.NET 中事件源的示例和教程
Tutorial, practical samples and other resources about Event Sourcing in .NET. See also my similar repositories for JVM and NodeJS.
Event Sourcing is a design pattern in which results of business operations are stored as a series of events.
It is an alternative way to persist data. In contrast with state-oriented persistence that only keeps the latest version of the entity state, Event Sourcing stores each state change as a separate event.
Thanks to that, no business data is lost. Each operation results in the event stored in the database. That enables extended auditing and diagnostics capabilities (both technically and business-wise). What's more, as events contains the business context, it allows wide business analysis and reporting.
In this repository I'm showing different aspects and patterns around Event Sourcing from the basic to advanced practices.
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Events represent facts in the past. They carry information about something accomplished. It should be named in the past tense, e.g. "user added", "order confirmed". Events are not directed to a specific recipient - they're broadcasted information. It's like telling a story at a party. We hope that someone listens to us, but we may quickly realise that no one is paying attention.
Events:
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Events are logically grouped into streams. In Event Sourcing, streams are the representation of the entities. All the entity state mutations end up as the persisted events. Entity state is retrieved by reading all the stream events and applying them one by one in the order of appearance.
A stream should have a unique identifier representing the specific object. Each event has its own unique position within a stream. This position is usually represented by a numeric, incremental value. This number can be used to define the order of the events while retrieving the state. It can also be used to detect concurrency issues.
Technically events are messages.
They may be represented, e.g. in JSON, Binary, XML format. Besides the data, they usually contain:
correlation id, causation id, etc.Sample event JSON can look like:
{
"id": "e44f813c-1a2f-4747-aed5-086805c6450e",
"type": "invoice-issued",
"streamId": "INV/2021/11/01",
"streamPosition": 1,
"timestamp": "2021-11-01T00:05:32.000Z",
"data":
{
"issuedTo": {
"name": "Oscar the Grouch",
"address": "123 Sesame Street"
},
"amount": 34.12,
"number": "INV/2021/11/01",
"issuedAt": "2021-11-01T00:05:32.000Z"
},
"metadata":
{
"correlationId": "1fecc92e-3197-4191-b929-bd306e1110a4",
"causationId": "c3cf07e8-9f2f-4c2d-a8e9-f8a612b4a7f1"
}
}
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Event Sourcing is not related to any type of storage implementation. As long as it fulfills the assumptions, it can be implemented having any backing database (relational, document, etc.). The state has to be represented by the append-only log of events. The events are stored in chronological order, and new events are appended to the previous event. Event Stores are the databases' category explicitly designed for such purpose.
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In Event Sourcing, the state is stored in events. Events are logically grouped into streams. Streams can be thought of as the entities' representation. Traditionally (e.g. in relational or document approach), each entity is stored as a separate record.
| Id | IssuerName | IssuerAddress | Amount | Number | IssuedAt |
|---|---|---|---|---|---|
| e44f813c | Oscar the Grouch | 123 Sesame Street | 34.12 | INV/2021/11/01 | 2021-11-01 |
In Event Sourcing, the entity is stored as the series of events that happened for this specific object, e.g. InvoiceInitiated, InvoiceIssued, InvoiceSent.
…
All of those events share the stream id ("streamId": "INV/2021/11/01"), and have incremented stream positions.
In Event Sourcing each entity is represented by its stream: the sequence of events correlated by the stream id ordered by stream position.
To get the current state of an entity we need to perform the stream aggregation process. We're translating the set of events into a single entity. This can be done with the following steps:
This process is called also stream aggregation or state rehydration.
We could implement that as:
…
and use it as:
…
and generalise this into Aggregate base c
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