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https://issues.apache.org/jira/browse/FLINK-4469?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15448024#comment-15448024
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Jark Wu edited comment on FLINK-4469 at 8/30/16 4:51 AM:
---------------------------------------------------------
Agree with that. Using a collector will be more user-friendly and can optimize
further. Instead of defining a {{Collector}} as parameter, I prefer to define a
{{collect(T)}} protected method. So that user do not need to define the
`Collector` in {{eval}} method's parameter list, and the parameter list can
keep consistent with the calling in SQL.
Maybe the UDTF signature looks like this:
{code}
abstract class UDTF[T] {
var collector: Collector[T] = null
def setCollector(collector: Collector[T]): Unit = {
this.collector = collector
}
def collect(ele: T): Unit = {
collector.collect(ele)
}
}
{code}
And we can define specific UDTF like this:
{code}
public class SplitStringUDTF extends UDTF<Word> {
public void eval(String str) {
if (str != null) {
for (String s : str.split(",")) {
Word word = new Word(s, s.length());
collect(word);
}
}
}
}
// calling in SQL as usual
tableEnv.registerFunction("split", new SplitStringUDTF())
tableEnv.sql("SELECT a, b, t.* FROM MyTable CROSS APPLY split(c) AS t(w,l)")
{code}
was (Author: jark):
Agree with that. Using a collector will be more user-friendly and can optimize
further. Instead of defining a {Collector} as parameter, I prefer to define a
{collect(T)} protected method. So that user do not need to define the
`Collector` in {eval} method's parameter list, and the parameter list can keep
consistent with the calling in SQL.
Maybe the UDTF signature looks like this:
{code}
abstract class UDTF[T] {
var collector: Collector[T] = null
def setCollector(collector: Collector[T]): Unit = {
this.collector = collector
}
def collect(ele: T): Unit = {
collector.collect(ele)
}
}
{code}
And we can define specific UDTF like this:
{code}
public class SplitStringUDTF extends UDTF<Word> {
public void eval(String str) {
if (str != null) {
for (String s : str.split(",")) {
Word word = new Word(s, s.length());
collect(word);
}
}
}
}
// calling in SQL as usual
tableEnv.registerFunction("split", new SplitStringUDTF())
tableEnv.sql("SELECT a, b, t.* FROM MyTable CROSS APPLY split(c) AS t(w,l)")
{code}
> Add support for user defined table function in Table API & SQL
> --------------------------------------------------------------
>
> Key: FLINK-4469
> URL: https://issues.apache.org/jira/browse/FLINK-4469
> Project: Flink
> Issue Type: New Feature
> Components: Table API & SQL
> Reporter: Jark Wu
> Assignee: Jark Wu
>
> Normal user-defined functions, such as concat(), take in a single input row
> and output a single output row. In contrast, table-generating functions
> transform a single input row to multiple output rows. It is very useful in
> some cases, such as look up in HBase by rowkey and return one or more rows.
> Adding a user defined table function should:
> 1. inherit from UDTF class with specific generic type T
> 2. define one or more evel function.
> NOTE:
> 1. the eval method must be public and non-static.
> 2. eval should always return java.lang.Iterable or scala.collection.Iterable
> with the generic type T.
> 3. the generic type T is the row type returned by table function. Because of
> Java type erasure, we can’t extract T from the Iterable.
> 4. eval method can be overload. Blink will choose the best match eval method
> to call according to parameter types and number.
> {code}
> public class Word {
> public String word;
> public Integer length;
> }
> public class SplitStringUDTF extends UDTF<Word> {
> public Iterable<Word> eval(String str) {
> if (str == null) {
> return new ArrayList<>();
> } else {
> List<Word> list = new ArrayList<>();
> for (String s : str.split(",")) {
> Word word = new Word(s, s.length());
> list.add(word);
> }
> return list;
> }
> }
> }
> // in SQL
> tableEnv.registerFunction("split", new SplitStringUDTF())
> tableEnv.sql("SELECT a, b, t.* FROM MyTable CROSS APPLY split(c) AS t(w,l)")
> // in Java Table API
> tableEnv.registerFunction("split", new SplitStringUDTF())
> // rename split table columns to “w” and “l”
> table.crossApply("split(c)", "w, l")
> .select("a, b, w, l")
> // without renaming, we will use the origin field names in the POJO/case/...
> table.crossApply("split(c)")
> .select("a, b, word, length")
> // in Scala Table API
> val split = new SplitStringUDTF()
> table.crossApply(split('c), 'w, 'l)
> .select('a, 'b, 'w, 'l)
> // outerApply for outer join to a UDTF
> table.outerApply(split('c))
> .select('a, 'b, 'word, 'length)
> {code}
> Here we introduce CROSS/OUTER APPLY keywords to join table functions , which
> is used in SQL Server. We can discuss the API in the comment.
> Maybe the {{UDTF}} class should be replaced by {{TableFunction}} or something
> others, because we have introduced {{ScalarFunction}} for custom functions,
> we need to keep consistent. Although, I prefer {{UDTF}} rather than
> {{TableFunction}} as the former is more SQL-like and the latter maybe
> confused with DataStream functions.
> **This issue is blocked by CALCITE-1309, so we need to wait Calcite fix this
> and release.**
> See [1] for more information about UDTF design.
> [1]
> https://docs.google.com/document/d/15iVc1781dxYWm3loVQlESYvMAxEzbbuVFPZWBYuY1Ek/edit#
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