Table API and SQL Beta

Table API and SQL are experimental features

The Table API is a SQL-like expression language for relational stream and batch processing that can be easily embedded in Flink’s DataSet and DataStream APIs (Java and Scala). The Table API and SQL interface operate on a relational Table abstraction, which can be created from external data sources, or existing DataSets and DataStreams. With the Table API, you can apply relational operators such as selection, aggregation, and joins on Tables.

Tables can also be queried with regular SQL, as long as they are registered (see Registering Tables). The Table API and SQL offer equivalent functionality and can be mixed in the same program. When a Table is converted back into a DataSet or DataStream, the logical plan, which was defined by relational operators and SQL queries, is optimized using Apache Calcite and transformed into a DataSet or DataStream program.

• Using the Table API and SQL
• Registering Tables
  • Register a DataSet
  • Register a DataStream
  • Register a Table
  • Register an external Table using a TableSource
  • Available Table Sources
• Table API
  • Access a registered Table
  • Table API Operators
  • Expression Syntax
• SQL
  • SQL on Batch Tables
  • SQL on Streaming Tables
• Writing Tables to External Sinks
• Runtime Configuration
  • Null Handling

Using the Table API and SQL

The Table API and SQL are part of the flink-table Maven project. The following dependency must be added to your project in order to use the Table API and SQL:

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-table_2.10</artifactId>
  <version>1.1.5</version>
</dependency>

Note: The Table API is currently not part of the binary distribution. See linking with it for cluster execution here.

Registering Tables

TableEnvironments 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.

Note: DataSets or DataStreams can be directly converted into Tables without registering them in the TableEnvironment.

Register a DataSet

A DataSet is registered as a Table in a BatchTableEnvironment as follows:

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);

// register the DataSet cust as table "Customers" with fields derived from the dataset
tableEnv.registerDataSet("Customers", cust)

// register the DataSet ord as table "Orders" with fields user, product, and amount
tableEnv.registerDataSet("Orders", ord, "user, product, amount");

val env = ExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)

// register the DataSet cust as table "Customers" with fields derived from the dataset
tableEnv.registerDataSet("Customers", cust)

// register the DataSet ord as table "Orders" with fields user, product, and amount
tableEnv.registerDataSet("Orders", ord, 'user, 'product, 'amount)

Note: The name of a DataSet Table must not match the ^_DataSetTable_[0-9]+ pattern which is reserved for internal use only.

Register a DataStream

A DataStream is registered as a Table in a StreamTableEnvironment as follows:

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);

// register the DataStream cust as table "Customers" with fields derived from the datastream
tableEnv.registerDataStream("Customers", cust)

// register the DataStream ord as table "Orders" with fields user, product, and amount
tableEnv.registerDataStream("Orders", ord, "user, product, amount");

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)

// register the DataStream cust as table "Customers" with fields derived from the datastream
tableEnv.registerDataStream("Customers", cust)

// register the DataStream ord as table "Orders" with fields user, product, and amount
tableEnv.registerDataStream("Orders", ord, 'user, 'product, 'amount)

Note: The name of a DataStream Table must not match the ^_DataStreamTable_[0-9]+ pattern which is reserved for internal use only.

Register a Table

A Table that originates from a Table API operation or a SQL query is registered in a TableEnvironment as follows:

// works for StreamExecutionEnvironment identically
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);

// convert a DataSet into a Table
Table custT = tableEnv
  .toTable(custDs, "name, zipcode")
  .where("zipcode = '12345'")
  .select("name")

// register the Table custT as table "custNames"
tableEnv.registerTable("custNames", custT)

// works for StreamExecutionEnvironment identically
val env = ExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)

// convert a DataSet into a Table
val custT = custDs
  .toTable(tableEnv, 'name, 'zipcode)
  .where('zipcode === "12345")
  .select('name)

// register the Table custT as table "custNames"
tableEnv.registerTable("custNames", custT)

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.

Register an external Table using a TableSource

An external table is registered in a TableEnvironment using a TableSource as follows:

// works for StreamExecutionEnvironment identically
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);

TableSource custTS = new CsvTableSource("/path/to/file", ...)

// register a `TableSource` as external table "Customers"
tableEnv.registerTableSource("Customers", custTS)

// works for StreamExecutionEnvironment identically
val env = ExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)

val custTS: TableSource = new CsvTableSource("/path/to/file", ...)

// register a `TableSource` as external table "Customers"
tableEnv.registerTableSource("Customers", custTS)

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, …).

Currently, Flink provides the CsvTableSource to read CSV files and the Kafka08JsonTableSource/Kafka09JsonTableSource to read JSON objects from Kafka. A custom TableSource can be defined by implementing the BatchTableSource or StreamTableSource interface.

Available Table Sources

All sources that come with the flink-table dependency can be directly used by your Table programs. For all other table sources, you have to add the respective dependency in addition to the flink-table dependency.

KafkaJsonTableSource

To use the Kafka JSON source, you have to add the Kafka connector dependency to your project:

• flink-connector-kafka-0.8 for Kafka 0.8, and
• flink-connector-kafka-0.9 for Kafka 0.9, respectively.

You can then create the source as follows (example for Kafka 0.8):

// The JSON field names and types
String[] fieldNames =  new String[] { "id", "name", "score"};
Class<?>[] fieldTypes = new Class<?>[] { Integer.class, String.class, Double.class };

KafkaJsonTableSource kafkaTableSource = new Kafka08JsonTableSource(
    kafkaTopic,
    kafkaProperties,
    fieldNames,
    fieldTypes);

By default, a missing JSON field does not fail the source. You can configure this via:

// Fail on missing JSON field
tableSource.setFailOnMissingField(true);

You can work with the Table as explained in the rest of the Table API guide:

tableEnvironment.registerTableSource("kafka-source", kafkaTableSource);
Table result = tableEnvironment.ingest("kafka-source");

CsvTableSource

The CsvTableSource is already included in flink-table without additional dependecies.

It can be configured with the following properties:

• path The path to the CSV file, required.
• fieldNames The names of the table fields, required.
• fieldTypes The types of the table fields, required.
• fieldDelim The field delimiter, "," by default.
• rowDelim The row delimiter, "\n" by default.
• quoteCharacter An optional quote character for String values, null by default.
• ignoreFirstLine Flag to ignore the first line, false by default.
• ignoreComments An optional prefix to indicate comments, null by default.
• lenient Flag to skip records with parse error instead to fail, false by default.

You can create the source as follows:

CsvTableSource csvTableSource = new CsvTableSource(
    "/path/to/your/file.csv",
    new String[] { "name", "id", "score", "comments" },
    new TypeInformation<?>[] {
      Types.STRING(),
      Types.INT(),
      Types.DOUBLE(),
      Types.STRING()
    },
    "#",    // fieldDelim
    "$",    // rowDelim
    null,   // quoteCharacter
    true,   // ignoreFirstLine
    "%",    // ignoreComments
    false); // lenient

val csvTableSource = new CsvTableSource(
    "/path/to/your/file.csv",
    Array("name", "id", "score", "comments"),
    Array(
      Types.STRING,
      Types.INT,
      Types.DOUBLE,
      Types.STRING
    ),
    fieldDelim = "#",
    rowDelim = "$",
    ignoreFirstLine = true,
    ignoreComments = "%")

You can work with the Table as explained in the rest of the Table API guide in both stream and batch TableEnvironments:

tableEnvironment.registerTableSource("mycsv", csvTableSource);

Table streamTable = streamTableEnvironment.ingest("mycsv");

Table batchTable = batchTableEnvironment.scan("mycsv");

tableEnvironment.registerTableSource("mycsv", csvTableSource)

val streamTable = streamTableEnvironment.ingest("mycsv")

val batchTable = batchTableEnvironment.scan("mycsv")

Table API

The Table API provides methods to apply relational operations on DataSets and Datastreams both in Scala and Java.

The central concept of the Table API is a Table which represents a table with relational schema (or relation). Tables can be created from a DataSet or DataStream, converted into a DataSet or DataStream, or registered in a table catalog using a TableEnvironment. A Table is always bound to a specific TableEnvironment. It is not possible to combine Tables of different TableEnvironments.

Note: The only operations currently supported on streaming Tables are selection, projection, and union.

public class WC {

  public WC(String word, int count) {
    this.word = word; this.count = count;
  }

  public WC() {} // empty constructor to satisfy POJO requirements

  public String word;
  public int count;
}

...

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment tEnv = TableEnvironment.getTableEnvironment(env);

DataSet<WC> input = env.fromElements(
        new WC("Hello", 1),
        new WC("Ciao", 1),
        new WC("Hello", 1));

Table table = tEnv.fromDataSet(input);

Table wordCounts = table
        .groupBy("word")
        .select("word, count.sum as count");

DataSet<WC> result = tableEnv.toDataSet(wordCounts, WC.class);

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);

// register the DataSet cust as table "Customers" with fields derived from the dataset
tableEnv.registerDataSet("Customers", cust)

// register the DataSet ord as table "Orders" with fields user, product, and amount
tableEnv.registerDataSet("Orders", ord, "user, product, amount");

import org.apache.flink.api.scala._
import org.apache.flink.api.scala.table._

case class WC(word: String, count: Int)

val env = ExecutionEnvironment.getExecutionEnvironment
val tEnv = TableEnvironment.getTableEnvironment(env)

val input = env.fromElements(WC("hello", 1), WC("hello", 1), WC("ciao", 1))
val expr = input.toTable(tEnv)
val result = expr
               .groupBy('word)
               .select('word, 'count.sum as 'count)
               .toDataSet[WC]

case class MyResult(a: String, d: Int)

val input1 = env.fromElements(...).toTable(tEnv).as('a, 'b)
val input2 = env.fromElements(...).toTable(tEnv, 'c, 'd)

val joined = input1.join(input2)
               .where("a = c && d > 42")
               .select("a, d")
               .toDataSet[MyResult]

import org.apache.flink.api.scala._
import org.apache.flink.api.scala.table._

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tEnv = TableEnvironment.getTableEnvironment(env)

val inputStream = env.addSource(...)
val result = inputStream
                .toTable(tEnv, 'a, 'b, 'c)
                .filter('a === 3)
val resultStream = result.toDataStream[Row]

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Access a registered Table

A registered table can be accessed from a TableEnvironment as follows:

• tEnv.scan("tName") scans a Table that was registered as "tName" in a BatchTableEnvironment.
• tEnv.ingest("tName") ingests a Table that was registered as "tName" in a StreamTableEnvironment.

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Table API Operators

The Table API features a domain-specific language to execute language-integrated queries on structured data in Scala and Java. This section gives a brief overview of the available operators. You can find more details of operators in the Javadoc.

Table in = tableEnv.fromDataSet(ds, "a, b, c");
Table result = in.select("a, c as d");

Table in = tableEnv.fromDataSet(ds, "a, b, c");
Table result = in.as("d, e, f");

Table in = tableEnv.fromDataSet(ds, "a, b, c");
Table result = in.where("b = 'red'");

Table in = tableEnv.fromDataSet(ds, "a, b, c");
Table result = in.filter("a % 2 = 0");

Table in = tableEnv.fromDataSet(ds, "a, b, c");
Table result = in.groupBy("a").select("a, b.sum as d");

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "d, e, f");
Table result = left.join(right).where("a = d").select("a, b, e");

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "d, e, f");
Table result = left.leftOuterJoin(right, "a = d").select("a, b, e");

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "d, e, f");
Table result = left.rightOuterJoin(right, "a = d").select("a, b, e");

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "d, e, f");
Table result = left.fullOuterJoin(right, "a = d").select("a, b, e");

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "a, b, c");
Table result = left.union(right);

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "a, b, c");
Table result = left.unionAll(right);

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "d, e, f");
Table result = left.intersect(right);

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "d, e, f");
Table result = left.intersectAll(right);

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "a, b, c");
Table result = left.minus(right);

Table left = tableEnv.fromDataSet(ds1, "a, b, c");
Table right = tableEnv.fromDataSet(ds2, "a, b, c");
Table result = left.minusAll(right);

Table in = tableEnv.fromDataSet(ds, "a, b, c");
Table result = in.distinct();

Table in = tableEnv.fromDataSet(ds, "a, b, c");
Table result = in.orderBy("a.asc");

val in = ds.toTable(tableEnv, 'a, 'b, 'c);
val result = in.select('a, 'c as 'd);

val in = ds.toTable(tableEnv).as('a, 'b, 'c);

val in = ds.toTable(tableEnv, 'a, 'b, 'c);
val result = in.filter('a % 2 === 0)

val in = ds.toTable(tableEnv, 'a, 'b, 'c);
val result = in.where('b === "red");

val in = ds.toTable(tableEnv, 'a, 'b, 'c);
val result = in.groupBy('a).select('a, 'b.sum as 'd);

val left = ds1.toTable(tableEnv, 'a, 'b, 'c);
val right = ds2.toTable(tableEnv, 'd, 'e, 'f);
val result = left.join(right).where('a === 'd).select('a, 'b, 'e);

val left = tableEnv.fromDataSet(ds1, 'a, 'b, 'c)
val right = tableEnv.fromDataSet(ds2, 'd, 'e, 'f)
val result = left.leftOuterJoin(right, 'a === 'd).select('a, 'b, 'e)

val left = tableEnv.fromDataSet(ds1, 'a, 'b, 'c)
val right = tableEnv.fromDataSet(ds2, 'd, 'e, 'f)
val result = left.rightOuterJoin(right, 'a === 'd).select('a, 'b, 'e)

val left = tableEnv.fromDataSet(ds1, 'a, 'b, 'c)
val right = tableEnv.fromDataSet(ds2, 'd, 'e, 'f)
val result = left.fullOuterJoin(right, 'a === 'd).select('a, 'b, 'e)

val left = ds1.toTable(tableEnv, 'a, 'b, 'c);
val right = ds2.toTable(tableEnv, 'a, 'b, 'c);
val result = left.union(right);

val left = ds1.toTable(tableEnv, 'a, 'b, 'c);
val right = ds2.toTable(tableEnv, 'a, 'b, 'c);
val result = left.unionAll(right);

val left = ds1.toTable(tableEnv, 'a, 'b, 'c);
val right = ds2.toTable(tableEnv, 'e, 'f, 'g);
val result = left.intersect(right);

val left = ds1.toTable(tableEnv, 'a, 'b, 'c);
val right = ds2.toTable(tableEnv, 'e, 'f, 'g);
val result = left.intersectAll(right);

val left = ds1.toTable(tableEnv, 'a, 'b, 'c);
val right = ds2.toTable(tableEnv, 'a, 'b, 'c);
val result = left.minus(right);

val left = ds1.toTable(tableEnv, 'a, 'b, 'c);
val right = ds2.toTable(tableEnv, 'a, 'b, 'c);
val result = left.minusAll(right);

val in = ds.toTable(tableEnv, 'a, 'b, 'c);
val result = in.distinct();

val in = ds.toTable(tableEnv, 'a, 'b, 'c);
val result = in.orderBy('a.asc);

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Expression Syntax

Some of the operators in previous sections expect one or more expressions. Expressions can be specified using an embedded Scala DSL or as Strings. Please refer to the examples above to learn how expressions can be specified.

This is the EBNF grammar for expressions:

expressionList = expression , { "," , expression } ;

expression = alias ;

alias = logic | ( logic , "AS" , fieldReference ) ;

logic = comparison , [ ( "&&" | "||" ) , comparison ] ;

comparison = term , [ ( "=" | "===" | "!=" | "!==" | ">" | ">=" | "<" | "<=" ) , term ] ;

term = product , [ ( "+" | "-" ) , product ] ;

product = unary , [ ( "*" | "/" | "%") , unary ] ;

unary = [ "!" | "-" ] , composite ;

composite = suffixed | atom ;

suffixed = cast | as | aggregation | nullCheck | if | functionCall ;

cast = composite , ".cast(" , dataType , ")" ;

dataType = "BYTE" | "SHORT" | "INT" | "LONG" | "FLOAT" | "DOUBLE" | "BOOL" | "BOOLEAN" | "STRING" | "DECIMAL" | "DATE" | "TIME" | "TIMESTAMP";

as = composite , ".as(" , fieldReference , ")" ;

aggregation = composite , ( ".sum" | ".min" | ".max" | ".count" | ".avg" ) , [ "()" ] ;

nullCheck = composite , ( ".isNull" | ".isNotNull" ) , [ "()" ] ;

if = composite , ".?(" , expression , "," , expression , ")" ;

functionCall = composite , "." , functionIdentifier , "(" , [ expression , { "," , expression } ] , ")"

atom = ( "(" , expression , ")" ) | literal | nullLiteral | fieldReference ;

nullLiteral = "Null(" , dataType , ")" ;

Here, literal is a valid Java literal, fieldReference specifies a column in the data, and functionIdentifier specifies a supported scalar function. The column names and function names follow Java identifier syntax. Expressions specified as Strings can also use prefix notation instead of suffix notation to call operators and functions.

If working with exact numeric values or large decimals is required, the Table API also supports Java’s BigDecimal type. In the Scala Table API decimals can be defined by BigDecimal("123456") and in Java by appending a “p” for precise e.g. 123456p.

In order to work with temporal values the Table API supports Java SQL’s Date, Time, and Timestamp types. In the Scala Table API literals can be defined by using java.sql.Date.valueOf("2016-06-27"), java.sql.Time.valueOf("10:10:42"), or java.sql.Timestamp.valueOf("2016-06-27 10:10:42.123"). The Java and Scala Table API also support calling "2016-06-27".toDate(), "10:10:42".toTime(), and "2016-06-27 10:10:42.123".toTimestamp() for converting Strings into temporal types. Note: Since Java’s temporal SQL types are time zone dependent, please make sure that the Flink Client and all TaskManagers use the same time zone.

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SQL

SQL queries are specified using the sql() method of the TableEnvironment. The method returns the result of the SQL query as a Table which can be converted into a DataSet or DataStream, used in subsequent Table API queries, or written to a TableSink (see Writing Tables to External Sinks). SQL and Table API queries can seamlessly mixed and are holistically optimized and translated into a single DataStream or DataSet program.

A Table, DataSet, DataStream, or external TableSource must be registered in the TableEnvironment in order to be accessible by a SQL query (see Registering Tables).

Note: Flink’s SQL support is not feature complete, yet. Queries that include unsupported SQL features will cause a TableException. The limitations of SQL on batch and streaming tables are listed in the following sections.

SQL on Batch Tables

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);

// read a DataSet from an external source
DataSet<Tuple3<Long, String, Integer>> ds = env.readCsvFile(...);
// register the DataSet as table "Orders"
tableEnv.registerDataSet("Orders", ds, "user, product, amount");
// run a SQL query on the Table and retrieve the result as a new Table
Table result = tableEnv.sql(
  "SELECT SUM(amount) FROM Orders WHERE product LIKE '%Rubber%'");

val env = ExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)

// read a DataSet from an external source
val ds: DataSet[(Long, String, Integer)] = env.readCsvFile(...)
// register the DataSet under the name "Orders"
tableEnv.registerDataSet("Orders", ds, 'user, 'product, 'amount)
// run a SQL query on the Table and retrieve the result as a new Table
val result = tableEnv.sql(
  "SELECT SUM(amount) FROM Orders WHERE product LIKE '%Rubber%'")

Limitations

The current version supports selection (filter), projection, inner equi-joins, grouping, non-distinct aggregates, and sorting on batch tables.

Among others, the following SQL features are not supported, yet:

• Time interval data type (INTERVAL)
• Timestamps are limited to milliseconds precision
• Advanced types such as generic types, composite types (e.g. POJOs), and arrays within rows
• Distinct aggregates (e.g., COUNT(DISTINCT name))
• Non-equi joins and Cartesian products
• Result selection by order position (ORDER BY OFFSET FETCH)
• Grouping sets

Note: Tables are joined in the order in which they are specified in the FROM clause. In some cases the table order must be manually tweaked to resolve Cartesian products.

SQL on Streaming Tables

SQL queries can be executed on streaming Tables (Tables backed by DataStream or StreamTableSource) by using the SELECT STREAM keywords instead of SELECT. Please refer to the Apache Calcite’s Streaming SQL documentation for more information on the Streaming SQL syntax.

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);

// ingest a DataStream from an external source
DataStream<Tuple3<Long, String, Integer>> ds = env.addSource(...);
// register the DataStream as table "Orders"
tableEnv.registerDataStream("Orders", ds, "user, product, amount");
// run a SQL query on the Table and retrieve the result as a new Table
Table result = tableEnv.sql(
  "SELECT STREAM product, amount FROM Orders WHERE product LIKE '%Rubber%'");

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tEnv = TableEnvironment.getTableEnvironment(env)

// read a DataStream from an external source
val ds: DataStream[(Long, String, Integer)] = env.addSource(...)
// register the DataStream under the name "Orders"
tableEnv.registerDataStream("Orders", ds, 'user, 'product, 'amount)
// run a SQL query on the Table and retrieve the result as a new Table
val result = tableEnv.sql(
  "SELECT STREAM product, amount FROM Orders WHERE product LIKE '%Rubber%'")

Limitations

The current version of streaming SQL only supports SELECT, FROM, WHERE, and UNION clauses. Aggregations or joins are not supported yet.

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Writing Tables to External Sinks

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.

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.

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);

// compute the 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
env.execute();

val env = ExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)

// compute the result Table using Table API operators and/or SQL queries
val result: Table = ...

// create a TableSink
val sink: TableSink = new CsvTableSink("/path/to/file", fieldDelim = "|")
// write the result Table to the TableSink
result.writeToSink(sink)

// execute the program
env.execute()

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Runtime Configuration

The Table API provides a configuration (the so-called TableConfig) to modify runtime behavior. It can be accessed either through TableEnvironment or passed to the toDataSet/toDataStream method when using Scala implicit conversion.

Null Handling

By default, the Table API supports null values. Null handling can be disabled to improve preformance by setting the nullCheck property in the TableConfig to false.

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