Stream Processing Topology
- A stream is the most important abstraction provided by Kafka Streams: it represents an unbounded, continuously updating data set. A stream is an ordered, replayable, and fault-tolerant sequence of immutable data records, where a data record is defined as a key-value pair.
- A stream processing application is any program that makes use of the Kafka Streams library. It defines its computational logic through one or more processor topologies, where a processor topology is a graph of stream processors (nodes) that are connected by streams (edges).
- - A stream processor is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors.
+ - A stream processor is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors.
There are two special processors in the topology:
diff --git a/docs/streams/developer-guide/memory-mgmt.html b/docs/streams/developer-guide/memory-mgmt.html
index 6c6fd2f76ccb2..a73a814be116f 100644
--- a/docs/streams/developer-guide/memory-mgmt.html
+++ b/docs/streams/developer-guide/memory-mgmt.html
@@ -55,9 +55,9 @@
For such KTable instances, the record cache is used for:
- Internal caching and compacting of output records before they are written by the underlying stateful
- processor node to its internal state stores.
+ processor node to its internal state stores.
- Internal caching and compacting of output records before they are forwarded from the underlying stateful
- processor node to any of its downstream processor nodes.
+ processor node to any of its downstream processor nodes.
Use the following example to understand the behaviors with and without record caching. In this example, the input is a
KStream<String, Integer> with the records <K,V>: <A, 1>, <D, 5>, <A, 20>, <A, 300>. The focus in this example is