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Introducing Facebook Graph API v10.0 and Marketing API v10.0

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Today, we are releasing Facebook Graph API v10.0 and Marketing API v10.0.

This release has many announcements, updates, deprecations and “undeprecations” that may impact how your application(s) integrate with our platform. This post outlines these updates and the necessary steps developers need to take to avoid disruption where applicable.

Detailed information regarding all the changes is provided below. For technical specifications, please visit our v10.0 changelog.

Visit the Platform Initiatives Hub to learn more about our Developer Platform.

Announcements and Updates

Data Use Checkup certification

To avoid losing your Facebook Platform API access, please complete Data Use Checkup (DUC) within 60 days of being enrolled. You can complete DUC by going to the “My Apps” page in your App Dashboard.

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Last fall, Facebook launched Data Use Checkup (DUC), a new annual requirement for Developers to certify their API access and data use comply with the Facebook Platform Terms and Developer Policies.

As part of our gradual rollout to all apps, you’ll receive a due date and further instructions via a developer alert, along with an email to the registered contact address and a notification in your Required Actions list on the App Dashboard.

For detailed instructions on completing DUC, you can refer to our developer documentation. You can also find out more in the DUC dedicated FAQs section.

Groups API 90-Day Data Access Levels and Data Limits:

Starting on May 24, 2021, when utilizing the Groups API, applications’ available data access window will be limited to 90 days.

We are also introducing tiered Groups API access for consumer applications based on if they are in Development Mode or Live Mode. Groups API permissions will now behave similarly to Standard and Advanced Access for business applications.

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Today, apps in Development Mode can access all contents of Public Groups & Private Groups where the developer is an admin. After completing App Review, these apps can access all content from groups that have installed the app.

On May 24, 2021, apps in Development Mode can only access their own content in Public Groups & Private Groups where the developer is an admin. This means the app will not have access to content that other Groups users (without roles on the app) have published, such as posts and comments.

Learn more in the v10.0 changelog

Access Levels for public_profile and email permissions for Business Apps

As part of our ongoing initiatives to safeguard and protect user data, we’ve added the public_profile (i.e. Default Public Profile Fields) and email permissions to the access levels model we announced with Graph v8.0 for new and existing business type apps.

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Newly created business type apps now start with Standard Access for the public_profile and email permissions. When an app has Standard Access to public_profile, only users with a role on the app or business that’s claimed the app can search for or log into the app. If you need external users to log into your app, app admins can request Advanced Access in the App Dashboard. This access will be auto-granted.

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Learn more in the v10.0 changelog

Data Deletion Requests Reminders

As announced on November 10, 2020, Consumer & Gaming app types submitting for App Review or transitioning from Development Mode to Live Mode will be required to provide a data deletion request callback, or a URL with explicit instructions for users on how to request deletion of their data. We plan to enforce this requirement by November 10, 2022 and will remind app developers 60-days prior to enforcement.

Graph Ads Insight API Change Options for Retention Reduction Project

Ads Manager UI will no longer support reporting of Ads Insights metrics data older than 37 months. The API will return an error when the request contains date ranges beyond the 37 month retention. We are disabling the ‘date_preset = lifetime’ and replacing it with ‘date_preset = maximum.’

Graph API v10.0 changes to Ads Insight, except for ‘date_preset = lifetime,’ will be applied to all other versions of the Graph API after 90 days. The specific field called does not need to change. There will be preset functionality, and all “Lifetime” calls will default to 37 month retention as maximum.

This change will not impact data related to ad creatives, audiences, delivery settings, or ad object names, and there will be no change to the calculation of existing metrics. This change will apply across Ads Reporting surfaces, including Ads Manager, Ads Reporting, and Lightweight Ad Experiences.

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  • Day of Release 2/23/2021:
    • Version 10.0
      • Enable ‘date_preset = maximum’
      • Disable ‘date_preset = lifetime’
      • Return error when request contains explicit date ranges that are beyond the 37 month retention
    • Versions 9.0 and below
      • No change to current functionality
  • 90 Days Post Release – 05/24/2021:
    • Versions 9.0 and below
      • Enable ‘date_preset = maximum’ (same as new version)
      • Return error when request contains explicit date ranges that are beyond the 37 month retention (same as new version)
      • date_preset = lifetime’ can still work but it only return data within 37 months, that is to say, it behaves like maximum
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Learn more in the v10.0 changelog

New Deletion Policy for externally-owned data on ThreatExchange

Beginning May 24, 2021, when partners set an expire_time on the data they upload to ThreatExchange, we will “hard delete” data at the expiration time mentioned. If you wish to indicate data is no longer valid, set the “expired_on” field to delete automatically.

Formerly, “soft” delete meant that we label expired content as expired. We no longer support soft deletes, and after content expires, it will be hard deleted and will no longer be visible in ThreatExchange.

Additionally, all non-Facebook ThreatDescriptors in ThreatExchange will be hard deleted once they expire. The expiration time will continue to be set by the creator of the ThreatDescriptor. Deletion will then begin on all expired ThreatDescriptors not created by Facebook. If your application currently has expired ThreatDescriptors that you do not want to delete when this policy goes into effect, you must push back the expiration date or set it to “0” to ensure that the data never expires.

Click here to learn more about the changes

Targeting Changes in new API

On May 24, 2021, we’ll no longer ask for location_spec and country parameters when creating a Lookalike Custom Audience across all versions of the API.

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When creating a Lookalike Audience, the user selects the Lookalike source and the desired ratio. The location for the Lookalikes will be defined by the country location in the campaign’s targeting specification. There will be no impact on existing campaigns given this change. This change will only impact new and edited campaigns.

Launching new Platform Live Status page

The new Facebook’s Business Status page will increase transparency and proactive communication with external developers using Facebook’s Platform Products. This page will provide a centralized place to check the platform status of Facebook’s business products (Ads, WhatsApp Business API, Facebook Developer Platform, etc.). The page is currently live for Ads Manager and Whatsapp Business API, and additional business products will be added through May 2021.

Deprecations

Deprecation of the Go Live Dialog

On May 24, 2021, we will be deprecating the Go Live Dialog (GLD) SDK. This deprecation of a legacy live production tool will occur across all versions of the API. This change will only impact live broadcasting on Facebook. We encourage developers to use the Live Producer and Live API for all bespoke live production solutions.

Solutions to go live once the GLD SDK is deprecated:

  • Access Live Producer directly via facebook.com/live/producer (or other entry points on Facebook such as the “Live Video” button on a Page or Profile)
  • Integrate any custom solutions with the Live API
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Learn more in the v10.0 changelog

Connections Targeting Deprecation

On May 24, 2021, Connections Targeting will no longer be supported when creating/editing an adset. This requirement will only impact new and edited campaigns, and there will be no impact on existing campaigns.

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Learn more in the v10.0 changelog

Undeprecations

Undeprecate endpoints gr:post:Business/business_users, gr:post:Business/system_users, and gr:post:User/access_tokens

When we launched Graph API v9.0, we restricted access to three business endpoints:

  • gr:post:Business/business_users
  • gr:post:Business/system_users
  • gr:post:User/access_tokens

As of the launch of Graph v10.0, we will re-open these endpoints to API calls when apps meet either of these two conditions:

  • The target business is the owning business of the calling app
  • The target business is a child business (in a Business 2-Tier model) of the owning business of the calling app

Learn more in the v10.0 changelog

Action Item Calendar

Marketing API Version Deprecations:

As part of our Marketing API versioning schedule, please note the upcoming Marketing API version deprecations:

  • March 3, 2021: Marketing API v7.0 will be deprecated removed from the platform
  • May 4, 2021: Marketing API v8.0 will be deprecated removed from the platform
  • August 25, 2021: Marketing API v9.0 will be deprecated removed from the platform

To avoid disruption to your business, we recommend migrating all calls to the latest Marketing API version that launched today. (Visit the changelog for more details).

Graph API Version Deprecations:

As part of our Graph API versioning schedule, please note the upcoming Graph API version deprecations listed below:

  • May 4, 2021: Graph API v3.2 will be deprecated removed from the platform
  • August 3,2021: Graph API v3.3 will be deprecated removed from the platform

General Deprecations

  • May 24, 2021:
    • Go Live Dialog will be deprecated and removed from the platform
    • Connections Targeting will be deprecated and removed from the platform

Developer Action Required

  • Rolling Enrollment: Data Use Checkup certification
    • Once enrolled, completion required within 60 days
    • DUC dedicated FAQs section.
  • February 23, 2021: Launch of Graph Ads Insight API changes only for Graph v10.0
  • May 24, 2021:
    • Launch of Graph Ads Insight API changes for Graph v9.0 and below
    • Changes to Access Levels Based on App Review go into effect
      • Groups API data access limited to 90 days
      • Groups API tiered data levels implemented
      • Change to public_profile and Email Permissions for Business Apps goes into effect
    • New “Hard Deletion” data policy on ThreatExchange is implemented
  • November 10, 2022: Data Deletion Requests Deadline

Facebook Developers

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Upcoming Restriction Period for US ads about social issues, elections, or politics

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In recent years, Meta has developed a comprehensive approach to protecting elections on our technologies. These efforts continue in advance of the US 2022 Midterms, which you can read more about in our Newsroom.

Implementing a restriction period for ads about social issues, elections or politics in the US

Consistent with our approach during the US 2020 General Election, we are introducing a restriction period for ads about social issues, elections or politics in the US. The restriction period will run from 12:01 AM PT on Tuesday, November 1, 2022 through 11:59 PM PT on Tuesday, November 8, 2022.

We are putting this restriction period in place again because we found that the restriction period achieves the right balance of giving campaigns a voice while providing additional time for scrutiny of issue, electoral, and political ads in the Ad Library. We are sharing the requirements and key dates ahead of time, so advertisers are able to prepare their campaigns in the months and weeks ahead.

What to know about the ad restriction period in the US

We will not allow any new ads about social issues, elections or politics in the US from 12:01 AM PT on Tuesday, November 1, 2022 through 11:59 PM PT on Tuesday, November 8, 2022.

In order to run ads about social issues, elections or politics in the US during the restriction period, the ads must be created with a valid disclaimer and have delivered an impression prior to 12:01 AM PT on Tuesday, November 1, 2022, but with limited editing capabilities.

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What advertisers can do during the restriction period for eligible ads:

  • Edit bid amount, budget amount and scheduled end date
  • Pause and unpause eligible ads that have already served at least 1 impression with a valid disclaimer prior to the restriction period going into effect
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What advertisers cannot do during the restriction period for eligible ads, includes but is not limited to:

  • Editing certain aspects of eligible ads, such as ad creative (including ad copy, image/video assets, website URL)
  • Editing targeting, placement, optimization or campaign objective
  • Removing or adding a disclaimer
  • Copy, duplicating or boosting ads

See the Help Center for detailed requirements of what is or isn’t allowed during the restriction period.

Planning ahead for key dates

Keep in mind the following dates as you plan your campaign to avoid delays or disapprovals that may prevent your ads from running during the restriction period:

  • By Tuesday, October 18, 2022: Complete the ad authorization process to get authorized to run ads about social issues, elections or politics, which includes setting up an approved disclaimer for your ads.

  • By Tuesday, October 25, 2022: Submit your issue, electoral or political ads in order to best ensure that your ads are live and have delivered at least 1 impression with a valid disclaimer before the restriction period begins.
    • Please ensure that you add your approved disclaimer to these ads by choosing ISSUES_ELECTIONS_POLITICS in the special_ad_categories field. You will not be able to add a disclaimer after 12:01 AM PT on Tuesday, November 1, 2022.

  • Between Tuesday, November 1, 2022 and Tuesday, November 8, 2022: The ad restriction period will be in effect. We will not allow any new ads to run about social issues, elections or politics in the US starting 12:01 AM PT on Tuesday, November 1 through 11:59 PM PT on Tuesday, November 8, 2022.
  • At 12:00 AM PT on Wednesday, November 9, 2022: We will allow new ads about social issues, elections or politics to be published.

As the restriction period approaches, we encourage you to review these ad restriction period best practices to properly prepare ahead of time.

We will continue to provide updates on our approach to elections integrity or on any changes regarding the restriction period via this blog.

Visit the Elections Hub or our FAQ for more advertising resources.

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Signals in prod: dangers and pitfalls

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In this blog post, Chris Down, a Kernel Engineer at Meta, discusses the pitfalls of using Linux signals in Linux production environments and why developers should avoid using signals whenever possible.

What are Linux Signals?

A signal is an event that Linux systems generate in response to some condition. Signals can be sent by the kernel to a process, by a process to another process, or a process to itself. Upon receipt of a signal, a process may take action.

Signals are a core part of Unix-like operating environments and have existed since more or less the dawn of time. They are the plumbing for many of the core components of the operating system—core dumping, process life cycle management, etc.—and in general, they’ve held up pretty well in the fifty or so years that we have been using them. As such, when somebody suggests that using them for interprocess communication (IPC) is potentially dangerous, one might think these are the ramblings of someone desperate to invent the wheel. However, this article is intended to demonstrate cases where signals have been the cause of production issues and offer some potential mitigations and alternatives.

Signals may appear attractive due to their standardization, wide availability and the fact that they don’t require any additional dependencies outside of what the operating system provides. However, they can be difficult to use safely. Signals make a vast number of assumptions which one must be careful to validate to match their requirements, and if not, one must be careful to configure correctly. In reality, many applications, even widely known ones, do not do so, and may have hard-to-debug incidents in the future as a result.

Let us look into a recent incident that occurred in the Meta production environment, reinforcing the pitfalls of using signals. We’ll go briefly over the history of some signals and how they led us to where we are today, and then we’ll contrast that with our current needs and issues that we’re seeing in production.

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The Incident

First, let’s rewind a bit. The LogDevice team cleaned up their codebase, removing unused code and features. One of the features that was deprecated was a type of log that documents certain operations performed by the service. This feature eventually became redundant, had no consumers and as such was removed. You can see the change here on GitHub. So far, so good.

The next little while after the change passed without much to speak about, production continued ticking on steadily and serving traffic as usual. A few weeks later, a report that service nodes were being lost at a staggering rate was received. It was something to do with the rollout of the new release, but what exactly was wrong was unclear. What was different now that had caused things to fall over?

The team in question narrowed the problem to the code change we mentioned earlier, deprecating these logs. But why? What’s wrong with that code? If you don’t already know the answer, we invite you to look at that diff and try to work out what’s wrong because it’s not immediately obvious, and it’s a mistake anyone could make.

logrotate, Enter the Ring

logrotate is more or less the standard tool for log rotation when using Linux. It’s been around for almost thirty years now, and the concept is simple: manage the life cycle of logs by rotating and vacuuming them.

logrotate doesn’t send any signals by itself, so you won’t find much, if anything, about them in the logrotate main page or its documentation. However, logrotate can take arbitrary commands to execute before or after its rotations. Just as a basic example from the default logrotate configuration in CentOS, you can see this configuration:

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 /var/log/cron /var/log/maillog /var/log/messages /var/log/secure /var/log/spooler {     sharedscripts     postrotate         /bin/kill -HUP `cat /var/run/syslogd.pid 2> /dev/null` 2> /dev/null || true     endscript } 

A bit brittle, but we’ll forgive that and assume that this works as intended. This configuration says that after logrotate rotates any of the files listed, it should send SIGHUP to the pid contained in /var/run/syslogd.pid, which should be that of the running syslogd instance.

This is all well and good for something with a stable public API like syslog, but what about something internal where the implementation of SIGHUP is an internal implementation detail that could change at any time?

A History of Hangups

One of the problems here is that, except for signals which cannot be caught in user space and thus have only one meaning, like SIGKILL and SIGSTOP, the semantic meaning of signals is up to application developers and users to interpret and program. In some cases, the distinction is largely academic, like SIGTERM, which is pretty much universally understood to mean “terminate gracefully as soon as possible.” However, in the case of SIGHUP, the meaning is significantly less clear.

SIGHUP was invented for serial lines and was originally used to indicate that the other end of the connection had dropped the line. Nowadays, we still carry our lineage with us of course, so SIGHUP is still sent for its modern equivalent: where a pseudo or virtual terminal is closed (hence tools like nohup, which mask it).

In the early days of Unix, there was a need to implement daemon reloading. This usually consists at least of configuration/log file reopening without restarting, and signals seemed like a dependency-free way to achieve that. Of course, there was no signal for such a thing, but as these daemons have no controlling terminal, there should be no reason to receive SIGHUP, so it seemed like a convenient signal to piggyback onto without any obvious side effects.

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There is a small hitch with this plan though. The default state for signals is not “ignored,” but signal-specific. So, for example, programs don’t have to configure SIGTERM manually to terminate their application. As long as they don’t set any other signal handler, the kernel just terminates their program for free, without any code needed in user space. Convenient!

What’s not so convenient though, is that SIGHUP also has the default behavior of terminating the program immediately. This works great for the original hangup case, where these applications likely aren’t needed anymore, but is not so great for this new meaning.

This would be fine of course, if we removed all the places which could potentially send SIGHUP to the program. The problem is that in any large, mature codebase, that is difficult. SIGHUP is not like a tightly controlled IPC call for which you can easily grep the codebase for. Signals can come from anywhere, at any time, and there are few checks on their operation (other than the most basic “are you this user or have CAP_KILL“). The bottom line is that it’s hard to determine where signals could come from, but with more explicit IPC, we would know that this signal doesn’t mean anything to us and should be ignored.

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From Hangup to Hazard

By now, I suppose you may have started to guess what happened. A LogDevice release started one fateful afternoon containing the aforementioned code change. At first, nothing had gone awry, but at midnight the next day, everything mysteriously started falling over. The reason is the following stanza in the machine’s logrotate configuration, which sends a now unhandled (and therefore fatal) SIGHUP to the logdevice daemon:

 /var/log/logdevice/audit.log {   daily   # [...]   postrotate     pkill -HUP logdeviced   endscript } 

Missing just one short stanza of a logrotate configuration is incredibly easy and common when removing a large feature. Unfortunately, it’s also hard to be certain that every last vestige of its existence was removed at once. Even in cases that are easier to validate than this, it’s common to mistakenly leave remnants when doing code cleanup. Still, usually, it’s without any destructive consequences, that is, the remaining detritus is just dead or no-op code.

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Conceptually, the incident itself and its resolution are simple: don’t send SIGHUP, and spread LogDevice actions out more over time (that is, don’t run this at midnight on the dot). However, it’s not just this one incident’s nuances that we should focus on here. This incident, more than anything, has to serve as a platform to discourage the use of signals in production for anything other than the most basic, essential cases.

The Dangers of Signals

What Signals are Good For

First, using signals as a mechanism to affect changes in the process state of the operating system is well founded. This includes signals like SIGKILL, which are impossible to install a signal handler for and does exactly what you would expect, and the kernel-default behavior of SIGABRT, SIGTERM, SIGINT, SIGSEGV, and SIGQUIT and the like, which are generally well understood by users and programmers.

What these signals all have in common is that once you’ve received them, they’re all progressing towards a terminal end state within the kernel itself. That is, no more user space instructions will be executed once you get a SIGKILL or SIGTERM with no user space signal handler.

A terminal end state is important because it usually means you’re working towards decreasing the complexity of the stack and code currently being executed. Other desired states often result in the complexity actually becoming higher and harder to reason about as concurrency and code flow become more muddled.

Dangerous Default Behavior

You may notice that we didn’t mention some other signals that also terminate by default. Here’s a list of all of the standard signals that terminate by default (excluding core dump signals like SIGABRT or SIGSEGV, since they’re all sensible):

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  • SIGALRM
  • SIGEMT
  • SIGHUP
  • SIGINT
  • SIGIO
  • SIGKILL
  • SIGLOST
  • SIGPIPE
  • SIGPOLL
  • SIGPROF
  • SIGPWR
  • SIGSTKFLT
  • SIGTERM
  • SIGUSR1
  • SIGUSR2
  • SIGVTALRM

At first glance, these may seem reasonable, but here are a few outliers:

  • SIGHUP: If this was used only as it was originally intended, defaulting to terminate would be sensible. With the current mixed usage meaning “reopen files,” this is dangerous.
  • SIGPOLL and SIGPROF: These are in the bucket of “these should be handled internally by some standard function rather than your program.” However, while probably harmless, the default behavior to terminate still seems nonideal.
  • SIGUSR1 and SIGUSR2: These are “user-defined signals” that you can ostensibly use however you like. But because these are terminal by default, if you implement USR1 for some specific need and later don’t need that, you can’t just safely remove the code. You have to consciously think to explicitly ignore the signal. That’s really not going to be obvious even to every experienced programmer.

So that’s almost one-third of terminal signals, which are at best questionable and, at worst, actively dangerous as a program’s needs change. Worse still, even the supposedly “user-defined” signals are a disaster waiting to happen when someone forgets to explicitly SIG_IGN it. Even an innocuous SIGUSR1 or SIGPOLL may cause incidents.

This is not simply a question of familiarity. No matter how well you know how signals work, it’s still extremely hard to write signal-correct code the first time around because, despite their appearance, signals are far more complex than they seem.

Code flow, Concurrency, and the Myth of SA_RESTART

Programmers generally do not spend their entire day thinking about the inner workings of signals. This means that when it comes to actually implementing signal handling, they often subtly do the wrong thing.

I’m not even talking about the “trivial” cases, like safety in a signal handling function, which is mostly solved by only bumping a sig_atomic_t, or using C++’s atomic signal fence stuff. No, that’s mostly easily searchable and memorable as a pitfall by anyone after their first time through signal hell. What’s a lot harder is reasoning about the code flow of the nominal portions of a complex program when it receives a signal. Doing so requires either constantly and explicitly thinking about signals at every part of the application life cycle (hey, what about EINTR, is SA_RESTART enough here? What flow should we go into if this terminates prematurely? I now have a concurrent program, what are the implications of that?), or setting up a sigprocmask or pthread_setmask for some part of your application life cycle and praying that the code flow never changes (which is certainly not a good guess in an atmosphere of fast-paced development). signalfd or running sigwaitinfo in a dedicated thread can help somewhat here, but both of these have enough edge cases and usability concerns to make them hard to recommend.

We like to believe that most experienced programmers know by now that even a facetious example of correctly writing thread-safe code is very hard. Well, if you thought correctly writing thread-safe code was hard, signals are significantly harder. Signal handlers must only rely on strictly lock-free code with atomic data structures, respectively, because the main flow of execution is suspended and we don’t know what locks it’s holding, and because the main flow of execution could be performing non-atomic operations. They must also be fully reentrant, that is, they must be able to nest within themselves since signal handlers can overlap if a signal is sent multiple times (or even with one signal, with SA_NODEFER). That’s one of the reasons why you can’t use functions like printf or malloc in a signal handler because they rely on global mutexes for synchronization. If you were holding that lock when the signal was received and then called a function requiring that lock again, your application would end up deadlocked. This is really, really hard to reason about. That’s why many people simply write something like the following as their signal handling:

 static volatile sig_atomic_t received_sighup;   static void sighup(int sig __attribute__((unused))) { received_sighup = 1; }  static int configure_signal_handlers(void) {   return sigaction(     SIGHUP,     &(const struct sigaction){.sa_handler = sighup, .sa_flags = SA_RESTART},     NULL); }  int main(int argc, char *argv[]) {   if (configure_signal_handlers()) {        /* failed to set handlers */   }    /* usual program flow */    if (received_sighup) {     /* reload */     received_sighup = 0;   }    /* usual program flow */ }  

The problem is that, while this, signalfd, or other attempts at async signal handling might look fairly simple and robust, it ignores the fact that the point of interruption is just as important as the actions performed after receiving the signal. For example, suppose your user space code is doing I/O or changing the metadata of objects that come from the kernel (like inodes or FDs). In this case, you’re probably actually in a kernel space stack at the time of interruption. For example, here’s how a thread might look when it’s trying to close a file descriptor:

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# cat /proc/2965230/stack  [<0>] schedule+0x43/0xd0  [<0>] io_schedule+0x12/0x40  [<0>] wait_on_page_bit+0x139/0x230  [<0>] filemap_write_and_wait+0x5a/0x90  [<0>] filp_close+0x32/0x70  [<0>] __x64_sys_close+0x1e/0x50  [<0>] do_syscall_64+0x4e/0x140  [<0>] entry_SYSCALL_64_after_hwframe+0x44/0xa9

Here, __x64_sys_close is the x86_64 variant of the close system call, which closes a file descriptor. At this point in its execution, we’re waiting for the backing storage to be updated (that’s this wait_on_page_bit). Since I/O work is usually several orders of magnitude slower than other operations, schedule here is a way of voluntarily hinting to the kernel’s CPU scheduler that we are about to perform a high-latency operation (like disk or network I/O) and that it should consider finding another process to schedule instead of the current process for now. This is good, as it allows us to signal to the kernel that it is a good idea to go ahead and pick a process that will actually make use of the CPU instead of wasting time on one which can’t continue until it’s finished waiting for a response from something that may take a while.

Imagine that we send a signal to the process we were running. The signal that we have sent has a user space handler in the receiving thread, so we’ll resume in user space. One of the many ways this race can end up is that the kernel will try to come out of schedule, further unwind the stack and eventually return an errno of ESYSRESTART or EINTR to user space to indicate that we were interrupted. But how far did we get in closing it? What’s the state of the file descriptor now?

Now that we’ve returned to user space, we’ll run the signal handler. When the signal handler exits, we’ll propagate the error to the user space libc’s close wrapper, and then to the application, which, in theory, can do something about the situation encountered. We say “in theory” because it’s really hard to know what to do about many of these situations with signals, and many services in production do not handle the edge cases here very well. That might be fine in some applications where data integrity isn’t that important. However, in production applications that do care about data consistency and integrity, this presents a significant problem: the kernel doesn’t expose any granular way to understand how far it got, what it achieved and didn’t and what we should actually do about the situation. Even worse, if close returns with EINTR, the state of the file descriptor is now unspecified:

“If close() is interrupted by a signal [...] the state of [the file descriptor] is unspecified.”

Good luck trying to reason about how to handle that safely and securely in your application. In general, handling EINTR even for well-behaved syscalls is complicated. There are plenty of subtle issues forming a large part of the reason why SA_RESTART is not enough. Not all system calls are restartable, and expecting every single one of your application’s developers to understand and mitigate the deep nuances of getting a signal for every single syscall at every single call site is asking for outages. From man 7 signal:

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“The following interfaces are never restarted after being interrupted by a signal handler, regardless of the use of SA_RESTART; they always fail with the error EINTR [...]”

Likewise, using a sigprocmask and expecting code flow to remain static is asking for trouble as developers do not typically spend their lives thinking about the bounds of signal handling or how to produce or preserve signal-correct code. The same goes for handling signals in a dedicated thread with sigwaitinfo, which can easily end up with GDB and similar tools being unable to debug the process. Subtly wrong code flows or error handling can result in bugs, crashes, difficult to debug corruptions, deadlocks and many more issues that will send you running straight into the warm embrace of your preferred incident management tool.

High Complexity in Multithreaded Environments

If you thought all this talk of concurrency, reentrancy and atomicity was bad enough, throwing multithreading into the mix makes things even more complicated. This is especially important when considering the fact that many complex applications run separate threads implicitly, for example, as part of jemalloc, GLib, or similar. Some of these libraries even install signal handlers themselves, opening a whole other can of worms.

Overall, man 7 signal has this to say on the matter:

“A signal may be generated (and thus pending) for a process as a whole (e.g., when sent using kill(2)) or for a specific thread [...] If more than one of the threads has the signal unblocked, then the kernel chooses an arbitrary thread to which to deliver the signal.”

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More succinctly, “for most signals, the kernel sends the signal to any thread that doesn’t have that signal blocked with sigprocmask“. SIGSEGV, SIGILL and the like resemble traps, and have the signal explicitly directed at the offending thread. However, despite what one might think, most signals cannot be explicitly sent to a single thread in a thread group, even with tgkill or pthread_kill.

This means that you can’t trivially change overall signal handling characteristics as soon as you have a set of threads. If a service needs to do periodic signal blocking with sigprocmask in the main thread, you need to somehow communicate to other threads externally about how they should handle that. Otherwise, the signal may be swallowed by another thread, never to be seen again. Of course, you can block signals in child threads to avoid this, but if they need to do their own signal handling, even for primitive things like waitpid, it will end up making things complex.

Just as with everything else here, these aren’t technically insurmountable problems. However, one would be negligent in ignoring the fact that the complexity of synchronization required to make this work correctly is burdensome and lays the groundwork for bugs, confusion and worse.

Lack of Definition and Communication of Success or Failure

Signals are propagated asynchronously in the kernel. The kill syscall returns as soon as the pending signal is recorded for the process or thread’s task_struct in question. Thus, there’s no guarantee of timely delivery, even if the signal isn’t blocked.

Even if there is timely delivery of the signal, there’s no way to communicate back to the signal issuer what the status of their request for action is. As such, any meaningful action should not be delivered by signals, since they only implement fire-and-forget with no real mechanism to report the success or failure of delivery and subsequent actions. As we’ve seen above, even seemingly innocuous signals can be dangerous when they are not configured in user space.

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Anyone using Linux for long enough has undoubtedly run into a case where they want to kill some process but find that the process is unresponsive even to supposedly always fatal signals like SIGKILL. The problem is that misleadingly, kill(1)’s purpose isn’t to kill processes, but just to queue a request to the kernel (with no indication about when it will be serviced) that someone has requested some action to be taken.

The kill syscall’s job is to mark the signal as pending in the kernel’s task metadata, which it does successfully even when a SIGKILL task doesn’t die. In the case of SIGKILL in particular, the kernel guarantees that no more user mode instructions will be executed, but we may still have to execute instructions in kernel mode to complete actions that otherwise may result in data corruption or to release resources. For this reason, we still succeed even if the state is D (uninterruptible sleep). Kill itself doesn’t fail unless you provided an invalid signal, you don’t have permission to send that signal or the pid that you requested to send a signal to does not exist and is thus not useful to reliably propagate non-terminal states to applications.

In Conclusion

  • Signals are fine for terminal state handled purely in-kernel with no user space handler. For signals that you actually would like to immediately kill your program, leave those signals alone for the kernel to handle. This also means that the kernel may be able to exit early from its work, freeing up your program resources more quickly, whereas a user space IPC request would have to wait for the user space portion to start executing again.
  • A way to avoid getting into trouble handling signals is to not handle them at all. However, for applications handling state processing that must do something about cases like SIGTERM, ideally use a high-level API like folly::AsyncSignalHandler where a number of the warts have already been made more intuitive.

  • Avoid communicating application requests with signals. Use self-managed notifications (like inotify) or user space RPC with a dedicated part of the application life cycle to handle it instead of relying on interrupting the application.
  • Where possible, limit the scope of signals to a subsection of your program or threads with sigprocmask, reducing the amount of code that needs to be regularly scrutinized for signal-correctness. Bear in mind that if code flows or threading strategies change, the mask may not have the effect you intended.
  • At daemon start, mask terminal signals that are not uniformly understood and could be repurposed at some point in your program to avoid falling back to kernel default behavior. My suggestion is the following:
 signal(SIGHUP, SIG_IGN); signal(SIGQUIT, SIG_IGN); signal(SIGUSR1, SIG_IGN); signal(SIGUSR2, SIG_IGN); 

Signal behavior is extremely complicated to reason about even in well-authored programs, and its use presents an unnecessary risk in applications where other alternatives are available. In general, do not use signals for communicating with the user space portion of your program. Instead, either have the program transparently handle events itself (for example, with inotify), or use user space communication that can report back errors to the issuer and is enumerable and demonstrable at compile time, like Thrift, gRPC or similar.

I hope this article has shown you that signals, while they may ostensibly appear simple, are in reality anything but. The aesthetics of simplicity that promote their use as an API for user space software belie a series of implicit design decisions that do not fit most production use cases in the modern era.

Let’s be clear: there are valid use cases for signals. Signals are fine for basic communication with the kernel about a desired process state when there’s no user space component, for example, that a process should be killed. However, it is difficult to write signal-correct code the first time around when signals are expected to be trapped in user space.

Signals may seem attractive due to their standardization, wide availability and lack of dependencies, but they come with a significant number of pitfalls that will only increase concern as your project grows. Hopefully, this article has provided you with some mitigations and alternative strategies that will allow you to still achieve your goals, but in a safer, less subtly complex and more intuitive way.

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Meet the Developers – Linux Kernel Team (David Vernet)

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Credit: Larry Ewing (lewing@isc.tamu.edu) and The GIMP for the original design of Tux the penguin.

Intro

For today’s interview, we have David Vernet, a core systems engineer on the Kernel team at Meta. He works on the BPF (Berkeley Packet Filter) and the Linux kernel scheduler. This series highlights Meta Software Engineers who contribute to the Linux kernel. The Meta Linux Kernel team works with the broader Linux community to add new features to the kernel and makes sure that the kernel works well in Meta production data centers. Engineers on the team work with peers in the industry to make the kernel better for Meta’s workloads and to make Linux better for everyone.

Tell us about yourself.

I’m a systems engineer who’s spent a good chunk of his career in the kernel space, and some time in the user-space as well working on a microkernel. Right now, I’m focusing most of my time on BPF and the Linux kernel scheduler.

I started my career as a web developer after getting a degree in math. After going to grad school, I realized that I was happiest when hacking on low-level systems and figuring out how computers work.

As a kernel developer at Meta, what does your typical day look like?

I’m not a maintainer of any subsystems in the kernel, so my typical day is filled with almost exclusively coding and engineering. That being said, participating in the upstream Linux kernel community is one of the coolest parts of being on the kernel team, so I still spend some time reading over upstream discussions. A typical day goes something like this:

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  1. Read over some of the discussions taking place on various upstream lists, such as BPF and mm. I usually spend about 30-60 minutes or so per day on this, though it depends on the day.

  2. Hack on the project that I’m working on. Lately, that’s adding a user-space ringbuffer map type to BPF.

  3. Work on drafting an article for lwn.net.

What have you been excited about or incredibly proud of lately?

I recently submitted a patch-set to enable a new map type in BPF. This allows user-space to publish messages to BPF programs in the kernel over the ringbuffer. This map type is exciting because it sets the stage to enable frameworks for user-space to drive logic in BPF programs in a performant way.

Is there something especially exciting about being a kernel developer at a company like Meta?

The Meta kernel team has a strong upstream-first culture. Bug fixes that we find in our Meta kernel, and features that we’d like to add, are almost always first submitted to the upstream kernel, and then they are backported to our internal kernel.

Do you have a favorite part of the kernel dev life cycle?

I enjoy architecting and designing APIs. Kernel code can never crash and needs to be able to run forever. I find it gratifying to architect systems in the kernel that make it easy to reason about correctness and robustness and provide intuitive APIs that make it easy for other parts of the kernel to use your code.

I also enjoy iterating with the upstream community. It’s great that your patches have a whole community of people looking at them to help you find bugs in your code and suggest improvements that you may never have considered on your own. A lot of people find this process to be cumbersome, but I find that it’s a small price to pay for what you get out of it.

Tell us a bit about the topic you presented at the Linux Plumbers Conference this year.

We presented the live patch feature in the Linux kernel, describing how we have utilized it at Meta and how our hyper-scale has shown some unique challenges with the feature.

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What are some of the misconceptions about kernel or open source software development that you have encountered in your career?

The biggest misconception is that it’s an exclusive, invite-only club to contribute to the Linux kernel. You certainly must understand operating systems to be an effective contributor and be ready to receive constructive criticism when there is scope for improvement in your code. Still, the community always welcomes people who come in with an open mind and want to contribute.

What resources are helpful in getting started in kernel development?

There is a lot of information out there that people have written on how to get integrated into the Linux kernel community. I wrote a blog post on how to get plugged into Linux kernel upstream mailing list discussions, and another on how to submit your first patch. There is also a video on writing and submitting your first Linux kernel patch from Greg Kroah-Hartman.

In terms of resources to learn about the kernel itself, there are many resources and books, such as:

Where can people find you and follow your work?

I have a blog where I talk about my experiences as a systems engineer: https://www.bytelab.codes/. I publish articles that range from topics that are totally newcomer friendly to more advanced topics that discuss kernel code in more detail. Feel free to check it out and let me know if there’s anything you’d like me to discuss.

To learn more about Meta Open Source, visit our open source site, subscribe to our YouTube channel, or follow us on Twitter, Facebook and LinkedIn.

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