Kafka Streams KStreams Materialized Sınıfı - State Store İçindir

Giriş

Şu satırı dahil ederiz

import org.apache.kafka.streams.kstream.Materialized;

Aggregated values bir başka topic'e yazılabilir ancak bu şart değil. Sonuçlar local state store içinde de saklanabilir. 

State Store İlişkisi

Açıklaması şöyle. Yani KStreams ile istenirse sonuçlar bir Materialized View ile saklanabilir. Materialized View arkada bir State Store kullanıyor

In Kafka Streams, a materialized view is a stateful data structure that represents the result of processing a stream of events. 

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When you process a Kafka stream using Kafka Streams, you can define a materialized view as part of your processing logic. 

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Materialized views in Kafka Streams are backed by a state store...

How is materialized view refreshed in Kafka streams?

Açıklaması şöyle. 

In Kafka Streams, materialized views are refreshed automatically as new events are processed in the underlying Kafka topics. Kafka Streams follows an incremental processing model, which means that it only processes new events as they arrive, allowing materialized views to be continuously updated in real-time.

Query Access

Açıklaması şöyle. 

The updated state in the materialized view’s state store is accessible for querying using the Kafka Streams API. You can use the API to query the state store and retrieve the current results of the materialized view.

What are the cases when materialized view has stale data in kafka streams?

Açıklaması şöyle. 

In Kafka Streams, materialized views may have stale data under certain circumstances. Stale data refers to the data in the materialized view that is not up-to-date with the latest changes in the underlying Kafka topics. Here are some cases when materialized views can have stale data:

Time Windows and Grace Period: If your Kafka Streams application uses time-based windows for aggregation (e.g., tumbling, hopping, or sliding windows), there may be a grace period defined to allow late-arriving events to be included in the window’s computation. During this grace period, the data in the materialized view may be considered stale as it includes events that arrived after the window’s actual end time.

Stream-Stream Joins: When performing stream-stream joins, the materialized view might have stale data if the join operation depends on events from both input streams. If one of the input streams has late-arriving events, the join result may not reflect the most recent data.

Out-of-Order Events: If your Kafka topic receives out-of-order events (events with timestamps older than expected), the materialized view may temporarily have stale data until the correct ordering is restored during the processing.

Processing Lag: If there is a processing delay in your Kafka Streams application due to high load, insufficient resources, or complex processing logic, the materialized view may have stale data during the lag period.

Faulty Processing: Bugs or errors in the processing logic can lead to stale data in the materialized view if certain events are not correctly processed or if updates are skipped.

as metodu

Örnek

Şöyle yaparız

StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> stream = builder.stream(INPUT_TOPIC);
stream.groupByKey().count(Materialized.as("count-store"));