Queries

SELECT queries are specified with the sqlQuery() method of the TableEnvironment. The method returns the result of the SELECT query as a Table. A Table can be used in subsequent SQL and Table API queries, be converted into a DataSet or DataStream, or written to a TableSink. SQL and Table API queries can be seamlessly mixed and are holistically optimized and translated into a single program.

In order to access a table in a SQL query, it must be registered in the TableEnvironment. A table can be registered from a TableSource, Table, CREATE TABLE statement, DataStream, or DataSet. Alternatively, users can also register catalogs in a TableEnvironment to specify the location of the data sources.

For convenience, Table.toString() automatically registers the table under a unique name in its TableEnvironment and returns the name. So, Table objects can be directly inlined into SQL queries as shown in the examples below.

Note: Queries that include unsupported SQL features cause a TableException. The supported features of SQL on batch and streaming tables are listed in the following sections.

Specifying a Query

The following examples show how to specify a SQL queries on registered and inlined tables.

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
// ingest a DataStream from an external source
DataStream<Tuple3<Long, String, Integer>> ds = env.addSource(...);
// SQL query with an inlined (unregistered) table
Table table = tableEnv.fromDataStream(ds, "user, product, amount");
Table result = tableEnv.sqlQuery(
  "SELECT SUM(amount) FROM " + table + " WHERE product LIKE '%Rubber%'");
// SQL query with a registered table
// register the DataStream as view "Orders"
tableEnv.createTemporaryView("Orders", ds, "user, product, amount");
// run a SQL query on the Table and retrieve the result as a new Table
Table result2 = tableEnv.sqlQuery(
  "SELECT product, amount FROM Orders WHERE product LIKE '%Rubber%'");
// SQL update with a registered table
// create and register a TableSink
final Schema schema = new Schema()
    .field("product", DataTypes.STRING())
    .field("amount", DataTypes.INT());
tableEnv.connect(new FileSystem("/path/to/file"))
    .withFormat(...)
    .withSchema(schema)
    .createTemporaryTable("RubberOrders");
// run a SQL update query on the Table and emit the result to the TableSink
tableEnv.sqlUpdate(
  "INSERT INTO RubberOrders SELECT product, amount FROM Orders WHERE product LIKE '%Rubber%'");

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = StreamTableEnvironment.create(env)
// read a DataStream from an external source
val ds: DataStream[(Long, String, Integer)] = env.addSource(...)
// SQL query with an inlined (unregistered) table
val table = ds.toTable(tableEnv, 'user, 'product, 'amount)
val result = tableEnv.sqlQuery(
  s"SELECT SUM(amount) FROM $table WHERE product LIKE '%Rubber%'")
// SQL query with a registered table
// register the DataStream under the name "Orders"
tableEnv.createTemporaryView("Orders", ds, 'user, 'product, 'amount)
// run a SQL query on the Table and retrieve the result as a new Table
val result2 = tableEnv.sqlQuery(
  "SELECT product, amount FROM Orders WHERE product LIKE '%Rubber%'")
// SQL update with a registered table
// create and register a TableSink
val schema = new Schema()
    .field("product", DataTypes.STRING())
    .field("amount", DataTypes.INT())
tableEnv.connect(new FileSystem("/path/to/file"))
    .withFormat(...)
    .withSchema(schema)
    .createTemporaryTable("RubberOrders")
// run a SQL update query on the Table and emit the result to the TableSink
tableEnv.sqlUpdate(
  "INSERT INTO RubberOrders SELECT product, amount FROM Orders WHERE product LIKE '%Rubber%'")

env = StreamExecutionEnvironment.get_execution_environment()
table_env = StreamTableEnvironment.create(env)
# SQL query with an inlined (unregistered) table
# elements data type: BIGINT, STRING, BIGINT
table = table_env.from_elements(..., ['user', 'product', 'amount'])
result = table_env \
    .sql_query("SELECT SUM(amount) FROM %s WHERE product LIKE '%%Rubber%%'" % table)
# SQL update with a registered table
# create and register a TableSink
t_env.connect(FileSystem().path("/path/to/file")))
    .with_format(Csv()
                 .field_delimiter(',')
                 .deriveSchema())
    .with_schema(Schema()
                 .field("product", DataTypes.STRING())
                 .field("amount", DataTypes.BIGINT()))
    .create_temporary_table("RubberOrders")
# run a SQL update query on the Table and emit the result to the TableSink
table_env \
    .sql_update("INSERT INTO RubberOrders SELECT product, amount FROM Orders WHERE product LIKE '%Rubber%'")

Supported Syntax

Flink parses SQL using Apache Calcite, which supports standard ANSI SQL.

The following BNF-grammar describes the superset of supported SQL features in batch and streaming queries. The Operations section shows examples for the supported features and indicates which features are only supported for batch or streaming queries.

query:
  values
  | {
      select
      | selectWithoutFrom
      | query UNION [ ALL ] query
      | query EXCEPT query
      | query INTERSECT query
    }
    [ ORDER BY orderItem [, orderItem ]* ]
    [ LIMIT { count | ALL } ]
    [ OFFSET start { ROW | ROWS } ]
    [ FETCH { FIRST | NEXT } [ count ] { ROW | ROWS } ONLY]
orderItem:
  expression [ ASC | DESC ]
select:
  SELECT [ ALL | DISTINCT ]
  { * | projectItem [, projectItem ]* }
  FROM tableExpression
  [ WHERE booleanExpression ]
  [ GROUP BY { groupItem [, groupItem ]* } ]
  [ HAVING booleanExpression ]
  [ WINDOW windowName AS windowSpec [, windowName AS windowSpec ]* ]
selectWithoutFrom:
  SELECT [ ALL | DISTINCT ]
  { * | projectItem [, projectItem ]* }
projectItem:
  expression [ [ AS ] columnAlias ]
  | tableAlias . *
tableExpression:
  tableReference [, tableReference ]*
  | tableExpression [ NATURAL ] [ LEFT | RIGHT | FULL ] JOIN tableExpression [ joinCondition ]
joinCondition:
  ON booleanExpression
  | USING '(' column [, column ]* ')'
tableReference:
  tablePrimary
  [ matchRecognize ]
  [ [ AS ] alias [ '(' columnAlias [, columnAlias ]* ')' ] ]
tablePrimary:
  [ TABLE ] [ [ catalogName . ] schemaName . ] tableName
  | LATERAL TABLE '(' functionName '(' expression [, expression ]* ')' ')'
  | UNNEST '(' expression ')'
values:
  VALUES expression [, expression ]*
groupItem:
  expression
  | '(' ')'
  | '(' expression [, expression ]* ')'
  | CUBE '(' expression [, expression ]* ')'
  | ROLLUP '(' expression [, expression ]* ')'
  | GROUPING SETS '(' groupItem [, groupItem ]* ')'
windowRef:
    windowName
  | windowSpec
windowSpec:
    [ windowName ]
    '('
    [ ORDER BY orderItem [, orderItem ]* ]
    [ PARTITION BY expression [, expression ]* ]
    [
        RANGE numericOrIntervalExpression {PRECEDING}
      | ROWS numericExpression {PRECEDING}
    ]
    ')'
matchRecognize:
      MATCH_RECOGNIZE '('
      [ PARTITION BY expression [, expression ]* ]
      [ ORDER BY orderItem [, orderItem ]* ]
      [ MEASURES measureColumn [, measureColumn ]* ]
      [ ONE ROW PER MATCH ]
      [ AFTER MATCH
            ( SKIP TO NEXT ROW
            | SKIP PAST LAST ROW
            | SKIP TO FIRST variable
            | SKIP TO LAST variable
            | SKIP TO variable )
      ]
      PATTERN '(' pattern ')'
      [ WITHIN intervalLiteral ]
      DEFINE variable AS condition [, variable AS condition ]*
      ')'
measureColumn:
      expression AS alias
pattern:
      patternTerm [ '|' patternTerm ]*
patternTerm:
      patternFactor [ patternFactor ]*
patternFactor:
      variable [ patternQuantifier ]
patternQuantifier:
      '*'
  |   '*?'
  |   '+'
  |   '+?'
  |   '?'
  |   '??'
  |   '{' { [ minRepeat ], [ maxRepeat ] } '}' ['?']
  |   '{' repeat '}'

Flink SQL uses a lexical policy for identifier (table, attribute, function names) similar to Java:

• The case of identifiers is preserved whether or not they are quoted.
• After which, identifiers are matched case-sensitively.
• Unlike Java, back-ticks allow identifiers to contain non-alphanumeric characters (e.g. "SELECT a AS my field FROM t").

String literals must be enclosed in single quotes (e.g., SELECT 'Hello World'). Duplicate a single quote for escaping (e.g., SELECT 'It''s me.'). Unicode characters are supported in string literals. If explicit unicode code points are required, use the following syntax:

• Use the backslash (\) as escaping character (default): SELECT U&'\263A'
• Use a custom escaping character: SELECT U&'#263A' UESCAPE '#'

Operations

Show and Use

SHOW CATALOGS;

SHOW DATABASES;

SHOW TABLES;

USE CATALOG mycatalog;

USE mydatabase;

Scan, Projection, and Filter

SELECT  FROM OrdersSELECT a, c AS d FROM Orders

SELECT  FROM Orders WHERE b = 'red'SELECT  FROM Orders WHERE a % 2 = 0

SELECT PRETTY_PRINT(user) FROM Orders

Aggregations

SELECT a, SUM(b) as dFROM OrdersGROUP BY a

SELECT user, SUM(amount)FROM OrdersGROUP BY TUMBLE(rowtime, INTERVAL '1' DAY), user

SELECT COUNT(amount) OVER (  PARTITION BY user  ORDER BY proctime  ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)FROM OrdersSELECT COUNT(amount) OVER w, SUM(amount) OVER wFROM OrdersWINDOW w AS (  PARTITION BY user  ORDER BY proctime  ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)

SELECT DISTINCT users FROM Orders

SELECT SUM(amount)FROM OrdersGROUP BY GROUPING SETS ((user), (product))

SELECT SUM(amount)FROM OrdersGROUP BY usersHAVING SUM(amount) > 50

SELECT MyAggregate(amount)FROM OrdersGROUP BY users

Joins

SELECT FROM Orders INNER JOIN Product ON Orders.productId = Product.id

SELECT FROM Orders LEFT JOIN Product ON Orders.productId = Product.idSELECT FROM Orders RIGHT JOIN Product ON Orders.productId = Product.idSELECT FROM Orders FULL OUTER JOIN Product ON Orders.productId = Product.id

SELECT FROM Orders o, Shipments sWHERE o.id = s.orderId AND      o.ordertime BETWEEN s.shiptime - INTERVAL '4' HOUR AND s.shiptime

SELECT users, tagFROM Orders CROSS JOIN UNNEST(tags) AS t (tag)

SELECT users, tagFROM Orders, LATERAL TABLE(unnestudtf(tags)) t AS tag

SELECT users, tagFROM Orders LEFT JOIN LATERAL TABLE(unnest_udtf(tags)) t AS tag ON TRUE

SELECT  o_amount, r_rateFROM  Orders,  LATERAL TABLE (Rates(o_proctime))WHERE  r_currency = o_currency

SELECT  o.amout, o.currency, r.rate, o.amount  r.rateFROM  Orders AS o  JOIN LatestRates FOR SYSTEM_TIME AS OF o.proctime AS r  ON r.currency = o.currency

Set Operations

SELECT FROM (    (SELECT user FROM Orders WHERE a % 2 = 0)  UNION    (SELECT user FROM Orders WHERE b = 0))

SELECT FROM (    (SELECT user FROM Orders WHERE a % 2 = 0)  UNION ALL    (SELECT user FROM Orders WHERE b = 0))

SELECT FROM (    (SELECT user FROM Orders WHERE a % 2 = 0)  INTERSECT    (SELECT user FROM Orders WHERE b = 0))

SELECT FROM (    (SELECT user FROM Orders WHERE a % 2 = 0)  EXCEPT    (SELECT user FROM Orders WHERE b = 0))

SELECT user, amountFROM OrdersWHERE product IN (    SELECT product FROM NewProducts)

SELECT user, amountFROM OrdersWHERE product EXISTS (    SELECT product FROM NewProducts)

OrderBy & Limit

SELECT FROM OrdersORDER BY orderTime

SELECT FROM OrdersORDER BY orderTimeLIMIT 3

Top-N

Attention Top-N is only supported in Blink planner.

Top-N queries ask for the N smallest or largest values ordered by columns. Both smallest and largest values sets are considered Top-N queries. Top-N queries are useful in cases where the need is to display only the N bottom-most or the N top-most records from batch/streaming table on a condition. This result set can be used for further analysis.

Flink uses the combination of a OVER window clause and a filter condition to express a Top-N query. With the power of OVER window PARTITION BY clause, Flink also supports per group Top-N. For example, the top five products per category that have the maximum sales in realtime. Top-N queries are supported for SQL on batch and streaming tables.

The following shows the syntax of the TOP-N statement:

SELECT [column_list]
FROM (
   SELECT [column_list],
     ROW_NUMBER() OVER ([PARTITION BY col1[, col2...]]
       ORDER BY col1 [asc|desc][, col2 [asc|desc]...]) AS rownum
   FROM table_name)
WHERE rownum <= N [AND conditions]

Parameter Specification:

• ROW_NUMBER(): Assigns an unique, sequential number to each row, starting with one, according to the ordering of rows within the partition. Currently, we only support ROW_NUMBER as the over window function. In the future, we will support RANK() and DENSE_RANK().
• PARTITION BY col1[, col2…]: Specifies the partition columns. Each partition will have a Top-N result.
• ORDER BY col1 [asc|desc][, col2 [asc|desc]…]: Specifies the ordering columns. The ordering directions can be different on different columns.
• WHERE rownum <= N: The rownum <= N is required for Flink to recognize this query is a Top-N query. The N represents the N smallest or largest records will be retained.
• [AND conditions]: It is free to add other conditions in the where clause, but the other conditions can only be combined with rownum <= N using AND conjunction.

Attention in Streaming Mode The TopN query is Result Updating. Flink SQL will sort the input data stream according to the order key, so if the top N records have been changed, the changed ones will be sent as retraction/update records to downstream.It is recommended to use a storage which supports updating as the sink of Top-N query. In addition, if the top N records need to be stored in external storage, the result table should have the same unique key with the Top-N query.

The unique keys of Top-N query is the combination of partition columns and rownum column. Top-N query can also derive the unique key of upstream. Take following job as an example, say product_id is the unique key of the ShopSales, then the unique keys of the Top-N query are [category, rownum] and [product_id].

The following examples show how to specify SQL queries with Top-N on streaming tables. This is an example to get “the top five products per category that have the maximum sales in realtime” we mentioned above.

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
// ingest a DataStream from an external source
DataStream<Tuple3<String, String, String, Long>> ds = env.addSource(...);
// register the DataStream as table "ShopSales"
tableEnv.createTemporaryView("ShopSales", ds, "product_id, category, product_name, sales");
// select top-5 products per category which have the maximum sales.
Table result1 = tableEnv.sqlQuery(
  "SELECT * " +
  "FROM (" +
  "   SELECT *," +
  "       ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC) as row_num" +
  "   FROM ShopSales)" +
  "WHERE row_num <= 5");

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)
// read a DataStream from an external source
val ds: DataStream[(String, String, String, Long)] = env.addSource(...)
// register the DataStream under the name "ShopSales"
tableEnv.createTemporaryView("ShopSales", ds, 'product_id, 'category, 'product_name, 'sales)
// select top-5 products per category which have the maximum sales.
val result1 = tableEnv.sqlQuery(
    """
      |SELECT *
      |FROM (
      |   SELECT *,
      |       ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC) as row_num
      |   FROM ShopSales)
      |WHERE row_num <= 5
    """.stripMargin)

No Ranking Output Optimization

As described above, the rownum field will be written into the result table as one field of the unique key, which may lead to a lot of records being written to the result table. For example, when the record (say product-1001) of ranking 9 is updated and its rank is upgraded to 1, all the records from ranking 1 ~ 9 will be output to the result table as update messages. If the result table receives too many data, it will become the bottleneck of the SQL job.

The optimization way is omitting rownum field in the outer SELECT clause of the Top-N query. This is reasonable because the number of the top N records is usually not large, thus the consumers can sort the records themselves quickly. Without rownum field, in the example above, only the changed record (product-1001) needs to be sent to downstream, which can reduce much IO to the result table.

The following example shows how to optimize the above Top-N example in this way:

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
// ingest a DataStream from an external source
DataStream<Tuple3<String, String, String, Long>> ds = env.addSource(...);
// register the DataStream as table "ShopSales"
tableEnv.createTemporaryView("ShopSales", ds, "product_id, category, product_name, sales");
// select top-5 products per category which have the maximum sales.
Table result1 = tableEnv.sqlQuery(
  "SELECT product_id, category, product_name, sales " + // omit row_num field in the output
  "FROM (" +
  "   SELECT *," +
  "       ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC) as row_num" +
  "   FROM ShopSales)" +
  "WHERE row_num <= 5");

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)
// read a DataStream from an external source
val ds: DataStream[(String, String, String, Long)] = env.addSource(...)
// register the DataStream under the name "ShopSales"
tableEnv.createTemporaryView("ShopSales", ds, 'product_id, 'category, 'product_name, 'sales)
// select top-5 products per category which have the maximum sales.
val result1 = tableEnv.sqlQuery(
    """
      |SELECT product_id, category, product_name, sales  -- omit row_num field in the output
      |FROM (
      |   SELECT *,
      |       ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC) as row_num
      |   FROM ShopSales)
      |WHERE row_num <= 5
    """.stripMargin)

Attention in Streaming Mode In order to output the above query to an external storage and have a correct result, the external storage must have the same unique key with the Top-N query. In the above example query, if the product_id is the unique key of the query, then the external table should also has product_id as the unique key.

Deduplication

Attention Deduplication is only supported in Blink planner.

Deduplication is removing rows that duplicate over a set of columns, keeping only the first one or the last one. In some cases, the upstream ETL jobs are not end-to-end exactly-once, this may result in there are duplicate records in the sink in case of failover. However, the duplicate records will affect the correctness of downstream analytical jobs (e.g. SUM, COUNT). So a deduplication is needed before further analysis.

Flink uses ROW_NUMBER() to remove duplicates just like the way of Top-N query. In theory, deduplication is a special case of Top-N which the N is one and order by the processing time or event time.

The following shows the syntax of the Deduplication statement:

SELECT [column_list]
FROM (
   SELECT [column_list],
     ROW_NUMBER() OVER ([PARTITION BY col1[, col2...]]
       ORDER BY time_attr [asc|desc]) AS rownum
   FROM table_name)
WHERE rownum = 1

Parameter Specification:

• ROW_NUMBER(): Assigns an unique, sequential number to each row, starting with one.
• PARTITION BY col1[, col2…]: Specifies the partition columns, i.e. the deduplicate key.
• ORDER BY time_attr [asc|desc]: Specifies the ordering column, it must be a time attribute. Currently only support proctime attribute. Rowtime atttribute will be supported in the future. Ordering by ASC means keeping the first row, ordering by DESC means keeping the last row.
• WHERE rownum = 1: The rownum = 1 is required for Flink to recognize this query is deduplication.

The following examples show how to specify SQL queries with Deduplication on streaming tables.

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
// ingest a DataStream from an external source
DataStream<Tuple3<String, String, String, Integer>> ds = env.addSource(...);
// register the DataStream as table "Orders"
tableEnv.createTemporaryView("Orders", ds, "order_id, user, product, number, proctime.proctime");
// remove duplicate rows on order_id and keep the first occurrence row,
// because there shouldn't be two orders with the same order_id.
Table result1 = tableEnv.sqlQuery(
  "SELECT order_id, user, product, number " +
  "FROM (" +
  "   SELECT *," +
  "       ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY proctime ASC) as row_num" +
  "   FROM Orders)" +
  "WHERE row_num = 1");

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)
// read a DataStream from an external source
val ds: DataStream[(String, String, String, Int)] = env.addSource(...)
// register the DataStream under the name "Orders"
tableEnv.createTemporaryView("Orders", ds, 'order_id, 'user, 'product, 'number, 'proctime.proctime)
// remove duplicate rows on order_id and keep the first occurrence row,
// because there shouldn't be two orders with the same order_id.
val result1 = tableEnv.sqlQuery(
    """
      |SELECT order_id, user, product, number
      |FROM (
      |   SELECT *,
      |       ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY proctime DESC) as row_num
      |   FROM Orders)
      |WHERE row_num = 1
    """.stripMargin)

Group Windows

Group windows are defined in the GROUP BY clause of a SQL query. Just like queries with regular GROUP BY clauses, queries with a GROUP BY clause that includes a group window function compute a single result row per group. The following group windows functions are supported for SQL on batch and streaming tables.

Time Attributes

For SQL queries on streaming tables, the time_attr argument of the group window function must refer to a valid time attribute that specifies the processing time or event time of rows. See the documentation of time attributes to learn how to define time attributes.

For SQL on batch tables, the time_attr argument of the group window function must be an attribute of type TIMESTAMP.

Selecting Group Window Start and End Timestamps

The start and end timestamps of group windows as well as time attributes can be selected with the following auxiliary functions:

Note: Auxiliary functions must be called with exactly same arguments as the group window function in the GROUP BY clause.

The following examples show how to specify SQL queries with group windows on streaming tables.

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
// ingest a DataStream from an external source
DataStream<Tuple3<Long, String, Integer>> ds = env.addSource(...);
// register the DataStream as table "Orders"
tableEnv.createTemporaryView("Orders", ds, "user, product, amount, proctime.proctime, rowtime.rowtime");
// compute SUM(amount) per day (in event-time)
Table result1 = tableEnv.sqlQuery(
  "SELECT user, " +
  "  TUMBLE_START(rowtime, INTERVAL '1' DAY) as wStart,  " +
  "  SUM(amount) FROM Orders " +
  "GROUP BY TUMBLE(rowtime, INTERVAL '1' DAY), user");
// compute SUM(amount) per day (in processing-time)
Table result2 = tableEnv.sqlQuery(
  "SELECT user, SUM(amount) FROM Orders GROUP BY TUMBLE(proctime, INTERVAL '1' DAY), user");
// compute every hour the SUM(amount) of the last 24 hours in event-time
Table result3 = tableEnv.sqlQuery(
  "SELECT product, SUM(amount) FROM Orders GROUP BY HOP(rowtime, INTERVAL '1' HOUR, INTERVAL '1' DAY), product");
// compute SUM(amount) per session with 12 hour inactivity gap (in event-time)
Table result4 = tableEnv.sqlQuery(
  "SELECT user, " +
  "  SESSION_START(rowtime, INTERVAL '12' HOUR) AS sStart, " +
  "  SESSION_ROWTIME(rowtime, INTERVAL '12' HOUR) AS snd, " +
  "  SUM(amount) " +
  "FROM Orders " +
  "GROUP BY SESSION(rowtime, INTERVAL '12' HOUR), user");

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = StreamTableEnvironment.create(env)
// read a DataStream from an external source
val ds: DataStream[(Long, String, Int)] = env.addSource(...)
// register the DataStream under the name "Orders"
tableEnv.createTemporaryView("Orders", ds, 'user, 'product, 'amount, 'proctime.proctime, 'rowtime.rowtime)
// compute SUM(amount) per day (in event-time)
val result1 = tableEnv.sqlQuery(
    """
      |SELECT
      |  user,
      |  TUMBLE_START(rowtime, INTERVAL '1' DAY) as wStart,
      |  SUM(amount)
      | FROM Orders
      | GROUP BY TUMBLE(rowtime, INTERVAL '1' DAY), user
    """.stripMargin)
// compute SUM(amount) per day (in processing-time)
val result2 = tableEnv.sqlQuery(
  "SELECT user, SUM(amount) FROM Orders GROUP BY TUMBLE(proctime, INTERVAL '1' DAY), user")
// compute every hour the SUM(amount) of the last 24 hours in event-time
val result3 = tableEnv.sqlQuery(
  "SELECT product, SUM(amount) FROM Orders GROUP BY HOP(rowtime, INTERVAL '1' HOUR, INTERVAL '1' DAY), product")
// compute SUM(amount) per session with 12 hour inactivity gap (in event-time)
val result4 = tableEnv.sqlQuery(
    """
      |SELECT
      |  user,
      |  SESSION_START(rowtime, INTERVAL '12' HOUR) AS sStart,
      |  SESSION_END(rowtime, INTERVAL '12' HOUR) AS sEnd,
      |  SUM(amount)
      | FROM Orders
      | GROUP BY SESSION(rowtime(), INTERVAL '12' HOUR), user
    """.stripMargin)

Pattern Recognition

SELECT T.aid, T.bid, T.cidFROM MyTableMATCH_RECOGNIZE (  PARTITION BY userid  ORDER BY proctime  MEASURES    A.id AS aid,    B.id AS bid,    C.id AS cid  PATTERN (A B C)  DEFINE    A AS name = 'a',    B AS name = 'b',    C AS name = 'c') AS T