翻譯自
https://microservices.io/patterns/data/database-per-service.html

寫在前面

公司采用微服務(wù)模式重構(gòu)了產(chǎn)品線模叙，但是在微服務(wù)化過程中遇到一些問題，例如：
1.分布式事務(wù)；
2.基礎(chǔ)數(shù)據(jù)共享彭则；
3.跨庫關(guān)聯(lián)查詢夸盟；
4.單個(gè)服務(wù)部署集群遇到的問題糕篇，像緩存一致性绳矩、定時(shí)任務(wù)單點(diǎn)處理平项、請(qǐng)求負(fù)載均衡危虱、topic消息消費(fèi)等榨了；

針對(duì)基礎(chǔ)數(shù)據(jù)共享拐云，筆者開發(fā)了一個(gè)數(shù)據(jù)同步j(luò)ar包已經(jīng)較好的解決了該問題，但是對(duì)于分布式事務(wù)和跨服務(wù)數(shù)據(jù)關(guān)聯(lián)依然存在很多疑惑。在查閱資料過程中痘儡，發(fā)現(xiàn)了https://microservices.io/
該博客，因此對(duì)其中部分內(nèi)容進(jìn)行摘錄和解讀猜极。

Pattern: Database per service

Context

Let’s imagine you are developing an online store application using the Microservice architecture pattern. Most services need to persist data in some kind of database. For example, the Order Service stores information about orders and the Customer Service stores information about customers.

image

Problem

What’s the database architecture in a microservices application?

Forces

• Services must be loosely coupled so that they can be developed, deployed and scaled independently

• Some business transactions must enforce invariants that span multiple services. For example, the Place Order use case must verify that a new Order will not exceed the customer’s credit limit. Other business transactions, must update data owned by multiple services.

• Some business transactions need to query data that is owned by multiple services. For example, the View Available Credit use must query the Customer to find the creditLimit and Orders to calculate the total amount of the open orders.

• Some queries must join data that is owned by multiple services. For example, finding customers in a particular region and their recent orders requires a join between customers and orders.

• Databases must sometimes be replicated and sharded in order to scale. See the Scale Cube.

• Different services have different data storage requirements. For some services, a relational database is the best choice. Other services might need a NoSQL database such as MongoDB, which is good at storing complex, unstructured data, or Neo4J, which is designed to efficiently store and query graph data.

Solution

Keep each microservice’s persistent data private to that service and accessible only via its API. A service’s transactions only involve its database.

The following diagram shows the structure of this pattern.

image

The service’s database is effectively part of the implementation of that service. It cannot be accessed directly by other services.

There are a few different ways to keep a service’s persistent data private. You do not need to provision a database server for each service. For example, if you are using a relational database then the options are:

• Private-tables-per-service – each service owns a set of tables that must only be accessed by that service
• Schema-per-service – each service has a database schema that’s private to that service
• Database-server-per-service – each service has it’s own database server.

Private-tables-per-service and schema-per-service have the lowest overhead. Using a schema per service is appealing since it makes ownership clearer. Some high throughput services might need their own database server.

It is a good idea to create barriers that enforce this modularity. You could, for example, assign a different database user id to each service and use a database access control mechanism such as grants. Without some kind of barrier to enforce encapsulation, developers will always be tempted to bypass a service’s API and access it’s data directly.

Example

The FTGO application is an example of an application that uses this approach. Each service has database credentials that only grant it access its own (logical) database on a shared MySQL server. For more information, see this blog post.

Resulting context

每個(gè)服務(wù)使用獨(dú)立的數(shù)據(jù)持久化有以下優(yōu)點(diǎn)：
1.松耦合缨叫，改變一個(gè)服務(wù)的數(shù)據(jù)庫不影響其他服務(wù)
2.每個(gè)服務(wù)可以使用最合適的數(shù)據(jù)庫類型，例如es剪勿，mongo等

Using a database per service has the following benefits:

• Helps ensure that the services are loosely coupled. Changes to one service’s database does not impact any other services.

• Each service can use the type of database that is best suited to its needs. For example, a service that does text searches could use ElasticSearch. A service that manipulates a social graph could use Neo4j.

也會(huì)有下面的缺點(diǎn)
1.分布式事務(wù)
2.跨庫聯(lián)查（join）
3.管理多種不同數(shù)據(jù)庫是一個(gè)麻煩事

Using a database per service has the following drawbacks:

• Implementing business transactions that span multiple services is not straightforward. Distributed transactions are best avoided because of the CAP theorem. Moreover, many modern (NoSQL) databases don’t support them.

• Implementing queries that join data that is now in multiple databases is challenging.

• Complexity of managing multiple SQL and NoSQL databases

There are various patterns/solutions for implementing transactions and queries that span services:

• Implementing transactions that span services - use the Saga pattern.

• Implementing queries that span services:

  • API Composition - the application performs the join rather than the database. For example, a service (or the API gateway) could retrieve a customer and their orders by first retrieving the customer from the customer service and then querying the order service to return the customer’s most recent orders.

  • Command Query Responsibility Segregation (CQRS) - maintain one or more materialized views that contain data from multiple services. The views are kept by services that subscribe to events that each services publishes when it updates its data. For example, the online store could implement a query that finds customers in a particular region and their recent orders by maintaining a view that joins customers and orders. The view is updated by a service that subscribes to customer and order events.

Related patterns

• Microservice architecture pattern creates the need for this pattern
• Saga pattern is a useful way to implement eventually consistent transactions
• The API Composition and Command Query Responsibility Segregation (CQRS) pattern are useful ways to implement queries
• The Shared Database anti-pattern describes the problems that result from microservices sharing a database