rust performance profiling

There, we can set the amount of users we want to simulate and how fast they should spawn (per second). There are different ways of collecting data about a program's execution. However, since the clients_lock stays in the scope, especially for the whole duration of our fake DB call (sleep), that means we lock the resource for the whole duration of this handler! Next, armed with a great way to load test our web application, well do some actual profiling to get a deeper look into what happens under the hood of our web handlers. Then we define a @task called read, and this client simply makes a GET request to /read using the HTTP client Locust provides. Activate your 30 day free trialto continue reading. instructions, and adding the following lines to the config.toml file: This is a hassle, but may be worth the effort in some cases. InfluxDB IOx Tech Talks: Performance Profiling in Rust. Weve added many new features and published a couple of releases on crates.io. You can't do line-by-line profiling in a language like Rust, because lines will be destroyed or combined in complicated ways by the optimizer. More info and buy. This is best done via profiling. contributed,sponsor-scylladb,sponsored,sponsored-post-contributed. FuturesUnordered has a list of futures ready for polling, and it assumes that once polled, the futures will not need to be polled again. Image Sharpening: Use Global Settings (Off) Anisotropic Filtering: 16x. Free access to premium services like Tuneln, Mubi and more. Docker base image First of all, I suggest to start with a Debian testing base image. I'm a software developer originally from Graz but living in Vienna, Austria. How Intuits Platform Engineering Team Chose an App Definition, Install Dozzle, a Simple Log File Viewer for Docker, The Next Evolution of Virtualization Infrastructure. So far, so good. Stage 2: Plotting your own performance profile. # performance # profiling profiling This crate provides a very thin abstraction over other profiler crates. Clipping is a handy way to collect important slides you want to go back to later. (Given that our own product, ScyllaDB, is API-compatible with Apache Cassandra, we at ScyllaDB especially appreciate such attributes!). Profiling Rust applications Profiling is indispensable for building high-performance software. The response time was really impressive! Thats it! The combination of Tokios preemptive scheduling trick and FuturesUnordereds implementation is the heart of the problem. It looks like the equivalent to Gos pprof package supporting all kinds of profilers. In this example, we will only do CPU time analysis, which is supported by cargo-flamegraph. Since then, its development and adoption accelerated a lot. (The width indicates time spent on executing a particular operation.). Since such futures are not polled more than once, when put in the ready list, the amortized time needed to serve them all is constant. Time to investigate! What Is Supply Chain Security and How Does It Work? After running this load-test on our profiled application and stopping the running Rust web server using CTRL+C, we get a flame graph like this: You can see the benefit of this visualization. Tracing support is unstable features in Tokio. You found pprof-rs? Rust port of the FlameGraph performance profiling tool suite v0.11.12 135 K bin+lib #perf #flamegraph #profiling blake2b_simd a pure Rust BLAKE2b implementation with dynamic SIMD v1.0.0 277 K #blake2b #blake2bp #blake2 firestorm A low overhead intrusive flamegraph profiler v0.5.1 142 K #flamegraph #profiler brunch A simple micro-benchmark runner In this chapter, we will improve the performance of our Game of Life implementation. What Do 'Cloud Native' and 'Kubernetes' Even Mean? Piotr graduated from University of Warsaw with a master's degree in computer science. However, a local setup quickly verified that this overhead is not negligible at all. The best you can hope for is associating samples with hunks of code, which is basically what perf report tries to help you do. _RMCsno73SFvQKx_1cINtB0_3StrKRe616263_E. Higher-level optimizations, in theory, improve the performance of the code greatly, but they might have bugs that could change the behavior of the program. First, lets build a handler so we get a nice visualization: In this (also rather contrived) example, we re-use the base of the /fast handler, but we extend the calculation to run inside a long loop. debug info. After the handler comes back from sleep, we do another operation on the user_ids, parsing them to numbers, reversing them, adding them up, and returning them to the user. Interpreting flame graphs is explained in detail in the link above, but a rule of thumb is to look for operations that take up the majority of the total width of the graph. We've been working hard to develop and improve the scylla-rust-driver. In this case, we want to spawn 3000 users with 100/s. We'll discuss our experiences with tooling aimed at finding and fixing performance problems in a production Rust application, as experienced through the eyes of somebody who's more familiar with the Go ecosystem but grew to love Rust. Improving Rust Performance Through Profiling and Benchmarking. Along the way, we also stumbled upon a few interesting performance bottlenecks to investigate and overcome. Fixing this is pretty easy, we simply remove the .cloned() as we dont need it here anyway, but as you might have noticed, unnecessary cloning can lead to big performance impacts, especially within hot code. There are many different profilers available, each with their strengths and weaknesses. We see, stacked up, where we spend most of the time during the load test. Next, we define some helpers to initialize and propagate our Clients: The with_clients Warp Filter is simply a way we can make resources available to routes in the Warp web framework. Currently, I work at Timeular. Click here to review the details. The field of performance optimization in Rust is vast, and this tutorial can only hope to scratch the surface. Names like these can be manually demangled using rustfilt. Activate your 30 day free trialto unlock unlimited reading. We've added many new features and published a couple of releases on crates.io. I'll explain profilers for async Rust, in comparison with Go, designed to support various. Using dataform to improve data quality in BigQuery. Running cargo build --release again produces the same artifacts, sample.exe and sample.pdb. 2. The world of async programming in Rust is still young, but very actively developed. Hopefully you'll find hidden hot spots, fix them, and then see the improvement on the next criterion run. In order to fully grasp the problem, you need to understand how Rust async runtimes work. Rust's compiler is a great tool to find bugs. In async Rust, if one task keeps polling futures in a loop and these futures always happen to be ready due to sufficient load, then theres a potential problem of starving other tasks. A few existing profilers met these requirements, including the Linux perf tool. In Tokio, and other runtimes, the act of giving back control can be issued explicitly by calling yield_now, but the runtime itself is not able to force an await to become a yield point. While I've only focussed on Criterion, valgrind, kcachegrind - your needs may be better suited by flame graphs and flamer. This effectively turns off cooperative scheduling in Tokio, which removes one of the necessary conditions for the regression to appear. profiler is unaware of this scheme, its output may contain symbol names The following profilers have been used successfully on Rust programs. Interpolated data is simply the last known data point repeated until another known data point is found. Rahul Sharma | Vesa Kaihlavirta (2019) Mastering Rust. There are many different profilers available, each with their strengths and CPU and RAM profiling of long-running Rust services in a Kubernetes environment is not terribly complicated, it . Table of Contents. The goal of profiling is to receive a better inclination of the code base. In the original implementation, neither sending the requests nor receiving the responses used any kind of buffering, so each request was sent/received as soon as it was popped from the queue. Michael Hall [InfluxData] | Become an InfluxDB Pro in 20 Minutes | InfluxDays Emily Kurze [InfluxData] | Accelerate Time to Awesome at InfluxDB University Hall, Dotis-Georgiou [InfluxData] | Getting Involved in the InfluxDB Communit Mya Longmire [InfluxData] | Time to Awesome Demo of the Client Libraries and Vinay Kumar [InfluxData] | InfluxDB API Overview | InfluxDays 2022. Tap here to review the details. The WebResult is simply a helper type for the result of our web handlers. All experiments seemed to prove that scylla-rust-driver is at least as fast as the other drivers and often provides better throughput and latency than all the tested alternatives. perf is generally a CPU oriented profiler, but it can track some non-CPU related metrics. A latency of 1 millisecond means that we wont be able to send more than 1,000 requests per second. We've updated our privacy policy. You shouldn't have to instrument or even re-run your application to get observability. For looking into memory usage of the rustc bootstrap process, we'll want to select the following items: CPU usage. Note: pink in the graphs represent data points that are interpolated due to missing data. It is capable of lightweight profiling. Gilmore, Palani [InfluxData] | Use Case: Monitoring / Observability | InfluxD Gilmore, Palani [InfluxData] | Use Case: Crypto & Fintech | InfluxDays 2022, Charles Mahler [InfluxData] | Use Case: Networking Monitoring | InfluxDays 2022, Anais Dotis-Georgiou [InfluxData] | Becoming a Flux Pro | InfluxDays 2022. You embed static instrumentations in your application and implement functions that are executed when trace events happen. If you are new to Rust and want to learn more, The Rust Programming Language online book is a great place to start. Usage In Applications In order to record trace events, executables have to use a collector implementation compatible with tracing. However, once the budget is spent, Tokio may force such ready futures to return a pending status! perf is powerful: it can instrument CPU performance counters, tracepoints, kprobes, and uprobes (dynamic tracing). Using cargo-flamegraph is as easy as running the binary, and it produces an interactive flamegraph.svg file, which can then be browsed to look for potential bottlenecks. This is a client-side driver for ScyllaDB written in pure Rust with a fully async API using Tokio. If we run the load test for a while, at least until all users were spawned and the response times stabilize, we might see something like this, upon stopping it: We see that we managed to get a measly 19.5 requests per second and the requests took an average of 18+ seconds. That translates to issuing a system call per each request and response. Apache MXNet Distributed Training Explained In Depth by Viacheslav Kovalevsky 9:40 am InfluxDB 2.0 and Flux The Road Ahead Paul Dix, Founder and CTO | HadoopCon 2016 - Jupyter Notebook Hold Spark Machine Learning , Developing High Performance Application with Aerospike & Go. Profiling the Rust compiler is much easier and more enjoyable than profiling Firefox, for example. Introduction This is the wiki page for the Linux perf command, also called perf_events. Especially that the observed performance of the test program based on FuturesUnordered, even though it stopped being quadratic, it was still considerably slower than the task::unconstrained one, which suggests there's room for improvement. However, any other load-testing application (such as Gatling) or your own tool to send and measure lots of requests to a web server, will suffice. Tokios mutex code doesnt implement such feature. Go has the built-in runtime but Rust supports multiple asynchronous runtimes. 0.1% vs 3%; the . You can read the details below. Functions are often inlined, so even measuring the time spent in a function can give incorrect results - or else change the performance . , readings-probe, rust_hawktracer, time-graph, optick, embedded-profiling, superluminal-perf, superluminal-perf-sys, microprofile. We want our tests to be as close as possible to production environments, so they always run in a distributed environment. Rust offers many convenient utilities and combinators for futures, and some of them maintain their own scheduling policies that might interfere with the semantics described above. What is relevant is that this resource will be shared across our whole application and multiple endpoints will access it simultaneously. This is best done via profiling. Flame graphs can also be used to do, among other analyses, Off-CPU Analysis, which can help find issues where threads are waiting for I/O a lot, for example. Afterward, make the following tweaks. It's an open-source ScyllaDB (and Apache Cassandra) driver for Rust, written in pure Rust with a fully async API using Tokio.You can read more regarding its benchmark results and how our developers solved a performance regression.. _ZN28_$u7b$$u7b$closure$u7d$$u7d$E or How Idit Levines Athletic Past Fueled Solo.ios Startup, Have Some CAKE: The New (Stateful) Serverless Stack, Hazelcast Aims to Democratize Real-Time Data with Serverless, Forrester Identifies Best Practices for Serverless Development, Early Days for Quantum Developers, But Serverless Coming, Connections Problem: Finding the Right Path through a Graph, Accelerating SQL Queries on a Modern Real-Time Database, New ScyllaDB Go Driver: Faster than GoCQL and Rust Counterpart, The Unfortunate Reality about Data Pipelines, Monitoring Network Outages at the Edge and in the Cloud, The Race to Be Figma for Devs: CodeSandbox vs. StackBlitz, What Developers Told Us about Vercel's Next.js Update. Time Profiling This section describes how to profile Web pages using Rust and WebAssembly where the goal is improving throughput or latency. nmdaniels 9 mo. We also define the wait_time property, which controls how long to wait in between requests. To follow along, all you need is a recent Rust installation (1.45+) and a Python3 installation with the ability to run Locust. We all enjoy a good DIY project, but putting up a shelf or some flat-pack or Raamaturiiul furniture is not the same as . Blockchain + AI + Crypto Economics Are We Creating a Code Tsunami? Node Interactive Debugging Node.js In Production. APIdays Paris 2019 - Innovation @ scale, APIs as Digital Factories' New Machi Mammalian Brain Chemistry Explains Everything. Rust in Visual Studio Code. tracing is maintained by the Tokio project, but does not require the tokio runtime to be used. You may have to increase the number of open files allowed for the locust process using a command such as ulimit -n 200000 in the terminal where you run Locust. If youre interested in this type of thing and want to dive deeper, there is a huge rabbit hole waiting for you and you can use the resources mentioned in The Rust Performance Book as a starting point on your journey towards lightning fast Rust code. Then it's a back and forth: identify bottleneck with profiling, smooth it out, check impact on timings, do it all over again until performance is satisfactory. Since FuturesUnordered was also used in latte, it became the candidate for causing this regression. That fits perfectly with the elevated number of syscalls, which need CPU to be handled. Hotspot and Firefox Profiler are good for viewing data recorded by . It is primarily for RUST server owners offering large public servers with high player slots (100+) where performance becomes increasingly important. To profile throughput, you must specify a progress point. The optimiser does its job by completely reorganising the code you wrote and finding the minimal machine code that behaves the same as what you intended. Related titles. By default, Rust will perform level 3 optimizations in the code. After we were able to reliably reproduce the results, it was time to look at profiling results, both the ones provided in the original issue and the ones generated by our tests. Full system profiling is outside of the scope of this book. Profiling After we were able to reliably reproduce the results, it was time to look at profiling results - both the ones provided in the original issue and the ones generated by our tests. Since then, its development and adoption accelerated a lot. The rust-unmangle script is optional but nice.. Also check out @dlaehnemann's more detailed walkthrough about generating flamegraphs . He previously developed an open source distributed file system (LizardFS) and had a brief adventure with the Linux kernel during an apprenticeship at Samsung Electronics. The Compile Times section also contains some techniques that will improve the compile times of Rust programs. https://twitter.com/brewaddict. It maintains its own list of ready futures that it iterates over when it is polled. It is a very nice consensus between turning off cooperative scheduling altogether and spawning each task separately instead of combining them into FuturesUnordered. The nice thing about using these more high-level tools is that you not only get a static .svg file, which hides some of the details, but you can zoom around in your profile! build your own version of the compiler and standard library, following these Piotr is a software engineer very keen on open source projects and C++. Async Rust in Practice: Performance, Pitfalls, Profiling. One is to run the program inside a profiler (such as perf) and another is to create an instrumented binary, that is, a binary that has data collection built into it, and run that. The Rust ecosystem is great at testing various small changes introduced on the dependencies of your project. Now, at this point you might roll your eyes a bit at this contrived example, and I do agree that this probably isnt an issue you will run into a lot in real systems. To avoid starving other tasks, Tokio resorted to a neat trick: Each task is assigned a budget, and once that budget is spent, all resources controlled by Tokio start returning a pending status (even though they might be ready) in order to force the budgetless task to yield. One important thing to note when optimizing performance in Rust, is to always compile in release mode. Abhishek Chanda (2018) Network Programming with Rust. 09, 2021 2 likes 741 views Download Now Download to read offline Technology We'll discuss our experiences with tooling aimed at finding and fixing performance problems in a production Rust application, as experienced through the eyes of somebody who's more familiar with the Go ecosystem but grew to love Rust. Whats even better is that the Rust ecosystem already has fantastic support for generating flame graphs integrated into the build system: cargo-flamegraph. This is a rather obvious performance issue, but when youre juggling references and fighting with the borrow checker, its possible the odd superfluous .clone() makes it in your code which, inside of hot loops, might lead to performance issues. This requires you have flamegraph available in your path. What happened? Allows you to store large volumes of high cardinality It was reported that, despite our care in designing the driver to be efficient, it proved to be unpleasantly slower than one of the competing drivers, cassandra-cpp, which is a Rust wrapper of a C++ CQL driver. The suspicion was confirmed after trying out a modified version of latte that did not rely on FuturesUnordered. In this article, were going to have a look at some techniques to analyze and improve the performance of Rust web applications. Jay Clifford [InfluxData] | Tips & Tricks for Analyzing IIoT in Real-Time | I Brian Gilmore [InfluxData] | Use Case: IIoT Overview | InfluxDays 2022. Can You Now Safely Remove the Service Mesh Sidecar? Using perf: $ perf record -g binary $ perf script | stackcollapse-perf.pl | rust-unmangle | flamegraph.pl > flame.svg NOTE: See @GabrielMajeri's comments below about the -g option.. Low-overhead Agents. Its been a while since the Tokio-based Rust Driver for ScyllaDB, a high-performance low-latency NoSQL database, was born during ScyllaDBs internal developer hackathon. The Rust Performance Book Profiling When optimizing a program, you also need a way to determine which parts of the program are "hot" (executed frequently enough to affect runtime) and worth modifying. Building Distributed System with Celery on Docker Swarm - PyCon JP 2016, Non-Relational Postgres / Bruce Momjian (EnterpriseDB), 2017-03-11 02 . Design Like a Dev: What's Happened to Self-Driving Cars? Confluent: Have We Entered the Age of Streaming? Has very low overheads: This is required for a continuous (always-on) profiler, which was desirable for making performance profiling as low-effort as possible. This was invaluable when comparing the various fixes applied to scylla-rust-driver without having to publish anything on crates. Rust was created to provide high performance, comparable to C and C++, with a strong emphasis on the code's safety. If you miss on these Nvidia Settings, you are likely to see a 20% performance loss, which translates into lots of FPS. In fact, often with profiling we can reveal slow areas of code that we may not have suspected at all. [profile.release] debug = true If you need it, the kind folk at Embark Studios have helpfully published a crateto make using our API super simple from Rust. But Tracing crate enables you to get diagnostic information that can be used for profiling. This is not very surprising as we added .cloned() to the iterator, which, for each loop iteration, clones the contents of the list before processing the data. A breakthrough came when we compared the testing environments, which should have been our first step from the start. Select the chrome_profiler.json file we created. If youre looking for memory-related performance issues specifically, you might want to take a look at the tools mentioned within the Profiling section of The Rust Performance Book, namely heaptrack, DHAT, or cachegrind. Experiment Description If we review the code in our read_handler, we might notice that were doing something very inefficient when it comes to the Mutex lock: We acquire the lock, access the data, and at that point, were actually done with clients and dont need it anymore. Lib.rs is an unofficial list of Rust/Cargo crates. Optimizations get divided into levels depending on how complex they are. This is best done via profiling. A Client has a user_id and a list of subscribed topics, but thats not particularly relevant for our example. Now, with Locust installed, lets create locust folder in our project, where we can add some load testing definitions: Writing a locustfile is relatively straightforward, but if you want to dive deeper, the Locust documentation is fantastic. This should give us quite a speed boost lets check. eBPF or Not, Sidecars are the Future of the Service Mesh. In Rust, most of these problems are detected during the compilation process. Make everything reproducible I previously worked as a fullstack web developer before quitting my job to work as a freelancer and explore open source. We use profiling to measure the performance of our applications - generally in a more fine-grained manner than benchmarking alone can provide. Recompilation with an option is required. Yes, an experiment performed by one of our engineers hinted that using a combinator for Rust futures, FuturesUnordered, appears to cause quadratic rise of execution time, compared to a similar problem being expressed without the combinator, by using Tokios spawn utility directly. In initialize_clients, we add some hard-coded values to our shared Clients map, but the actual values arent particularly relevant for the example. LogRocket also monitors your apps performance, reporting metrics like client CPU load, client memory usage, and more. Both run in use mode and use OS timer facilities without depending any special CPU features. In the above read.py example, we create a class called Basic based on HttpUser, which will give us all the Locust helpers within the class. weaknesses. Collaborating with Internal Dev Experience and Tool Teams, Hub and Spoke: A Better Way to Architect Your Tech Stack, When 99% Service Level Objectives Are Overrated (and Too Expensive), Latest Enhancements to HashiCorp Terraform and Terraform Cloud. Next.js 13 Debuts a Faster, Rust-Based Bundler, The Challenges of Marketing Software Tools to Developers, Deep Work: A Better Way to Measure Developer Velocity, Case Study: How BOK Financial Managed Its Cloud Migration, What Observability Must Learn from Your IDE, A Tactical Field Guide to Optimizing APM Bills, Amazon Web Services Gears Elastic Kubernetes Service for Batch Work, MC2: Secure Collaborative Analytics for Machine Learning. If you want to test a particular change made to one of your dependencies before it is published (or even on your own fork, where you applied some experimental changes yourself! Well get a flame graph like this: Thats quite a difference! Weve updated our privacy policy so that we are compliant with changing global privacy regulations and to provide you with insight into the limited ways in which we use your data. This means programmers need to take care not to write a program that causes memory violation or data races. Now Rust has no gprof support, but on Linux there are a number of options available to profile code based on the DWARF debugging information in a binary (plus supplied source). Link with the /PROFILE linker switch. Our driver manages the requests internally by queueing them into a per-connection router, which is responsible for taking the requests from the queue and sending them to target nodes and reading their responses asynchronously. Rust uses a mangling scheme to encode function names in compiled code. 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