We first summarize the key concepts of Kafka Streams.
+A stream processor is a node in the processor topology that represents a single processing step. +
A stream processor is a node in the processor topology that represents a single processing step. With the Processor API, you can define arbitrary stream processors that processes one received record at a time, and connect these processors with their associated state stores to compose the processor topology.
You can define a customized stream processor by implementing the Processor interface, which provides the process() API method.
diff --git a/11/streams/core-concepts.html b/11/streams/core-concepts.html
index 473a268b7a5..bd930de910a 100644
--- a/11/streams/core-concepts.html
+++ b/11/streams/core-concepts.html
@@ -57,13 +57,14 @@
We first summarize the key concepts of Kafka Streams.
+A stream processor is a node in the processor topology that represents a single processing step. +
A stream processor is a node in the processor topology that represents a single processing step. With the Processor API, you can define arbitrary stream processors that processes one received record at a time, and connect these processors with their associated state stores to compose the processor topology.
You can define a customized stream processor by implementing the Processor interface, which provides the process() API method.
diff --git a/20/streams/core-concepts.html b/20/streams/core-concepts.html
index 594efaa5ed9..e522f3041c3 100644
--- a/20/streams/core-concepts.html
+++ b/20/streams/core-concepts.html
@@ -57,13 +57,14 @@
We first summarize the key concepts of Kafka Streams.
+
Any stream processing technology must therefore provide first-class support for streams and tables.
- Kafka's Streams API provides such functionality through its core abstractions for
- streams <streams_concepts_kstream> and
- tables <streams_concepts_ktable>, which we will talk about in a minute.
- Now, an interesting observation is that there is actually a close relationship between streams and tables,
- the so-called stream-table duality.
- And Kafka exploits this duality in many ways: for example, to make your applications
+ Kafka's Streams API provides such functionality through its core abstractions for
+ streams
+ <streams_concepts_kstreams>
+ and tables
+ <streams_concepts_kstreams>,
+ which we will talk about in a minute. Now, an interesting observation is that there is actually a close relationship between streams and tables,
+ the so-called stream-table duality. And Kafka exploits this duality in many ways: for example, to make your applications
+ elastic
elastic <streams_developer-guide_execution-scaling>,
- to support fault-tolerant stateful processing <streams_developer-guide_state-store_fault-tolerance>,
- or to run interactive queries <streams_concepts_interactive-queries>
+ to support fault-tolerant stateful processing
+ <streams_developer-guide_state-store_fault-tolerance>,
+ or to run interactive queries
+ <streams_concepts_interactive-queries>
against your application's latest processing results. And, beyond its internal usage, the Kafka Streams API
also allows developers to exploit this duality in their own applications.
- Before we discuss concepts such as aggregations <streams_concepts_aggregations>
+ Before we discuss concepts such as aggregations
+ <streams_concepts_aggregations>,
in Kafka Streams we must first introduce tables in more detail, and talk about the aforementioned stream-table duality.
Essentially, this duality means that a stream can be viewed as a table, and a table can be viewed as a stream.
We first summarize the key concepts of Kafka Streams.
- +
Any stream processing technology must therefore provide first-class support for streams and tables.
- Kafka's Streams API provides such functionality through its core abstractions for
- streams <streams_concepts_kstream> and
- tables <streams_concepts_ktable>, which we will talk about in a minute.
- Now, an interesting observation is that there is actually a close relationship between streams and tables,
- the so-called stream-table duality.
- And Kafka exploits this duality in many ways: for example, to make your applications
- elastic <streams_developer-guide_execution-scaling>,
- to support fault-tolerant stateful processing <streams_developer-guide_state-store_fault-tolerance>,
- or to run interactive queries <streams_concepts_interactive-queries>
+ Kafka's Streams API provides such functionality through its core abstractions for
+ streams
+ and tables,
+ which we will talk about in a minute. Now, an interesting observation is that there is actually a close relationship between streams and tables,
+ the so-called stream-table duality. And Kafka exploits this duality in many ways: for example, to make your applications
+ elastic,
+ to support fault-tolerant stateful processing,
+ or to run interactive queries
against your application's latest processing results. And, beyond its internal usage, the Kafka Streams API
also allows developers to exploit this duality in their own applications.
- Before we discuss concepts such as aggregations <streams_concepts_aggregations>
- in Kafka Streams we must first introduce tables in more detail, and talk about the aforementioned stream-table duality.
+ Before we discuss concepts such as aggregations
+ in Kafka Streams, we must first introduce tables in more detail, and talk about the aforementioned stream-table duality.
Essentially, this duality means that a stream can be viewed as a table, and a table can be viewed as a stream.
We first summarize the key concepts of Kafka Streams.
- +
Any stream processing technology must therefore provide first-class support for streams and tables.
- Kafka's Streams API provides such functionality through its core abstractions for
- streams <streams_concepts_kstream> and
- tables <streams_concepts_ktable>, which we will talk about in a minute.
- Now, an interesting observation is that there is actually a close relationship between streams and tables,
- the so-called stream-table duality.
- And Kafka exploits this duality in many ways: for example, to make your applications
- elastic <streams_developer-guide_execution-scaling>,
- to support fault-tolerant stateful processing <streams_developer-guide_state-store_fault-tolerance>,
- or to run interactive queries <streams_concepts_interactive-queries>
+ Kafka's Streams API provides such functionality through its core abstractions for
+ streams
+ and tables,
+ which we will talk about in a minute. Now, an interesting observation is that there is actually a close relationship between streams and tables,
+ the so-called stream-table duality. And Kafka exploits this duality in many ways: for example, to make your applications
+ elastic,
+ to support fault-tolerant stateful processing,
+ or to run interactive queries
against your application's latest processing results. And, beyond its internal usage, the Kafka Streams API
also allows developers to exploit this duality in their own applications.
- Before we discuss concepts such as aggregations <streams_concepts_aggregations>
- in Kafka Streams we must first introduce tables in more detail, and talk about the aforementioned stream-table duality.
+ Before we discuss concepts such as aggregations
+ in Kafka Streams, we must first introduce tables in more detail, and talk about the aforementioned stream-table duality.
Essentially, this duality means that a stream can be viewed as a table, and a table can be viewed as a stream.
Kafka Streams makes your stream processing applications elastic and scalable. You can add and remove processing capacity dynamically during application runtime without any downtime or data loss. This makes your applications resilient in the face of failures and for allows you to perform maintenance as needed (e.g. rolling upgrades).