SLF4J VS PMD

A few years ago, my team moved from Java to Go. Working on Go projects, we encountered a wide variety of logging frameworks with different APIs, configuration, and formatting. We soon found ourselves longing for a logging abstraction layer like Java’s slf4j, which had proven invaluable for use in reusable libraries or configuring and debugging production systems. So, not long after moving to Go, we began working toward replacing what we had lost in slf4j.

I'm not familiar with Serilog as I code mostly in Java, use slf4j (logs to stdout) and our apps send logs to Cloudwatch using the task definition's awslogs configuration. I prefer it this way because I can customize the log configurations in my task definitions. Also the default stream name has this format prefix-name/container-name/ecs-task-id so I can easily identify the logs of the task I want to look at. I haven't experienced any downsides with this approach and our apps publish a shit ton of logs. Cloudwatch approach looks like you can customize the stream name?

slf4j is open source. You can look at the code.

Java -> Logback, Log4j2, JDK (Java Util Logging), Slf4j, e.t.c.

slf4j para padronização dos logs;

SLF4J

> My God. Logging in protobuf?

Yes, or any other data format and/or transport protocol.

I'm surprised this is up for debate.

> Logging is the lowest of all debugging utilities - its the first thing you ever do writing software - “hello world”. And, while I admire structural logging, the truth is printing strings remains (truly) the lowest common denominator across software developers.

This sort of comment is terribly miopic. You can have a logging API, and then configure your logging to transport the events anywhere, any way. This is a terribly basic feature and requirement, and one that comes out of the box with some systems. Check how SLF4J[1] is pervasive in Java, and how any SLF4J implementation offers logging to stdout or a local file as a very specific and basic usecase.

It turns out that nowadays most developers write software that runs on many computers that aren't stashed over or under their desks, and thus they need efficient and convenient ways to check what's happening either in a node or in all deployments.

[1] https://www.slf4j.org/

While the security findings can be pretty elaborate and helpful, the code quality and performance focused findings are not that impressive and can often be detected by more basic or powerful tools like SonarQube (paying) or PMD (free). To see what I mean you can have a look at the list of Java code quality detectors, which is pretty short and contains a lot of simple findings like:

Also saw this convo has a couple years worth of ideas going on … https://github.com/pmd/pmd/issues/3413

PMD and Checkstyle are static analysis tools that check your code on each project build. Gradle allows to apply them easily.

The generated classes should be put into .gitignore. Otherwise, if you have Checkstyle, PMD, or SonarQube in your project, then generated classes can violate some rules. Besides, if you don't put them into .gitignore, then each pull request might become huge due to the fact that even a slightest fix can lead to lots of changes in the generated classes.

- PMD Static Code Analysis tool: https://pmd.github.io/

PMD, Spotbugs, Nullaway: Java linting/static analysis (https://pmd.github.io, https://spotbugs.github.io, https://github.com/uber/NullAway)

PMD, and checkstyle as well.

Open up the config/ruleset.xml file, and you’ll find an XML document that lists several rules. These rules map to the issues which PMD will report on. Believe it or not, there are hundreds of Apex rules, and you can find the full set at the PMD repo. You have complete control over which rules to enable. Typically, you’d determine which ones are important by agreeing with your teammates on the ones that matter most. After all, their code will be statically analyzed, too!

While at it you could also point them to static code analyzers such as error_prone, spotbugs and pmd (use all 3 at once - they complement each other in detecting different issues).