高速档与低速档中的测试驱动开发 (TDD)
We’ve introduced the service layer to capture some of the additional orchestration responsibilities we need from a working application. The service layer helps us clearly define our use cases and the workflow for each: what we need to get from our repositories, what pre-checks and current state validation we should do, and what we save at the end.
我们引入了服务层来承担一些实际应用程序中所需的额外协调职责。服务层帮助我们清晰地定义用例以及每个用例的工作流程：我们需要从仓储中获取什么数据， 我们应该进行哪些预检查和当前状态验证，以及最终需要保存什么内容。
But currently, many of our unit tests operate at a lower level, acting directly on the model. In this chapter we’ll discuss the trade-offs involved in moving those tests up to the service-layer level, and some more general testing guidelines.
但目前，我们的许多单元测试运行在较低的层级，直接操作模型。在本章中，我们将讨论将这些测试上移到服务层级别时涉及的权衡， 以及一些更为通用的测试指南。
Here are a few words from Harry directly:
以下是 Harry 的几句话：
I was initially skeptical of all Bob’s architectural patterns, but seeing an actual test pyramid made me a convert.
起初我对 Bob 的所有架构模式持怀疑态度，但看到一个实际的测试金字塔让我彻底信服了。
Once you implement domain modeling and the service layer, you really actually can get to a stage where unit tests outnumber integration and end-to-end tests by an order of magnitude. Having worked in places where the E2E test build would take hours ("wait 'til tomorrow," essentially), I can’t tell you what a difference it makes to be able to run all your tests in minutes or seconds.
一旦你实现了领域建模和服务层，你真的可以达到这样一个阶段：单元测试的数量能够比集成测试和端到端测试多出一个数量级。曾经我在一些地方工作时， 端到端测试的构建需要花费数小时（基本上是“等到明天吧”），我没法描述能够在几分钟甚至几秒内运行完所有测试带来的巨大改变。
Read on for some guidelines on how to decide what kinds of tests to write and at which level. The high gear versus low gear way of thinking really changed my testing life.
继续阅读，了解一些关于如何决定编写哪些类型的测试以及在哪个层级编写的指南。高速档与低速档的思维方式确实改变了我的测试工作方式。
我们的测试金字塔看起来如何？
Let’s see what this move to using a service layer, with its own service-layer tests, does to our test pyramid:
让我们来看看引入服务层以及为其编写服务层测试对我们的测试金字塔有何影响：
$ grep -c test_ */*/test_*.py
tests/unit/test_allocate.py:4
tests/unit/test_batches.py:8
tests/unit/test_services.py:3
tests/integration/test_orm.py:6
tests/integration/test_repository.py:2
tests/e2e/test_api.py:2Not bad! We have 15 unit tests, 8 integration tests, and just 2 end-to-end tests. That’s already a healthy-looking test pyramid.
不错！我们有 15 个单元测试，8 个集成测试，以及仅仅 2 个端到端测试。这已经是一个非常健康的测试金字塔了。
领域层测试是否应该移到服务层？
Let’s see what happens if we take this a step further. Since we can test our software against the service layer, we don’t really need tests for the domain model anymore. Instead, we could rewrite all of the domain-level tests from [chapter_01_domain_model] in terms of the service layer:
让我们看看再进一步会发生什么。由于我们可以针对服务层测试我们的软件，因此实际上我们不再需要领域模型的测试了。 相反，我们可以根据服务层，重写所有来自[chapter_01_domain_model]的领域层级测试：
# domain-layer test:
def test_prefers_current_stock_batches_to_shipments():
in_stock_batch = Batch("in-stock-batch", "RETRO-CLOCK", 100, eta=None)
shipment_batch = Batch("shipment-batch", "RETRO-CLOCK", 100, eta=tomorrow)
line = OrderLine("oref", "RETRO-CLOCK", 10)
allocate(line, [in_stock_batch, shipment_batch])
assert in_stock_batch.available_quantity == 90
assert shipment_batch.available_quantity == 100
# service-layer test:
def test_prefers_warehouse_batches_to_shipments():
in_stock_batch = Batch("in-stock-batch", "RETRO-CLOCK", 100, eta=None)
shipment_batch = Batch("shipment-batch", "RETRO-CLOCK", 100, eta=tomorrow)
repo = FakeRepository([in_stock_batch, shipment_batch])
session = FakeSession()
line = OrderLine('oref', "RETRO-CLOCK", 10)
services.allocate(line, repo, session)
assert in_stock_batch.available_quantity == 90
assert shipment_batch.available_quantity == 100Why would we want to do that?
为什么我们会想要这么做呢？
Tests are supposed to help us change our system fearlessly, but often we see teams writing too many tests against their domain model. This causes problems when they come to change their codebase and find that they need to update tens or even hundreds of unit tests.
测试的目的是帮助我们无所畏惧地更改系统，但我们经常看到团队针对领域模型编写了过多的测试。这会在需要更改代码库时引发问题， 因为他们可能发现需要更新几十甚至上百个单元测试。
This makes sense if you stop to think about the purpose of automated tests. We use tests to enforce that a property of the system doesn’t change while we’re working. We use tests to check that the API continues to return 200, that the database session continues to commit, and that orders are still being allocated.
如果你停下来思考一下自动化测试的目的，这就说得通了。我们使用测试是为了确保在我们工作时，系统的某些属性不会发生变化。 我们使用测试来检查 API 是否仍然返回 200，数据库会话是否仍旧提交，以及订单是否仍被分配。
If we accidentally change one of those behaviors, our tests will break. The flip side, though, is that if we want to change the design of our code, any tests relying directly on that code will also fail.
如果我们意外更改了这些行为之一，那么我们的测试就会失败。不过，反过来说，如果我们想更改代码的设计，任何直接依赖该代码的测试也会失败。
As we get further into the book, you’ll see how the service layer forms an API for our system that we can drive in multiple ways. Testing against this API reduces the amount of code that we need to change when we refactor our domain model. If we restrict ourselves to testing only against the service layer, we won’t have any tests that directly interact with "private" methods or attributes on our model objects, which leaves us freer to refactor them.
随着我们进一步阅读本书，你会看到服务层如何为我们的系统形成一个 API，这个 API 能以多种方式进行驱动。针对这个 API 进行测试可以 减少在重构领域模型时需要更改的代码量。如果我们只限制自己测试服务层，那么就不会有任何测试直接与模型对象的“私有”方法或属性交互， 这使得我们可以更自由地对它们进行重构。
|
Tip
|
Every line of code that we put in a test is like a blob of glue, holding the system in a particular shape. The more low-level tests we have, the harder it will be to change things. 我们在测试中编写的每一行代码都像是一滴胶水，将系统固定成特定的形状。低层级测试越多，改变系统就会变得越困难。 |
关于如何决定编写哪些类型的测试
You might be asking yourself, "Should I rewrite all my unit tests, then? Is it wrong to write tests against the domain model?" To answer those questions, it’s important to understand the trade-off between coupling and design feedback (see The test spectrum（测试光谱）).
你可能会问自己：“那我是否应该重写所有的单元测试呢？针对领域模型编写测试是不是错的？”要回答这些问题， 理解耦合与设计反馈之间的取舍非常重要（参见The test spectrum（测试光谱））。
[ditaa, apwp_0501] | Low feedback High feedback | | Low barrier to change High barrier to change | | High system coverage Focused coverage | | | | <--------- ----------> | | | | API Tests Service–Layer Tests Domain Tests |
Extreme programming (XP) exhorts us to "listen to the code." When we’re writing tests, we might find that the code is hard to use or notice a code smell. This is a trigger for us to refactor, and to reconsider our design.
极限编程（XP）敦促我们“倾听代码的声音”。当我们编写测试时，可能会发现代码难以使用，或者察觉到代码有异味。 这就是一个触发点，提醒我们进行重构并重新审视我们的设计。
We only get that feedback, though, when we’re working closely with the target code. A test for the HTTP API tells us nothing about the fine-grained design of our objects, because it sits at a much higher level of abstraction.
然而，只有当我们与目标代码密切合作时，才能获得这种反馈。针对 HTTP API 的测试无法告诉我们对象的细粒度设计情况， 因为它处于更高的抽象层级。
On the other hand, we can rewrite our entire application and, so long as we don’t change the URLs or request formats, our HTTP tests will continue to pass. This gives us confidence that large-scale changes, like changing the database schema, haven’t broken our code.
另一方面，我们可以重写整个应用程序，只要不更改 URL 或请求格式，HTTP 测试仍然会通过。这让我们有信心进行大规模的更改， 例如修改数据库模式，而不会破坏我们的代码。
At the other end of the spectrum, the tests we wrote in [chapter_01_domain_model] helped us to flesh out our understanding of the objects we need. The tests guided us to a design that makes sense and reads in the domain language. When our tests read in the domain language, we feel comfortable that our code matches our intuition about the problem we’re trying to solve.
在光谱的另一端，我们在[chapter_01_domain_model]中编写的测试帮助我们完善了对所需对象的理解。这些测试引导我们实现了一个合理的设计， 并使用了领域语言。当我们的测试以领域语言编写时，我们会感到安心，因为代码与我们试图解决的问题直观认识是一致的。
Because the tests are written in the domain language, they act as living documentation for our model. A new team member can read these tests to quickly understand how the system works and how the core concepts interrelate.
由于这些测试是用领域语言编写的，它们可以作为我们模型的动态文档。新团队成员可以通过阅读这些测试快速了解系统的工作原理以及核心概念之间的关系。
We often "sketch" new behaviors by writing tests at this level to see how the code might look. When we want to improve the design of the code, though, we will need to replace or delete these tests, because they are tightly coupled to a particular implementation.
我们经常通过在这个层级编写测试来“勾勒”新行为，来试试看代码可能会是什么样子。然而，当我们想改进代码设计时，就需要替换或删除这些测试， 因为它们与特定的 实现 紧密耦合。
高速档与低速档
Most of the time, when we are adding a new feature or fixing a bug, we don’t need to make extensive changes to the domain model. In these cases, we prefer to write tests against services because of the lower coupling and higher coverage.
大多数情况下，当我们添加新功能或修复一个错误时，并不需要对领域模型进行大规模更改。在这些情况下，我们更倾向于针对服务编写测试， 因为这样可以降低耦合且提高覆盖率。
For example, when writing an add_stock function or a cancel_order feature,
we can work more quickly and with less coupling by writing tests against the
service layer.
例如，在编写 add_stock 函数或 cancel_order 功能时，通过针对服务层编写测试，我们可以以更快的速度完成工作，并减少耦合。
When starting a new project or when hitting a particularly gnarly problem, we will drop back down to writing tests against the domain model so we get better feedback and executable documentation of our intent.
当启动一个新项目或遇到一个特别棘手的问题时，我们会退回到针对领域模型编写测试，以获得更好的反馈以及可执行的意图文档。
The metaphor we use is that of shifting gears. When starting a journey, the bicycle needs to be in a low gear so that it can overcome inertia. Once we’re off and running, we can go faster and more efficiently by changing into a high gear; but if we suddenly encounter a steep hill or are forced to slow down by a hazard, we again drop down to a low gear until we can pick up speed again.
我们使用的比喻是换挡。当开始一段旅程时，自行车需要处于低速档以克服惯性。一旦起步并行进， 我们可以换到高速档以更快、更高效地行驶；但如果突然遇到陡坡或由于障碍被迫减速，我们会再次降到低速档，直到能够重新提速。
将服务层测试与领域完全解耦
We still have direct dependencies on the domain in our service-layer tests, because we use domain objects to set up our test data and to invoke our service-layer functions.
我们的服务层测试中仍然直接依赖于领域模型，因为我们使用领域对象来设置测试数据并调用服务层函数。
To have a service layer that’s fully decoupled from the domain, we need to rewrite its API to work in terms of primitives.
要让服务层与领域模型完全解耦，我们需要重写其 API，使其基于基础数据类型（primitives）工作。
Our service layer currently takes an OrderLine domain object:
我们的服务层当前接收一个 OrderLine 领域对象：
allocate 接受一个领域对象）def allocate(line: OrderLine, repo: AbstractRepository, session) -> str:How would it look if its parameters were all primitive types?
如果其参数全是基础数据类型，会是什么样子呢？
allocate 接受字符串和整数）def allocate(
orderid: str, sku: str, qty: int,
repo: AbstractRepository, session
) -> str:We rewrite the tests in those terms as well:
我们也用这些基础数据类型重写测试：
def test_returns_allocation():
batch = model.Batch("batch1", "COMPLICATED-LAMP", 100, eta=None)
repo = FakeRepository([batch])
result = services.allocate("o1", "COMPLICATED-LAMP", 10, repo, FakeSession())
assert result == "batch1"But our tests still depend on the domain, because we still manually instantiate
Batch objects. So, if one day we decide to massively refactor how our Batch
model works, we’ll have to change a bunch of tests.
但是我们的测试仍然依赖于领域模型，因为我们仍需手动实例化 Batch 对象。因此，如果有一天我们决定对 Batch 模型的工作方式进行大规模重构，
就不得不修改许多测试。
缓解措施：将所有领域依赖集中在固定装置函数中
We could at least abstract that out to a helper function or a fixture
in our tests. Here’s one way you could do that, adding a factory
function on FakeRepository:
我们至少可以将其抽象为测试中的一个辅助函数或固定装置（fixture）。以下是实现这一点的一种方式，通过在 FakeRepository 上添加一个工厂函数：
class FakeRepository(set):
@staticmethod
def for_batch(ref, sku, qty, eta=None):
return FakeRepository([
model.Batch(ref, sku, qty, eta),
])
...
def test_returns_allocation():
repo = FakeRepository.for_batch("batch1", "COMPLICATED-LAMP", 100, eta=None)
result = services.allocate("o1", "COMPLICATED-LAMP", 10, repo, FakeSession())
assert result == "batch1"At least that would move all of our tests' dependencies on the domain into one place.
至少这样可以将我们所有测试对领域的依赖集中到一个地方。
添加一个缺失的服务
We could go one step further, though. If we had a service to add stock, we could use that and make our service-layer tests fully expressed in terms of the service layer’s official use cases, removing all dependencies on the domain:
不过，我们还可以更进一步。如果我们有一个用于添加库存的服务，就可以使用该服务，使我们的服务层测试完全基于服务层的官方用例， 从而移除对领域模型的所有依赖：
add_batch 服务）def test_add_batch():
repo, session = FakeRepository([]), FakeSession()
services.add_batch("b1", "CRUNCHY-ARMCHAIR", 100, None, repo, session)
assert repo.get("b1") is not None
assert session.committed|
Tip
|
In general, if you find yourself needing to do domain-layer stuff directly in your service-layer tests, it may be an indication that your service layer is incomplete. 通常情况下，如果你发现在服务层测试中需要直接处理领域层的内容，这可能表明你的服务层还不够完善。 |
And the implementation is just two lines:
而实现代码只有两行：
add_batch 的新服务）def add_batch(
ref: str, sku: str, qty: int, eta: Optional[date],
repo: AbstractRepository, session,
) -> None:
repo.add(model.Batch(ref, sku, qty, eta))
session.commit()
def allocate(
orderid: str, sku: str, qty: int,
repo: AbstractRepository, session
) -> str:|
Note
|
Should you write a new service just because it would help remove
dependencies from your tests? Probably not. But in this case, we
almost definitely would need an add_batch service one day anyway.
你是否应该仅仅为了帮助移除测试中的依赖而编写一个新服务？可能不必如此。但在这种情况下，我们几乎可以确定有一天我们会
需要一个 add_batch 服务 无论如何。
|
That now allows us to rewrite all of our service-layer tests purely in terms of the services themselves, using only primitives, and without any dependencies on the model:
现在，这使得我们可以将 所有 服务层测试纯粹以服务本身为基础重写，只使用基础数据类型（primitives），而无需任何对模型的依赖：
def test_allocate_returns_allocation():
repo, session = FakeRepository([]), FakeSession()
services.add_batch("batch1", "COMPLICATED-LAMP", 100, None, repo, session)
result = services.allocate("o1", "COMPLICATED-LAMP", 10, repo, session)
assert result == "batch1"
def test_allocate_errors_for_invalid_sku():
repo, session = FakeRepository([]), FakeSession()
services.add_batch("b1", "AREALSKU", 100, None, repo, session)
with pytest.raises(services.InvalidSku, match="Invalid sku NONEXISTENTSKU"):
services.allocate("o1", "NONEXISTENTSKU", 10, repo, FakeSession())This is a really nice place to be in. Our service-layer tests depend on only the service layer itself, leaving us completely free to refactor the model as we see fit.
这真是一个令人愉快的境地。我们的服务层测试仅依赖于服务层本身，使我们可以完全自由地按照需要重构模型。
将改进扩展到端到端（E2E）测试
In the same way that adding add_batch helped decouple our service-layer
tests from the model, adding an API endpoint to add a batch would remove
the need for the ugly add_stock fixture, and our E2E tests could be free
of those hardcoded SQL queries and the direct dependency on the database.
就像添加 add_batch 帮助将我们的服务层测试与模型解耦一样，添加一个用于添加批次的 API 端点可以去除丑陋的 add_stock 测试夹具的需求，
而我们的端到端（E2E）测试也可以摆脱那些硬编码的 SQL 查询以及对数据库的直接依赖。
Thanks to our service function, adding the endpoint is easy, with just a little JSON wrangling and a single function call required:
多亏了我们的服务函数，添加这个端点非常简单，只需处理一点点 JSON，并进行一次函数调用：
@app.route("/add_batch", methods=["POST"])
def add_batch():
session = get_session()
repo = repository.SqlAlchemyRepository(session)
eta = request.json["eta"]
if eta is not None:
eta = datetime.fromisoformat(eta).date()
services.add_batch(
request.json["ref"],
request.json["sku"],
request.json["qty"],
eta,
repo,
session,
)
return "OK", 201|
Note
|
Are you thinking to yourself, POST to /add_batch? That’s not very RESTful! You’re quite right. We’re being happily sloppy, but if you’d like to make it all more RESTy, maybe a POST to /batches, then knock yourself out! Because Flask is a thin adapter, it’ll be easy. See the next sidebar. 你是否在心里想，POST 到 /add_batch？这不太符合 RESTful！你完全正确。我们在这里确实有点随意， 但如果你想让它更符合 REST 的风格，或许可以考虑 POST 到 /batches，那就随你喜欢了！因为 Flask 是一个轻量级的适配器， 这会很容易实现。参见 下一侧边栏。 |
And our hardcoded SQL queries from conftest.py get replaced with some API calls, meaning the API tests have no dependencies other than the API, which is also nice:
我们在 conftest.py 中的那些硬编码 SQL 查询被一些 API 调用取代了，这意味着 API 测试除了依赖 API 本身之外没有其他依赖，这也非常不错：
def post_to_add_batch(ref, sku, qty, eta):
url = config.get_api_url()
r = requests.post(
f"{url}/add_batch", json={"ref": ref, "sku": sku, "qty": qty, "eta": eta}
)
assert r.status_code == 201
@pytest.mark.usefixtures("postgres_db")
@pytest.mark.usefixtures("restart_api")
def test_happy_path_returns_201_and_allocated_batch():
sku, othersku = random_sku(), random_sku("other")
earlybatch = random_batchref(1)
laterbatch = random_batchref(2)
otherbatch = random_batchref(3)
post_to_add_batch(laterbatch, sku, 100, "2011-01-02")
post_to_add_batch(earlybatch, sku, 100, "2011-01-01")
post_to_add_batch(otherbatch, othersku, 100, None)
data = {"orderid": random_orderid(), "sku": sku, "qty": 3}
url = config.get_api_url()
r = requests.post(f"{url}/allocate", json=data)
assert r.status_code == 201
assert r.json()["batchref"] == earlybatch总结
Once you have a service layer in place, you really can move the majority of your test coverage to unit tests and develop a healthy test pyramid.
一旦你建立了服务层，确实可以将大部分测试覆盖移到单元测试中，从而构建一个合理的测试金字塔。
- Aim for one end-to-end test per feature（每个功能目标实现一个端到端测试）
-
This might be written against an HTTP API, for example. The objective is to demonstrate that the feature works, and that all the moving parts are glued together correctly. 例如，这可能是针对一个 HTTP API 编写的。目标是证明该功能可以正常工作，并且所有的组件都正确地结合在一起。
- Write the bulk of your tests against the service layer（将大部分测试编写在服务层上）
-
These edge-to-edge tests offer a good trade-off between coverage, runtime, and efficiency. Each test tends to cover one code path of a feature and use fakes for I/O. This is the place to exhaustively cover all the edge cases and the ins and outs of your business logic.[1] 这些端到端的测试在覆盖范围、运行时间和效率之间提供了良好的权衡。每个测试通常覆盖一个功能的代码路径，并使用假对象（fakes）来处理 I/O。 这是全面覆盖所有边界情况以及业务逻辑内部细节的最佳位置。脚注：[一个关于在更高层级编写测试的合理担忧是，对于更复杂的用例， 这可能会导致组合爆炸的风险。在这种情况下，针对各个协作域对象的较低层次单元测试可能是有用的。 但同时也可以参考 [chapter_08_events_and_message_bus] 和 [fake_message_bus]。]
- Maintain a small core of tests written against your domain model（维护一小部分针对领域模型编写的核心测试）
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These tests have highly focused coverage and are more brittle, but they have the highest feedback. Don’t be afraid to delete these tests if the functionality is later covered by tests at the service layer. 这些测试具有非常集中的覆盖范围，但相对来说更脆弱，但它们提供了最高的反馈速度。如果这些功能后来被服务层的测试覆盖了，不要害怕删除这些测试。
- Error handling counts as a feature（错误处理也算作一个功能。）
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Ideally, your application will be structured such that all errors that bubble up to your entrypoints (e.g., Flask) are handled in the same way. This means you need to test only the happy path for each feature, and to reserve one end-to-end test for all unhappy paths (and many unhappy path unit tests, of course). 理想情况下，你的应用程序结构应确保所有冒泡到入口点（例如，Flask）的错误都以相同的方式处理。这意味着你只需为每个功能测试其正常路径， 并专门保留一个端到端测试用于测试所有异常路径（当然，还需要许多单元测试来覆盖各种异常路径）。
A few things will help along the way:
以下几点会对你有所帮助：
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Express your service layer in terms of primitives rather than domain objects. 用原语（primitives）而不是领域对象来表达你的服务层。
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In an ideal world, you’ll have all the services you need to be able to test entirely against the service layer, rather than hacking state via repositories or the database. This pays off in your end-to-end tests as well. 在理想情况下，你应该拥有所有需要的服务，能够完全针对服务层进行测试，而不是通过仓储或数据库来操作状态。 这在你的端到端测试中也会有所收益。
Onto the next chapter!
进入下一章！