title: “Table API and SQL” is_beta: true
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.
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:
{% highlight xml %} org.apache.flink flink-table{{ site.scala_version_suffix }} {{site.version }} {% endhighlight %}
Note: The Table API is currently not part of the binary distribution. See linking with it for cluster execution [here]({{ site.baseurl }}/apis/cluster_execution.html#linking-with-modules-not-contained-in-the-binary-distribution).
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.
A DataSet is registered as a Table in a BatchTableEnvironment as follows:
// 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”); {% endhighlight %}
// 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) {% endhighlight %}
Note: The name of a DataSet Table must not match the ^_DataSetTable_[0-9]+ pattern which is reserved for internal use only.
A DataStream is registered as a Table in a StreamTableEnvironment as follows:
// 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”); {% endhighlight %}
// 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) {% endhighlight %}
Note: The name of a DataStream Table must not match the ^_DataStreamTable_[0-9]+ pattern which is reserved for internal use only.
A Table that originates from a Table API operation or a SQL query is registered in a TableEnvironment as follows:
// 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) {% endhighlight %}
// 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) {% endhighlight %}
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.
An external table is registered in a TableEnvironment using a TableSource as follows:
TableSource custTS = new CsvTableSource(“/path/to/file”, ...)
// register a TableSource as external table “Customers” tableEnv.registerTableSource(“Customers”, custTS) {% endhighlight %}
val custTS: TableSource = new CsvTableSource(“/path/to/file”, ...)
// register a TableSource as external table “Customers” tableEnv.registerTableSource(“Customers”, custTS)
{% endhighlight %}
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.
| Class name | Maven dependency | Batch? | Streaming? | Description | CsvTableSouce | flink-table | Y | Y | A simple source for CSV files with up to 25 fields. | Kafka08JsonTableSource | flink-connector-kafka-0.8 | N | Y | A Kafka 0.8 source for JSON data. | Kafka09JsonTableSource | flink-connector-kafka-0.9 | N | Y | A Kafka 0.9 source for JSON data.
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.
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, andflink-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");
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:
You can work with the Table as explained in the rest of the Table API guide in both stream and batch TableEnvironments:
Table streamTable = streamTableEnvironment.ingest(“mycsv”);
Table batchTable = batchTableEnvironment.scan(“mycsv”); {% endhighlight %}
val streamTable = streamTableEnvironment.ingest(“mycsv”)
val batchTable = batchTableEnvironment.scan(“mycsv”) {% endhighlight %}
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.
DataSet is converted to a Table,Table is converted back to a DataSet.{% highlight java %} 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 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 result = tableEnv.toDataSet(wordCounts, WC.class); {% endhighlight %}
With Java, expressions must be specified by Strings. The embedded expression DSL is not supported.
{% highlight java %} 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”); {% endhighlight %}
Please refer to the Javadoc for a full list of supported operations and a description of the expression syntax.
DataSet is converted to a Table,Table is converted back to a DataSet.{% highlight scala %} 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] {% endhighlight %}
The expression DSL uses Scala symbols to refer to field names and code generation to transform expressions to efficient runtime code. Please note that the conversion to and from Tables only works when using Scala case classes or Java POJOs. Please refer to the [Type Extraction and Serialization]({{ site.baseurl }}/internals/types_serialization.html) section to learn the characteristics of a valid POJO.
Another example shows how to join two Tables:
{% highlight scala %} 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] {% endhighlight %}
Notice, how the field names of a Table can be changed with as() or specified with toTable() when converting a DataSet to a Table. In addition, the example shows how to use Strings to specify relational expressions.
Creating a Table from a DataStream works in a similar way. The following example shows how to convert a DataStream to a Table and filter it with the Table API.
{% highlight scala %} 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] {% endhighlight %}
Please refer to the Scaladoc for a full list of supported operations and a description of the expression syntax.
{% top %}
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.{% top %}
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.
<tr>
<td><strong>As</strong></td>
<td>
<p>Renames fields.</p>
{% highlight java %} Table in = tableEnv.fromDataSet(ds, “a, b, c”); Table result = in.as(“d, e, f”); {% endhighlight %}
<tr>
<td><strong>Where / Filter</strong></td>
<td>
<p>Similar to a SQL WHERE clause. Filters out rows that do not pass the filter predicate.</p>
{% highlight java %} Table in = tableEnv.fromDataSet(ds, “a, b, c”); Table result = in.where(“b = ‘red’”); {% endhighlight %} or {% highlight java %} Table in = tableEnv.fromDataSet(ds, “a, b, c”); Table result = in.filter(“a % 2 = 0”); {% endhighlight %} GroupBy Similar to a SQL GROUPBY clause. Groups the rows on the grouping keys, with a following aggregation operator to aggregate rows group-wise. {% highlight java %} Table in = tableEnv.fromDataSet(ds, “a, b, c”); Table result = in.groupBy(“a”).select(“a, b.sum as d”); {% endhighlight %}
<tr>
<td><strong>Join</strong></td>
<td>
<p>Similar to a SQL JOIN clause. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined through join operator or using a where or filter operator.</p>
{% highlight java %} 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”); {% endhighlight %}
<tr>
<td><strong>LeftOuterJoin</strong></td>
<td>
<p>Similar to a SQL LEFT OUTER JOIN clause. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined.</p>
{% highlight java %} 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”); {% endhighlight %}
<tr>
<td><strong>RightOuterJoin</strong></td>
<td>
<p>Similar to a SQL RIGHT OUTER JOIN clause. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined.</p>
{% highlight java %} 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”); {% endhighlight %}
<tr>
<td><strong>FullOuterJoin</strong></td>
<td>
<p>Similar to a SQL FULL OUTER JOIN clause. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined.</p>
{% highlight java %} 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”); {% endhighlight %}
<tr>
<td><strong>Union</strong></td>
<td>
<p>Similar to a SQL UNION clause. Unions two tables with duplicate records removed. Both tables must have identical field types.</p>
{% highlight java %} Table left = tableEnv.fromDataSet(ds1, “a, b, c”); Table right = tableEnv.fromDataSet(ds2, “a, b, c”); Table result = left.union(right); {% endhighlight %}
<tr>
<td><strong>UnionAll</strong></td>
<td>
<p>Similar to a SQL UNION ALL clause. Unions two tables. Both tables must have identical field types.</p>
{% highlight java %} Table left = tableEnv.fromDataSet(ds1, “a, b, c”); Table right = tableEnv.fromDataSet(ds2, “a, b, c”); Table result = left.unionAll(right); {% endhighlight %}
<tr>
<td><strong>Intersect</strong></td>
<td>
<p>Similar to a SQL INTERSECT clause. Intersect returns records that exist in both tables. If a record is present one or both tables more than once, it is returned just once, i.e., the resulting table has no duplicate records. Both tables must have identical field types.</p>
{% highlight java %} Table left = tableEnv.fromDataSet(ds1, “a, b, c”); Table right = tableEnv.fromDataSet(ds2, “d, e, f”); Table result = left.intersect(right); {% endhighlight %}
<tr>
<td><strong>IntersectAll</strong></td>
<td>
<p>Similar to a SQL INTERSECT ALL clause. IntersectAll returns records that exist in both tables. If a record is present in both tables more than once, it is returned as many times as it is present in both tables, i.e., the resulting table might have duplicate records. Both tables must have identical field types.</p>
{% highlight java %} Table left = tableEnv.fromDataSet(ds1, “a, b, c”); Table right = tableEnv.fromDataSet(ds2, “d, e, f”); Table result = left.intersectAll(right); {% endhighlight %}
<tr>
<td><strong>Minus</strong></td>
<td>
<p>Similar to a SQL EXCEPT clause. Minus returns records from the left table that do not exist in the right table. Duplicate records in the left table are returned exactly once, i.e., duplicates are removed. Both tables must have identical field types.</p>
{% highlight java %} Table left = tableEnv.fromDataSet(ds1, “a, b, c”); Table right = tableEnv.fromDataSet(ds2, “a, b, c”); Table result = left.minus(right); {% endhighlight %}
<tr>
<td><strong>MinusAll</strong></td>
<td>
<p>Similar to a SQL EXCEPT ALL clause. MinusAll returns the records that do not exist in the right table. A record that is present n times in the left table and m times in the right table is returned (n - m) times, i.e., as many duplicates as are present in the right table are removed. Both tables must have identical field types.</p>
{% highlight java %} Table left = tableEnv.fromDataSet(ds1, “a, b, c”); Table right = tableEnv.fromDataSet(ds2, “a, b, c”); Table result = left.minusAll(right); {% endhighlight %}
<tr>
<td><strong>Distinct</strong></td>
<td>
<p>Similar to a SQL DISTINCT clause. Returns rows with distinct value combinations.</p>
{% highlight java %} Table in = tableEnv.fromDataSet(ds, “a, b, c”); Table result = in.distinct(); {% endhighlight %}
<tr>
<td><strong>Order By</strong></td>
<td>
<p>Similar to a SQL ORDER BY clause. Returns rows globally sorted across all parallel partitions.</p>
{% highlight java %} Table in = tableEnv.fromDataSet(ds, “a, b, c”); Table result = in.orderBy(“a.asc”); {% endhighlight %}
<tr>
<td><strong>As</strong></td>
<td>
<p>Renames fields.</p>
{% highlight scala %} val in = ds.toTable(tableEnv).as('a, 'b, 'c); {% endhighlight %}
<tr>
<td><strong>Where / Filter</strong></td>
<td>
<p>Similar to a SQL WHERE clause. Filters out rows that do not pass the filter predicate.</p>
{% highlight scala %} val in = ds.toTable(tableEnv, 'a, 'b, 'c); val result = in.filter('a % 2 === 0) {% endhighlight %} or {% highlight scala %} val in = ds.toTable(tableEnv, 'a, 'b, 'c); val result = in.where('b === “red”); {% endhighlight %}
<tr>
<td><strong>GroupBy</strong></td>
<td>
<p>Similar to a SQL GROUPBY clause. Groups rows on the grouping keys, with a following aggregation
operator to aggregate rows group-wise.</p>
{% highlight scala %} val in = ds.toTable(tableEnv, 'a, 'b, 'c); val result = in.groupBy('a).select('a, 'b.sum as 'd); {% endhighlight %}
<tr>
<td><strong>Join</strong></td>
<td>
<p>Similar to a SQL JOIN clause. Joins two tables. Both tables must have distinct field names and an equality join predicate must be defined using a where or filter operator.</p>
{% highlight scala %} 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); {% endhighlight %}
<tr>
<td><strong>LeftOuterJoin</strong></td>
<td>
<p>Similar to a SQL LEFT OUTER JOIN clause. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined.</p>
{% highlight scala %} 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) {% endhighlight %}
<tr>
<td><strong>RightOuterJoin</strong></td>
<td>
<p>Similar to a SQL RIGHT OUTER JOIN clause. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined.</p>
{% highlight scala %} 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) {% endhighlight %}
<tr>
<td><strong>FullOuterJoin</strong></td>
<td>
<p>Similar to a SQL FULL OUTER JOIN clause. Joins two tables. Both tables must have distinct field names and at least one equality join predicate must be defined.</p>
{% highlight scala %} 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) {% endhighlight %}
<tr>
<td><strong>Union</strong></td>
<td>
<p>Similar to a SQL UNION clause. Unions two tables with duplicate records removed, both tables must have identical field types.</p>
{% highlight scala %} val left = ds1.toTable(tableEnv, 'a, 'b, 'c); val right = ds2.toTable(tableEnv, 'a, 'b, 'c); val result = left.union(right); {% endhighlight %}
<tr>
<td><strong>UnionAll</strong></td>
<td>
<p>Similar to a SQL UNION ALL clause. Unions two tables, both tables must have identical field types.</p>
{% highlight scala %} val left = ds1.toTable(tableEnv, 'a, 'b, 'c); val right = ds2.toTable(tableEnv, 'a, 'b, 'c); val result = left.unionAll(right); {% endhighlight %}
<tr>
<td><strong>Intersect</strong></td>
<td>
<p>Similar to a SQL INTERSECT clause. Intersect returns records that exist in both tables. If a record is present in one or both tables more than once, it is returned just once, i.e., the resulting table has no duplicate records. Both tables must have identical field types.</p>
{% highlight scala %} val left = ds1.toTable(tableEnv, 'a, 'b, 'c); val right = ds2.toTable(tableEnv, 'e, 'f, 'g); val result = left.intersect(right); {% endhighlight %}
<tr>
<td><strong>IntersectAll</strong></td>
<td>
<p>Similar to a SQL INTERSECT ALL clause. IntersectAll returns records that exist in both tables. If a record is present in both tables more than once, it is returned as many times as it is present in both tables, i.e., the resulting table might have duplicate records. Both tables must have identical field types.</p>
{% highlight scala %} val left = ds1.toTable(tableEnv, 'a, 'b, 'c); val right = ds2.toTable(tableEnv, 'e, 'f, 'g); val result = left.intersectAll(right); {% endhighlight %}
<tr>
<td><strong>Minus</strong></td>
<td>
<p>Similar to a SQL EXCEPT clause. Minus returns records from the left table that do not exist in the right table. Duplicate records in the left table are returned exactly once, i.e., duplicates are removed. Both tables must have identical field types.</p>
{% highlight scala %} val left = ds1.toTable(tableEnv, 'a, 'b, 'c); val right = ds2.toTable(tableEnv, 'a, 'b, 'c); val result = left.minus(right); {% endhighlight %}
<tr>
<td><strong>MinusAll</strong></td>
<td>
<p>Similar to a SQL EXCEPT ALL clause. MinusAll returns the records that do not exist in the right table. A record that is present n times in the left table and m times in the right table is returned (n - m) times, i.e., as many duplicates as are present in the right table are removed. Both tables must have identical field types.</p>
{% highlight scala %} val left = ds1.toTable(tableEnv, 'a, 'b, 'c); val right = ds2.toTable(tableEnv, 'a, 'b, 'c); val result = left.minusAll(right); {% endhighlight %}
<tr>
<td><strong>Distinct</strong></td>
<td>
<p>Similar to a SQL DISTINCT clause. Returns rows with distinct value combinations.</p>
{% highlight scala %} val in = ds.toTable(tableEnv, 'a, 'b, 'c); val result = in.distinct(); {% endhighlight %}
<tr>
<td><strong>Order By</strong></td>
<td>
<p>Similar to a SQL ORDER BY clause. Returns rows globally sorted across all parallel partitions.</p>
{% highlight scala %} val in = ds.toTable(tableEnv, 'a, 'b, 'c); val result = in.orderBy('a.asc); {% endhighlight %}
{% top %}
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:
{% highlight ebnf %}
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 , “)” ;
{% endhighlight %}
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.
{% top %}
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.
// 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%’”); {% endhighlight %}
// 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%’”) {% endhighlight %}
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:
INTERVAL)COUNT(DISTINCT name))ORDER BY OFFSET FETCH)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 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.
// 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%’”); {% endhighlight %}
// 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%’”) {% endhighlight %}
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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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.
// 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(); {% endhighlight %}
// 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() {% endhighlight %}
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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.
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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