读书摘要，Hackable Projects

2023-07-20 07:53:10

完整读完Google的三篇谈Hackable Projects的文章，以及一篇从Test Pyramid看UnitTest的比重、一篇谈Optimal Logging的文章，感觉这5篇在测试、日志两个方面对工程的速度、大小、解耦三个方面做了深入而系统的解读，非常值得一看，专业的测试角度剖析工程，有一种解剖学的感觉，这刷新了我对测试的理解，我估计职业的开发工程师里能有这么全面的视角的估计比例本就不高。此处简要摘录做个笔记，结合自己的经验体会，在这方面会有系统化的视角。

Hackable Projects - Pillar 1: Code Health

  • Readable Code
  • Presubmit Test
  • Single Branch And Reducing Risk
  • Loose Couping
    • SOLID
  • Aggressively Reduce Technical Debt
    • dependency enforcement

Hackable Projects - Pillar 2: Debuggability

Hackable Projects - Pillar 3: Infrastructure

  • Build Systems Speed
    • Replace make with ninja
    • Use the gold linker instead of ld
    • Detect and delete dead code in your project
    • Reduce the number of dependencies
    • enforce dependency rules so new ones are not added lightly
    • Give the developers faster machines
    • Use distributed build, which is available with many open-source continuous integration systems
  • feedback cycles kill hackability, for many reasons:
    • Build and test times longer than a handful of seconds cause many developers’ minds to wander, taking them out of the zone.
    • Excessive build or release times* makes tinkering and refactoring much harder.
  • The main axes of improvement are:
    • Reduce the amount of code being compiled.
    • Replace tools with faster counterparts.
    • Increase processing power, maybe through parallelization or distributed systems.
  • Continuous Integration and Presubmit Queues
    • You should build and run tests on all platforms you release on.
    • At a minimum, you should build and test on all platforms, but it’s even better if you do it in presubmit.
    • Chromium:It has developed over the years so that a normal patch builds and tests on about 30 different build configurations before commit.
  • Test Speed:
    • if it takes more than a minute to execute, its value is greatly diminished
    • Sharding and parallelization.
    • Continuously measure how long it takes to run one build+test cycle in your continuous build, and have someone take action when it gets slower.
    • Remove tests that don’t pull their weight.
    • If you have tests that bring up a local server stack, for instance inter-server integration tests, making your servers boot faster is going to make the tests faster as well.
  • Workflow Speed
    • You need to keep track of your core workflows as your project grows.
    • Your version control system may work fine for years, but become too slow once the project becomes big enough.
  • Release Often
  • Easy Reverts:
    • If you look in the commit log for the Chromium project, you will see that a significant percentage of the commits are reverts of a previous commits.
  • Performance Tests: Measure Everything:
    • Is it critical that your app starts up within a second?
    • Should your app always render at 60 fps when it’s scrolled up or down?
    • Should your web server always serve a response within 100 ms?
    • Should your mobile app be smaller than 8 MB?

Just Say No to More End-to-End Tests

Good ideas often fail in practice, and in the world of testing, one pervasive good idea that often fails in practice is a testing strategy built around end-to-end tests.

What Went Wrong for End-to-End test:

  • The team completed their coding milestone a week late (and worked a lot of overtime).
  • Finding the root cause for a failing end-to-end test is painful and can take a long time.
  • Partner failures and lab failures ruined the test results on multiple days.
  • Many smaller bugs were hidden behind bigger bugs.
  • End-to-end tests were flaky at times.
  • Developers had to wait until the following day to know if a fix worked or not.

The True Value of Tests:

  • A failing test does not directly benefit the user.
  • A bug fix directly benefits the user.

Building the Right Feedback Loop:

  • It's fast
  • It's reliable(smaller)
  • It isolates failures

Think Smaller, Not Larger

Unit Test

Unit tests take a small piece of the product and test that piece in isolation.

  • Unit tests are fast.
  • Unit tests are reliable.
  • Unit tests isolate failures.

Writing effective unit tests requires skills in areas:

  • dependency management
  • mocking
  • hermetic testing

With end-to-end tests, you have to wait: first for the entire product to be built, then for it to be deployed, and finally for all end-to-end tests to run.

Although end-to-end tests do a better job of simulating real user scenarios, this advantage quickly becomes outweighed by all the disadvantages of the end-to-end feedback loop:

  • NOT fast
  • NOT Reliable
  • NOT Isolates Failures

Integration Tests

Unit tests do have one major disadvantage: even if the units work well in isolation, you do not know if they work well together.

But even then, you do not necessarily need end-to-end tests. For that, you can use an integration test.

An integration test takes a small group of units, often two units, and tests their behavior as a whole, verifying that they coherently work together.

Testing Pyramid

Even with both unit tests and integration tests, you probably still will want a small number of end-to-end tests to verify the system as a whole.

To find the right balance between all three test types, the best visual aid to use is the testing pyramid:
读书摘要，Hackable Projects

As a good first guess, Google often suggests a 70/20/10 split: 70% unit tests, 20% integration tests, and 10% end-to-end tests.

Optimal Logging

Channeling Goldilocks:

Massive, disk-quota burning logs are a clear indicator that little thought was put in to logging.

  • Never log too much:

Goals of logging:

  • help with bug investigation
  • event confirmation

If your log can’t explain the cause of a bug or whether a certain transaction took place, you are logging too little.

  • The only thing worse than logging too much is logging too little.

Good things to log:

  • Important startup configuration
  • Errors
  • Warnings
  • Changes to persistent data
  • Requests and responses between major system components
  • Significant state changes
  • User interactions
  • Calls with a known risk of failure
  • Waits on conditions that could take measurable time to satisfy
  • Periodic progress during long-running tasks
  • Significant branch points of logic and conditions that led to the branch
  • Summaries of processing steps or events from high level functions - Avoid logging every step of a complex process in low-level functions.

Bad things to log:

  • Function entry - Don’t log a function entry unless it is significant or logged at the debug level.
  • Data within a loop - Avoid logging from many iterations of a loop. It is OK to log from iterations of small loops or to log periodically from large loops.
  • Content of large messages or files - Truncate or summarize the data in some way that will be useful to debugging.
  • Benign errors - Errors that are not really errors can confuse the log reader. This sometimes happens when exception handling is part of successful execution flow.
  • Repetitive errors - Do not repetitively log the same or similar error. This can quickly fill a log and hide the actual cause. Frequency of error types is best handled by monitoring. Logs only need to capture detail for some of those errors.

There is More Than One Level

Test logs should always contain:

  • Test execution environment
  • Initial state
  • Setup steps
  • Test case steps
  • Interactions with the system
  • Expected results
  • Actual results
  • Teardown steps

Conditional Verbosity With Temporary Log Queues

to create temporary, in-memory log queues. Throughout processing of a transaction, append verbose details about each step to the queue. If the transaction completes successfully, discard the queue and log a summary. If an error is encountered, log the content of the entire queue and the error.

Failures and Flakiness Are Opportunities

If you have a hard time determining the cause of an error, it's a great opportunity to improve your logging. Before fixing the problem, fix your logging so that the logs clearly show the cause.

Might As Well Log Performance Data

Logged timing data can help debug performance issues.

Following the Trail Through Many Threads and Processes

You should create unique identifiers for transactions that involve processing across many threads and/or processes

Monitoring and Logging Complement Each Other

a monitoring alert is simply a trigger for you to start an investigation. Monitoring shows the symptoms of problems. Logs provide details and state on individual transactions

上一篇：数据结构之,线性表去除等于x的元素


下一篇：【python】为什么用python