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Ktable Kafka

KTable is an abstraction of a changelog stream from a primary-keyed table.

Each record in this changelog stream is an update on the primary-keyed table with the record key as the primary key.

A KTable is either defined from a single Kafka topic that is consumed message by message or the result of a KTable transformation.

An aggregation of a KStream also yields a KTable.

08/10/2018 KTables are again equivalent to DB tables, and as in these, using a KTable means that you just care about the latest state of the row/entity, which means that any previous states can be safely thrown away.

Not in vain a KTable is backed up by a compacted topic.

So this becomes an excellent test to know if it is appropriate to use a KTable :, KTable is an abstraction of a changelog stream from a primary-keyed table.

Each record in this changelog stream is an update on the primary-keyed table with the record key as the primary key.

A KTable is either defined from a single Kafka topic that is consumed message by message or the result of a KTable transformation.

An aggregation of a KStream also yields a KTable.

Like a KTable , a GlobalKTable is an abstraction of a changelog stream, where each data record represents an update.

A GlobalKTable differs from a KTable in the data that they are being populated with, i.

e.

which data from the underlying Kafka topic is being read into the respective table.

24 rows KTable is an abstraction of a changelog stream from a primary-keyed table.

Each record in #, KTable is an abstraction on a Kafka topic that can represent the latest state of a key/value pair.

The underlying Kafka topic is likely enabled with log compaction.

When I was first learning about KTables, the idea of UPSERTS immediately came to mind.

KTable is an abstraction of a changelog stream from a primary-keyed table.

Each record in this changelog stream is an update on the primary-keyed table with the record key as the primary key.

A KTable is either defined from a single Kafka topic that is consumed message by message or the result of a KTable transformation.

An aggregation of a KStream also yields a KTable.

In option 2, Kafka Streams will create an internal changelog topic to back up the KTable for fault tolerance.

Thus, both approaches require some additional storage in Kafka and result in additional network traffic.

Overall, it#s a trade-off between slightly more complex code in option 2 versus manual topic management in option 1.

01/10/2019 And each instance is reading data from a separate partition of the underlying Kafka topic.

Now, each instance will have its own copy of the KTable.

This local KTable will be populated with data from only that particular partition assigned to that instance of the application.

So none of the local KTables has all the data required.

05/01/2017 Kafka Streams don#t need any new infrastructure, depending only on the Kafka cluster (and the Kafka #s Zookeeper cluster until KIP-90 is done).

Apart from a nice functional API similar to Java 8 streams, Kafka Streams introduces the concept of a KTable.

Let#s try to explain what a KTable given the requirements we have.

ktable kafka