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Primer Stream

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Amazon Photos. Performing an aggregation on an event stream is the opposite: the stream acts as the input change stream for the table.

Figure 3. Aggregating an event stream into a table. The change stream can be used to react in real time to table changes, for example, to generate alerts.

It can also be used for operational purposes, such as migrating a table from machine A to machine B in the case of infrastructure failure or when elastically scaling an application in and out.

Figure 4. Every table has its own change stream also called a changelog. We will return to the stream-table duality concept later in this series, as it is not only helpful when writing your own applications but is also so foundational that Kafka leverages it for elastic scalability and fault tolerance, to name a few!

This completes the first part of this series, where we learned about the basic elements of an event streaming platform: events, streams, and tables.

But of course, this was just the beginning! Part 2 will take a deep dive into Kafka topics, partitions, and storage fundamentals, where we explore topics and—in my opinion, the most important concept in Kafka: partitions.

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Learn More. Stream Processing. As such it provides, next to many other features, three key functionalities in a scalable, fault-tolerant, and reliable manner: It lets you publish and subscribe to events It lets you store events for as long as you want It lets you process and analyze events This sounds like a very attractive piece of technology—but what is an event in this context?

A stream provides immutable data. It supports only inserting appending new events, whereas existing events cannot be changed.

Streams are persistent, durable, and fault tolerant. A table provides mutable data. New events—rows—can be inserted, and existing rows can be updated and deleted.

Like streams, tables are persistent, durable, and fault tolerant. Today, a table behaves much like an RDBMS materialized view because it is being changed automatically as soon as any of its input streams or tables change, rather than letting you directly run insert, update, or delete operations against it.

More on this later. Aggregating an event stream into a table Here are the code examples for continuously aggregating a stream into a table: ksqlDB :.

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This sounds like a very attractive piece of technology—but what is an event in this context? Conceptually, an event has a key, value, and timestamp.

A concrete event could be a plain notification without any additional information, but it could also include the full details of what exactly happened to facilitate subsequent processing.

For instance:. Events are captured by an event streaming platform into event streams. An event stream records the history of what has happened in the world as a sequence of events.

An example stream is a sales ledger or the sequence of moves in a chess match. With Kafka, such a stream may record the history of your business for hundreds of years.

This history is an ordered sequence or chain of events, so we know which event happened before another event to infer causality e.

A stream thus represents both the past and the present: as we go from today to tomorrow—or from one millisecond to the next—new events are constantly being appended to the history.

A table is a view of an event stream, and this view is continuously being updated whenever a new event is captured. Figure 1.

Streams record history. Tables represent state. Streams and tables in Kafka differ in a few ways, notably with regard to whether their contents can be changed, i.

I am not talking about state stores, which we will cover later on. Notwithstanding their differences, we can observe that there is a close relationship between a stream and a table.

We call this the stream-table duality. What this means is:. In fact, a table is fully defined by its underlying change stream.

If you have ever worked with a relational database such as Oracle or MySQL, these change streams exist there, too! Here, however, they are a hidden implementation detail—albeit an absolutely critical one—and have names like redo log or binary log.

In event streaming, the redo log is much more than an implementation detail. We can turn streams into tables and tables into streams, which is one reason why we say that event streaming and Kafka are turning the database inside out.

Figure 2. Because of the stream-table duality, we can easily turn a stream into a table, and vice versa.

Even more, we can do this in a continuous, streaming manner so that both the stream and the table are always up to date with the latest events.

For illustrative reasons, I do not show event timestamps. The table is continuously updated as new events arrive in the stream, similar to a materialized view in relational databases, but it supports millions of events per second.

Think of this as performing CDC on a table yielding an output change stream of the table. Performing an aggregation on an event stream is the opposite: the stream acts as the input change stream for the table.

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