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This Week in Apps: Spotify debuts a Clubhouse rival, Facebook tests Audio Rooms in US, Amazon …

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Welcome back to This Week in Apps, the weekly TechCrunch series that recaps the latest in mobile OS news, mobile applications and the overall app economy.

The app industry continues to grow, with a record 218 billion downloads and $143 billion in global consumer spend in 2020. Consumers last year also spent 3.5 trillion minutes using apps on Android devices alone. And in the U.S., app usage surged ahead of the time spent watching live TV. Currently, the average American watches 3.7 hours of live TV per day, but now spends four hours per day on their mobile devices.

Apps aren’t just a way to pass idle hours — they’re also a big business. In 2019, mobile-first companies had a combined $544 billion valuation, 6.5x higher than those without a mobile focus. In 2020, investors poured $73 billion in capital into mobile companies — a figure that’s up 27% year-over-year.

This week we’re looking at more Clubhouse competitors, including Facebook’s first test of its Live Audio Rooms in the U.S. and Spotify’s launch of its Greenroom app for live discussions across an array of topics. Also, Amazon is reducing its Appstore fees, after similar moves by Apple and Google.

This Week in Apps will soon be a newsletter! Sign up here: techcrunch.com/newsletters

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Spotify launches its Clubhouse competitor

Image Credits: Spotify

In March, Spotify announced it was acquiring the company behind the sports-focused audio app Locker Room to help speed its entry into the live audio market. This week, the company made good on that deal with the launch of Spotify Greenroom, a new mobile app and likely Clubhouse rival, that allows Spotify users worldwide to join or host live audio rooms, and optionally turn those conversations into podcasts.

The Spotify Greenroom app itself is based on Locker Room’s existing code, with the earlier Locker Room app basically updating to become Greenroom. To join the new app, Spotify users sign in with their current Spotify account information. They’re then walked through an onboarding experience designed to connect them with their interests. Spotify considers the app a soft launch, as it has plans to announce shows later this summer. It’s also funding shows through a new Creator Fund, whose details have not yet been revealed at this time.

Longer-term, the company believes it will be able to take advantage of its personalization tech to make smart recommendations about live shows, based on what music or podcasts a user listens to, and could notify users when favorite creators go live.

The bigger advantage Spotify has here is that its Greenroom sessions are recorded. After a show wraps, the creator can request an audio file which they can then turn into a podcast episode. This ability to straddle both worlds of live and recorded audio could prove to be more useful as the post-COVID world opens up, and users are no longer stuck at home, bored, able to tune in at any time to audio programs.

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Amazon lowers its cut of app developer revenues

Amazon this week quietly announced it would follow in the footsteps of app store giants Apple and Google with its introduction of the Amazon Appstore Small Business Accelerator Program. The new program will reduce the commissions Amazon takes on app developer revenues for qualifying smaller businesses. Previously, Amazon’s Appstore took a 30% cut of revenue, including that from in-app purchases. Now, it will take only 20% from developers who earned up to $1 million in the prior calendar year. The company also said developers with less than $1 million in Appstore revenue in a calendar year will receive 10% of their revenue as promotional credit for AWS services, bringing the total program benefits up to an equivalent of 90% of revenue.

The program’s overall structure is similar to Apple’s App Store Small Business Program, announced in late 2020, which reduced Apple’s cut to 15% for developers who earn up to a $1 million threshold, after which they’re moved to the higher 30% standard rate. This rate then continues as they enter the following year. Google, more recently, took a slightly different course, by lowering the commissions to 15% on the first $1 million of developer revenue earned through the Play billing system each year.

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The lack of attention to Amazon’s announcement, both in the developer community and by press, demonstrates how inconsequential Amazon’s own Appstore has become in the greater app ecosystem.

Platforms: Google

Android announced several new features which will roll out this summer, including starring text messages to easily find them later, getting contextual Emoji Kitchen suggestions depending on what you’re typing, as well as updates that emphasize security, safety and accessibility. The latter include updates to Google Assistant, Android Auto and Google’s Gaze detection feature.

A teardown of the newly released Google Play Services app (v.21.24.13) suggests Google is working on a “Find My Device” network that would allow Android users to locate your phone and other devices, similar to Apple’s “Find My.”

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Google apps will return to Honor devices with the launch of the Honor 50 series devices. The company had not been able to ship Google apps, including the Play store, on its phones due to parent company Huawei’s placement on the U.S.’s entity list, which forced Google to pull its license. But Huawei sold off Honor last year, allowing it to work with Google again.

Google introduced AppSearch in Jetpack, which is now available in Alpha. AppSearch is an on-device search library that provides high-performance and feature-rich full-text search functionality, said Google, and lives completely on-device, allowing for offline search.

E-commerce/Marketplaces

Mobile-first marketplace OfferUp, which connects local buyers and sellers, hired a new CEO. The company brought on former Booking.com managing director Todd Dunlap as CEO, while co-founder and former CEO Nick Huzar will remain as chief product officer.

Social

After lawsuits, injuries and deaths, Snapchat finally removed its controversial “speed filter” which displays how fast a user was going at the time of posting. Critics argued the sticker encouraged reckless driving, as teens would try to post themselves traveling at excess speeds.

Snapchat launched Creative Kit for Spotlight, which will allow third-party apps to publish directly to Snap’s TikTok rival, Spotlight, similar to TikTok’s SDK. Early adopters include Videoleap, Beatleap by Lightricks, Splice, Powder and Pinata Farms.

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ByteDance revenues more than doubled in the past year thanks to TikTok. According to an internal memo, ByteDance saw a 111% increase in revenues, to $34.3 billion, and a 93% increase in gross profit, to $19 billion in 2020.

Instagram’s TikTok rival, Reels, is rolling out ads worldwide. The ads will be up to 30 seconds in length, like Reels themselves, and vertical in format, similar to ads found in Instagram Stories. Also like Reels, the new ads will loop, and people will be able to like, comment on, and save them, the same as other Reels videos.

Twitter said it’s considering a new feature that would allow users to untag themselves from tweets, in order to control unwanted attention, like harassment and abuse. The feature could be useful when troll armies attack at scale before a user can block and report attacks or Twitter has a chance to respond.

Messaging

WhatsApp for iOS is making it easier for users to search for stickers. With a coming update, already live on TestFlight, users will be able to type a word or emoji and WhatsApp will animate the sticker button if a matching sticker is found.

Streaming & Entertainment

Image Credits: Apple

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Apple Podcasts Subscriptions went live across more than 170 countries and regions this week. First unveiled this spring, subscriptions allow listeners to unlock additional benefits for their favorite podcasts, including things like ad-free listening, early access to new episodes, bonus material, exclusives or whatever else the podcast creator believes will be something their fans will pay for.

✨ Facebook CEO Mark Zuckerberg this week hosted the first test of Facebook’s Clubhouse competitor, Live Audio Rooms, in the U.S. The exec was joined by Facebook VP and Head of Facebook Reality Labs Andrew “Boz” Bosworth, Head of Facebook App Fidji Simo and three Facebook Gaming creators. It’s pretty incredible that Zuckerberg only months ago was appearing on Clubhouse to talk about the future of audio-based networking before essentially cloning the Clubhouse experience for Facebook’s own platform.

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Streaming app Deezer launched a new iOS app, Deezer for Creators, which allows musicians and podcasters to track trends, audience insights and more, similar to Spotify for Artists.

An app for pirated movies and TV that disguised itself as a Sudoku game climbed up the App Store charts this week, before being pulled by Apple. The app, Zoshy+, seems to have circumvented App Review by taking advantage of server-side controls.

In a change that represents a significant shift underway in the creators economy, TikTok signed on as creator conference VidCon’s title sponsor for 2021, taking the spot formerly held by YouTube. The latter will still be involved as a secondary sponsor.

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Apple-owned music identification and discovery app Shazam announced this week it had surpassed 1 billion Shazams per month. The company noted it took 10 years for Shazam to reach its first billion tags. Less than 10 years after that, Shazam has crossed 1 billion monthly recognitions and has successfully matched over 50 billion tags with over 51 million songs. At WWDC, Apple announced its plans for Shazam’s future with the launch of ShazamKit, which brings Shazam’s audio identification capabilities to third-party apps.

Gaming

Popular mobile game PUBG Mobile returned to India after being banned more than nine months ago. The game was banned as part of the country’s decision to boot out over 200 apps with links to China due to national security concerns. The new game has been rebranded to Battlegrounds Mobile India, but is largely the same same as before, but “with data compliance, green blood, and a constant reminder that you’re in a ‘virtual world’ with such messaging present as you start a game and when you’re in menus,” said IGN India editor Rishi Alwani.

Pokémon Go creator Niantic is working with Hasbro on a new AR game. Transformers: Heavy Metal, is being built by Very Very Spaceship for Niantic, and is scheduled for a 2021 release. The company has around a dozen games in development, including a collaboration with Nintendo to adapt its Pikmin game, and a game based on the board game Settlers of Catan.

An upcoming Apple Arcade update will bring a new, special edition of Alto’s Odyssey, a new Angry Birds title called Angry Birds Reloaded and a remastered Doodle God Universe. The update will be the largest since April.

Amazon’s cross-platform cloud gaming service Luna will open up priority access during Prime Day, June 21-22, meaning all Prime members will be able to access the service without an invite.

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Mobile users worldwide downloaded 30% more games in the first quarter of 2021 than in the fourth quarter of 2019, and spent a record-breaking $1.7 billion per week in mobile games in Q1 2021, up 40% from pre-pandemic levels, per a new App Annie/IDC report.

Image Credits: App Annie

Productivity

An email that surfaced during the Epic trial discussed the issue of Apple’s Files app ranking first when users searched for a competitor’s app, Dropbox, for 11 months. The app had been manually boosted, the emails seemed to reveal. But Apple this week stated the issue was due to the Files app having a Dropbox integration. Apple put Dropbox in the metadata, causing it to rank higher — an explanation that doesn’t match up with the internal emails.

Home Automation

Third-party Alexa devices can now incorporate setup for their products in the Alexa app, thanks to an update to Alexa Voice Services.

Although Samsung’s SmartThings is no longer making its own smart home hardware, the company this week launched a new SmartThings mobile app on Android, which aims to make it simpler to get to actions and automations. The app includes a new Favorites section to replace the existing home screen, a Life section where users can explore new SmartThings services, plus pages for Devices, Automations and a Menu. The iPhone version will arrive soon.

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An update to the Wyze mobile app added support for Google Home and Google Assistant, allowing users to control smart home devices with voice commands.

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Government & Policy

A report published this week by U.S. advocacy group Fight for the Future and China-based GreatFire highlighted government censorship of LGBTQ+ apps around the world, due to government restrictions. It documented 1,377 cases of app access restrictions across 152 App Stores. However, the study contained several inaccuracies, Apple pointed out. For example, Grindr and Scruff are both available worldwide in the App Store, despite what the report said. Also, none of the 27 apps mentioned in the report with regard to China had been removed by Apple. Of the total 64 apps monitored, only four had been removed by a particular country because of legal issues.

Security & Privacy

A security bug in Google’s Android app, installed over 5 billion times, could have allowed attackers to steal personal data from a user’s device. Google says it fixed the vulnerability last month and found no evidence it had ever been exploited.

💰 Messaging social network IRL raised $170 million in a Series C round led by SoftBank’s Vision Fund 2, valuing the social events calendar and group chat app at $1.17 billion. New investor Dragoneer also participated in the oversubscribed round, alongside returning investors Goodwater Capital, Founders Fund and Floodgate. To date, IRL has raised over $200 million.

🤝 Delivery service Gopuff, which is available on web and mobile, acquired fleet management platform rideOS for $115 million. This acquisition comes a few months after the delivery startup announced a $1.15 billion funding round at a $8.9 billion valuation.

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🤝 Spotify acquired Podz, a podcast delivery platform focused on solving issues around podcast discovery. Podz has been using machine learning to choose clips that can help introduce shows to new listeners. The startup had raised $2.5 million in pre-seed funding ahead of its acquisition. Deal terms weren’t disclosed.

📈  PUBG Mobile maker Krafton is preparing to raise $5 billion in a South Korean IPO, expected to be the country’s largest ever. The company will sell more than 10 million shares at 458,000 won to 557,000 won apiece, a filing said. It will finalize the price July 9 and list on July 22.

💰 Mobile banking app Novo, which targets an SMB customer base, raised $40.7 million in Series A funding, after growing its user base to 100,000 businesses.

💰 Mobile banking app FamPay, aimed at Indian teens, raised $38 million in Series A funding. Investors include Elevation Capital, General Catalyst, Rocketship VC, Greenoaks Capital, and others, and makes for one of India’s largest Series A rounds to date.

💰 Apna, a jobs app built by an Apple alum, raised $70 million in Series B funding co-led by Insight Partners and Tiger Global, valuing the business at $570 million. The app aims to help blue and gray-collar workers upskill themselves, find communities, and land jobs.

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🤝 WordPress.com owner Automattic acquired popular journaling app Day One. The app has been downloaded more than 15 million times since its March 2011 launch on the Mac and iTunes App Store, offering users a private place to share their thoughts. Since then, it’s been awarded the App Store Editor’s Choice, App of the Year and the Apple Design Award, along with praise from various reviewers. Deal terms were not disclosed. Day One had been bootstrapped and self-funded for 10 years. The app will further integrate with other Automattic-owned writing platforms, including WordPress.com and Tumblr.

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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.

First seen at developers.facebook.com

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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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