From commits-return-8900-archive-asf-public=cust-asf.ponee.io@kafka.apache.org Wed Feb 7 22:17:36 2018 Return-Path: X-Original-To: archive-asf-public@eu.ponee.io Delivered-To: archive-asf-public@eu.ponee.io Received: from cust-asf.ponee.io (cust-asf.ponee.io [163.172.22.183]) by mx-eu-01.ponee.io (Postfix) with ESMTP id 5EBA518065B for ; Wed, 7 Feb 2018 22:17:36 +0100 (CET) Received: by cust-asf.ponee.io (Postfix) id 4E8CE160C5B; Wed, 7 Feb 2018 21:17:36 +0000 (UTC) Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by cust-asf.ponee.io (Postfix) with SMTP id 52FAC160C3A for ; Wed, 7 Feb 2018 22:17:34 +0100 (CET) Received: (qmail 35416 invoked by uid 500); 7 Feb 2018 21:17:33 -0000 Mailing-List: contact commits-help@kafka.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@kafka.apache.org Delivered-To: mailing list commits@kafka.apache.org Received: (qmail 35407 invoked by uid 99); 7 Feb 2018 21:17:33 -0000 Received: from ec2-52-202-80-70.compute-1.amazonaws.com (HELO gitbox.apache.org) (52.202.80.70) by apache.org (qpsmtpd/0.29) with ESMTP; Wed, 07 Feb 2018 21:17:33 +0000 Received: by gitbox.apache.org (ASF Mail Server at gitbox.apache.org, from userid 33) id A520D82315; Wed, 7 Feb 2018 21:17:32 +0000 (UTC) Date: Wed, 07 Feb 2018 21:17:32 +0000 To: "commits@kafka.apache.org" Subject: [kafka-site] branch asf-site updated: MINOR: fix broken links of javadocs in Streams developer guide MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 8bit Message-ID: <151803825261.4232.2539534166597309310@gitbox.apache.org> From: guozhang@apache.org X-Git-Host: gitbox.apache.org X-Git-Repo: kafka-site X-Git-Refname: refs/heads/asf-site X-Git-Reftype: branch X-Git-Oldrev: 5cd44e460909c7b800ebe1e8f4a557065066810d X-Git-Newrev: a28d75ec02acf23cc62804001a4cc11ca80b5809 X-Git-Rev: a28d75ec02acf23cc62804001a4cc11ca80b5809 X-Git-NotificationType: ref_changed_plus_diff X-Git-Multimail-Version: 1.5.dev Auto-Submitted: auto-generated This is an automated email from the ASF dual-hosted git repository. guozhang pushed a commit to branch asf-site in repository https://gitbox.apache.org/repos/asf/kafka-site.git The following commit(s) were added to refs/heads/asf-site by this push: new a28d75e MINOR: fix broken links of javadocs in Streams developer guide a28d75e is described below commit a28d75ec02acf23cc62804001a4cc11ca80b5809 Author: slim AuthorDate: Thu Feb 8 00:17:30 2018 +0300 MINOR: fix broken links of javadocs in Streams developer guide --- 10/streams/developer-guide/dsl-api.html | 108 ++++++++++++++++---------------- 1 file changed, 54 insertions(+), 54 deletions(-) diff --git a/10/streams/developer-guide/dsl-api.html b/10/streams/developer-guide/dsl-api.html index b81da39..f7d8ccb 100644 --- a/10/streams/developer-guide/dsl-api.html +++ b/10/streams/developer-guide/dsl-api.html @@ -119,7 +119,7 @@

Creates a KStream from the specified Kafka input topics and interprets the data as a record stream. A KStream represents a partitioned record stream. - (details)

+ (details)

In the case of a KStream, the local KStream instance of every application instance will be populated with data from only a subset of the partitions of the input topic. Collectively, across all application instances, all input topic partitions are read and processed.

@@ -153,7 +153,7 @@

Reads the specified Kafka input topic into a KTable. The topic is interpreted as a changelog stream, where records with the same key are interpreted as UPSERT aka INSERT/UPDATE (when the record value is not null) or as DELETE (when the value is null) for that key. - (details)

+ (details)

In the case of a KStream, the local KStream instance of every application instance will be populated with data from only a subset of the partitions of the input topic. Collectively, across all application instances, all input topic partitions are read and processed.

@@ -178,7 +178,7 @@

Reads the specified Kafka input topic into a GlobalKTable. The topic is interpreted as a changelog stream, where records with the same key are interpreted as UPSERT aka INSERT/UPDATE (when the record value is not null) or as DELETE (when the value is null) for that key. - (details)

+ (details)

In the case of a GlobalKTable, the local GlobalKTable instance of every application instance will be populated with data from only a subset of the partitions of the input topic. Collectively, across all application instances, all input topic partitions are read and processed.

@@ -250,7 +250,7 @@

Branch (or split) a KStream based on the supplied predicates into one or more KStream instances. - (details)

+ (details)

Predicates are evaluated in order. A record is placed to one and only one output stream on the first match: if the n-th predicate evaluates to true, the record is placed to n-th stream. If no predicate matches, the the record is dropped.

@@ -278,8 +278,8 @@

Evaluates a boolean function for each element and retains those for which the function returns true. - (KStream details, - KTable details)

+ (KStream details, + KTable details)

KStream<String, Long> stream = ...;
 
 // A filter that selects (keeps) only positive numbers
@@ -305,8 +305,8 @@
                         
                     
                         
Evaluates a boolean function for each element and drops those for which the function returns true.
-                            (KStream details,
-                            KTable details)

+                            (KStream details,
+                            KTable details)

                             
KStream<String, Long> stream = ...;
 
 // An inverse filter that discards any negative numbers or zero
@@ -332,7 +332,7 @@
                     
                         
Takes one record and produces zero, one, or more records.  You can modify the record keys and values, including
                             their types.
-                            (details)

+                            (details)

                             
Marks the stream for data re-partitioning:
                                 Applying a grouping or a join after flatMap will result in re-partitioning of the records.
                                 If possible use flatMapValues instead, which will not cause data re-partitioning.

@@ -361,7 +361,7 @@
                     
                         
Takes one record and produces zero, one, or more records, while retaining the key of the original record.
                             You can modify the record values and the value type.
-                            (details)

+                            (details)

                             
flatMapValues is preferable to flatMap because it will not cause data re-partitioning.  However, you
                                 cannot modify the key or key type like flatMap does.

                             
// Split a sentence into words.
@@ -381,7 +381,7 @@
                         
                     
                         
Terminal operation.  Performs a stateless action on each record.
-                            (details)

+                            (details)

                             
You would use foreach to cause side effects based on the input data (similar to peek) and then stop
                                 further processing of the input data (unlike peek, which is not a terminal operation).

                             
Note on processing guarantees: Any side effects of an action (such as writing to external systems) are not
@@ -410,7 +410,7 @@
                         
                     
                         
Groups the records by the existing key.
-                            (details)

+                            (details)

                             
Grouping is a prerequisite for aggregating a stream or a table
                                 and ensures that data is properly partitioned (“keyed”) for subsequent operations.

                             
When to set explicit SerDes:
@@ -455,8 +455,8 @@
                         
Groups the records by a new key, which may be of a different key type.
                             When grouping a table, you may also specify a new value and value type.
                             groupBy is a shorthand for selectKey(...).groupByKey().
-                            (KStream details,
-                            KTable details)

+                            (KStream details,
+                            KTable details)

                             
Grouping is a prerequisite for aggregating a stream or a table
                                 and ensures that data is properly partitioned (“keyed”) for subsequent operations.

                             
When to set explicit SerDes:
@@ -532,7 +532,7 @@
                         
                     
                         
Takes one record and produces one record.  You can modify the record key and value, including their types.
-                            (details)

+                            (details)

                             
Marks the stream for data re-partitioning:
                                 Applying a grouping or a join after map will result in re-partitioning of the records.
                                 If possible use mapValues instead, which will not cause data re-partitioning.

@@ -564,8 +564,8 @@
                     
                         
Takes one record and produces one record, while retaining the key of the original record.
                             You can modify the record value and the value type.
-                            (KStream details,
-                            KTable details)

+                            (KStream details,
+                            KTable details)

                             
mapValues is preferable to map because it will not cause data re-partitioning.  However, it does not
                                 allow you to modify the key or key type like map does.

                             
KStream<byte[], String> stream = ...;
@@ -591,7 +591,7 @@
                         
                     
                         
Performs a stateless action on each record, and returns an unchanged stream.
-                            (details)

+                            (details)

                             
You would use peek to cause side effects based on the input data (similar to foreach) and continue
                                 processing the input data (unlike foreach, which is a terminal operation).  peek returns the input
                                 stream as-is;  if you need to modify the input stream, use map or mapValues instead.

@@ -623,7 +623,7 @@
                     
                         
Terminal operation.  Prints the records to System.out.  See Javadocs for serde and toString()
                             caveats.
-                            (details)

+                            (details)

                             
Calling print() is the same as calling foreach((key, value) -> System.out.println(key + ", " + value))

                             
KStream<byte[], String> stream = ...;
 // print to sysout
@@ -641,7 +641,7 @@
                         
                     
                         
Assigns a new key – possibly of a new key type – to each record.
-                            (details)

+                            (details)

                             
Calling selectKey(mapper) is the same as calling map((key, value) -> mapper(key, value), value).

                             
Marks the stream for data re-partitioning:
                                 Applying a grouping or a join after selectKey will result in re-partitioning of the records.

@@ -669,7 +669,7 @@
                         
                     
                         
Get the changelog stream of this table.
-                            (details)

+                            (details)

                             
KTable<byte[], String> table = ...;
 
 // Also, a variant of `toStream` exists that allows you
@@ -773,8 +773,8 @@
                             
Rolling aggregation. Aggregates the values of (non-windowed) records by the grouped key.
                                 Aggregating is a generalization of reduce and allows, for example, the aggregate value to have a different
                                 type than the input values.
-                                (KGroupedStream details,
-                                KGroupedTable details)

+                                (KGroupedStream details,
+                                KGroupedTable details)

                                 
When aggregating a grouped stream, you must provide an initializer (e.g., aggValue = 0) and an “adder”
                                     aggregator (e.g., aggValue + curValue).  When aggregating a grouped table, you must provide a
                                     “subtractor” aggregator (think: aggValue - oldValue).

@@ -876,8 +876,8 @@
                                 Aggregates the values of records, per window, by the grouped key.
                                 Aggregating is a generalization of reduce and allows, for example, the aggregate value to have a different
                                 type than the input values.
-                                (TimeWindowedKStream details,
-                                SessionWindowedKStream details)

+                                (TimeWindowedKStream details,
+                                SessionWindowedKStream details)

                                 
You must provide an initializer (e.g.,  aggValue = 0), “adder” aggregator (e.g.,  aggValue + curValue),
                                     and a window.  When windowing based on sessions, you must additionally provide a “session merger” aggregator
                                     (e.g.,  mergedAggValue = leftAggValue + rightAggValue).

@@ -971,8 +971,8 @@
                             
                         
                             
Rolling aggregation. Counts the number of records by the grouped key.
-                                (KGroupedStream details,
-                                KGroupedTable details)

+                                (KGroupedStream details,
+                                KGroupedTable details)

                                 
Several variants of count exist, see Javadocs for details.

                                 
KGroupedStream<String, Long> groupedStream = ...;
 KGroupedTable<String, Long> groupedTable = ...;
@@ -1002,8 +1002,8 @@
                         
                             
Windowed aggregation.
                                 Counts the number of records, per window, by the grouped key.
-                                (TimeWindowedKStream details,
-                                SessionWindowedKStream details)

+                                (TimeWindowedKStream details,
+                                SessionWindowedKStream details)

                                 
The windowed count turns a TimeWindowedKStream<K, V> or SessionWindowedKStream<K, V>
                                     into a windowed KTable<Windowed<K>, V>.

                                 
Several variants of count exist, see Javadocs for details.

@@ -1036,8 +1036,8 @@
                             
Rolling aggregation. Combines the values of (non-windowed) records by the grouped key.
                                 The current record value is combined with the last reduced value, and a new reduced value is returned.
                                 The result value type cannot be changed, unlike aggregate.
-                                (KGroupedStream details,
-                                KGroupedTable details)

+                                (KGroupedStream details,
+                                KGroupedTable details)

                                 
When reducing a grouped stream, you must provide an “adder” reducer (e.g.,  aggValue + curValue).
                                     When reducing a grouped table, you must additionally provide a “subtractor” reducer (e.g.,
                                     aggValue - oldValue).

@@ -1120,8 +1120,8 @@
                                 The current record value is combined with the last reduced value, and a new reduced value is returned.
                                 Records with null key or value are ignored.
                                 The result value type cannot be changed, unlike aggregate.
-                                (TimeWindowedKStream details,
-                                SessionWindowedKStream details)

+                                (TimeWindowedKStream details,
+                                SessionWindowedKStream details)

                                 
The windowed reduce turns a turns a TimeWindowedKStream<K, V> or a SessionWindowedKStream<K, V>
                                     into a windowed KTable<Windowed<K>, V>.

                                 
Several variants of reduce exist, see Javadocs for details.

@@ -1646,7 +1646,7 @@
                             
                                 
Performs an INNER JOIN of this stream with another stream.
                                     Even though this operation is windowed, the joined stream will be of type KStream<K, ...> rather than KStream<Windowed<K>, ...>.
-                                    (details)

+                                    (details)

                                     
Data must be co-partitioned: The input data for both sides must be co-partitioned.

                                     
Causes data re-partitioning of a stream if and only if the stream was marked for re-partitioning (if both are marked, both are re-partitioned).

                                     
Several variants of join exists, see the Javadocs for details.

@@ -1705,7 +1705,7 @@
                             
                                 
Performs a LEFT JOIN of this stream with another stream.
                                     Even though this operation is windowed, the joined stream will be of type KStream<K, ...> rather than KStream<Windowed<K>, ...>.
-                                    (details)

+                                    (details)

                                     
Data must be co-partitioned: The input data for both sides must be co-partitioned.

                                     
Causes data re-partitioning of a stream if and only if the stream was marked for re-partitioning (if both are marked, both are re-partitioned).

                                     
Several variants of leftJoin exists, see the Javadocs for details.

@@ -1767,7 +1767,7 @@
                             
                                 
Performs an OUTER JOIN of this stream with another stream.
                                     Even though this operation is windowed, the joined stream will be of type KStream<K, ...> rather than KStream<Windowed<K>, ...>.
-                                    (details)

+                                    (details)

                                     
Data must be co-partitioned: The input data for both sides must be co-partitioned.

                                     
Causes data re-partitioning of a stream if and only if the stream was marked for re-partitioning (if both are marked, both are re-partitioned).

                                     
Several variants of outerJoin exists, see the Javadocs for details.

@@ -1828,7 +1828,7 @@
                             The semantics of the various stream-stream join variants are explained below.
                             To improve the readability of the table, assume that (1) all records have the same key (and thus the key in the table is omitted), (2) all records belong to a single join window, and (3) all records are processed in timestamp order.
                             The columns INNER JOIN, LEFT JOIN, and OUTER JOIN denote what is passed as arguments to the user-supplied
-                            ValueJoiner for the join, leftJoin, and
+                            ValueJoiner for the join, leftJoin, and
                             outerJoin methods, respectively, whenever a new input record is received on either side of the join.  An empty table
                             cell denotes that the ValueJoiner is not called at all.

                         
@@ -1994,7 +1994,7 @@
                             

                                 
Performs an INNER JOIN of this table with another table.
                                     The result is an ever-updating KTable that represents the “current” result of the join.
-                                    (details)

+                                    (details)

                                     
Data must be co-partitioned: The input data for both sides must be co-partitioned.

                                     
KTable<String, Long> left = ...;
 KTable<String, Double> right = ...;
@@ -2040,7 +2040,7 @@
                                 
                             

                                 
Performs a LEFT JOIN of this table with another table.
-                                    (details)

+                                    (details)

                                     
Data must be co-partitioned: The input data for both sides must be co-partitioned.

                                     
KTable<String, Long> left = ...;
 KTable<String, Double> right = ...;
@@ -2089,7 +2089,7 @@
                                 
                             

                                 
Performs an OUTER JOIN of this table with another table.
-                                    (details)

+                                    (details)

                                     
Data must be co-partitioned: The input data for both sides must be co-partitioned.

                                     
KTable<String, Long> left = ...;
 KTable<String, Double> right = ...;
@@ -2138,7 +2138,7 @@
                             The semantics of the various table-table join variants are explained below.
                             To improve the readability of the table, you can assume that (1) all records have the same key (and thus the key in the table is omitted) and that (2) all records are processed in timestamp order.
                             The columns INNER JOIN, LEFT JOIN, and OUTER JOIN denote what is passed as arguments to the user-supplied
-                            ValueJoiner for the join, leftJoin, and
+                            ValueJoiner for the join, leftJoin, and
                             outerJoin methods, respectively, whenever a new input record is received on either side of the join.  An empty table
                             cell denotes that the ValueJoiner is not called at all.

                         
@@ -2302,7 +2302,7 @@
                                 

                             
                                 
Performs an INNER JOIN of this stream with the table, effectively doing a table lookup.
-                                    (details)

+                                    (details)

                                     
Data must be co-partitioned: The input data for both sides must be co-partitioned.

                                     
Causes data re-partitioning of the stream if and only if the stream was marked for re-partitioning.

                                     
Several variants of join exists, see the Javadocs for details.

@@ -2355,7 +2355,7 @@
                                 
                             		
                                 
Performs a LEFT JOIN of this stream with the table, effectively doing a table lookup.
-                                    (details)

+                                    (details)

                                     
Data must be co-partitioned: The input data for both sides must be co-partitioned.

                                     
Causes data re-partitioning of the stream if and only if the stream was marked for re-partitioning.

                                     
Several variants of leftJoin exists, see the Javadocs for details.

@@ -2411,7 +2411,7 @@
                             To improve the readability of the table we assume that (1) all records have the same key (and thus we omit the key in
                             the table) and that (2) all records are processed in timestamp order.
                             The columns INNER JOIN and LEFT JOIN denote what is passed as arguments to the user-supplied
-                            ValueJoiner for the join and leftJoin
+                            ValueJoiner for the join and leftJoin
                             methods, respectively, whenever a new input record is received on either side of the join.  An empty table
                             cell denotes that the ValueJoiner is not called at all.

                         
@@ -2573,7 +2573,7 @@
                                 
@@ -2938,14 +2938,14 @@ t=5 (blue), which lead to a merge of sessions and an extension of a session, res
                     
@@ -3051,7 +3051,7 @@ t=5 (blue), which lead to a merge of sessions and an extension of a session, res
                     

                             
                                 
Performs an INNER JOIN of this stream with the global table, effectively doing a table lookup.
-                                    (details)

+                                    (details)

                                     
The GlobalKTable is fully bootstrapped upon (re)start of a KafkaStreams instance, which means the table is fully populated with all the data in the underlying topic that is
                                         available at the time of the startup.  The actual data processing begins only once the bootstrapping has completed.

                                     
Causes data re-partitioning of the stream if and only if the stream was marked for re-partitioning.

@@ -2627,7 +2627,7 @@
                                 
                             		
                                 
Performs a LEFT JOIN of this stream with the global table, effectively doing a table lookup.
-                                    (details)

+                                    (details)

                                     
The GlobalKTable is fully bootstrapped upon (re)start of a KafkaStreams instance, which means the table is fully populated with all the data in the underlying topic that is
                                         available at the time of the startup.  The actual data processing begins only once the bootstrapping has completed.

                                     
Causes data re-partitioning of the stream if and only if the stream was marked for re-partitioning.

@@ -2903,11 +2903,11 @@ t=5 (blue), which lead to a merge of sessions and an extension of a session, res
                     		
                         
Terminal operation.  Applies a Processor to each record.
                             process() allows you to leverage the Processor API from the DSL.
-                            (details)

+                            (details)

                             
This is essentially equivalent to adding the Processor via Topology#addProcessor() to your
                                 processor topology.

                             
An example is available in the
-                                javadocs.

+                                javadocs.

                         		
                     

                     
Transform

@@ -2917,7 +2917,7 @@ t=5 (blue), which lead to a merge of sessions and an extension of a session, res
                     		
                         
Applies a Transformer to each record.
                             transform() allows you to leverage the Processor API from the DSL.
-                            (details)

+                            (details)

                             
Each input record is transformed into zero, one, or more output records (similar to the stateless flatMap).
                                 The Transformer must return null for zero output.
                                 You can modify the record’s key and value, including their types.

@@ -2927,7 +2927,7 @@ t=5 (blue), which lead to a merge of sessions and an extension of a session, res
                             
transform is essentially equivalent to adding the Transformer via Topology#addProcessor() to your
                                 processor topology.

                             
An example is available in the
-                                javadocs.
+                                javadocs.
                                

                         		
                     

                         
Applies a ValueTransformer to each record, while retaining the key of the original record.
                             transformValues() allows you to leverage the Processor API from the DSL.
-                            (details)

+                            (details)

                             
Each input record is transformed into exactly one output record (zero output records or multiple output records are not possible).
                                 The ValueTransformer may return null as the new value for a record.

                             
transformValues is preferable to transform because it will not cause data re-partitioning.

                             
transformValues is essentially equivalent to adding the ValueTransformer via Topology#addProcessor() to your
                                 processor topology.

                             
An example is available in the
-                                javadocs.

+                                javadocs.

                         		
                     

                     
                 
                     
Terminal operation.  Write the records to a Kafka topic.
-                        (KStream details)

+                        (KStream details)

                         
When to provide serdes explicitly:

                         
    
                             
  • If you do not specify SerDes explicitly, the default SerDes from the
@@ -3098,7 +3098,7 @@ t=5 (blue), which lead to a merge of sessions and an extension of a session, res
                 

                     
Write the records to a Kafka topic and create a new stream/table from that topic.
                         Essentially a shorthand for KStream#to() followed by StreamsBuilder#stream(), same for tables.
-                        (KStream details)

+                        (KStream details)

                         
When to provide SerDes explicitly:

                         
    
                             
  • If you do not specify SerDes explicitly, the default SerDes from the

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