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2. Common build actions

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2. Common build actions

A note on languages

Throughout this primer we will often use examples building and testing Java and C++ because they are languages with clear (and different) build steps, but the principles apply to most languages.

You do not need to know Java or C++ in order to follow this primer.

This is an interesting and common theme when working with build systems: we can often achieve a lot of things with a language without actually knowing many details of that language.

Tempting though it is to go down the rabbitholes of learning languages, we recommend you don’t get too distracted with this unless you have a clear goal in doing so.

There are example projects for each of these languages in https://github.com/CodeYourFuture/immersive-go-course/tree/main/example-projects. Try to build them yourself on the command line to get a feel for each of these actions.

Fetching external dependencies

When we depend on code that isn’t part of our current project (perhaps because someone else wrote it, or because we re-use it across many projects), we need to fetch those dependencies onto our machine before we can build with it.

There are often two stages in this process: resolving and fetching. Most languages’ tooling exposes this to users as one operation (e.g. npm install), rather than as two steps, but internally the tooling probably still does both.

Resolving

The input to resolving is a list of dependencies. For instance, we may know we need “guava at exactly version v33.2.1-jre”. Or we may know we need “guava at least version v31.0-jre”. Or we may know we need “guava”.

The output of resolving is a complete list of dependencies, including exact version numbers. For instance, a resolver may tell you: “You need guava at exactly version v33.2.1-jre, and guava needs junit at exactly version 5.10.2, so you need that as well”. Dependencies your project direectly needs are referred to as direct dependencies and dependencies of your dependencies (or their dependencies) are referred to as transitive dependencies.

Different resolvers use different strategies for picking which versions you should use if your input doesn’t have exact version requirements, but they always return exact versions. This means that if the inputs said “We need guava at least version v31.0-jre”, the output of resolving will be “exactly v31.0-jre” or “exactly v33.2.1-jre” or some other exact version.

Fetching

Fetching typically just involves downloading a file and putting it on disk in the right place for your project to use it (possibly unzipping it on the way). Where “on disk in the right place” is depends on the build system being used. Using npm in JavaScript, for instance, it’s a directory named after the dependency, in a directory called node_modules in the same directory as your project’s package.json.

Compiling

Compiling is the process of taking source code (intended for humans to read/write) and converting it into a format better suited for computers to consume and run.

C++ (a “native” language)

In C++, compiling takes a .cc file and generates a .o file. This .o file contains actual machine code that an operating system knows how to run directly. There are different compilers for C++: common ones are called clang++, g++, or c++.

A typical command line to compile C++ with some dependency is:

clang++ formatting/formatting.cpp -I "." -c -o formatting/formatting.o

The -o flag specifies where the generated object file should be placed.

The -I flag points at a directory containing .h files, telling the compiler “if you need to include a .h file, here’s a place to look for them”. It may be specified multiple times. .h files are header files, which give the compiler enough information about code you depend on for your code to be able to use it (generally type definitions and function signatures).

Java (a “JIT” language)

In Java, compiling takes a .java file and generates a .class file. This .class file contains JVM bytecode which a java process (called a JVM) knows how to run. When running, the JVM converts the bytecode to machine code that an operating system knows how to run directly.

A typical command line to compile Java with some dependency is:

javac -cp third_party/guava-33.2.1-jre.jar com/example/fmt/Formatting.java

The -cp flag specifies directories or jar files (a jar file is a zip file with an expected structure inside) where .class files you depend on can be found.

JavaScript (an “interpreted” language)

JavaScript does not have a standard compile step - it is an interpreted language, which means that when you run node (or load a file in a browser), the JavaScript engine reads that file as text, line-by-line, and executes it. But JavaScript development often involves steps like bundling (e.g. with webpack, which often creates a new .js file from an existing one) which in some ways resemble compilation.

Comparisons

These three compilation modes offer different trade-offs:

  • Native machine code is very fast to run (because it’s directly run by the operating system), and during the compile step optimisations may be applied to speed up the code further (at the cost of taking even longer to compile).
  • Interpreted languages are much slower to run (because they can’t be optimised in advance), but don’t require a compile step in order to run (so are often faster to iterate on when writing code).
  • Java compilation does some compilation ahead of time (which takes some time, but typically less time than compiling native machine code), but by deferring some decisions until runtime, the JVM can observe how code is actually used with real-world data and perform optimisations in response to that (this is called Just-In-Time compilation or JIT).

Linking

Linking is the process of taking several pieces of compiled code (e.g. object files of machine code) and combining them into one executable.

C++

Before linking, an object file may reference some symbols (e.g. function names) that it just knows the name of. We can run nm on an object file and see what symbols it contains and which ones it knows it needs but doesn’t know about. In the output of nm, the first column is the address of the symbol in the object file (if it’s present), the second describes its type (where U means “undefined” - not in this object file, but something in this file needs it), and the third column is the symbol’s name.

Click to expand an nm invocation from the example project’s main.o.

 
% nm main.o
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0000000000002af0 s GCC_except_table103
0000000000002b00 s GCC_except_table110
0000000000002b10 s GCC_except_table111
0000000000002b20 s GCC_except_table115
0000000000002b30 s GCC_except_table116
0000000000002b64 s GCC_except_table118
0000000000002a68 s GCC_except_table12
0000000000002b7c s GCC_except_table138
0000000000002b8c s GCC_except_table143
0000000000002b9c s GCC_except_table147
0000000000002bb0 s GCC_except_table150
0000000000002bc0 s GCC_except_table153
0000000000002a78 s GCC_except_table21
0000000000002a8c s GCC_except_table28
0000000000002a50 s GCC_except_table4
0000000000002a9c s GCC_except_table41
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The linker’s job is to make sure it can find a definition for each symbol any object file references, and to stitch them all together so that when one function tries to call another one, the other function can be found in the executable and called.

Packaging

When preparing software to be deployed somewhere, it often needs to be packaged in some format.

That may be a directory with a certain structure, a zip file with a certain structure, or something more complicated. This may be tied specifically to the language being built, or may be dictated by the environment where it’s being deployed.

Pre-processing / generating code

Sometimes code may be very repetitive, or may be derived from some data.

For instance, if we are using protobuf files to define types and functions that may be used, we need to run the protocol buffer compiler to generate the per-language bindings from the .proto files.

A different example: if we have a CSV of categories, and need to populate some constants or a hashmap with them, we can generate code to do this at compile-time rather than needing to parse the CSV at run-time. This may be beneficial for a number of reasons: we don’t need to distribute the CSV with our code, we don’t need to worry about the speed of access of a file from disk, we don’t need to handle possible errors from reading, etc.

These are examples which may take some input (e.g. a .proto or .csv file) and generate some code, which we then need to compile. This generation needs to be done before we can compile the code, but once we have generated the code it can be treated like any other code that’s in our project.

Post-processing

Sometimes after we have produced some output (e.g. by compiling some code), we have extra steps we want to perform (perhaps before packaging it, or perhaps to analyse the output in some way).

Some examples:

  • There may be some optimisations (e.g. minifying code to reduce binary size, or doing profile-guided optimisations) we want to perform which edit a compiled output to improve it in some way.
  • We may want to scan the compiled output for secrets which shouldn’t be compiled in the code and which should instead be read from a file/environment - if the secrets are detected, we want to give an error indicating someone should fix this.

Testing

Output

In many ways, testing is one of the most unusual kind of build actions.

People may have many different signals they’re looking for from tests, depending on their context.

A lot of the time, people just want to know the answer to a boolean question: Did all the tests pass?

If some tests failed, they probably want to know which ones, and what error messages were reported.

A lot of testing frameworks will also produce structured output (often an XML file) which other tools can consume. For example, an IDE may display the results of the tests. Or if every time tests are run the XML is uploaded to some service, it can track and present data like what are your slowest tests, or what tests fail the most often.

Reliability

Tests are also unusual because they are often less consistent than other kinds of build actions. If you compile some code, you should always end up with the same output (or the same error, if it couldn’t compile!). If you package some files in a zip file, you should end up with the same zip file. If you run tests, sometimes you don’t get the same output.

Sometimes this is intentional. When tests run, they may log information, and that may include things like timestamps, to help you understand what happened when the test was running.

Sometimes this is accidental. Some tests may depend on how fast something happens, and if your computer was too busy (so the test ran slowly) it may fail. Some tests only work if the computer they’re running on is set to a particular time zone, so you may get different results than if one of your colleagues runs the test.

Sometimes we want to run tests lots of times, e.g. to find out whether they sometimes randomly fail, so that we can detect problems and fix them.

This is not usually the case with other kinds of build actions. Compiling is consistent. Packaging is consistent. Good tests are consistent, but not all tests are good tests. Often, this means build systems need to treat test actions differently from other actions.

How are tests actually run?

Tests are run via a program called a test harness.

In some languages, the test harness is a standalone program which is given the tests to run. This is quite common in interpreted languages, e.g. in JavaScript, jest tests are run by running a program called jest, and that program will look for test files it should run.

In other languages, when you want to run tests, a program gets compiled (where perhaps a main function is generated based on what tests exist), and then to run the tests you run that program. This means every time a test is changed or added, some code may need to be re-generated, and a whole program needs to be compiled.

The important thing to take away from this is that running tests is really just running a program. The tests are considered to have passed if that program exits 0. The tests are considered to have failed if the program exits non-zero. The test program will write output to stdout and stderr, and maybe produce structured output (e.g. an XML file) somewhere too. But running tests is generally just: running a program, and interpreting its output.

Observations

What do all of these kinds of build action have in common?

  • They typically have some input files (maybe source code, maybe a CSV file, maybe a compiled test program).
  • They typically have some output files (maybe object files, maybe source code, maybe test results).
  • They are generally performed by running a process.
  • They can succeed or fail, and this is generally signalled by the exit code of a process.

Build actions may also have ordering constraints. If we’re generating code, we need to do that before we can compile it. If we’re running tests, we need to compile the tests before we can run them. If we’re running tests which include generated code, we need to generate the code before we can compile the code before we can run the tests.