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  1. Application Development
  2. Table API & SQL
  3. SQL

SQL

SQL queries are specified with the sql() method of the TableEnvironment. The method returns the result of the SQL 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 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, DataStream, or DataSet. Alternatively, users can also register external 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. Hence, Table objects can be directly inlined into SQL queries (by string concatenation) as shown in the examples below.

Note: Flink’s SQL support is not yet feature complete. 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 = TableEnvironment.getTableEnvironment(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.toTable(ds, "user, product, amount");
Table result = tableEnv.sql(
  "SELECT SUM(amount) FROM " + table + " WHERE product LIKE '%Rubber%'");

// SQL query with a registered table
// 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 result2 = tableEnv.sql(
  "SELECT product, amount FROM Orders WHERE product LIKE '%Rubber%'");
val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(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.sql(
  s"SELECT SUM(amount) FROM $table WHERE product LIKE '%Rubber%'")

// SQL query with a registered table
// 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 result2 = tableEnv.sql(
  "SELECT product, amount FROM Orders WHERE product LIKE '%Rubber%'")

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Supported Syntax

Flink parses SQL using Apache Calcite, which supports standard ANSI SQL. DML and DDL statements are not supported by Flink.

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 ]

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
  [ [ 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 ]* ')'

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").

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Operations

Scan, Projection, and Filter

Operation Description
Scan / Select / As
Batch Streaming 
SELECT * FROM Orders

SELECT a, c AS d FROM Orders
Where / Filter
Batch Streaming 
SELECT * FROM Orders WHERE b = 'red'

SELECT * FROM Orders WHERE a % 2 = 0
User-defined Scalar Functions (Scalar UDF)
Batch Streaming

UDFs must be registered in the TableEnvironment. See the UDF documentation for details on how to specify and register scalar UDFs.

SELECT PRETTY_PRINT(user) FROM Orders

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Aggregations

Operation Description
GroupBy Aggregation
Batch Streaming
Result Updating

Note: GroupBy on a streaming table produces an updating result. See the Streaming Concepts page for details. 

SELECT a, SUM(b) as d 
FROM Orders 
GROUP BY a
GroupBy Window Aggregation
Batch Streaming

Use a group window to compute a single result row per group. See Group Windows section for more details.

SELECT user, SUM(amount) 
FROM Orders 
GROUP BY TUMBLE(rowtime, INTERVAL '1' DAY), user
Over Window aggregation
Streaming

Note: All aggregates must be defined over the same window, i.e., same partitioning, sorting, and range. Currently, only windows with PRECEDING (UNBOUNDED and bounded) to CURRENT ROW range are supported. Ranges with FOLLOWING are not supported yet. ORDER BY must be specified on a single time attribute

SELECT COUNT(amount) OVER (
  PARTITION BY user 
  ORDER BY proctime 
  ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) 
FROM Orders
Distinct
Batch 
SELECT DISTINCT users FROM Orders
Grouping sets, Rollup, Cube
Batch 
SELECT SUM(amount) 
FROM Orders 
GROUP BY GROUPING SETS ((user), (product))
Having
Batch Streaming 
SELECT SUM(amount) 
FROM Orders 
GROUP BY users 
HAVING SUM(amount) > 50
User-defined Aggregate Functions (UDAGG)
Batch Streaming

UDAGGs must be registered in the TableEnvironment. See the UDF documentation for details on how to specify and register UDAGGs.

SELECT MyAggregate(amount) 
FROM Orders 
GROUP BY users

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Joins

Operation Description
Inner Equi-join / Outer Equi-join
Batch

Currently, only equi-joins are supported, i.e., joins that have at least one conjunctive condition with an equality predicate. Arbitrary cross or theta joins are not supported.

Note: The order of joins is not optimized. Tables are joined in the order in which they are specified in the FROM clause. Make sure to specify tables in an order that does not yield a cross join (Cartesian product) which are not supported and would cause a query to fail.

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

SELECT * 
FROM Orders LEFT JOIN Product ON Orders.productId = Product.id
Expanding arrays into a relation
Batch Streaming

Unnesting WITH ORDINALITY is not supported yet.

SELECT users, tag 
FROM Orders CROSS JOIN UNNEST(tags) AS t (tag)
Join with User Defined Table Functions (UDTF)
Batch Streaming

UDTFs must be registered in the TableEnvironment. See the UDF documentation for details on how to specify and register UDTFs.

Inner Join

SELECT users, tag
FROM Orders, LATERAL TABLE(unnest_udtf(tags)) t AS tag

Left Outer Join

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

Note: Currently, only literal TRUE is supported as predicate for a left outer join against a lateral table.

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Set Operations

Operation Description
Union
Batch 
SELECT * 
FROM (
    (SELECT user FROM Orders WHERE a % 2 = 0)
  UNION
    (SELECT user FROM Orders WHERE b = 0)
)
UnionAll
Batch Streaming 
SELECT * 
FROM (
    (SELECT user FROM Orders WHERE a % 2 = 0)
  UNION ALL
    (SELECT user FROM Orders WHERE b = 0)
)
Intersect / Except
Batch 
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)
)

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OrderBy & Limit

Operation Description
Order By
Batch 
SELECT * 
FROM Orders 
ORDER BY users
Limit
Batch 
SELECT * 
FROM Orders 
LIMIT 3

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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.

Group Window Function Description
TUMBLE(time_attr, interval) Defines a tumbling time window. A tumbling time window assigns rows to non-overlapping, continuous windows with a fixed duration (interval). For example, a tumbling window of 5 minutes groups rows in 5 minutes intervals. Tumbling windows can be defined on event-time (stream + batch) or processing-time (stream).
HOP(time_attr, interval, interval) Defines a hopping time window (called sliding window in the Table API). A hopping time window has a fixed duration (second interval parameter) and hops by a specified hop interval (first interval parameter). If the hop interval is smaller than the window size, hopping windows are overlapping. Thus, rows can be assigned to multiple windows. For example, a hopping window of 15 minutes size and 5 minute hop interval assigns each row to 3 different windows of 15 minute size, which are evaluated in an interval of 5 minutes. Hopping windows can be defined on event-time (stream + batch) or processing-time (stream).
SESSION(time_attr, interval) Defines a session time window. Session time windows do not have a fixed duration but their bounds are defined by a time interval of inactivity, i.e., a session window is closed if no event appears for a defined gap period. For example a session window with a 30 minute gap starts when a row is observed after 30 minutes inactivity (otherwise the row would be added to an existing window) and is closed if no row is added within 30 minutes. Session windows can work on event-time (stream + batch) or processing-time (stream).

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 can be selected with the following auxiliary functions:

Auxiliary Function Description
TUMBLE_START(time_attr, interval)
HOP_START(time_attr, interval, interval)
SESSION_START(time_attr, interval)
 Returns the start timestamp of the corresponding tumbling, hopping, and session window.
TUMBLE_END(time_attr, interval)
HOP_END(time_attr, interval, interval)
SESSION_END(time_attr, interval)
 Returns the end timestamp of the corresponding tumbling, hopping, and session window.

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 = 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, proctime.proctime, rowtime.rowtime");

// compute SUM(amount) per day (in event-time)
Table result1 = tableEnv.sql(
  "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.sql(
  "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.sql(
  "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.sql(
  "SELECT user, " +
  "  SESSION_START(rowtime, INTERVAL '12' HOUR) AS sStart, " +
  "  SESSION_END(rowtime, INTERVAL '12' HOUR) AS snd, " +
  "  SUM(amount) " +
  "FROM Orders " +
  "GROUP BY SESSION(rowtime, INTERVAL '12' HOUR), user");
val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = TableEnvironment.getTableEnvironment(env)

// read a DataStream from an external source
val ds: DataStream[(Long, String, Int)] = env.addSource(...)
// register the DataStream under the name "Orders"
tableEnv.registerDataStream("Orders", ds, 'user, 'product, 'amount, 'proctime.proctime, 'rowtime.rowtime)

// compute SUM(amount) per day (in event-time)
val result1 = tableEnv.sql(
    """
      |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.sql(
  "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.sql(
  "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.sql(
    """
      |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)

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Data Types

The SQL runtime is built on top of Flink’s DataSet and DataStream APIs. Internally, it also uses Flink’s TypeInformation to define data types. Fully supported types are listed in org.apache.flink.table.api.Types. The following table summarizes the relation between SQL Types, Table API types, and the resulting Java class.

Table API SQL Java type
Types.STRING VARCHAR java.lang.String
Types.BOOLEAN BOOLEAN java.lang.Boolean
Types.BYTE TINYINT java.lang.Byte
Types.SHORT SMALLINT java.lang.Short
Types.INT INTEGER, INT java.lang.Integer
Types.LONG BIGINT java.lang.Long
Types.FLOAT REAL, FLOAT java.lang.Float
Types.DOUBLE DOUBLE java.lang.Double
Types.DECIMAL DECIMAL java.math.BigDecimal
Types.DATE DATE java.sql.Date
Types.TIME TIME java.sql.Time
Types.TIMESTAMP TIMESTAMP(3) java.sql.Timestamp
Types.INTERVAL_MONTHS INTERVAL YEAR TO MONTH java.lang.Integer
Types.INTERVAL_MILLIS INTERVAL DAY TO SECOND(3) java.lang.Long
Types.PRIMITIVE_ARRAY ARRAY e.g. int[]
Types.OBJECT_ARRAY ARRAY e.g. java.lang.Byte[]
Types.MAP MAP java.util.HashMap

Generic types and composite types (e.g., POJOs or Tuples) can be fields of a row as well. Generic types are treated as a black box and can be passed on or processed by user-defined functions. Composite types can be accessed with built-in functions (see Value access functions section).

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Built-In Functions

Flink’s SQL support comes with a set of built-in functions for data transformations. This section gives a brief overview of the available functions.

The Flink SQL functions (including their syntax) are a subset of Apache Calcite’s built-in functions. Most of the documentation has been adopted from the Calcite SQL reference.

Comparison functions Description 
value1 = value2

Equals.

 
value1 <> value2

Not equal.

 
value1 > value2

Greater than.

 
value1 >= value2

Greater than or equal.

 
value1 < value2

Less than.

 
value1 <= value2

Less than or equal.

 
value IS NULL

Returns TRUE if value is null.

 
value IS NOT NULL

Returns TRUE if value is not null.

 
value1 IS DISTINCT FROM value2

Returns TRUE if two values are not equal, treating null values as the same.

 
value1 IS NOT DISTINCT FROM value2

Returns TRUE if two values are equal, treating null values as the same.

 
value1 BETWEEN [ASYMMETRIC | SYMMETRIC] value2 AND value3

Returns TRUE if value1 is greater than or equal to value2 and less than or equal to value3.

 
value1 NOT BETWEEN value2 AND value3

Returns TRUE if value1 is less than value2 or greater than value3.

 
string1 LIKE string2 [ ESCAPE string3 ]

Returns TRUE if string1 matches pattern string2. An escape character can be defined if necessary.

 
string1 NOT LIKE string2 [ ESCAPE string3 ]

Returns TRUE if string1 does not match pattern string2. An escape character can be defined if necessary.

 
string1 SIMILAR TO string2 [ ESCAPE string3 ]

Returns TRUE if string1 matches regular expression string2. An escape character can be defined if necessary.

 
string1 NOT SIMILAR TO string2 [ ESCAPE string3 ]

Returns TRUE if string1 does not match regular expression string2. An escape character can be defined if necessary.

 
value IN (value [, value]* )

Returns TRUE if value is equal to a value in a list.

 
value NOT IN (value [, value]* )

Returns TRUE if value is not equal to every value in a list.

 
EXISTS (sub-query)

Returns TRUE if sub-query returns at least one row. Only supported if the operation can be rewritten in a join and group operation.
Logical functions Description 
boolean1 OR boolean2

Returns TRUE if boolean1 is TRUE or boolean2 is TRUE. Supports three-valued logic.

 
boolean1 AND boolean2

Returns TRUE if boolean1 and boolean2 are both TRUE. Supports three-valued logic.

 
NOT boolean

Returns TRUE if boolean is not TRUE; returns UNKNOWN if boolean is UNKNOWN.

 
boolean IS FALSE

Returns TRUE if boolean is FALSE; returns FALSE if boolean is UNKNOWN.

 
boolean IS NOT FALSE

Returns TRUE if boolean is not FALSE; returns TRUE if boolean is UNKNOWN.

 
boolean IS TRUE

Returns TRUE if boolean is TRUE; returns FALSE if boolean is UNKNOWN.

 
boolean IS NOT TRUE

Returns TRUE if boolean is not TRUE; returns TRUE if boolean is UNKNOWN.

 
boolean IS UNKNOWN

Returns TRUE if boolean is UNKNOWN.

 
boolean IS NOT UNKNOWN

Returns TRUE if boolean is not UNKNOWN.
Arithmetic functions Description 
+ numeric

Returns numeric.

 
- numeric

Returns negative numeric.

 
numeric1 + numeric2

Returns numeric1 plus numeric2.

 
numeric1 - numeric2

Returns numeric1 minus numeric2.

 
numeric1 * numeric2

Returns numeric1 multiplied by numeric2.

 
numeric1 / numeric2

Returns numeric1 divided by numeric2.

 
POWER(numeric1, numeric2)

Returns numeric1 raised to the power of numeric2.

 
ABS(numeric)

Returns the absolute value of numeric.

 
MOD(numeric1, numeric2)

Returns the remainder (modulus) of numeric1 divided by numeric2. The result is negative only if numeric1 is negative.

 
SQRT(numeric)

Returns the square root of numeric.

 
LN(numeric)

Returns the natural logarithm (base e) of numeric.

 
LOG10(numeric)

Returns the base 10 logarithm of numeric.

 
EXP(numeric)

Returns e raised to the power of numeric.

 
CEIL(numeric)
CEILING(numeric)

Rounds numeric up, and returns the smallest number that is greater than or equal to numeric.

 
FLOOR(numeric)

Rounds numeric down, and returns the largest number that is less than or equal to numeric.

 
SIN(numeric)

Calculates the sine of a given number.

 
COS(numeric)

Calculates the cosine of a given number.

 
TAN(numeric)

Calculates the tangent of a given number.

 
COT(numeric)

Calculates the cotangent of a given number.

 
ASIN(numeric)

Calculates the arc sine of a given number.

 
ACOS(numeric)

Calculates the arc cosine of a given number.

 
ATAN(numeric)

Calculates the arc tangent of a given number.

 
DEGREES(numeric)

Converts numeric from radians to degrees.

 
RADIANS(numeric)

Converts numeric from degrees to radians.

 
SIGN(numeric)

Calculates the signum of a given number.

 
ROUND(numeric, int)

Rounds the given number to integer places right to the decimal point.

 
PI()

Returns a value that is closer than any other value to pi.
String functions Description 
string || string

Concatenates two character strings.

 
CHAR_LENGTH(string)

Returns the number of characters in a character string.

 
CHARACTER_LENGTH(string)

As CHAR_LENGTH(string).

 
UPPER(string)

Returns a character string converted to upper case.

 
LOWER(string)

Returns a character string converted to lower case.

 
POSITION(string1 IN string2)

Returns the position of the first occurrence of string1 in string2.

 
TRIM( { BOTH | LEADING | TRAILING } string1 FROM string2)

Removes leading and/or trailing characters from string2. By default, whitespaces at both sides are removed.

 
OVERLAY(string1 PLACING string2 FROM integer [ FOR integer2 ])

Replaces a substring of string1 with string2.

 
SUBSTRING(string FROM integer)

Returns a substring of a character string starting at a given point.

 
SUBSTRING(string FROM integer FOR integer)

Returns a substring of a character string starting at a given point with a given length.

 
INITCAP(string)

Returns string with the first letter of each word converter to upper case and the rest to lower case. Words are sequences of alphanumeric characters separated by non-alphanumeric characters.
Conditional functions Description 
CASE value
WHEN value1 [, value11 ]* THEN result1
[ WHEN valueN [, valueN1 ]* THEN resultN ]*
[ ELSE resultZ ]
END

Simple case.

 
CASE
WHEN condition1 THEN result1
[ WHEN conditionN THEN resultN ]*
[ ELSE resultZ ]
END

Searched case.

 
NULLIF(value, value)

Returns NULL if the values are the same. For example, NULLIF(5, 5) returns NULL; NULLIF(5, 0) returns 5.

 
COALESCE(value, value [, value ]* )

Provides a value if the first value is null. For example, COALESCE(NULL, 5) returns 5.
Type conversion functions Description 
CAST(value AS type)

Converts a value to a given type.
Temporal functions Description 
DATE string

Parses a date string in the form "yy-mm-dd" to a SQL date.

 
TIME string

Parses a time string in the form "hh:mm:ss" to a SQL time.

 
TIMESTAMP string

Parses a timestamp string in the form "yy-mm-dd hh:mm:ss.fff" to a SQL timestamp.

 
INTERVAL string range

Parses an interval string in the form "dd hh:mm:ss.fff" for SQL intervals of milliseconds or "yyyy-mm" for SQL intervals of months. An interval range might be e.g. DAY, MINUTE, DAY TO HOUR, or DAY TO SECOND for intervals of milliseconds; YEAR or YEAR TO MONTH for intervals of months. E.g. INTERVAL '10 00:00:00.004' DAY TO SECOND, INTERVAL '10' DAY, or INTERVAL '2-10' YEAR TO MONTH return intervals.

 
CURRENT_DATE

Returns the current SQL date in UTC time zone.

 
CURRENT_TIME

Returns the current SQL time in UTC time zone.

 
CURRENT_TIMESTAMP

Returns the current SQL timestamp in UTC time zone.

 
LOCALTIME

Returns the current SQL time in local time zone.

 
LOCALTIMESTAMP

Returns the current SQL timestamp in local time zone.

 
EXTRACT(timeintervalunit FROM temporal)

Extracts parts of a time point or time interval. Returns the part as a long value. E.g. EXTRACT(DAY FROM DATE '2006-06-05') leads to 5.

 
FLOOR(timepoint TO timeintervalunit)

Rounds a time point down to the given unit. E.g. FLOOR(TIME '12:44:31' TO MINUTE) leads to 12:44:00.

 
CEIL(timepoint TO timeintervalunit)

Rounds a time point up to the given unit. E.g. CEIL(TIME '12:44:31' TO MINUTE) leads to 12:45:00.

 
QUARTER(date)

Returns the quarter of a year from a SQL date. E.g. QUARTER(DATE '1994-09-27') leads to 3.

 
(timepoint, temporal) OVERLAPS (timepoint, temporal)

Determines whether two anchored time intervals overlap. Time point and temporal are transformed into a range defined by two time points (start, end). The function evaluates leftEnd >= rightStart && rightEnd >= leftStart. E.g. (TIME '2:55:00', INTERVAL '1' HOUR) OVERLAPS (TIME '3:30:00', INTERVAL '2' HOUR) leads to true; (TIME '9:00:00', TIME '10:00:00') OVERLAPS (TIME '10:15:00', INTERVAL '3' HOUR) leads to false.
Aggregate functions Description 
COUNT(value [, value]* )

Returns the number of input rows for which value is not null.

 
COUNT(*)

Returns the number of input rows.

 
AVG(numeric)

Returns the average (arithmetic mean) of numeric across all input values.

 
SUM(numeric)

Returns the sum of numeric across all input values.

 
MAX(value)

Returns the maximum value of value across all input values.

 
MIN(value)

Returns the minimum value of value across all input values.

 
STDDEV_POP(value)

Returns the population standard deviation of the numeric field across all input values.

 
STDDEV_SAMP(value)

Returns the sample standard deviation of the numeric field across all input values.

 
VAR_POP(value)

Returns the population variance (square of the population standard deviation) of the numeric field across all input values.

 
VAR_SAMP(value)

Returns the sample variance (square of the sample standard deviation) of the numeric field across all input values.
Grouping functions Description 
GROUP_ID()

Returns an integer that uniquely identifies the combination of grouping keys.

 
GROUPING(expression)

Returns 1 if expression is rolled up in the current row’s grouping set, 0 otherwise.

 
GROUPING_ID(expression [, expression]* )

Returns a bit vector of the given grouping expressions.
Value access functions Description 
tableName.compositeType.field

Accesses the field of a Flink composite type (such as Tuple, POJO, etc.) by name and returns it's value.

 
tableName.compositeType.*

Converts a Flink composite type (such as Tuple, POJO, etc.) and all of its direct subtypes into a flat representation where every subtype is a separate field.
Array functions Description 
ARRAY ‘[’ value [, value ]* ‘]’

Creates an array from a list of values.

 
CARDINALITY(ARRAY)

Returns the number of elements of an array.

 
array ‘[’ index ‘]’

Returns the element at a particular position in an array. The index starts at 1.

 
ELEMENT(ARRAY)

Returns the sole element of an array with a single element. Returns null if the array is empty. Throws an exception if the array has more than one element.

Unsupported Functions

The following functions are not supported yet:

  • Binary string operators and functions
  • System functions
  • Collection functions
  • Distinct aggregate functions like COUNT DISTINCT

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Reserved Keywords

Although not every SQL feature is implemented yet, some string combinations are already reserved as keywords for future use. If you want to use one of the following strings as a field name, make sure to surround them with backticks (e.g. `value`, `count`).

A, ABS, ABSOLUTE, ACTION, ADA, ADD, ADMIN, AFTER, ALL, ALLOCATE, ALLOW, ALTER, ALWAYS, AND, ANY, ARE, ARRAY, AS, ASC, ASENSITIVE, ASSERTION, ASSIGNMENT, ASYMMETRIC, AT, ATOMIC, ATTRIBUTE, ATTRIBUTES, AUTHORIZATION, AVG, BEFORE, BEGIN, BERNOULLI, BETWEEN, BIGINT, BINARY, BIT, BLOB, BOOLEAN, BOTH, BREADTH, BY, C, CALL, CALLED, CARDINALITY, CASCADE, CASCADED, CASE, CAST, CATALOG, CATALOG_NAME, CEIL, CEILING, CENTURY, CHAIN, CHAR, CHARACTER, CHARACTERISTICTS, CHARACTERS, CHARACTER_LENGTH, CHARACTER_SET_CATALOG, CHARACTER_SET_NAME, CHARACTER_SET_SCHEMA, CHAR_LENGTH, CHECK, CLASS_ORIGIN, CLOB, CLOSE, COALESCE, COBOL, COLLATE, COLLATION, COLLATION_CATALOG, COLLATION_NAME, COLLATION_SCHEMA, COLLECT, COLUMN, COLUMN_NAME, COMMAND_FUNCTION, COMMAND_FUNCTION_CODE, COMMIT, COMMITTED, CONDITION, CONDITION_NUMBER, CONNECT, CONNECTION, CONNECTION_NAME, CONSTRAINT, CONSTRAINTS, CONSTRAINT_CATALOG, CONSTRAINT_NAME, CONSTRAINT_SCHEMA, CONSTRUCTOR, CONTAINS, CONTINUE, CONVERT, CORR, CORRESPONDING, COUNT, COVAR_POP, COVAR_SAMP, CREATE, CROSS, CUBE, CUME_DIST, CURRENT, CURRENT_CATALOG, CURRENT_DATE, CURRENT_DEFAULT_TRANSFORM_GROUP, CURRENT_PATH, CURRENT_ROLE, CURRENT_SCHEMA, CURRENT_TIME, CURRENT_TIMESTAMP, CURRENT_TRANSFORM_GROUP_FOR_TYPE, CURRENT_USER, CURSOR, CURSOR_NAME, CYCLE, DATA, DATABASE, DATE, DATETIME_INTERVAL_CODE, DATETIME_INTERVAL_PRECISION, DAY, DEALLOCATE, DEC, DECADE, DECIMAL, DECLARE, DEFAULT, DEFAULTS, DEFERRABLE, DEFERRED, DEFINED, DEFINER, DEGREE, DELETE, DENSE_RANK, DEPTH, DEREF, DERIVED, DESC, DESCRIBE, DESCRIPTION, DESCRIPTOR, DETERMINISTIC, DIAGNOSTICS, DISALLOW, DISCONNECT, DISPATCH, DISTINCT, DOMAIN, DOUBLE, DOW, DOY, DROP, DYNAMIC, DYNAMIC_FUNCTION, DYNAMIC_FUNCTION_CODE, EACH, ELEMENT, ELSE, END, END-EXEC, EPOCH, EQUALS, ESCAPE, EVERY, EXCEPT, EXCEPTION, EXCLUDE, EXCLUDING, EXEC, EXECUTE, EXISTS, EXP, EXPLAIN, EXTEND, EXTERNAL, EXTRACT, FALSE, FETCH, FILTER, FINAL, FIRST, FIRST_VALUE, FLOAT, FLOOR, FOLLOWING, FOR, FOREIGN, FORTRAN, FOUND, FRAC_SECOND, FREE, FROM, FULL, FUNCTION, FUSION, G, GENERAL, GENERATED, GET, GLOBAL, GO, GOTO, GRANT, GRANTED, GROUP, GROUPING, HAVING, HIERARCHY, HOLD, HOUR, IDENTITY, IMMEDIATE, IMPLEMENTATION, IMPORT, IN, INCLUDING, INCREMENT, INDICATOR, INITIALLY, INNER, INOUT, INPUT, INSENSITIVE, INSERT, INSTANCE, INSTANTIABLE, INT, INTEGER, INTERSECT, INTERSECTION, INTERVAL, INTO, INVOKER, IS, ISOLATION, JAVA, JOIN, K, KEY, KEY_MEMBER, KEY_TYPE, LABEL, LANGUAGE, LARGE, LAST, LAST_VALUE, LATERAL, LEADING, LEFT, LENGTH, LEVEL, LIBRARY, LIKE, LIMIT, LN, LOCAL, LOCALTIME, LOCALTIMESTAMP, LOCATOR, LOWER, M, MAP, MATCH, MATCHED, MAX, MAXVALUE, MEMBER, MERGE, MESSAGE_LENGTH, MESSAGE_OCTET_LENGTH, MESSAGE_TEXT, METHOD, MICROSECOND, MILLENNIUM, MIN, MINUTE, MINVALUE, MOD, MODIFIES, MODULE, MONTH, MORE, MULTISET, MUMPS, NAME, NAMES, NATIONAL, NATURAL, NCHAR, NCLOB, NESTING, NEW, NEXT, NO, NONE, NORMALIZE, NORMALIZED, NOT, NULL, NULLABLE, NULLIF, NULLS, NUMBER, NUMERIC, OBJECT, OCTETS, OCTET_LENGTH, OF, OFFSET, OLD, ON, ONLY, OPEN, OPTION, OPTIONS, OR, ORDER, ORDERING, ORDINALITY, OTHERS, OUT, OUTER, OUTPUT, OVER, OVERLAPS, OVERLAY, OVERRIDING, PAD, PARAMETER, PARAMETER_MODE, PARAMETER_NAME, PARAMETER_ORDINAL_POSITION, PARAMETER_SPECIFIC_CATALOG, PARAMETER_SPECIFIC_NAME, PARAMETER_SPECIFIC_SCHEMA, PARTIAL, PARTITION, PASCAL, PASSTHROUGH, PATH, PERCENTILE_CONT, PERCENTILE_DISC, PERCENT_RANK, PLACING, PLAN, PLI, POSITION, POWER, PRECEDING, PRECISION, PREPARE, PRESERVE, PRIMARY, PRIOR, PRIVILEGES, PROCEDURE, PUBLIC, QUARTER, RANGE, RANK, READ, READS, REAL, RECURSIVE, REF, REFERENCES, REFERENCING, REGR_AVGX, REGR_AVGY, REGR_COUNT, REGR_INTERCEPT, REGR_R2, REGR_SLOPE, REGR_SXX, REGR_SXY, REGR_SYY, RELATIVE, RELEASE, REPEATABLE, RESET, RESTART, RESTRICT, RESULT, RETURN, RETURNED_CARDINALITY, RETURNED_LENGTH, RETURNED_OCTET_LENGTH, RETURNED_SQLSTATE, RETURNS, REVOKE, RIGHT, ROLE, ROLLBACK, ROLLUP, ROUTINE, ROUTINE_CATALOG, ROUTINE_NAME, ROUTINE_SCHEMA, ROW, ROWS, ROW_COUNT, ROW_NUMBER, SAVEPOINT, SCALE, SCHEMA, SCHEMA_NAME, SCOPE, SCOPE_CATALOGS, SCOPE_NAME, SCOPE_SCHEMA, SCROLL, SEARCH, SECOND, SECTION, SECURITY, SELECT, SELF, SENSITIVE, SEQUENCE, SERIALIZABLE, SERVER, SERVER_NAME, SESSION, SESSION_USER, SET, SETS, SIMILAR, SIMPLE, SIZE, SMALLINT, SOME, SOURCE, SPACE, SPECIFIC, SPECIFICTYPE, SPECIFIC_NAME, SQL, SQLEXCEPTION, SQLSTATE, SQLWARNING, SQL_TSI_DAY, SQL_TSI_FRAC_SECOND, SQL_TSI_HOUR, SQL_TSI_MICROSECOND, SQL_TSI_MINUTE, SQL_TSI_MONTH, SQL_TSI_QUARTER, SQL_TSI_SECOND, SQL_TSI_WEEK, SQL_TSI_YEAR, SQRT, START, STATE, STATEMENT, STATIC, STDDEV_POP, STDDEV_SAMP, STREAM, STRUCTURE, STYLE, SUBCLASS_ORIGIN, SUBMULTISET, SUBSTITUTE, SUBSTRING, SUM, SYMMETRIC, SYSTEM, SYSTEM_USER, TABLE, TABLESAMPLE, TABLE_NAME, TEMPORARY, THEN, TIES, TIME, TIMESTAMP, TIMESTAMPADD, TIMESTAMPDIFF, TIMEZONE_HOUR, TIMEZONE_MINUTE, TINYINT, TO, TOP_LEVEL_COUNT, TRAILING, TRANSACTION, TRANSACTIONS_ACTIVE, TRANSACTIONS_COMMITTED, TRANSACTIONS_ROLLED_BACK, TRANSFORM, TRANSFORMS, TRANSLATE, TRANSLATION, TREAT, TRIGGER, TRIGGER_CATALOG, TRIGGER_NAME, TRIGGER_SCHEMA, TRIM, TRUE, TYPE, UESCAPE, UNBOUNDED, UNCOMMITTED, UNDER, UNION, UNIQUE, UNKNOWN, UNNAMED, UNNEST, UPDATE, UPPER, UPSERT, USAGE, USER, USER_DEFINED_TYPE_CATALOG, USER_DEFINED_TYPE_CODE, USER_DEFINED_TYPE_NAME, USER_DEFINED_TYPE_SCHEMA, USING, VALUE, VALUES, VARBINARY, VARCHAR, VARYING, VAR_POP, VAR_SAMP, VERSION, VIEW, WEEK, WHEN, WHENEVER, WHERE, WIDTH_BUCKET, WINDOW, WITH, WITHIN, WITHOUT, WORK, WRAPPER, WRITE, XML, YEAR, ZONE

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