SELECT statements and VALUES statements are specified with the sqlQuery() method of the TableEnvironment. The method returns the result of the SELECT statement (or the VALUES statements) as a Table. A Table can be used in [subsequent SQL and Table API queries]({{ site.baseurl }}/dev/table/common.html#mixing-table-api-and-sql), be [converted into a DataSet or DataStream]({{ site.baseurl }}/dev/table/common.html#integration-with-datastream-and-dataset-api), or [written to a TableSink]({{ site.baseurl }}/dev/table/common.html#emit-a-table). 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]({{ site.baseurl }}/dev/table/common.html#register-tables-in-the-catalog). A table can be registered from a [TableSource]({{ site.baseurl }}/dev/table/common.html#register-a-tablesource), [Table]({{ site.baseurl }}/dev/table/common.html#register-a-table), CREATE TABLE statement, [DataStream, or DataSet]({{ site.baseurl }}/dev/table/common.html#register-a-datastream-or-dataset-as-table). Alternatively, users can also [register catalogs in a TableEnvironment]({{ site.baseurl }}/dev/table/catalogs.html) 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.
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.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%’”);
// 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 an INSERT SQL on the Table and emit the result to the TableSink tableEnv.executeSql( “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.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%’”)
// 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 an INSERT SQL on the Table and emit the result to the TableSink tableEnv.executeSql( “INSERT INTO RubberOrders SELECT product, amount FROM Orders WHERE product LIKE ‘%Rubber%’”) {% endhighlight %}
table = table_env.from_elements(..., [‘user’, ‘product’, ‘amount’]) result = table_env
.sql_query(“SELECT SUM(amount) FROM %s WHERE product LIKE ‘%%Rubber%%’” % table)
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”)
table_env
.execute_sql(“INSERT INTO RubberOrders SELECT product, amount FROM Orders WHERE product LIKE ‘%Rubber%’”) {% endhighlight %}
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A SELECT statement or a VALUES statement can be executed to collect the content to local through the TableEnvironment.executeSql() method. The method returns the result of the SELECT statement (or the VALUES statement) as a TableResult. Similar to a SELECT statement, a Table object can be executed using the Table.execute() method to collect the content of the query to the local client. TableResult.collect() method returns a closeable row iterator. The select job will not be finished unless all result data has been collected. We should actively close the job to avoid resource leak through the CloseableIterator#close() method. We can also print the select result to client console through the TableResult.print() method. The result data in TableResult can be accessed only once. Thus, collect() and print() must not be called after each other.
For streaming job, TableResult.collect() method or TableResult.print method guarantee end-to-end exactly-once record delivery. This requires the checkpointing mechanism to be enabled. By default, checkpointing is disabled. To enable checkpointing, we can set checkpointing properties (see the checkpointing config for details) through TableConfig. So a result record can be accessed by client only after its corresponding checkpoint completes.
Notes: For streaming mode, only append-only query is supported now.
tableEnv.executeSql(“CREATE TABLE Orders (user BIGINT, product STRING, amount INT) WITH (...)”);
// execute SELECT statement TableResult tableResult1 = tableEnv.executeSql(“SELECT * FROM Orders”); // use try-with-resources statement to make sure the iterator will be closed automatically try (CloseableIterator it = tableResult1.collect()) { while(it.hasNext()) { Row row = it.next(); // handle row } }
// execute Table TableResult tableResult2 = tableEnv.sqlQuery(“SELECT * FROM Orders”).execute(); tableResult2.print();
{% endhighlight %}
tableEnv.executeSql(“CREATE TABLE Orders (user BIGINT, product STRING, amount INT) WITH (...)”)
// execute SELECT statement val tableResult1 = tableEnv.executeSql(“SELECT * FROM Orders”) val it = tableResult1.collect() try while (it.hasNext) { val row = it.next // handle row } finally it.close() // close the iterator to avoid resource leak
// execute Table val tableResult2 = tableEnv.sqlQuery(“SELECT * FROM Orders”).execute() tableResult2.print()
{% endhighlight %}
table_env.execute_sql(“CREATE TABLE Orders (user BIGINT, product STRING, amount INT) WITH (...)”)
table_result1 = table_env.execute_sql(“SELECT * FROM Orders”) table_result1.print()
table_result2 = table_env.sql_query(“SELECT * FROM Orders”).execute() table_result2.print()
{% endhighlight %}
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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.
{% highlight sql %} 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 [ dynamicTableOptions ] | LATERAL TABLE ‘(’ functionName ‘(’ expression [, expression ]* ‘)’ ‘)’ | UNNEST ‘(’ expression ‘)’
dynamicTableOptions: /+ OPTIONS(key=val [, key=val]) */
key: stringLiteral
val: stringLiteral
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 ‘}’
{% endhighlight %}
Flink SQL uses a lexical policy for identifier (table, attribute, function names) similar to Java:
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:
\) as escaping character (default): SELECT U&'\263A'SELECT U&'#263A' UESCAPE '#'{% top %}
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 Query Configuration for details. Grouping sets, Rollup, Cube
Batch Streaming Result Updating {% highlight sql %} SELECT SUM(amount) FROM Orders GROUP BY GROUPING SETS ((user), (product)) {% endhighlight %} Note: Streaming mode Grouping sets, Rollup and Cube are only supported in Blink planner. 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 RIGHT JOIN Product ON Orders.productId = Product.id
SELECT * FROM Orders FULL OUTER JOIN Product ON Orders.productId = Product.id {% endhighlight %} Note: For streaming queries the required state to compute the query result might grow infinitely depending on the number of distinct input rows. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details. Interval Join
Batch Streaming Note: Interval joins are a subset of regular joins that can be processed in a streaming fashion.
<p>A interval 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="{{ site.baseurl }}/dev/table/streaming/time_attributes.html">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 interval 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>
{% 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 Table Function (UDTF)
Batch Streaming Joins a table with the results of a table function. Each row of the left (outer) table is joined with all rows produced by the corresponding call of the table function. User-defined table functions (UDTFs) must be registered before. See the UDF documentation for details on how to specify and register UDTFs.
<p><b>Inner Join</b></p>
<p>A row of the left (outer) table is dropped, if its table function call returns an empty result.</p>
{% highlight sql %} SELECT users, tag FROM Orders, LATERAL TABLE(unnest_udtf(tags)) t AS tag {% endhighlight %}
<p><b>Left Outer Join</b></p>
<p>If a table function call returns an empty result, the corresponding outer row is preserved and the result padded with null values.</p>
{% highlight sql %} SELECT users, tag FROM Orders LEFT JOIN LATERAL TABLE(unnest_udtf(tags)) t AS tag ON TRUE {% endhighlight %}
<p><b>Note:</b> Currently, only literal <code>TRUE</code> is supported as predicate for a left outer join against a lateral table.</p>
</td>
</tr>
<tr>
<td>
<strong>Join with Temporal Table Function</strong><br>
<span class="label label-primary">Streaming</span>
</td>
<td>
<p><a href="{{ site.baseurl }}/dev/table/streaming/temporal_tables.html">Temporal tables</a> are tables that track changes over time.</p>
<p>A <a href="{{ site.baseurl }}/dev/table/streaming/temporal_tables.html#temporal-table-functions">Temporal table function</a> provides access to the state of a temporal table at a specific point in time.
The syntax to join a table with a temporal table function is the same as in <i>Join with Table Function</i>.</p>
<p><b>Note:</b> Currently only inner joins with temporal tables are supported.</p>
<p>Assuming <i>Rates</i> is a <a href="{{ site.baseurl }}/dev/table/streaming/temporal_tables.html#temporal-table-functions">temporal table function</a>, the join can be expressed in SQL as follows:</p>
{% highlight sql %} SELECT o_amount, r_rate FROM Orders, LATERAL TABLE (Rates(o_proctime)) WHERE r_currency = o_currency {% endhighlight %} For more information please check the more detailed temporal tables concept description. Join with Temporal Table
Batch Streaming Temporal Tables are tables that track changes over time. A Temporal Table provides access to the versions of a temporal table at a specific point in time.
<p>Only inner and left joins with processing-time temporal tables are supported.</p>
<p>The following example assumes that <strong>LatestRates</strong> is a <a href="{{ site.baseurl }}/dev/table/streaming/temporal_tables.html#temporal-table">Temporal Table</a> which is materialized with the latest rate.</p>
{% highlight sql %} SELECT o.amout, o.currency, r.rate, o.amount * r.rate FROM Orders AS o JOIN LatestRates FOR SYSTEM_TIME AS OF o.proctime AS r ON r.currency = o.currency {% endhighlight %} For more information please check the more detailed Temporal Tables concept description. Only supported in Blink planner.
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<tr>
<td>
<strong>Intersect / Except</strong><br>
<span class="label label-primary">Batch</span>
</td>
<td>
{% 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 %}
<tr>
<td>
<strong>In</strong><br>
<span class="label label-primary">Batch</span> <span class="label label-primary">Streaming</span>
</td>
<td>
<p>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.</p>
{% highlight sql %} SELECT user, amount FROM Orders WHERE product IN ( SELECT product FROM NewProducts ) {% endhighlight %} Note: For streaming queries the operation is rewritten in a join and group operation. The required state to compute the query result might grow infinitely depending on the number of distinct input rows. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.
<tr>
<td>
<strong>Exists</strong><br>
<span class="label label-primary">Batch</span> <span class="label label-primary">Streaming</span>
</td>
<td>
<p>Returns true if the sub-query returns at least one row. Only supported if the operation can be rewritten in a join and group operation.</p>
{% highlight sql %} SELECT user, amount FROM Orders WHERE product EXISTS ( SELECT product FROM NewProducts ) {% endhighlight %} Note: For streaming queries the operation is rewritten in a join and group operation. The required state to compute the query result might grow infinitely depending on the number of distinct input rows. Please provide a query configuration with valid retention interval to prevent excessive state size. See Query Configuration for details.
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{% highlight sql %} SELECT * FROM Orders ORDER BY orderTime {% endhighlight %}
<tr>
<td><strong>Limit</strong><br>
<span class="label label-primary">Batch</span>
</td>
<td>
Note: The LIMIT clause requires an ORDER BY clause. {% highlight sql %} SELECT * FROM Orders ORDER BY orderTime LIMIT 3 {% endhighlight %}
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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:
{% highlight sql %} 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] {% endhighlight %}
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.
// 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”); {% endhighlight %}
// 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) {% endhighlight %}
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:
// 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”); {% endhighlight %}
// 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) {% endhighlight %}
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.
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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:
{% highlight sql %} 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 {% endhighlight %}
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]({{ site.baseurl }}/dev/table/streaming/time_attributes.html). Currently only support [proctime attribute]({{ site.baseurl }}/dev/table/streaming/time_attributes.html#processing-time). [Rowtime atttribute]({{ site.baseurl }}/dev/table/streaming/time_attributes.html#event-time) 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.
// 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”); {% endhighlight %}
// 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) {% 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]({{ site.baseurl }}/dev/table/streaming/time_attributes.html) 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.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”);
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// 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)
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{% highlight sql %} SELECT T.aid, T.bid, T.cid FROM MyTable MATCH_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 {% endhighlight %}
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