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17
Chris Wellons posted a good review of why large chunks of the C library are terrible, especially if you are coding on Windows - good fun if you like staring into the abyss. He followed up with let’s write a setjmp which is fun in a more positive way. I was also pleased to learn about __builtin_longjmp! There’s a small aside in this article about the signal mask, which skates past another horrible abyss - which might even make it sensible to DIY longjmp. Some of the nastiness can be seen in the POSIX rationale for sigsetjmp which says that on BSD-like systems, setjmp and _setjmp correspond to sigsetjmp and setjmp on System V Unixes. The effect is that setjmp might or might not involve a system call to adjust the signal mask. The syscall overhead might be OK for exceptional error recovery, such as Chris’s arena out of memory example, but it’s likely to be more troublesome if you are implementing coroutines. But why would they need to mess with the signal mask? Well, if you are using...
over a year ago

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More from Tony Finch's blog

constantly divisionless random numbers

Last year I wrote about inlining just the fast path of Lemire’s algorithm for nearly-divisionless unbiased bounded random numbers. The idea was to reduce code bloat by eliminating lots of copies of the random number generator in the rarely-executed slow paths. However a simple split prevented the compiler from being able to optimize cases like pcg32_rand(1 << n), so a lot of the blog post was toying around with ways to mitigate this problem. On Monday while procrastinating a different blog post, I realised that it’s possible to do better: there’s a more general optimization which gives us the 1 << n special case for free. nearly divisionless Lemire’s algorithm has about 4 neat tricks: use multiplication instead of division to reduce the output of a random number generator modulo some limit eliminate the bias in (1) by (counterintuitively) looking at the lower digits fun modular arithmetic to calculate the reject threshold for (2) arrange the reject tests to avoid the slow division in (3) in most cases The nearly-divisionless logic in (4) leads to two copies of the random number generator, in the fast path and the slow path. Generally speaking, compilers don’t try do deduplicate code that was written by the programmer, so they can’t simplify the nearly-divisionless algorithm very much when the limit is constant. constantly divisionless Two points occurred to me: when the limit is constant, the reject threshold (3) can be calculated at compile time when the division is free, there’s no need to avoid it using (4) These observations suggested that when the limit is constant, the function for random numbers less than a limit should be written: static inline uint32_t pcg32_rand_const(pcg32_t *rng, uint32_t limit) { uint32_t reject = -limit % limit; uint64_t sample; do sample = (uint64_t)pcg32_random(rng) * (uint64_t)limit); while ((uint32_t)(sample) < reject); return ((uint32_t)(sample >> 32)); } This has only one call to pcg32_random(), saving space as I wanted, and the compiler is able to eliminate the loop automatically when the limit is a power of two. The loop is smaller than a call to an out-of-line slow path function, so it’s better all round than the code I wrote last year. algorithm selection As before it’s possible to automatically choose the constantly-divisionless or nearly-divisionless algorithms depending on whether the limit is a compile-time constant or run-time variable, using arcane C tricks or GNU C __builtin_constant_p(). I have been idly wondering how to do something similar in other languages. Rust isn’t very keen on automatic specialization, but it has a reasonable alternative. The thing to avoid is passing a runtime variable to the constantly-divisionless algorithm, because then it becomes never-divisionless. Rust has a much richer notion of compile-time constants than C, so it’s possible to write a method like the follwing, which can’t be misused: pub fn upto<const LIMIT: u32>(&mut self) -> u32 { let reject = LIMIT.wrapping_neg().wrapping_rem(LIMIT); loop { let (lo, hi) = self.get_u32().embiggening_mul(LIMIT); if lo < reject { continue; } else { return hi; } } } assert!(rng.upto::<42>() < 42); (embiggening_mul is my stable replacement for the unstable widening_mul API.) This is a nugatory optimization, but there are more interesting cases where it makes sense to choose a different implementation for constant or variable arguments – that it, the constant case isn’t simply a constant-folded or partially-evaluated version of the variable case. Regular expressions might be lex-style or pcre-style, for example. It’s a curious question of language design whether it should be possible to write a library that provides a uniform API that automatically chooses constant or variable implementations, or whether the user of the library must make the choice explicit. Maybe I should learn some Zig to see how its comptime works.

a week ago 11 votes
random numbers from pcg32 at 200 Gbit/s

One of the neat things about the PCG random number generator by Melissa O’Neill is its use of instruction-level parallelism: the PCG state update can run in parallel with its output permutation. However, PCG only has a limited amount of ILP, about 3 instructions. Its overall speed is limited by the rate at which a CPU can run a sequence where the output of one multiply-add feeds into the next multiply-add. … Or is it? With some linear algebra and some AVX512, I can generate random numbers from a single instance of pcg32 at 200 Gbit/s on a single core. This is the same sequence of random numbers generated in the same order as normal pcg32, but more than 4x faster. You can look at the benchmark in my pcg-dxsm repository. skip ahead the insight multipliers trying it out results skip ahead One of the slightly weird features that PCG gets from its underlying linear congruential generator is “seekability”: you can skip ahead k steps in the stream of random numbers in log(k) time. The PCG paper (in section 4.3.1) cites Forrest Brown’s paper, random numbers with arbitrary strides, which explains that the skip-ahead feature is useful for reproducibility of monte carlo simulations. But what caught my eye is the skip-ahead formula. Rephrased in programmer style, state[n+k] = state[n] * pow(MUL, k) + inc * (pow(MUL, k) - 1) / (MUL - 1) the insight The skip-ahead formula says that we can calculate a future state using a couple of multiplications. The skip-ahead multipliers depend only on the LCG multiplier, not on the variable state, nor on the configurable increment. That means that for a fixed skip ahead, we can precalculate the multipliers before compile time. The skip-ahead formula allows us to unroll the PCG data dependency chain. Normally, four iterations of the PCG state update look like, state0 = rng->state state1 = state0 * MUL + rng->inc state2 = state1 * MUL + rng->inc state3 = state2 * MUL + rng->inc state4 = state3 * MUL + rng->inc rng->state = state4 With the skip-ahead multipliers it looks like, state0 = rng->state state1 = state0 * MULs1 + rng->inc * MULi1 state2 = state0 * MULs2 + rng->inc * MULi2 state3 = state0 * MULs3 + rng->inc * MULi3 state4 = state0 * MULs4 + rng->inc * MULi4 rng->state = state4 These state calculations can be done in parallel using NEON or AVX vector instructions. The disadvantage is that calculating future states in parallel requires more multiplications than doing so in series, but that’s OK because modern CPUs have lots of ALUs. multipliers The skip-ahead formula is useful for jumping ahead long distances, because (as Forrest Brown explained) you can do the exponentiation in log(k) time using repeated squaring. (The same technique is used in for modexp in RSA.) But I’m only interested in the first few skip-ahead multipliers. I’ll define the linear congruential generator as: lcg(s, inc) = s * MUL + inc Which is used in PCG’s normal state update like: rng->state = lcg(rng->state, rng->inc) To precalculate the first few skip-ahead multipliers, we iterate the LCG starting from zero and one, like this: MULs0 = 1 MULs1 = lcg(MULs0, 0) MULs2 = lcg(MULs1, 0) MULi0 = 0 MULi1 = lcg(MULi0, 1) MULi2 = lcg(MULi1, 1) My benchmark code’s commentary includes a proof by induction, which I wrote to convince myself that these multipliers are correct. trying it out To explore how well this skip-ahead idea works, I have written a couple of variants of my pcg32_bytes() function, which simply iterates pcg32 and writes the results to a byte array. The variants have an adjustable amount of parallelism. One variant is written as scalar code in a loop that has been unrolled by hand a few times. I wanted to see if standard C gets a decent speedup, perhaps from autovectorization. The other variant uses the GNU C portable vector extensions to calculate pcg32 in an explicitly parallel manner. The benchmark also ensures the output from every variant matches the baseline pcg32_bytes(). results The output from the benchmark harness lists: the function variant either the baseline version or uN for a scalar loop unrolled N times or xN for vector code with N lanes its speed in bytes per nanosecond (aka gigabytes per second) its performance relative to the baseline There are small differences in style between the baseline and u1 functions, but their performance ought to be basically the same. Apple clang 16, Macbook Pro M1 Pro. This compiler is eager and fairly effective at autovectorizing. ARM NEON isn’t big enough to get a speedup from 8 lanes of parallelism. __ 3.66 bytes/ns x 1.00 u1 3.90 bytes/ns x 1.07 u2 6.40 bytes/ns x 1.75 u3 7.66 bytes/ns x 2.09 u4 8.52 bytes/ns x 2.33 x2 7.59 bytes/ns x 2.08 x4 10.49 bytes/ns x 2.87 x8 10.40 bytes/ns x 2.84 The following results were from my AMD Ryzen 9 7950X running Debian 12 “bookworm”, comparing gcc vs clang, and AVX2 vs AVX512. gcc is less keen to autovectorize so it doesn’t do very well with the unrolled loops. (Dunno why u1 is so much slower than the baseline.) gcc 12.2 -march=x86-64-v3 __ 5.57 bytes/ns x 1.00 u1 5.13 bytes/ns x 0.92 u2 5.03 bytes/ns x 0.90 u3 7.01 bytes/ns x 1.26 u4 6.83 bytes/ns x 1.23 x2 3.96 bytes/ns x 0.71 x4 8.00 bytes/ns x 1.44 x8 12.35 bytes/ns x 2.22 clang 16.0 -march=x86-64-v3 __ 4.89 bytes/ns x 1.00 u1 4.08 bytes/ns x 0.83 u2 8.76 bytes/ns x 1.79 u3 10.43 bytes/ns x 2.13 u4 10.81 bytes/ns x 2.21 x2 6.67 bytes/ns x 1.36 x4 12.67 bytes/ns x 2.59 x8 15.27 bytes/ns x 3.12 gcc 12.2 -march=x86-64-v4 __ 5.53 bytes/ns x 1.00 u1 5.53 bytes/ns x 1.00 u2 5.55 bytes/ns x 1.00 u3 6.99 bytes/ns x 1.26 u4 6.79 bytes/ns x 1.23 x2 4.75 bytes/ns x 0.86 x4 17.14 bytes/ns x 3.10 x8 20.90 bytes/ns x 3.78 clang 16.0 -march=x86-64-v4 __ 5.53 bytes/ns x 1.00 u1 4.25 bytes/ns x 0.77 u2 7.94 bytes/ns x 1.44 u3 9.31 bytes/ns x 1.68 u4 15.33 bytes/ns x 2.77 x2 9.07 bytes/ns x 1.64 x4 21.74 bytes/ns x 3.93 x8 26.34 bytes/ns x 4.76 That last result is pcg32 generating random numbers at 200 Gbit/s.

4 weeks ago 14 votes
obfuscated C revisited

The International Obfuscated C Code Contest has a newly revamped web site, and the Judges have announced the 28th contest, to coincide with its 40th anniversary. (Or 41st?) The Judges have also updated the archive of past winners so that as many of them as possible work on modern systems. Accordingly, I took a look at my 1998 winner to see how much damage time hath wrought. When it is built, my program needs to go through the C preprocessor twice. There are a few reasons: It’s part of coercing the C compiler into compiling OFL, an obfuscated functional language. OFL has keywords l and b, short for let and be, so for example the function for constructing a pair is defined as l pair b (BB (B (B K)) C CI) In a less awful language that might be written let pair = λx λy λf λg (f x y) Anyway, the first pass of the C preprocessor turns a l (let) declaration into a macro #define pair b (BB (B (B K)) C CI) And the second pass expands the macros. (There’s a joke in the README that the OFL compiler has one optimization, function inlining (which is actually implemented by cpp macro expansion) but in fact inlining harms the performance of OFL.) The smaller the OFL interpreter, the more space there is for the program written in OFL. In the 1998 IOCCC rules, #define cost 7 characters, whereas l cost only one. I think the modern rules don’t count C or cpp keywords so there’s less reason to use this stupid trick to save space. Running the program through cpp twice is a horrible abuse of C and therefore just the kind of joke that the IOCCC encourages. (In fact the Makefile sends the program through cpp three times, twice explicitly and once as part of compiling to machine code. This is deliberately gratuitous, INABIAF.) There were a couple of ways this silliness caused problems. Modern headers are sensitive to which version of the C standard is in effect, wrt things like restrict keywords in standard library function declarations. The extra preprocessor invocations needed to be fixed to use consistent -std= options so that the final compilation doesn’t encounter language features from the future. Newer gcc emits #line directives around macro expansions. This caused problems for the declaration l ef E(EOF) which defines ef as a primitive value equal to EOF. After preprocessing this became #define ef E( #line 1213 "stdio.h" (-1) #line 69 "fanf.c" ) so the macro definition got truncated. The fix was to process the #include directives in the second preprocessor pass rather than the first. I vaguely remember some indecision when writing the program about whether to #include in the first or second pass, in particular whether preprocessing the headers twice would lead to trouble. First pass #include seemed to work and was shorter so that was what the original submission did. There’s one further change. The IOCCC Judges are trying to avoid compiler warnings about nonstandard arguments to main. To save a few characters, my entry had int main(int c) { ... } but the argument c isn’t used so I just removed it. The build commands still print “This may take some time to complete”, because in the 1990s if you tried to compile with optimization you would have been waiting a long time, if it completed at all. The revamped Makefile uses -O3, which takes gcc over 30 seconds and half a gigabyte of RAM. Quite a lot for less than 2.5 KiB of C!

2 months ago 52 votes
nsnotifyd-2.3 released

D’oh, I lost track of a bug report that should have been fixed in nsnotifyd-2.2. Thus, hot on the heels of [the previous release][prev], here’s nsnotifyd-2.3. Sorry for causing extra work to my uncountably many users! The nsnotifyd daemon monitors a set of DNS zones and runs a command when any of them change. It listens for DNS NOTIFY messages so it can respond to changes promptly. It also uses each zone’s SOA refresh and retry parameters to poll for updates if nsnotifyd does not receive NOTIFY messages more frequently. It comes with a client program nsnotify for sending notify messages. This nsnotifyd-2.3 release includes some bug fixes: When nsnotifyd receives a SIGINT or SIGTERM while running the command, it failed to handle it correctly. Now it exits promptly. Many thanks to Athanasius for reporting the bug! Miscellaneous minor code cleanup and compiler warning suppression. Thanks also to Dan Langille who sent me a lovely appreciation: Now that I think of it, nsnotifyd is in my favorite group of software. That group is software I forget I’m running, because they just run and do the work. For years. I haven’t touched, modified, or configured nsnotifyd and it just keeps doing the job.

3 months ago 53 votes
nsnotifyd-2.2 released

I have made a new release of nsnotifyd, a tiny DNS server that just listens for NOTIFY messages and runs a script when one of your zones changes. This nsnotifyd-2.2 release includes a new feature: nsnotify can now send NOTIFY messages from a specific source address Thanks to Adam Augustine for the suggestion. I like receiving messages that say things like, Thanks for making this useful tool available for free.

3 months ago 59 votes

More in programming

ChatGPT Would be a Decent Policy Advisor

Revealed: How the UK tech secretary uses ChatGPT for policy advice by Chris Stokel-Walker for the New Scientist

11 hours ago 3 votes
Setting policy for strategy.

This book’s introduction started by defining strategy as “making decisions.” Then we dug into exploration, diagnosis, and refinement: three chapters where you could argue that we didn’t decide anything at all. Clarifying the problem to be solved is the prerequisite of effective decision making, but eventually decisions do have to be made. Here in this chapter on policy, and the following chapter on operations, we finally start to actually make some decisions. In this chapter, we’ll dig into: How we define policy, and how setting policy differs from operating policy as discussed in the next chapter The structured steps for setting policy How many policies should you set? Is it preferable to have one policy, many policies, or does it not matter much either way? Recurring kinds of policies that appear frequently in strategies Why it’s valuable to be intentional about your strategy’s altitude, and how engineers and executives generally maintain different altitudes in their strategies Criteria to use for evaluating whether your policies are likely to be impactful How to develop novel policies, and why it’s rare Why having multiple bundles of alternative policies is generally a phase in strategy development that indicates a gap in your diagnosis How policies that ignore constraints sound inspirational, but accomplish little Dealing with ambiguity and uncertainty created by missing strategies from cross-functional stakeholders By the end, you’ll be ready to evaluate why an existing strategy’s policies are struggling to make an impact, and to start iterating on policies for strategy of your own. This is an exploratory, draft chapter for a book on engineering strategy that I’m brainstorming in #eng-strategy-book. As such, some of the links go to other draft chapters, both published drafts and very early, unpublished drafts. What is policy? Policy is interpreting your diagnosis into a concrete plan. That plan will be a collection of decisions, tradeoffs, and approaches. They’ll range from coding practices, to hiring mandates, to architectural decisions, to guidance about how choices are made within your organization. An effective policy solves the entirety of the strategy’s diagnosis, although the diagnosis itself is encouraged to specify which aspects can be ignored. For example, the strategy for working with private equity ownership acknowledges in its diagnosis that they don’t have clear guidance on what kind of reduction to expect: Based on general practice, it seems likely that our new Private Equity ownership will expect us to reduce R&D headcount costs through a reduction. However, we don’t have any concrete details to make a structured decision on this, and our approach would vary significantly depending on the size of the reduction. Faced with that uncertainty, the policy simply acknowledges the ambiguity and commits to reconsider when more information becomes available: We believe our new ownership will provide a specific target for Research and Development (R&D) operating expenses during the upcoming financial year planning. We will revise these policies again once we have explicit targets, and will delay planning around reductions until we have those numbers to avoid running two overlapping processes. There are two frequent points of confusion when creating policies that are worth addressing directly: Policy is a subset of strategy, rather than the entirety of strategy, because policy is only meaningful in the context of the strategy’s diagnosis. For example, the “N-1 backfill policy” makes sense in the context of new, private equity ownership. The policy wouldn’t work well in a rapidly expanding organization. Any strategy without a policy is useless, but you’ll also find policies without context aren’t worth much either. This is particularly unfortunate, because so often strategies are communicated without those critical sections. Policy describes how tradeoffs should be made, but it doesn’t verify how the tradeoffs are actually being made in practice. The next chapter on operations covers how to inspect an organization’s behavior to ensure policies are followed. When reworking a strategy to be more readable, it often makes sense to merge policy and operation sections together. However, when drafting strategy it’s valuable to keep them separate. Yes, you might use a weekly meeting to review whether the policy is being followed, but whether it’s an effective policy is independent of having such a meeting, and what operational mechanisms you use will vary depending on the number of policies you intend to implement. With this definition in mind, now we can move onto the more interesting discussion of how to set policy. How to set policy Every part of writing a strategy feels hard when you’re doing it, but I personally find that writing policy either feels uncomfortably easy or painfully challenging. It’s never a happy medium. Fortunately, the exploration and diagnosis usually come together to make writing your policy simple: although sometimes that simple conclusion may be a difficult one to swallow. The steps I follow to write a strategy’s policy are: Review diagnosis to ensure it captures the most important themes. It doesn’t need to be perfect, but it shouldn’t have omissions so obvious that you can immediately identify them. Select policies that address the diagnosis. Explicitly match each policy to one or more diagnoses that it addresses. Continue adding policies until every diagnosis is covered. This is a broad instruction, but it’s simpler than it sounds because you’ll typically select from policies identified during your exploration phase. However, there certainly is space to tweak those policies, and to reapply familiar policies to new circumstances. If you do find yourself developing a novel policy, there’s a later section in this chapter, Developing novel policies, that addresses that topic in more detail. Consolidate policies in cases where they overlap or adjoin. For example, two policies about specific teams might be generalized into a policy about all teams in the engineering organization. Backtest policy against recent decisions you’ve made. This is particularly effective if you maintain a decision log in your organization. Mine for conflict once again, much as you did in developing your diagnosis. Emphasize feedback from teams and individuals with a different perspective than your own, but don’t wholly eliminate those that you agree with. Just as it’s easy to crowd out opposing views in diagnosis if you don’t solicit their input, it’s possible to accidentally crowd out your own perspective if you anchor too much on others’ perspectives. Consider refinement if you finish writing, and you just aren’t sure your approach works – that’s fine! Return to the refinement phase by deploying one of the refinement techniques to increase your conviction. Remember that we talk about strategy like it’s done in one pass, but almost all real strategy takes many refinement passes. The steps of writing policy are relatively pedestrian, largely because you’ve done so much of the work already in the exploration, diagnosis, and refinement steps. If you skip those phases, you’d likely follow the above steps for writing policy, but the expected quality of the policy itself would be far lower. How many policies? Addressing the entirety of the diagnosis is often complex, which is why most strategies feature a set of policies rather than just one. The strategy for decomposing a monolithic application is not one policy deciding not to decompose, but a series of four policies: Business units should always operate in their own code repository and monolith. New integrations across business unit monoliths should be done using gRPC. Except for new business unit monoliths, we don’t allow new services. Merge existing services into business-unit monoliths where you can. Four isn’t universally the right number either. It’s simply the number that was required to solve that strategy’s diagnosis. With an excellent diagnosis, your policies will often feel inevitable, and perhaps even boring. That’s great: what makes a policy good is that it’s effective, not that it’s novel or inspiring. Kinds of policies While there are so many policies you can write, I’ve found they generally fall into one of four major categories: approvals, allocations, direction, and guidance. This section introduces those categories. Approvals define the process for making a recurring decision. This might require invoking an architecture advice process, or it might require involving an authority figure like an executive. In the Index post-acquisition integration strategy, there were a number of complex decisions to be made, and the approval mechanism was: Escalations come to paired leads: given our limited shared context across teams, all escalations must come to both Stripe’s Head of Traffic Engineering and Index’s Head of Engineering. This allowed the acquired and acquiring teams to start building trust between each other by ensuring both were consulted before any decision was finalized. On the other hand, the user data access strategy’s approval strategy was more focused on managing corporate risk: Exceptions must be granted in writing by CISO. While our overarching Engineering Strategy states that we follow an advisory architecture process as described in Facilitating Software Architecture, the customer data access policy is an exception and must be explicitly approved, with documentation, by the CISO. Start that process in the #ciso channel. These two different approval processes had different goals, so they made tradeoffs differently. There are so many ways to tweak approval, allowing for many different tradeoffs between safety, productivity, and trust. Allocations describe how resources are split across multiple potential investments. Allocations are the most concrete statement of organizational priority, and also articulate the organization’s belief about how productivity happens in teams. Some companies believe you go fast by swarming more people onto critical problems. Other companies believe you go fast by forcing teams to solve problems without additional headcount. Both can work, and teach you something important about the company’s beliefs. The strategy on Uber’s service migration has two concrete examples of allocation policies. The first describes the Infrastructure engineering team’s allocation between manual provision tasks and investing into creating a self-service provisioning platform: Constrain manual provisioning allocation to maximize investment in self-service provisioning. The service provisioning team will maintain a fixed allocation of one full time engineer on manual service provisioning tasks. We will move the remaining engineers to work on automation to speed up future service provisioning. This will degrade manual provisioning in the short term, but the alternative is permanently degrading provisioning by the influx of new service requests from newly hired product engineers. The second allocation policy is implicitly noted in this strategy’s diagnosis, where it describes the allocation policy in the Engineering organization’s higher altitude strategy: Within infrastructure engineering, there is a team of four engineers responsible for service provisioning today. While our organization is growing at a similar rate as product engineering, none of that additional headcount is being allocated directly to the team working on service provisioning. We do not anticipate this changing. Allocation policies often create a surprising amount of clarity for the team, and I include them in almost every policy I write either explicitly, or implicitly in a higher altitude strategy. Direction provides explicit instruction on how a decision must be made. This is the right tool when you know where you want to go, and exactly the way that you want to get there. Direction is appropriate for problems you understand clearly, and you value consistency more than empowering individual judgment. Direction works well when you need an unambiguous policy that doesn’t leave room for interpretation. For example, Calm’s policy for working in the monolith: We write all code in the monolith. It has been ambiguous if new code (especially new application code) should be written in our JavaScript monolith, or if all new code must be written in a new service outside of the monolith. This is no longer ambiguous: all new code must be written in the monolith. In the rare case that there is a functional requirement that makes writing in the monolith implausible, then you should seek an exception as described below. In that case, the team couldn’t agree on what should go into the monolith. Individuals would often make incompatible decisions, so creating consistency required removing personal judgment from the equation. Sometimes judgment is the issue, and sometimes consistency is difficult due to misaligned incentives. A good example of this comes in strategy on working with new Private Equity ownership: We will move to an “N-1” backfill policy, where departures are backfilled with a less senior level. We will also institute a strict maximum of one Principal Engineer per business unit. It’s likely that hiring managers would simply ignore this backfill policy if it was stated more softly, although sometimes less forceful policies are useful. Guidance provides a recommendation about how a decision should be made. Guidance is useful when there’s enough nuance, ambiguity, or complexity that you can explain the desired destination, but you can’t mandate the path to reaching it. One example of guidance comes from the Index acquisition integration strategy: Minimize changes to tokenization environment: because point-of-sale devices directly work with customer payment details, the API that directly supports the point-of-sale device must live within our secured environment where payment details are stored. However, any other functionality must not be added to our tokenization environment. This might read like direction, but it’s clarifying the desired outcome of avoiding unnecessary complexity in the tokenization environment. However, it’s not able to articulate what complexity is necessary, so ultimately it’s guidance because it requires significant judgment to interpret. A second example of guidance comes in the strategy on decomposing a monolithic codebase: Merge existing services into business-unit monoliths where you can. We believe that each choice to move existing services back into a monolith should be made “in the details” rather than from a top-down strategy perspective. Consequently, we generally encourage teams to wind down their existing services outside of their business unit’s monolith, but defer to teams to make the right decision for their local context. This is another case of knowing the desired outcome, but encountering too much uncertainty to direct the team on how to get there. If you ask five engineers about whether it’s possible to merge a given service back into a monolithic codebase, they’ll probably disagree. That’s fine, and highlights the value of guidance: it makes it possible to make incremental progress in areas where more concrete direction would cause confusion. When you’re working on a strategy’s policy section, it’s important to consider all of these categories. Which feel most natural to use will vary depending on your team and role, but they’re all usable: If you’re a developer productivity team, you might have to lean heavily on guidance in your policies and increased support for that guidance within the details of your platform. If you’re an executive, you might lean heavily on direction. Indeed, you might lean too heavily on direction, where guidance often works better for areas where you understand the direction but not the path. If you’re a product engineering organization, you might have to narrow the scope of your direction to the engineers within that organization to deal with the realities of complex cross-organization dynamics. Finally, if you have a clear approach you want to take that doesn’t fit cleanly into any of these categories, then don’t let this framework dissuade you. Give it a try, and adapt if it doesn’t initially work out. Maintaining strategy altitude The chapter on when to write engineering strategy introduced the concept of strategy altitude, which is being deliberate about where certain kinds of policies are created within your organization. Without repeating that section in its entirety, it’s particularly relevant when you set policy to consider how your new policies eliminate flexibility within your organization. Consider these two somewhat opposing strategies: Stripe’s Sorbet strategy only worked in an organization that enforced the use of a single programming language across (essentially) all teams Uber’s service migration strategy worked well in an organization that was unwilling to enforce consistent programming language adoption across teams Stripe’s organization-altitude policy took away the freedom of individual teams to select their preferred technology stack. In return, they unlocked the ability to centralize investment in a powerful way. Uber went the opposite way, unlocking the ability of teams to pick their preferred technology stack, while significantly reducing their centralized teams’ leverage. Both altitudes make sense. Both have consequences. Criteria for effective policies In The Engineering Executive’s Primer’s chapter on engineering strategy, I introduced three criteria for evaluating policies. They ought to be applicable, enforced, and create leverage. Defining those a bit: Applicable: it can be used to navigate complex, real scenarios, particularly when making tradeoffs. Enforced: teams will be held accountable for following the guiding policy. Create Leverage: create compounding or multiplicative impact. The last of these three, create leverage, made sense in the context of a book about engineering executives, but probably doesn’t make as much sense here. Some policies certainly should create leverage (e.g. empower developer experience team by restricting new services), but others might not (e.g. moving to an N-1 backfill policy). Outside the executive context, what’s important isn’t necessarily creating leverage, but that a policy solves for part of the diagnosis. That leaves the other two–being applicable and enforced–both of which are necessary for a policy to actually address the diagnosis. Any policy which you can’t determine how to apply, or aren’t willing to enforce, simply won’t be useful. Let’s apply these criteria to a handful of potential policies. First let’s think about policies we might write to improve the talent density of our engineering team: “We only hire world-class engineers.” This isn’t applicable, because it’s unclear what a world-class engineer means. Because there’s no mutually agreeable definition in this policy, it’s also not consistently enforceable. “We only hire engineers that get at least one ‘strong yes’ in scorecards.” This is applicable, because there’s a clear definition. This is enforceable, depending on the willingness of the organization to reject seemingly good candidates who don’t happen to get a strong yes. Next, let’s think about a policy regarding code reuse within a codebase: “We follow a strict Don’t Repeat Yourself policy in our codebase.” There’s room for debate within a team about whether two pieces of code are truly duplicative, but this is generally applicable. Because there’s room for debate, it’s a very context specific determination to decide how to enforce a decision. “Code authors are responsible for determining if their contributions violate Don’t Repeat Yourself, and rewriting them if they do.” This is much more applicable, because now there’s only a single person’s judgment to assess the potential repetition. In some ways, this policy is also more enforceable, because there’s no longer any ambiguity around who is deciding whether a piece of code is a repetition. The challenge is that enforceability now depends on one individual, and making this policy effective will require holding individuals accountable for the quality of their judgement. An organization that’s unwilling to distinguish between good and bad judgment won’t get any value out of the policy. This is a good example of how a good policy in one organization might become a poor policy in another. If you ever find yourself wanting to include a policy that for some reason either can’t be applied or can’t be enforced, stop to ask yourself what you’re trying to accomplish and ponder if there’s a different policy that might be better suited to that goal. Developing novel policies My experience is that there are vanishingly few truly novel policies to write. There’s almost always someone else has already done something similar to your intended approach. Calm’s engineering strategy is such a case: the details are particular to the company, but the general approach is common across the industry. The most likely place to find truly novel policies is during the adoption phase of a new widespread technology, such as the rise of ubiquitous mobile phones, cloud computing, or large language models. Even then, as explored in the strategy for adopting large-language models, the new technology can be engaged with as a generic technology: Develop an LLM-backed process for reactivating departed and suspended drivers in mature markets. Through modeling our driver lifecycle, we determined that improving onboarding time will have little impact on the total number of active drivers. Instead, we are focusing on mechanisms to reactivate departed and suspended drivers, which is the only opportunity to meaningfully impact active drivers. You could simply replace “LLM” with “data-driven” and it would be equally readable. In this way, policy can generally sidestep areas of uncertainty by being a bit abstract. This avoids being overly specific about topics you simply don’t know much about. However, even if your policy isn’t novel to the industry, it might still be novel to you or your organization. The steps that I’ve found useful to debug novel policies are the same steps as running a condensed version of the strategy process, with a focus on exploration and refinement: Collect a number of similar policies, with a focus on how those policies differ from the policy you are creating Create a systems model to articulate how this policy will work, and also how it will differ from the similar policies you’re considering Run a strategy testing cycle for your proto-policy to discover any unknown-unknowns about how it works in practice Whether you run into this scenario is largely a function of the extent of your, and your organization’s, experience. Early in my career, I found myself doing novel (for me) strategy work very frequently, and these days I rarely find myself doing novel work, instead focusing on adaptation of well-known policies to new circumstances. Are competing policy proposals an anti-pattern? When creating policy, you’ll often have to engage with the question of whether you should develop one preferred policy or a series of potential strategies to pick from. Developing these is a useful stage of setting policy, but rather than helping you refine your policy, I’d encourage you to think of this as exposing gaps in your diagnosis. For example, when Stripe developed the Sorbet ruby-typing tooling, there was debate between two policies: Should we build a ruby-typing tool to allow a centralized team to gradually migrate the company to a typed codebase? Should we migrate the codebase to a preexisting strongly typed language like Golang or Java? These were, initially, equally valid hypotheses. It was only by clarifying our diagnosis around resourcing that it became clear that incurring the bulk of costs in a centralized team was clearly preferable to spreading the costs across many teams. Specifically, recognizing that we wanted to prioritize short-term product engineering velocity, even if it led to a longer migration overall. If you do develop multiple policy options, I encourage you to move the alternatives into an appendix rather than including them in the core of your strategy document. This will make it easier for readers of your final version to understand how to follow your policies, and they are the most important long-term user of your written strategy. Recognizing constraints A similar problem to competing solutions is developing a policy that you cannot possibly fund. It’s easy to get enamored with policies that you can’t meaningfully enforce, but that’s bad policy, even if it would work in an alternate universe where it was possible to enforce or resource it. To consider a few examples: The strategy for controlling access to user data might have proposed requiring manual approval by a second party of every access to customer data. However, that would have gone nowhere. Our approach to Uber’s service migration might have required more staffing for the infrastructure engineering team, but we knew that wasn’t going to happen, so it was a meaningless policy proposal to make. The strategy for navigating private equity ownership might have argued that new ownership should not hold engineering accountable to a new standard on spending. But they would have just invalidated that strategy in the next financial planning period. If you find a policy that contemplates an impractical approach, it doesn’t only indicate that the policy is a poor one, it also suggests your policy is missing an important pillar. Rather than debating the policy options, the fastest path to resolution is to align on the diagnosis that would invalidate potential paths forward. In cases where aligning on the diagnosis isn’t possible, for example because you simply don’t understand the possibilities of a new technology as encountered in the strategy for adopting LLMs, then you’ve typically found a valuable opportunity to use strategy refinement to build alignment. Dealing with missing strategies At a recent company offsite, we were debating which policies we might adopt to deal with annual plans that kept getting derailed after less than a month. Someone remarked that this would be much easier if we could get the executive team to commit to a clearer, written strategy about which business units we were prioritizing. They were, of course, right. It would be much easier. Unfortunately, it goes back to the problem we discussed in the diagnosis chapter about reframing blockers into diagnosis. If a strategy from the company or a peer function is missing, the empowering thing to do is to include the absence in your diagnosis and move forward. Sometimes, even when you do this, it’s easy to fall back into the belief that you cannot set a policy because a peer function might set a conflicting policy in the future. Whether you’re an executive or an engineer, you’ll never have the details you want to make the ideal policy. Meaningful leadership requires taking meaningful risks, which is never something that gets comfortable. Summary After working through this chapter, you know how to develop policy, how to assemble policies to solve your diagnosis, and how to avoid a number of the frequent challenges that policy writers encounter. At this point, there’s only one phase of strategy left to dig into, operating the policies you’ve created.

16 hours ago 3 votes
Fast and random sampling in SQLite

I was building a small feature for the Flickr Commons Explorer today: show a random selection of photos from the entire collection. I wanted a fast and varied set of photos. This meant getting a random sample of rows from a SQLite table (because the Explorer stores all its data in SQLite). I’m happy with the code I settled on, but it took several attempts to get right. Approach #1: ORDER BY RANDOM() My first attempt was pretty naïve – I used an ORDER BY RANDOM() clause to sort the table, then limit the results: SELECT * FROM photos ORDER BY random() LIMIT 10 This query works, but it was slow – about half a second to sample a table with 2 million photos (which is very small by SQLite standards). This query would run on every request for the homepage, so that latency is unacceptable. It’s slow because it forces SQLite to generate a value for every row, then sort all the rows, and only then does it apply the limit. SQLite is fast, but there’s only so fast you can sort millions of values. I found a suggestion from Stack Overflow user Ali to do a random sort on the id column first, pick my IDs from that, and only fetch the whole row for the photos I’m selecting: SELECT * FROM photos WHERE id IN ( SELECT id FROM photos ORDER BY RANDOM() LIMIT 10 ) This means SQLite only has to load the rows it’s returning, not every row in the database. This query was over three times faster – about 0.15s – but that’s still slower than I wanted. Approach #2: WHERE rowid > (…) Scrolling down the Stack Overflow page, I found an answer by Max Shenfield with a different approach: SELECT * FROM photos WHERE rowid > ( ABS(RANDOM()) % (SELECT max(rowid) FROM photos) ) LIMIT 10 The rowid is a unique identifier that’s used as a primary key in most SQLite tables, and it can be looked up very quickly. SQLite automatically assigns a unique rowid unless you explicitly tell it not to, or create your own integer primary key. This query works by picking a point between the biggest and smallest rowid values used in the table, then getting the rows with rowids which are higher than that point. If you want to know more, Max’s answer has a more detailed explanation. This query is much faster – around 0.0008s – but I didn’t go this route. The result is more like a random slice than a random sample. In my testing, it always returned contiguous rows – 101, 102, 103, … – which isn’t what I want. The photos in the Commons Explorer database were inserted in upload order, so photos with adjacent row IDs were uploaded at around the same time and are probably quite similar. I’d get one photo of an old plane, then nine more photos of other planes. I want more variety! (This behaviour isn’t guaranteed – if you don’t add an ORDER BY clause to a SELECT query, then the order of results is undefined. SQLite is returning rows in rowid order in my table, and a quick Google suggests that’s pretty common, but that may not be true in all cases. It doesn’t affect whether I want to use this approach, but I mention it here because I was confused about the ordering when I read this code.) Approach #3: Select random rowid values outside SQLite Max’s answer was the first time I’d heard of rowid, and it gave me an idea – what if I chose random rowid values outside SQLite? This is a less “pure” approach because I’m not doing everything in the database, but I’m happy with that if it gets the result I want. Here’s the procedure I came up with: Create an empty list to store our sample. Find the highest rowid that’s currently in use: sqlite> SELECT MAX(rowid) FROM photos; 1913389 Use a random number generator to pick a rowid between 1 and the highest rowid: >>> import random >>> random.randint(1, max_rowid) 196476 If we’ve already got this rowid, discard it and generate a new one. (The rowid is a signed, 64-bit integer, so the minimum possible value is always 1.) Look for a row with that rowid: SELECT * FROM photos WHERE rowid = 196476 If such a row exists, add it to our sample. If we have enough items in our sample, we’re done. Otherwise, return to step 3 and generate another rowid. If such a row doesn’t exist, return to step 3 and generate another rowid. This requires a bit more code, but it returns a diverse sample of photos, which is what I really care about. It’s a bit slower, but still plenty fast enough (about 0.001s). This approach is best for tables where the rowid values are mostly contiguous – it would be slower if there are lots of rowids between 1 and the max that don’t exist. If there are large gaps in rowid values, you might try multiple missing entries before finding a valid row, slowing down the query. You might want to try something different, like tracking valid rowid values separately. This is a good fit for my use case, because photos don’t get removed from Flickr Commons very often. Once a row is written, it sticks around, and over 97% of the possible rowid values do exist. Summary Here are the four approaches I tried: Approach Performance (for 2M rows) Notes ORDER BY RANDOM() ~0.5s Slowest, easiest to read WHERE id IN (SELECT id …) ~0.15s Faster, still fairly easy to understand WHERE rowid > ... ~0.0008s Returns clustered results Random rowid in Python ~0.001s Fast and returns varied results, requires code outside SQL I’m using the random rowid in Python in the Commons Explorer, trading code complexity for speed. I’m using this random sample to render a web page, so it’s important that it returns quickly – when I was testing ORDER BY RANDOM(), I could feel myself waiting for the page to load. But I’ve used ORDER BY RANDOM() in the past, especially for asynchronous data pipelines where I don’t care about absolute performance. It’s simpler to read and easier to see what’s going on. Now it’s your turn – visit the Commons Explorer and see what random gems you can find. Let me know if you spot anything cool! [If the formatting of this post looks odd in your feed reader, visit the original article]

7 hours ago 1 votes
Choosing Languages
yesterday 2 votes
05 · Syncing Keyhive

How we sync Keyhive and Automerge

yesterday 1 votes