SQL queries are specified with the sqlQuery() 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.
The following examples show how to specify a SQL queries on registered and inlined tables.
// 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.sqlQuery( “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.sqlQuery( “SELECT product, amount FROM Orders WHERE product LIKE ‘%Rubber%’”);
// SQL update with a registered table // create and register a TableSink TableSink csvSink = new CsvTableSink(“/path/to/file”, ...); String[] fieldNames = {“product”, “amount”}; TypeInformation[] fieldTypes = {Types.STRING, Types.INT}; tableEnv.registerTableSink(“RubberOrders”, fieldNames, fieldTypes, csvSink); // 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%’”); {% endhighlight %}
// 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.registerDataStream(“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 TableSink csvSink = new CsvTableSink(“/path/to/file”, ...) val fieldNames: Array[String] = Array(“product”, “amount”) val fieldTypes: Array[TypeInformation[_]] = Array(Types.STRING, Types.INT) tableEnv.registerTableSink(“RubberOrders”, fieldNames, fieldTypes, csvSink) // 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%’”) {% endhighlight %}
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Flink parses SQL using Apache Calcite, which supports standard ANSI SQL. 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.
insert: INSERT INTO tableReference query 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 [ [ 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} ] ')'
Flink SQL uses a lexical policy for identifier (table, attribute, function names) similar to Java:
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SELECT a, c AS d FROM Orders {% endhighlight %} Where / Filter
Batch Streaming {% highlight sql %} SELECT * FROM Orders WHERE b = ‘red’
SELECT * FROM Orders WHERE a % 2 = 0 {% endhighlight %} 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. {% highlight sql %} SELECT PRETTY_PRINT(user) FROM Orders {% endhighlight %}
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SELECT COUNT(amount) OVER w, SUM(amount) OVER w FROM Orders WINDOW w AS ( PARTITION BY user ORDER BY proctime ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)
{% endhighlight %}
Distinct
Batch Streaming
Result Updating
{% highlight sql %}
SELECT DISTINCT users FROM Orders {% endhighlight %} Note: For streaming queries the required state to compute the query result might grow infinitely depending on the number of distinct fields. Please provide a query configuration with valid retention interval to prevent excessive state size. See Streaming Concepts for details. Grouping sets, Rollup, Cube
Batch {% highlight sql %} SELECT SUM(amount) FROM Orders GROUP BY GROUPING SETS ((user), (product)) {% endhighlight %} Having
Batch Streaming {% highlight sql %} SELECT SUM(amount) FROM Orders GROUP BY users HAVING SUM(amount) > 50 {% endhighlight %} 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. {% highlight sql %} SELECT MyAggregate(amount) FROM Orders GROUP BY users {% endhighlight %}
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SELECT * FROM Orders LEFT JOIN Product ON Orders.productId = Product.id {% endhighlight %} Time-windowed Join
Batch Streaming Note: Time-windowed joins are a subset of regular joins that can be processed in a streaming fashion.
<p>A time-windowed join requires at least one equi-join predicate and a join condition that bounds the time on both sides. Such a condition can be defined by two appropriate range predicates (<code><, <=, >=, ></code>), a <code>BETWEEN</code> predicate, or a single equality predicate that compares <a href="streaming.html#time-attributes">time attributes</a> of the same type (i.e., processing time or event time) of both input tables.</p>
<p>For example, the following predicates are valid window join conditions:</p>
<ul>
<li><code>ltime = rtime</code></li>
<li><code>ltime >= rtime AND ltime < rtime + INTERVAL '10' MINUTE</code></li>
<li><code>ltime BETWEEN rtime - INTERVAL '10' SECOND AND rtime + INTERVAL '5' SECOND</code></li>
</ul>
<p><b>Note:</b> Currently, only <code>INNER</code> time-windowed joins are supported.</p>
{% highlight sql %} SELECT * FROM Orders o, Shipments s WHERE o.id = s.orderId AND o.ordertime BETWEEN s.shiptime - INTERVAL ‘4’ HOUR AND s.shiptime {% endhighlight %}
The example above will join all orders with their corresponding shipments if the order was shipped four hours after the order was received. Expanding arrays into a relation
Batch Streaming Unnesting WITH ORDINALITY is not supported yet. {% highlight sql %} SELECT users, tag FROM Orders CROSS JOIN UNNEST(tags) AS t (tag) {% endhighlight %} 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 {% highlight sql %} SELECT users, tag FROM Orders, LATERAL TABLE(unnest_udtf(tags)) t AS tag {% endhighlight %} Left Outer Join {% highlight sql %} SELECT users, tag FROM Orders LEFT JOIN LATERAL TABLE(unnest_udtf(tags)) t AS tag ON TRUE {% endhighlight %}
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<strong>Intersect / Except</strong><br>
<span class="label label-primary">Batch</span>
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{% highlight sql %} SELECT * FROM ( (SELECT user FROM Orders WHERE a % 2 = 0) INTERSECT (SELECT user FROM Orders WHERE b = 0) ) {% endhighlight %} {% highlight sql %} SELECT * FROM ( (SELECT user FROM Orders WHERE a % 2 = 0) EXCEPT (SELECT user FROM Orders WHERE b = 0) ) {% endhighlight %}
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<strong>In</strong><br>
<span class="label label-primary">Batch</span>
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Returns true if an expression exists in a given table sub-query. The sub-query table must consist of one column. This column must have the same data type as the expression.
{% highlight sql %} SELECT user, amount FROM Orders WHERE product IN ( SELECT product FROM NewProducts ) {% endhighlight %}
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{% highlight sql %} SELECT * FROM Orders ORDER BY orderTime {% endhighlight %}
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<td><strong>Limit</strong><br>
<span class="label label-primary">Batch</span>
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{% highlight sql %} SELECT * FROM Orders LIMIT 3 {% endhighlight %}
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{% highlight sql %} INSERT INTO OutputTable SELECT users, tag FROM Orders {% endhighlight %}
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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.
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.
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.
// 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.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”);
{% endhighlight %}
// 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.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)
{% endhighlight %}
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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 |
Types.MULTISET | MULTISET | e.g. java.util.HashMap<String, Integer> for a multiset of String |
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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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.
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value1 <> value2 {% endhighlight %} Not equal.
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value1 > value2 {% endhighlight %} Greater than.
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value1 >= value2 {% endhighlight %} Greater than or equal.
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value1 < value2 {% endhighlight %} Less than.
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value1 <= value2 {% endhighlight %} Less than or equal.
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value IS NULL {% endhighlight %} Returns TRUE if value is null.
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value IS NOT NULL {% endhighlight %} Returns TRUE if value is not null.
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value1 IS DISTINCT FROM value2 {% endhighlight %} Returns TRUE if two values are not equal, treating null values as the same.
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value1 IS NOT DISTINCT FROM value2 {% endhighlight %} Returns TRUE if two values are equal, treating null values as the same.
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value1 BETWEEN [ASYMMETRIC | SYMMETRIC] value2 AND value3 {% endhighlight %} Returns TRUE if value1 is greater than or equal to value2 and less than or equal to value3.
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value1 NOT BETWEEN value2 AND value3 {% endhighlight %} Returns TRUE if value1 is less than value2 or greater than value3.
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string1 LIKE string2 [ ESCAPE string3 ] {% endhighlight %} Returns TRUE if string1 matches pattern string2. An escape character can be defined if necessary.
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string1 NOT LIKE string2 [ ESCAPE string3 ] {% endhighlight %} Returns TRUE if string1 does not match pattern string2. An escape character can be defined if necessary.
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string1 SIMILAR TO string2 [ ESCAPE string3 ] {% endhighlight %} Returns TRUE if string1 matches regular expression string2. An escape character can be defined if necessary.
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string1 NOT SIMILAR TO string2 [ ESCAPE string3 ] {% endhighlight %} Returns TRUE if string1 does not match regular expression string2. An escape character can be defined if necessary.
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value IN (value [, value]* ) {% endhighlight %} Returns TRUE if an expression exists in a given list of expressions. This is a shorthand for multiple OR conditions. If the testing set contains NULL, the result will be NULL if the element can not be found and TRUE if it can be found. If the element is NULL, the result is always NULL. E.g. “42 IN (1, 2, 3)” leads to FALSE.
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value NOT IN (value [, value]* ) {% endhighlight %} Returns TRUE if value is not equal to every value in a list.
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EXISTS (sub-query) {% endhighlight %} Returns TRUE if sub-query returns at least one row. Only supported if the operation can be rewritten in a join and group operation.
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value IN (sub-query) {% endhighlight %} Returns TRUE if value is equal to a row returned by sub-query. This operation is not supported in a streaming environment yet.
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value NOT IN (sub-query) {% endhighlight %} Returns TRUE if value is not equal to every row returned by sub-query. This operation is not supported in a streaming environment yet.
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boolean1 AND boolean2 {% endhighlight %} Returns TRUE if boolean1 and boolean2 are both TRUE. Supports three-valued logic.
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NOT boolean {% endhighlight %} Returns TRUE if boolean is not TRUE; returns UNKNOWN if boolean is UNKNOWN.
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boolean IS FALSE {% endhighlight %} Returns TRUE if boolean is FALSE; returns FALSE if boolean is UNKNOWN.
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boolean IS NOT FALSE {% endhighlight %} Returns TRUE if boolean is not FALSE; returns TRUE if boolean is UNKNOWN.
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boolean IS TRUE {% endhighlight %} Returns TRUE if boolean is TRUE; returns FALSE if boolean is UNKNOWN.
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boolean IS NOT TRUE {% endhighlight %} Returns TRUE if boolean is not TRUE; returns TRUE if boolean is UNKNOWN.
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boolean IS UNKNOWN {% endhighlight %} Returns TRUE if boolean is UNKNOWN.
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boolean IS NOT UNKNOWN {% endhighlight %} Returns TRUE if boolean is not UNKNOWN.
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numeric1 + numeric2 {% endhighlight %} Returns numeric1 plus numeric2.
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numeric1 - numeric2 {% endhighlight %} Returns numeric1 minus numeric2.
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numeric1 * numeric2 {% endhighlight %} Returns numeric1 multiplied by numeric2.
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numeric1 / numeric2 {% endhighlight %} Returns numeric1 divided by numeric2.
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POWER(numeric1, numeric2) {% endhighlight %} Returns numeric1 raised to the power of numeric2.
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ABS(numeric) {% endhighlight %} Returns the absolute value of numeric.
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MOD(numeric1, numeric2) {% endhighlight %} Returns the remainder (modulus) of numeric1 divided by numeric2. The result is negative only if numeric1 is negative.
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SQRT(numeric) {% endhighlight %} Returns the square root of numeric.
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LN(numeric) {% endhighlight %} Returns the natural logarithm (base e) of numeric.
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LOG10(numeric) {% endhighlight %} Returns the base 10 logarithm of numeric.
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EXP(numeric) {% endhighlight %} Returns e raised to the power of numeric.
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CEIL(numeric) CEILING(numeric) {% endhighlight %} Rounds numeric up, and returns the smallest number that is greater than or equal to numeric.
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FLOOR(numeric) {% endhighlight %} Rounds numeric down, and returns the largest number that is less than or equal to numeric.
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SIN(numeric) {% endhighlight %} Calculates the sine of a given number.
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COS(numeric) {% endhighlight %} Calculates the cosine of a given number.
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TAN(numeric) {% endhighlight %} Calculates the tangent of a given number.
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COT(numeric) {% endhighlight %} Calculates the cotangent of a given number.
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ASIN(numeric) {% endhighlight %} Calculates the arc sine of a given number.
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ACOS(numeric) {% endhighlight %} Calculates the arc cosine of a given number.
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ATAN(numeric) {% endhighlight %} Calculates the arc tangent of a given number.
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DEGREES(numeric) {% endhighlight %} Converts numeric from radians to degrees.
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RADIANS(numeric) {% endhighlight %} Converts numeric from degrees to radians.
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SIGN(numeric) {% endhighlight %} Calculates the signum of a given number.
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ROUND(numeric, int) {% endhighlight %} Rounds the given number to integer places right to the decimal point.
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PI() {% endhighlight %} Returns a value that is closer than any other value to pi. {% highlight text %} E() {% endhighlight %} Returns a value that is closer than any other value to e.
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RAND() {% endhighlight %} Returns a pseudorandom double value between 0.0 (inclusive) and 1.0 (exclusive).
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RAND(seed integer) {% endhighlight %} Returns a pseudorandom double value between 0.0 (inclusive) and 1.0 (exclusive) with a initial seed. Two RAND functions will return identical sequences of numbers if they have same initial seed.
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RAND_INTEGER(bound integer) {% endhighlight %} Returns a pseudorandom integer value between 0.0 (inclusive) and the specified value (exclusive).
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RAND_INTEGER(seed integer, bound integer) {% endhighlight %} Returns a pseudorandom integer value between 0.0 (inclusive) and the specified value (exclusive) with a initial seed. Two RAND_INTEGER functions will return identical sequences of numbers if they have same initial seed and same bound.
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LOG(x numeric), LOG(base numeric, x numeric) {% endhighlight %} Returns the natural logarithm of a specified number of a specified base. If called with one parameter, this function returns the natural logarithm of x. If called with two parameters, this function returns the logarithm of x to the base b. x must be greater than 0. b must be greater than 1.
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CHAR_LENGTH(string) {% endhighlight %} Returns the number of characters in a character string.
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CHARACTER_LENGTH(string) {% endhighlight %} As CHAR_LENGTH(string).
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UPPER(string) {% endhighlight %} Returns a character string converted to upper case.
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LOWER(string) {% endhighlight %} Returns a character string converted to lower case.
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POSITION(string1 IN string2) {% endhighlight %} Returns the position of the first occurrence of string1 in string2.
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TRIM( { BOTH | LEADING | TRAILING } string1 FROM string2) {% endhighlight %} Removes leading and/or trailing characters from string2. By default, whitespaces at both sides are removed.
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OVERLAY(string1 PLACING string2 FROM integer [ FOR integer2 ]) {% endhighlight %} Replaces a substring of string1 with string2.
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SUBSTRING(string FROM integer) {% endhighlight %} Returns a substring of a character string starting at a given point.
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SUBSTRING(string FROM integer FOR integer) {% endhighlight %} Returns a substring of a character string starting at a given point with a given length.
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INITCAP(string) {% endhighlight %} 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.
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CONCAT(string1, string2,...) {% endhighlight %} Returns the string that results from concatenating the arguments. Returns NULL if any argument is NULL. E.g. CONCAT(“AA”, “BB”, “CC”) returns AABBCC.
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CONCAT_WS(separator, string1, string2,...) {% endhighlight %} Returns the string that results from concatenating the arguments using a separator. The separator is added between the strings to be concatenated. Returns NULL If the separator is NULL. CONCAT_WS() does not skip empty strings. However, it does skip any NULL argument. E.g. CONCAT_WS(“~”, “AA”, “BB”, "", “CC”) returns AA~BB~~CC
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CASE WHEN condition1 THEN result1 [ WHEN conditionN THEN resultN ]* [ ELSE resultZ ] END {% endhighlight %} Searched case.
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NULLIF(value, value) {% endhighlight %} Returns NULL if the values are the same. For example, NULLIF(5, 5) returns NULL; NULLIF(5, 0) returns 5.
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COALESCE(value, value [, value ]* ) {% endhighlight %} Provides a value if the first value is null. For example, COALESCE(NULL, 5) returns 5.
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TIME string {% endhighlight %} Parses a time string in the form “hh:mm:ss” to a SQL time.
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TIMESTAMP string {% endhighlight %} Parses a timestamp string in the form “yy-mm-dd hh:mm:ss.fff” to a SQL timestamp.
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INTERVAL string range {% endhighlight %} 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.
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CURRENT_DATE {% endhighlight %} Returns the current SQL date in UTC time zone.
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CURRENT_TIME {% endhighlight %} Returns the current SQL time in UTC time zone.
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CURRENT_TIMESTAMP {% endhighlight %} Returns the current SQL timestamp in UTC time zone.
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LOCALTIME {% endhighlight %} Returns the current SQL time in local time zone.
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LOCALTIMESTAMP {% endhighlight %} Returns the current SQL timestamp in local time zone.
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EXTRACT(timeintervalunit FROM temporal) {% endhighlight %} 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.
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FLOOR(timepoint TO timeintervalunit) {% endhighlight %} Rounds a time point down to the given unit. E.g. FLOOR(TIME ‘12:44:31’ TO MINUTE) leads to 12:44:00.
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CEIL(timepoint TO timeintervalunit) {% endhighlight %} Rounds a time point up to the given unit. E.g. CEIL(TIME ‘12:44:31’ TO MINUTE) leads to 12:45:00.
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QUARTER(date) {% endhighlight %} Returns the quarter of a year from a SQL date. E.g. QUARTER(DATE ‘1994-09-27’) leads to 3.
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(timepoint, temporal) OVERLAPS (timepoint, temporal) {% endhighlight %} 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.
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DATE_FORMAT(timestamp, format) {% endhighlight %} Formats timestamp as a string using a specified format string. The format must be compatible with MySQL's date formatting syntax as used by the date_parse function. The format specification is given in the Date Format Specifier table below. For example DATE_FORMAT(ts, ‘%Y, %d %M’) results in strings formatted as “2017, 05 May”.
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TIMESTAMPADD(unit, interval, timestamp) {% endhighlight %} Adds a (signed) integer interval to a timestamp. The unit for the interval is given by the unit argument, which should be one of the following values: SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, or YEAR. E.g. TIMESTAMPADD(WEEK, 1, ‘2003-01-02’) leads to 2003-01-09.
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COUNT(*) {% endhighlight %} Returns the number of input rows.
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AVG(numeric) {% endhighlight %} Returns the average (arithmetic mean) of numeric across all input values.
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SUM(numeric) {% endhighlight %} Returns the sum of numeric across all input values.
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MAX(value) {% endhighlight %} Returns the maximum value of value across all input values.
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MIN(value) {% endhighlight %} Returns the minimum value of value across all input values. {% highlight text %} STDDEV_POP(value) {% endhighlight %} Returns the population standard deviation of the numeric field across all input values.
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VAR_POP(value) {% endhighlight %} Returns the population variance (square of the population standard deviation) of the numeric field across all input values.
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VAR_SAMP(value) {% endhighlight %} Returns the sample variance (square of the sample standard deviation) of the numeric field across all input values.
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COLLECT(value) {% endhighlight %} Returns a multiset of the values. null input value will be ignored. Return an empty multiset if only null values are added.
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GROUPING(expression) {% endhighlight %} Returns 1 if expression is rolled up in the current row’s grouping set, 0 otherwise.
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GROUPING_ID(expression [, expression]* ) {% endhighlight %} Returns a bit vector of the given grouping expressions.
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tableName.compositeType.* {% endhighlight %} 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.
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ARRAY ‘[’ value [, value ]* ‘]’ {% endhighlight %} Creates an array from a list of values.
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CARDINALITY(ARRAY) {% endhighlight %} Returns the number of elements of an array.
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array ‘[’ index ‘]’ {% endhighlight %} Returns the element at a particular position in an array. The index starts at 1.
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ELEMENT(ARRAY) {% endhighlight %} 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.
The following functions are not supported yet:
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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`).
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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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