From 6ea53d22e25e948b26c8f2c436f242edb0748df2 Mon Sep 17 00:00:00 2001 From: Joel Hamill <11722533+joel-hamill@users.noreply.github.com> Date: Mon, 29 Jan 2018 10:08:48 -0800 Subject: [PATCH] MINOR: Fix some streams web doc nits (#4411) Reviewers: Derrick Or , Guozhang Wang --- docs/streams/developer-guide/dsl-api.html | 17 ++++++++--------- .../developer-guide/interactive-queries.html | 4 ++-- docs/streams/developer-guide/processor-api.html | 2 +- 3 files changed, 11 insertions(+), 12 deletions(-) diff --git a/docs/streams/developer-guide/dsl-api.html b/docs/streams/developer-guide/dsl-api.html index 399d3fc9477..ba03e2e6f1b 100644 --- a/docs/streams/developer-guide/dsl-api.html +++ b/docs/streams/developer-guide/dsl-api.html @@ -418,7 +418,7 @@ must do if the key and/or value types of the resulting KGroupedStream do not match the configured default SerDes.

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Note

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Grouping vs. Windowing: A related operation is windowing, which lets you control how to “sub-group” the grouped records of the same key into so-called windows for stateful operations such as @@ -464,7 +464,7 @@ do if the key and/or value types of the resulting KGroupedStream or KGroupedTable do not match the configured default SerDes.

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Grouping vs. Windowing: A related operation is windowing, which lets you control how to “sub-group” the grouped records of the same key into so-called windows for stateful operations such as @@ -1204,7 +1204,7 @@

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Impact of record caches: For illustration purposes, the column “KTable aggregated” below shows the table’s state changes over time in a very granular way. In practice, you would observe state changes in such a granular way only when @@ -1334,7 +1334,7 @@

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Impact of record caches: For illustration purposes, the column “KTable aggregated” below shows the table’s state changes over time in a very granular way. In practice, you would observe state changes in such a granular way only when @@ -1531,7 +1531,6 @@ Not Supported Not Supported Not Supported - N/A @@ -1569,7 +1568,7 @@ each KafkaStreams instance, i.e. each instance has a full copy of the changelog stream. Further, a KeyValueMapper allows for non-key based joins from the KStream to the GlobalKTable.

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Kafka Streams partly verifies the co-partitioning requirement: During the partition assignment step, i.e. at runtime, Kafka Streams verifies whether the number of partitions for both sides of a join are the same. If they are not, a TopologyBuilderException (runtime exception) is being @@ -2691,7 +2690,7 @@ aggregations or joins into so-called windows. Windows are tracked per record key.

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A related operation is grouping, which groups all records that have the same key to ensure that data is properly partitioned (“keyed”) for subsequent operations. Once grouped, windowing allows you to further sub-group the records of a key.

@@ -2776,7 +2775,7 @@ become t=300,000).

hopping window with a size 5 minutes and an advance interval of 1 minute. Since hopping windows can overlap – and in general they do – a data record may belong to more than one such windows.

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Hopping windows vs. sliding windows: Hopping windows are sometimes called “sliding windows” in other stream processing tools. Kafka Streams follows the terminology in academic literature, where the semantics of sliding windows are different to those of hopping windows.

@@ -3146,7 +3145,7 @@ t=5 (blue), which lead to a merge of sessions and an extension of a session, res
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When you want to write to systems other than Kafka: Besides writing the data back to Kafka, you can also apply a custom processor as a stream sink at the end of the processing to, for diff --git a/docs/streams/developer-guide/interactive-queries.html b/docs/streams/developer-guide/interactive-queries.html index b55655ec035..0a37d5668cb 100644 --- a/docs/streams/developer-guide/interactive-queries.html +++ b/docs/streams/developer-guide/interactive-queries.html @@ -120,7 +120,7 @@

You can also implement your own QueryableStoreType as described in section Querying local custom state stores.

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Kafka Streams materializes one state store per stream partition. This means your application will potentially manage many underlying state stores. The API enables you to query all of the underlying stores without having to know which partition the data is in.

@@ -227,7 +227,7 @@

Querying local custom state stores

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Only the Processor API supports custom state stores.

Before querying the custom state stores you must implement these interfaces:

diff --git a/docs/streams/developer-guide/processor-api.html b/docs/streams/developer-guide/processor-api.html index 5ed569afc93..b92e85b845f 100644 --- a/docs/streams/developer-guide/processor-api.html +++ b/docs/streams/developer-guide/processor-api.html @@ -148,7 +148,7 @@
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Stateful processing with state stores: The WordCountProcessor defined above can access the currently received record in its process() method, and it can leverage state stores to maintain processing states to, for example, remember recently