|RFC-0003: Fuchsia logging guidelines|
Best practices for using log severities. Applications of log severities in testing and in-field metrics.
|Date submitted (year-month-day)||2020-06-03|
|Date reviewed (year-month-day)||2020-06-11|
Logging is used extensively throughout Fuchsia's code, but messages are logged with inconsistent severities. This diminishes the value of information in the logs. Below we propose semantic meaning for different log severity levels, and implications for logging at certain severities in various environments.
Motivation and problem statement
Logs are the most common way to send a diagnostics signal about a change in the internal state of running software. They offer valuable information that can be used to detect and correct faults in software and products.
Historically, logs contain a free-text message, as well as an enumerated severity value. The severity value offers an indication of how important the message is, differentiating the signal from the noise. However, the application of severity levels is made inconsistently throughout Fuchsia code and its dependencies, resulting in logs that are difficult to use both in manual troubleshooting and in automated analysis.
By providing common guidelines for how to use log severities, and by creating processes that attach consequences to certain ways of using the logs, we hope to create a virtuous cycle that will generate more diagnostics value out of logs while also improving the signal-to-noise ratio. For instance, we would like the frequency of ERROR log entries to be one of the proxies to system stability.
Log severity levels
Fuchsia supports a number of standard log severity levels:
This indicates an unrecoverable error and that the component should self-terminate shortly after logging the message. This typically indicates that a core invariant in the system has been violated and proceeding further could lead to data corruption or security vulnerabilities.
This log level should be used sparingly. This log level may indicate a hardware issue or software bug that needs to be fixed. Since the given message will be the final message logged before the component self-terminates, its contents should indicate the reason for termination.
This log level indicates an undesired event has occurred that the program can recover from. An ERROR log entry's appearance in the logs is an indication of incorrect program behavior that needs to be fixed. Developers should strive for a one-to-one correspondence between a bug in the bug tracker and the ERROR log entry. In other words, for every unique bug, there should be an ERROR log entry, and for every ERROR log entry, there should be a separate bug. Developers should attempt to maintain this correspondence in code where possible. Note that the bug might not be in the code issuing the log statement. The bug may be in one of the callers or one of its dependencies.
This log level and above will serve as a signal to system stability metrics. Additionally, this log level and above will typically be present in bug reports. This log message will be consumed by people outside the team that introduced the message and so it is expected to provide sufficient context to guide the reader toward the root cause of the problem.
This log level indicates an unexpected event has occurred within the normal operation of the system. These unexpected events may be environmental and therefore outside the control of the system itself. For example, a lost network connection may be logged as a WARNING or invalid input that prevents a program from proceeding further may be logged as WARNING. This log level can be used to find the root cause that led to a problem (typically an ERROR log entry). This log level may serve to make salient that an uncommon code path was taken as a result of the aforementioned unexpected event.
This is typically the lowest log level present in bug reports. This indicates that a noteworthy state change has occurred in a program. This log level will be used to indicate the context that led to a problem (typically an ERROR log entry). As with higher log levels, these logs will be consumed by other teams for diagnosis and so context is critical.
Long-lasting states should be reported using Component Inspection. Such conditions should not be logged if they have not changed. For example, instead of logging "[INFO] no configs found" at a fixed interval, the Inspect data could contain a flag "configs_found = false," or even "config_count = 0."
This log level is typically used in engineering environments, such as during tests or while reproducing an issue in a commit queue bot. Logs at severity level DEBUG and below are typically not collected in bug reports, so there should be no expectation to receive DEBUG and lower logs in bug reports. This log level is typically more verbose than INFO and helps individual teams better understand the state of the system they're developing.
This log level will typically be used by individual teams and perhaps in the commit queue and continuous integration. This log level is typically used to indicate that a stage in a multistage process has completed.
Consequences of logging
Having established guidelines for choosing log severity levels, we propose several ways in which we automatically treat logs based on their severity.
Logs as in-field analytics
We choose to treat the presence of an ERROR log entry as an indication of a software bug that needs to be fixed. It's safe to assume that a software author would be interested in knowing that their software is logging errors outside their local environment. Therefore we will count the presence of ERROR and FATAL log entries broken down by source component instance, report them via Cobalt, and track these counters as an in-field stability metric.
To view an overview of logging statistics on a device, use the following command:
Component Stats (cs) provides a log stats mode that parses Archivist's inspect tree to provide a summary view of logs generated per component. This view enables engineers to see the types of logs their component generates and the frequency with which it generates various logs relative to other components.
Logs as (un)expected behavior under test
It's safe to assume that a software author would be interested in knowing that their software is logging errors under controlled conditions set during a test, when such behavior is not expected. Therefore we will introduce behavior into the test runtime that will cause tests to fail if unexpected ERROR logs are observed within the test realm.
The test runtime behavior described above has been implemented, tested, and documented.
Logging with FATAL severity typically indicates that a core invariant in the system has been violated and proceeding further could lead to data corruption or security vulnerabilities. Termination is the safest course of action in this case.
The component framework can securely identify the source of a log to a component level. Within a component, modules will likely be self-attested and so cannot be trusted to the same degree as component-level identification.
Any logging analytics should avoid exposing any Personally Identifiable Information (PII). For example, we should not expose the full set of components on a user's device in off-device metrics nor the full text of any log messages that might contain any PII.
We intend to publish these logging guidelines as a Fuchsia Development Guide as well.
Drawbacks, alternatives, and unknowns
The proposed changes above create a system of incentives and disincentives to log in certain ways. The intention is to get more value out of logs - notice the important log entries by giving them appropriately high severity, while also reducing the number of less significant log entries with overly high severity.
We consider the risk that these incentives will promote unwanted behaviors, such as avoiding ERROR log entries entirely (even under conditions that call for it). We believe that the motivation to create better software will drive developers to make responsible decisions in how they react to the proposed changes.
We will test the above hypothesis by monitoring developer behavior via analysis of bug reports and occasional interviews with team members, and course-correct as necessary.
Prior art and references
The Google Testing Blog provides some basic guidelines on logging levels.