as table "CsvTable" +tableEnv.registerTableSource("CsvTable", csvSource) {% endhighlight %} </div> </div> -*Note: The name of a `DataSet` `Table` must not match the `^_DataSetTable_[0-9]+` pattern which is reserved for internal use only.* - {% top %} -### Register a DataStream +Register an External Catalog +---------------------------- + +An external catalog can provide information about external databases and tables such as their name, schema, statistics, and information for how to access data stored in an external database, table, or file. -A `DataStream` is registered as a `Table` in a `StreamTableEnvironment` as follows: +An external catalog can be created by implementing the `ExternalCatalog` interface and is registered in a `TableEnvironment` as follows: <div class="codetabs" markdown="1"> <div data-lang="java" markdown="1"> {% highlight java %} -StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); +// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); -// register the DataStream cust as table "Customers" with fields derived from the datastream -tableEnv.registerDataStream("Customers", cust); +// create an external catalog +ExternalCatalog catalog = new InMemoryExternalCatalog(); -// register the DataStream ord as table "Orders" with fields user, product, and amount -tableEnv.registerDataStream("Orders", ord, "user, product, amount"); +// register the ExternalCatalog catalog +tableEnv.registerExternalCatalog("InMemCatalog", catalog); {% endhighlight %} </div> <div data-lang="scala" markdown="1"> {% highlight scala %} -val env = StreamExecutionEnvironment.getExecutionEnvironment +// get a TableEnvironment val tableEnv = TableEnvironment.getTableEnvironment(env) -// register the DataStream cust as table "Customers" with fields derived from the datastream -tableEnv.registerDataStream("Customers", cust) +// create an external catalog +val catalog: ExternalCatalog = new InMemoryExternalCatalog -// register the DataStream ord as table "Orders" with fields user, product, and amount -tableEnv.registerDataStream("Orders", ord, 'user, 'product, 'amount) +// register the ExternalCatalog catalog +tableEnv.registerExternalCatalog("InMemCatalog", catalog) {% endhighlight %} </div> </div> -*Note: The name of a `DataStream` `Table` must not match the `^_DataStreamTable_[0-9]+` pattern which is reserved for internal use only.* +Once registered in a `TableEnvironment`, all tables defined in a `ExternalCatalog` can be accessed from Table API or SQL queries by specifying their full path, such as `catalog.database.table`. + +Currently, Flink provides an `InMemoryExternalCatalog` for demo and testing purposes. However, the `ExternalCatalog` interface can also be used to connect catalogs like HCatalog or Metastore to the Table API. {% top %} -### Register a TableSource +Query a Table +------------- -TableSources provided access to data stored in various storage systems such as databases (MySQL, HBase, ...), file formats (CSV, Apache Parquet, Avro, ORC, ...), or messaging systems (Apache Kafka, RabbitMQ, ...). Flink provides a TableSources for common data formats and storage systems. Please have a look at the [Table Sources and Sinks page]({{ site.baseurl }}/dev/table/sourceSinks.html) for a list of provided TableSources and documentation for how to built your own. +### Table API -An external table is registered in a `TableEnvironment` using a `TableSource` as follows: +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 [Table API]({{ site.baseurl }}/dev/table/tableapi.html) document describes all Table API operations that are supported on streaming and batch tables. + +The following example shows a simple Table API aggregation query: <div class="codetabs" markdown="1"> <div data-lang="java" markdown="1"> {% highlight java %} -// works for StreamExecutionEnvironment identically -ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); -BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); +// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); + +// register Orders table -TableSource custTS = new CsvTableSource("/path/to/file", ...); +// scan registered Orders table +Table orders = tableEnv.scan("Orders"); +// compute revenue for all customers from France +Table revenue = orders + .filter("cCountry === 'FRANCE'") + .groupBy("cID, cName") + .select("cID, cName, revenue.sum AS revSum"); -// register a `TableSource` as external table "Customers" -tableEnv.registerTableSource("Customers", custTS); +// emit or convert Table +// execute query {% endhighlight %} </div> <div data-lang="scala" markdown="1"> {% highlight scala %} -// works for StreamExecutionEnvironment identically -val env = ExecutionEnvironment.getExecutionEnvironment +// get a TableEnvironment val tableEnv = TableEnvironment.getTableEnvironment(env) -val custTS: TableSource = new CsvTableSource("/path/to/file", ...) +// register Orders table -// register a `TableSource` as external table "Customers" -tableEnv.registerTableSource("Customers", custTS) +// scan registered Orders table +Table orders = tableEnv.scan("Orders") +// compute revenue for all customers from France +Table revenue = orders + .filter('cCountry === "FRANCE") + .groupBy('cID, 'cName) + .select('cID, 'cName, 'revenue.sum AS 'revSum) +// emit or convert Table +// execute query {% endhighlight %} + +**Note:** The Scala Table API uses Scala Symbols, which start with a single tick (`'`) to reference the attributes of a `Table`. The Table API uses Scala implicits. Make sure to import `org.apache.flink.api.scala._` and `org.apache.flink.table.api.scala._` in order to use Scala implicit conversions. </div> </div> -A `TableSource` can provide access to data stored in various storage systems such as databases (MySQL, HBase, ...), file formats (CSV, Apache Parquet, Avro, ORC, ...), or messaging systems (Apache Kafka, RabbitMQ, ...). - {% top %} -Register an External Catalog ----------------------------- +### SQL + +Flink's SQL integration is based on [Apache Calcite](https://calcite.apache.org), which implements the SQL standard. SQL queries are specified as regular Strings. -An external catalog is defined by the `ExternalCatalog` interface and provides information about databases and tables such as their name, schema, statistics, and access information. An `ExternalCatalog` is registered in a `TableEnvironment` as follows: +The [SQL]({{ site.baseurl }}/dev/table/sql.html) 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. <div class="codetabs" markdown="1"> <div data-lang="java" markdown="1"> {% highlight java %} -// works for StreamExecutionEnvironment identically -ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); -BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); +// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); -ExternalCatalog customerCatalog = new InMemoryExternalCatalog(); +// register Orders table -// register the ExternalCatalog customerCatalog -tableEnv.registerExternalCatalog("Customers", customerCatalog); +// compute revenue for all customers from France +Table revenue = tableEnv.sql( + "SELECT cID, cName, SUM(revenue) AS revSum " + + "FROM Orders " + + "WHERE cCountry = 'FRANCE' " + + "GROUP BY cID, cName" + ); + +// emit or convert Table +// execute query {% endhighlight %} </div> <div data-lang="scala" markdown="1"> {% highlight scala %} -// works for StreamExecutionEnvironment identically -val env = ExecutionEnvironment.getExecutionEnvironment +// get a TableEnvironment val tableEnv = TableEnvironment.getTableEnvironment(env) -val customerCatalog: ExternalCatalog = new InMemoryExternalCatalog +// register Orders table -// register the ExternalCatalog customerCatalog -tableEnv.registerExternalCatalog("Customers", customerCatalog) +// compute revenue for all customers from France +Table revenue = tableEnv.sql(""" + |SELECT cID, cName, SUM(revenue) AS revSum + |FROM Orders + |WHERE cCountry = 'FRANCE' + |GROUP BY cID, cName + """.stripMargin) +// emit or convert Table +// execute query {% endhighlight %} + </div> </div> -Once registered in a `TableEnvironment`, all tables defined in a `ExternalCatalog` can be accessed from Table API or SQL queries by specifying their full path (`catalog`.`database`.`table`). +{% top %} -Currently, Flink provides an `InMemoryExternalCatalog` for demo and testing purposes. However, the `ExternalCatalog` interface can also be used to connect catalogs like HCatalog or Metastore to the Table API. +### Mixing Table API and SQL + +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 [registering the resulting Table](#register-a-table) in the `TableEnvironment` and referencing it in the `FROM` clause of the SQL query. {% top %} -Create a Table from a DataStream or DataSet -------------------------------------------- +Emit a Table +------------ -Besides registering a Table in a catalog, it is also possible to directly create a `Table` from a `DataStream` or `DataSet`. +In order to emit a `Table`, it can be written 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). -### Create a Table from a DataStream +A batch `Table` can only be written to a `BatchTableSink`, while a streaming table requires either an `AppendStreamTableSink`, a `RetractStreamTableSink`, or an `UpsertStreamTableSink`. -**TODO** +Please see the documentation about [Table Sources & Sinks]({{ site.baseurl }}/dev/table/sourceSinks.html) for details about available sinks and instructions for how to implement a custom `TableSink`. -{% top %} +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); -### Create a Table from a DataSet +// compute a result Table using Table API operators and/or SQL queries +Table result = ... + +// create a TableSink +TableSink sink = new CsvTableSink("/path/to/file", fieldDelim = "|"); + +// write the result Table to the TableSink +result.writeToSink(sink); + +// execute the program +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +// get a TableEnvironment +val tableEnv = TableEnvironment.getTableEnvironment(env) + +// compute a result Table using Table API operators and/or SQL queries +val result: Table = ... -**TODO** +// create a TableSink +val sink: TableSink = new CsvTableSink("/path/to/file", fieldDelim = "|") -### Scala Implicit Conversion +// write the result Table to the TableSink +result.writeToSink(sink) -If you use the Scala API, A `DataSet` or `DataStream` can be implicitly converted into a `Table`. +// execute the program +{% endhighlight %} +</div> +</div> {% top %} -Query a Table -------------- -### Table API +Translate and Execute a Query +----------------------------- + +Table API and SQL queries are translated into [DataStream]({{ site.baseurl }}/dev/datastream_api.html) or [DataSet]({{ site.baseurl }}/dev/batch) programs depending on whether their input is a streaming or batch input. A query is internally represented as a logical query plan and is translated in two phases: + +1. optimization of the logical plan, +2. translation into a DataStream or DataSet program. -**TODO** +A Table API or SQL query is translated when: + +* the `Table` is emitted to a `TableSink`, i.e., when `Table.writeToSink()` is called. +* the `Table` is converted into a `DataStream` or `DataSet` (see [Integration with DataStream and DataSet API](#integration-with-dataStream-and-dataSet-api)). + +Once translated, a Table API or SQL query is handled like a regular DataStream or DataSet program and is executed when `StreamExecutionEnvironment.execute()` or `ExecutionEnvironment.execute()` is called. {% top %} -### SQL +Integration with DataStream and DataSet API +------------------------------------------- -**TODO** +Table API and SQL queries can be easily integrated with and embedded into [DataStream]({{ site.baseurl }}/dev/datastream_api.html) and [DataSet]({{ site.baseurl }}/dev/batch) programs. For instance, it is possible to query an external table (for example from a RDBMS), do some pre-processing, such as filtering, projecting, aggregating, or joining with meta data, and then further process the data with either the DataStream or DataSet API (and any of the libraries built on top of these APIs, such as CEP or Gelly). Inversely, a Table API or SQL query can also be applied on the result of a DataStream or DataSet program. + +This interaction can be achieved by converting a `DataStream` or `DataSet` into a `Table` and vice versa. In this section, we describe how these conversions are done. + +### Implicit Conversion for Scala + +The Scala Table API features implicit conversions for the `DataSet`, `DataStream`, and `Table` classes. These conversions are enabled by importing the package `org.apache.flink.table.api.scala._` in addition to `org.apache.flink.api.scala._` for the Scala DataStream API. + +### Register a DataStream or DataSet as Table + +A `DataStream` or `DataSet` can be registered in a `TableEnvironment` as a Table. The schema of the resulting table depends on the data type of the registered `DataStream` or `DataSet`. Please check the section about [mapping of data types to table schema](#mapping-of-data-types-to-table-schema) for details. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +// get StreamTableEnvironment +// registration of a DataSet in a BatchTableEnvironment is equivalent +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); + +DataStream<Tuple2<Long, String>> stream = ... + +// register the DataStream as Table "myTable" with fields "f0", "f1" +tableEnv.registerDataStream("myTable", stream); + +// register the DataStream as table "myTable2" with fields "myLong", "myString" +tableEnv.registerDataStream("myTable2", stream, "myLong, myString"); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +// get TableEnvironment +// registration of a DataSet is equivalent +val tableEnv = TableEnvironment.getTableEnvironment(env) + +val stream: DataStream[(Long, String)] = ... + +// register the DataStream as Table "myTable" with fields "f0", "f1" +tableEnv.registerDataStream("myTable", stream) + +// register the DataStream as table "myTable2" with fields "myLong", "myString" +tableEnv.registerDataStream("myTable2", stream, 'myLong, 'myString) +{% endhighlight %} +</div> +</div> + +**Note:** The name of a `DataStream` `Table` must not match the `^_DataStreamTable_[0-9]+` pattern and the name of a `DataSet` `Table` must not match the `^_DataSetTable_[0-9]+` pattern. These patterns are reserved for internal use only. {% top %} -### Interoperability +### Convert a DataStream or DataSet into a Table + +Instead of registering a `DataStream` or `DataSet` in a `TableEnvironment`, it can also be directly converted into a `Table`. This is convenient if you want to use the Table in a Table API query. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +// get StreamTableEnvironment +// registration of a DataSet in a BatchTableEnvironment is equivalent +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); + +DataStream<Tuple2<Long, String>> stream = ... + +// Convert the DataStream into a Table with default fields "f0", "f1" +Table table1 = tableEnv.fromDataStream(stream); -**TODO** +// Convert the DataStream into a Table with fields "myLong", "myString" +Table table2 = tableEnv.fromDataStream(stream, "myLong, myString"); +{% endhighlight %} +</div> -* Mix SQL and Table as you like -* Table API to SQL requires registered tables, register Table -* SQL to Table API just use resulting table +<div data-lang="scala" markdown="1"> +{% highlight scala %} +// get TableEnvironment +// registration of a DataSet is equivalent +val tableEnv = TableEnvironment.getTableEnvironment(env) + +val stream: DataStream[(Long, String)] = ... + +// convert the DataStream into a Table with default fields '_1, '_2 +val table1: Table = tableEnv.fromDataStream(stream) + +// convert the DataStream into a Table with fields 'myLong, 'myString +val table2: Table = tableEnv.fromDataStream(stream, 'myLong, 'myString) +{% endhighlight %} +</div> +</div> {% top %} -Emit a Table ------------- +### Convert a Table into a DataStream or DataSet + +A `Table` can be converted into a `DataStream` or `DataSet`. In this way, custom DataStream or DataSet programs can be run on the result of a Table API or SQL query. -### Emit to a TableSink +When converting a `Table` into a `DataStream` or `DataSet`, you need to specify the data type of the resulting `DataStream` or `DataSet`, i.e., the data type into which the rows of the `Table` are to be converted. Often the most convenient conversion type is `Row`. The following list gives an overview of the features of the different options: -A `Table` can be written to a `TableSink`, which 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`, a streaming table requires a `StreamTableSink`. A `TableSink` can implement both interfaces at the same time. +- **Row**: fields are mapped by position, arbitrary number of fields, support for `null` values, no type-safe access. +- **POJO**: fields are mapped by name (POJO fields must be named as `Table` fields), arbitrary number of fields, support for `null` values, type-safe access. +- **Case Class**: fields are mapped by position, no support for `null` values, type-safe access. +- **Tuple**: fields are mapped by position, limitation to 22 (Scala) or 25 (Java) fields, no support for `null` values, type-safe access. +- **Atomic Type**: `Table` must have a single field, no support for `null` values, type-safe access. -Currently, Flink only provides a `CsvTableSink` that writes a batch or streaming `Table` to CSV-formatted files. A custom `TableSink` can be defined by implementing the `BatchTableSink` and/or `StreamTableSink` interface. +#### Convert a Table into a DataStream + +A `Table` that is the result of a streaming query will be updated dynamically, i.e., it is changing as new records arrive on the query's input streams. Hence, the `DataStream` into which such a dynamic query is converted needs to encode the updates of the table. + +There are two modes to convert a `Table` into a `DataStream`: + +1. **Append Mode**: This mode can only be used if the dynamic `Table` is only modified by `INSERT` changes, i.e, it is append-only and previously emitted results are never updated. +2. **Retract Mode**: This mode can always be used. It encodes `INSERT` and `DELETE` changes with a `boolean` flag. <div class="codetabs" markdown="1"> <div data-lang="java" markdown="1"> {% highlight java %} -ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); +// get StreamTableEnvironment. +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); + +// Table with two fields (String name, Integer age) +Table table = ... + +// convert the Table into an append DataStream of Row by specifying the class +DataStream<Row> dsRow = tableEnv.toAppendStream(table, Row.class); + +// convert the Table into an append DataStream of Tuple2<String, Integer> +// via a TypeInformation +TupleTypeInfo<Tuple2<String, Integer>> tupleType = new TupleTypeInfo<>( + Types.STRING(), + Types.INT()); +DataStream<Tuple2<String, Integer>> dsTuple = + tableEnv.toAppendStream(table, tupleType); + +// convert the Table into a retract DataStream of Row. +// A retract stream of type X is a DataStream<Tuple2<Boolean, X>>. +// The boolean field indicates the type of the change. +// True is INSERT, false is DELETE. +DataStream<Tuple2<Boolean, Row>> retractStream = + tableEnv.toRetractStream(table, Row.class); + +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +// get TableEnvironment. +// registration of a DataSet is equivalent +val tableEnv = TableEnvironment.getTableEnvironment(env) + +// Table with two fields (String name, Integer age) +val table: Table = ... + +// convert the Table into an append DataStream of Row +val dsRow: DataStream[Row] = tableEnv.toAppendStream[Row](table) + +// convert the Table into an append DataStream of Tuple2[String, Int] +val dsTuple: DataStream[(String, Int)] dsTuple = + tableEnv.toAppendStream[(String, Int)](table) + +// convert the Table into a retract DataStream of Row. +// A retract stream of type X is a DataStream[(Boolean, X)]. +// The boolean field indicates the type of the change. +// True is INSERT, false is DELETE. +val retractStream: DataStream[(Boolean, Row)] = tableEnv.toRetractStream[Row](table) +{% endhighlight %} +</div> +</div> + +**Note:** A detailed discussion about dynamic tables and their properties is given in the [Streaming Queries]({{ site.baseurl }}/dev/table/streaming.html) document. + +#### Convert a Table into a DataSet + +A `Table` is converted into a `DataSet` as follows: + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +// get BatchTableEnvironment BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); -// compute the result Table using Table API operators and/or SQL queries -Table result = ... +// Table with two fields (String name, Integer age) +Table table = ... -// create a TableSink -TableSink sink = new CsvTableSink("/path/to/file", fieldDelim = "|"); -// write the result Table to the TableSink -result.writeToSink(sink); +// convert the Table into a DataSet of Row by specifying a class +DataSet<Row> dsRow = tableEnv.toDataSet(table, Row.class); -// execute the program -env.execute(); +// convert the Table into a DataSet of Tuple2<String, Integer> via a TypeInformation +TupleTypeInfo<Tuple2<String, Integer>> tupleType = new TupleTypeInfo<>( + Types.STRING(), + Types.INT()); +DataStream<Tuple2<String, Integer>> dsTuple = + tableEnv.toAppendStream(table, tupleType); {% endhighlight %} </div> <div data-lang="scala" markdown="1"> {% highlight scala %} -val env = ExecutionEnvironment.getExecutionEnvironment +// get TableEnvironment +// registration of a DataSet is equivalent val tableEnv = TableEnvironment.getTableEnvironment(env) -// compute the result Table using Table API operators and/or SQL queries -val result: Table = ... +// Table with two fields (String name, Integer age) +val table: Table = ... -// create a TableSink -val sink: TableSink = new CsvTableSink("/path/to/file", fieldDelim = "|") -// write the result Table to the TableSink -result.writeToSink(sink) +// convert the Table into a DataSet of Row +val dsRow: DataSet[Row] = tableEnv.toDataSet[Row](table) -// execute the program -env.execute() +// convert the Table into a DataSet of Tuple2[String, Int] +val dsTuple: DataSet[(String, Int)] = tableEnv.toDataSet[(String, Int)](table) {% endhighlight %} </div> </div> {% top %} -### Convert to a DataStream +### Mapping of Data Types to Table Schema -**TODO** +Flink's DataStream and DataSet APIs support very diverse types, such as Tuples (built-in Scala and Flink Java tuples), POJOs, case classes, and atomic types. In the following we describe how the Table API converts these types into an internal row representation and show examples of converting a `DataStream` into a `Table`. -{% top %} +#### Atomic Types -### Convert to a DataSet +Flink treats primitives (`Integer`, `Double`, `String`) or generic types (types that cannot be analyzed and decomposed) as atomic types. A `DataStream` or `DataSet` of an atomic type is converted into a `Table` with a single attribute. The type of the attribute is inferred from the atomic type and the name of the attribute must be specified. -**TODO** +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); -### Scala Implicit Conversion +DataStream<Long> stream = ... +// convert DataStream into Table with field "myLong" +Table table = tableEnv.fromDataStream(stream, "myLong"); +{% endhighlight %} +</div> -If you use the Scala API, A `Table` can be implicitly converted into a `DataSet` or `DataStream`. +<div data-lang="scala" markdown="1"> +{% highlight scala %} +// get a TableEnvironment +val tableEnv = TableEnvironment.getTableEnvironment(env) -{% top %} +val stream: DataStream[Long] = ... +// convert DataStream into Table with field 'myLong +val table: Table = tableEnv.fromDataStream(stream, 'myLong) +{% endhighlight %} +</div> +</div> -Execute a Query ---------------- +#### Tuples (Scala and Java) and Case Classes (Scala only) -**TODO** +Flink supports Scala's built-in tuples and provides its own tuple classes for Java. DataStreams and DataSets of both kinds of tuples can be converted into tables. Fields can be renamed by providing names for all fields (mapping based on position). If no field names are specified, the default field names are used. -{% top %} +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); -Mappings Types to Table Schema ------------------------------- +DataStream<Tuple2<Long, String>> stream = ... -* Explain how types are mapped to table schema - * Atomic Types - * Row - * Tuples (Java / Scala) - * Pojos - * Case Classes +// convert DataStream into Table with field names "myLong", "myString" +Table table1 = tableEnv.fromDataStream(stream, "myLong, myString"); -**TODO** +// convert DataStream into Table with default field names "f0", "f1" +Table table2 = tableEnv.fromDataStream(stream); +{% endhighlight %} +</div> -{% top %} +<div data-lang="scala" markdown="1"> +{% highlight scala %} +// get a TableEnvironment +val tableEnv = TableEnvironment.getTableEnvironment(env) -Integration with DataSet and DataStream API -------------------------------------------- +val stream: DataStream[(Long, String)] = ... + +// convert DataStream into Table with field names 'myLong, 'myString +val table1: Table = tableEnv.fromDataStream(stream, 'myLong, 'myString) + +// convert DataStream into Table with default field names '_1, '_2 +val table2: Table = tableEnv.fromDataStream(stream) + +// define case class +case class Person(name: String, age: Int) +val streamCC: DataStream[Person] = ... + +// convert DataStream into Table with default field names 'name, 'age +val tableCC1 = tableEnv.fromDataStream(streamCC) + +// convert DataStream into Table with field names 'myName, 'myAge +val tableCC1 = tableEnv.fromDataStream(streamCC, 'myName, 'myAge) + +{% endhighlight %} +</div> +</div> + +#### POJO (Java and Scala) + +Flink supports POJOs as composite types. The rules for what determines a POJO are documented [here]({{ site.baseurl }}/dev/api_concepts.html#pojos). + +When converting a POJO `DataStream` or `DataSet` into a `Table` without specifying field names, the names of the original POJO fields are used. Renaming the original POJO fields requires the keyword `AS` because POJO fields have no inherent order. The name mapping requires the original names and cannot be done by positions. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); + +// Person is a POJO with fields "name" and "age" +DataStream<Person> stream = ... + +// convert DataStream into Table with field names "name", "age" +Table table1 = tableEnv.fromDataStream(stream); + +// convert DataStream into Table with field names "myName", "myAge" +Table table2 = tableEnv.fromDataStream(stream, "name as myName, age as myAge"); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +// get a TableEnvironment +val tableEnv = TableEnvironment.getTableEnvironment(env) + +// Person is a POJO with field names "name" and "age" +val stream: DataStream[Person] = ... + +// convert DataStream into Table with field names 'name, 'age +val table1: Table = tableEnv.fromDataStream(stream) + +// convert DataStream into Table with field names 'myName, 'myAge +val table2: Table = tableEnv.fromDataStream(stream, 'name as 'myName, 'age as 'myAge) +{% endhighlight %} +</div> +</div> + +#### Row + +The Row data type supports an arbitrary number of fields and fields with `null` values. Field names can be specified via a `RowTypeInfo` or when converting a `Row` `DataStream` or `DataSet` into a `Table` (based on position). + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +// get a StreamTableEnvironment, works for BatchTableEnvironment equivalently +StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env); + +// DataStream of Row with two fields "name" and "age" specified in `RowTypeInfo` +DataStream<Row> stream = ... -**TODO** +// convert DataStream into Table with field names "name", "age" +Table table1 = tableEnv.fromDataStream(stream); -* Create `Table` from `DataSet` and `DataStream` and back -* Easy integration with more expressive APIs and libraries - * CEP / Gelly / ML - * Ingestion and projection +// convert DataStream into Table with field names "myName", "myAge" +Table table2 = tableEnv.fromDataStream(stream, "myName, myAge"); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +// get a TableEnvironment +val tableEnv = TableEnvironment.getTableEnvironment(env) + +// DataStream of Row with two fields "name" and "age" specified in `RowTypeInfo` +val stream: DataStream[Row] = ... + +// convert DataStream into Table with field names 'name, 'age +val table1: Table = tableEnv.fromDataStream(stream) + +// convert DataStream into Table with field names 'myName, 'myAge +val table2: Table = tableEnv.fromDataStream(stream, 'myName, 'myAge) +{% endhighlight %} +</div> +</div> {% top %} + Query Optimization ------------------ -* No join order yet -* Filter / Projection push down -* Custom rules +Apache Flink leverages Apache Calcite to optimize and translate queries. The optimization currently performed include projection and filter push-down, subquery decorrelation, and other kinds of query rewriting. Flink does not yet optimize the order of joins, but executes them in the same order as defined in the query (order of Tables in the `FROM` clause and/or order of join predicates in the `WHERE` clause). + +It is possible to tweak the set of optimization rules which are applied in different phases by providing a `CalciteConfig` object. This can be created via a builder by calling `CalciteConfig.createBuilder())` and is provided to the TableEnvironment by calling `tableEnv.getConfig.setCalciteConfig(calciteConfig)`. ### 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 `TableEnvironment#explain(table)` method. It returns a string describing three plans: +This is done through the `TableEnvironment.explain(table)` method. It returns a String describing three plans: 1. the Abstract Syntax Tree of the relational query, i.e., the unoptimized logical query plan, 2. the optimized logical query plan, and @@ -485,8 +871,8 @@ DataStream<Tuple2<Integer, String>> stream2 = env.fromElements(new Tuple2<>(1, " Table table1 = tEnv.fromDataStream(stream1, "count, word"); Table table2 = tEnv.fromDataStream(stream2, "count, word"); Table table = table1 - .where("LIKE(word, 'F%')") - .unionAll(table2); + .where("LIKE(word, 'F%')") + .unionAll(table2); String explanation = tEnv.explain(table); System.out.println(explanation); @@ -501,8 +887,8 @@ val tEnv = TableEnvironment.getTableEnvironment(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) + .where('word.like("F%")) + .unionAll(table2) val explanation: String = tEnv.explain(table) println(explanation)
