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This article proposes adaptive pool testing which involves testing a combined sample from multiple people as a methodology to increase testing capacity for Sars-COv2 using PCR. Pool tests involve that if a pool is detected positive, each sample from the pool will be tested separately. Also, this article proposes an optimization algorithm for the pool size-determine the optimum number of samples that can be combined into a single test in order to obtain the best results with a minimum number of tests. Romania and other countries use PCR testing for diagnosing COVID -19; this method offers the best detection rate compared with other testing methods but is expensive and is limited by availability of kitting tests and PCR testing machines; current capacity in Romania is around 4000 test per day; Currently testing in Romania and other countries with limited testing capacity are conducted only for peoples with specific symptoms and for contacts of infected persons; A PCR test examines the presence of a unique genetic sequence of viruses in a sample taken from the patient and takes several hours. Researchers at Technion – Israel Institute of Technology and Rambam Health Care Campus have successfully tested pooling methodology for COVID 19 that enables simultaneous testing of dozens of samples. “According to the new pooling approach that we have currently tested, molecular testing can be performed on a “combined sample,” taken from 32 or 64 patients. This way we can significantly accelerate the testing rate. Only in those rare cases, where the joint sample is found to be positive, will we conduct an individual test for each of the specific samples.” According to Prof. Roy Kishony, head of the research group in the Faculty of Biology at Technion, “This is not a scientific breakthrough, but a demonstration of the effectivity of using the existing method and even the existing equipment to significantly increase the volume of samples tested per day. This is done by pooling multiple samples in a single test tube. Even when we conducted a joint examination of 64 samples in which only one was a positive carrier, the system identified that there was a positive sample. ( ) https://www.technion.ac.il/en/2020/03/pooling-method-for-accelerated-testing-of-covid-19/ Similar research was performed in Germany by Goethe-Universität Frankfurt am Main with combined sample size from 5 patients and results were 10% accurate. (more details here: ) https://idw-online.de/en/news743899 In mathematics, group testing was first studied by Robert Dorfman in 1943; is a procedure that breaks up the task of identifying certain objects into tests on groups of items, rather than on individual ones. This methodology was applied in epidemiology  for other diseases like malaria and HIV for PCR testing; a related article for malaria: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4301903/ Pooled adaptive PCR testing strategy based on prevalence estimation Facts: It's possible to test a pool of up to 64 people for SARS-CoV-2 at once and the test will say whether at least one person in the pool is positive; Based on previous testing results we can calculate the prevalence rate; for example in Romania (as of April 6, 2020): we have 3.864 detected cases and we performed 38.629  tests to detect these cases (only peoples with specific symptoms and contacts of infected persons were tested); in this case prevalence rate is around 10% (10% of total test are positive); Proposed solution: Develop a pooling testing protocol (algorithm) based on prevalence estimation; this algorithm will be adaptive and pool size will be changed frequently based on estimated prevalence rate and changes in prevalence rate in previous days. There are two potential strategies: in cases where prevalence rate is relatively high (between 1% and 30%); if the prevalence of positive samples is greater than 30% it is never worth pooling and the recommended strategy is individual tests; for the cases when prevalence rate is between 1% and 30% the objective is to determine the optimal pool size in order to obtain the minimum number of tests; strategy in this case is to test each pool of tests and if the results of a pool is positive to test individual each test from the pool; One pool strategy second strategy proposed it is recommended for a prevalence rate below 1%; a huge decrease in number of tests required to test a large population can be obtained using two pools; in this case first round of testing will use large pools (between 32 and 64 samples) and a second round of testing will be performed for positive large pools with smaller pools; only for positive small pools will be performed individual testing. Two pools strategy: Tests and pool calculator Based on the above data I developed a used to estimate the number of tests required based on prevalence rate and pool size; also, this calculator allow to determine the optimum pool size in order to use a minimum number of tests; calculator This calculator uses as input prevalence rate and pool size and the output is the estimated number of tests required using pool methodology; calculator was developed in Excel and is available for Also, this calculator determines the optimum pool size for a selected prevalence rate download. Figure 1: Calculator Proposed methodology for targeted tests Calculate prevalence rate in testing region based on previous data: Prevalence rate = number of positive tests divided by total number of tests performed; prevalence can be calculated at country level or region/state based Prevalence rate can be calculated using data from the beginning of testing; maximum benefits can be obtained by calculating prevalence rate based on last three days test results; also better results can be obtained if we calculate based on region prevalence Use Excel calculator to determine optimal pool size: enter number of cases and number of positive tests; prevalence is automatically calculated and also optimal pool size; you can use also a fixed pool size; calculator will show the number of test required for both situations (fixed and optimum pool size); Perform pool testing and individual tests for samples from positive pools Proposed methodology for large population tests Calculate prevalence rate in testing region based on previous data: Prevalence rate = number of positive tests divided by total population; prevalence can be calculated at country level or region/state based; adjust number by adding a coefficient for undetected case (asymptomatic cases) Use Excel calculator to determine optimal pool size Perform pool testing and individual tests for samples from positive pools Advantages Pooled adaptive PCR testing strategy based on prevalence estimation can offer a major increase in testing capacity with a big impact also in testing costs: Reduce the number of tests required for targeted testing with up to 50% Test large population with low prevalence rates using large pools (up to 64 tests); for example, you can test 100.000 persons with around 12.000 tests using a pool size of 30 at a prevalence rate of 0.25%. This methodology can be used also for prevention (i.e. test all passengers from a plane with only few pools and several individual tests) A “Two pools strategy” can be also developed; this strategy it is recommended for a prevalence rate below 1%; a huge decrease in number of tests required to test a large population can be obtained using two pools; in this case first round of testing will use large pools (between 32 and 64 samples) and a second round of testing will be performed for positive large pools with smaller pools (4-8 tests); only for positive small pools will be performed individual testing. Below table is based on Worldometer data ( ) as of April 8, 2020 show the optimum pool size for several countries and US states; also the table show the estimated number of tests required using adaptive pool methodology for the same number of patients and the gain in tests: https://www.worldometers.info/coronavirus/ Figure 2: Individual testing versus adaptive pool methodology The number of tests required can vary based on positive tests distribution (number can be higher or lower); It is obvious that major gains can be obtained for lower prevalence rates: Figure 3: Reduction rate using pool testing Very good results can be obtained for prevalence rate less than 20%. From a testing resources perspective with less than 2,000 tests we can test 10,000 persons (2% prevalence rate) which is a huge gain for testing capacity.

Pooled Adaptive PCR Testing

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 7 Conclusion We present a method that efficiently scales thermodynamic stability prediction from single mutations to higher-order mutations. Our key insight is that the effects of mutations on the same protein are correlated. Thus, for a target protein, it suffices to run a deep backbone once and decode the effect of all mutations simultaneously using a shallow decoder. With the AlphaFold model as our backbone, our method outperforms existing methods on a variety of single and multiple mutation benchmarks. 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Predicting a Protein’s Stability
under a Million Mutations: Conclusion, Acknowledgement & References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 2 Related Works 2.1 Protein Engineering Protein engineering is the process of mutating a natural protein to improve particular phenotypes, such as thermodynamic stability and function. The field has historically relied on rational design and stochastic methods, such as error-prone PCR [1], DNA shuffling [71], and directed evolution (DE) [4, 27], to identify gain-of-function mutations. Rational design is limited to proteins with solved structures and requires an extensive understanding of biochemistry and specific knowledge of the particular protein to select mutations highly likely to improve the target phenotypes [33, 79]. Directed evolution requires little to no knowledge of the protein and instead generates a library of protein variants that are then screened for the target phenotypes (e.g. fluorescence brightness, antibiotic resistance, stability, activity) [4, 27]. The library can be generated via site-saturated mutagenesis of a handful of positions in the sequence [12] or DNA shuffling, in which the gene is digested into random fragments and reassembled into full-length sequences [71]. After screening the library, the most "fit" variant is then selected as the initial sequence for the next round of directed evolution. This iterative process repeats until it obtains a protein variant with the desired phenotypes. Machine learning has demonstrated its ability to augment rational design and accelerate the stabilization of a variety of proteins [20, 28, 40, 52, 68]. Separately, machine learning-guided directed evolution (MLDE)[75] has been shown to improve the likelihood of obtaining the global fitness maximum by 81-fold compared to traditional DE [76]. MLDE has accelerated the engineering of several proteins, such as the enantioselectivity of enzymes for kinetic resolution of epoxides [23] and the activity and expression of a glutathione transferase [46]. Mutate Everything empowers the experimentalist to accelerate the stabilization of a protein for both rational design and MLDE. 2.2 Machine Learning for Protein Structure Prediction Recent advances in machine learning have led to remarkable progress in protein structure prediction. AlphaFold [31] has demonstrated that deep learning is highly effective at predicting protein structures from a sequence by using evolutionary history via a multiple sequence alignment (MSA). AlphaFold passes the MSA and a pairwise representation of the sequence into Evoformer to capture the coevolutionary patterns between residues. The Evoformer output is then processed by the Structure Module, which predicts the protein’s structure. We challenge prior works postulating that AlphaFold cannot be used for stability prediction and show that fine-tuning these rich co-evolutionary and structural features yield highly performant stability predictors [53]. Evolutionary Scale Modeling (ESM) [38, 62] has shown that protein structure prediction can be performed without MSAs and specialized architectures by leveraging large transformers pre-trained on masked token prediction. Other works extended this masked pre-training framework, including MSA-Transformer [60] which incorporates a sequence’s MSA as input, and Tranception [49], which develops a hybrid convolution and attention based autoregressive architecture. We show that finetuning these evolutionary representations excels at protein stability assessment without MSAs. 2.3 Protein Stability Assessment Traditional approaches to protein stability assessment relied on a combination of physics-based methods, statistical analysis, and traditional machine learning techniques. Physics-based tools, such as FoldX, Rosetta, and SDM, utilize energy functions and statistical patterns to assess how mutations affect a protein’s stability [32, 67, 77]. DDGun [44] directly inferred ∆∆G from heuristics, including Blosum substitution scores, differences in interaction energy with neighboring residues, and changes in hydrophobicity. Many traditional machine learning approaches used support vector machines and decision trees with physics-based feature engineering [11, 14, 15, 17, 37, 66]. Others ensemble existing machine learning methods [35, 56, 59, 63, 64]. Recently, deep learning-based approaches have recently begun to outperform existing physics and traditional machine learning approaches [13, 19, 34]. ACDC-NN [6] trains an asymmetric convolutional neural network for predicting forward and reverse mutation effects. Thermonet [36] voxelizes and feeds both the wild-type and mutant protein structures into a 3D-CNN to regress a ∆∆G value. PROSTATA feeds the wild-type and mutant protein sequence into a pre-trained ESM2 model and then regresses a ∆∆G value [74]. Stability Oracle takes the native and mutant amino acid type along with the local structure as input and regresses ∆∆G [18]. Several deep learning frameworks [64, 80, 81] model multiple mutations in addition to single mutations. In this paper, we develop a framework that models the protein to enable efficient enumeration of all mutation candidates.

Predicting a Protein’s Stability
under a Million Mutations: Related Works

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- that's what the experts say. And what is left for the naughty ones who want to efficiently search through a huge number of templates? "Don't use regular expressions, otherwise you'll have 2 problems instead of 1" Sure, there are cool solutions like or for this specific problem. However, for my project it seemed impractical to master these fine technologies for the time being. Ragel re2c In this article, we will look at alternatives to the standard regexp library in Go and benchmark them for speed and memory consumption. We will also consider the differences between them from a practical point of view. Analogues At the moment, I've found the following working alternatives to the default that can be used to find patterns in Go (the versions used in the benchmarks are given in brackets): regexp (1.3.0) - as simple as possible replaces the default regexp. Uses to improve performance when dealing with large inputs or complex expressions; go-re2 C++ re2 (1.10.0) - a feature-rich regexp engine for . It does not have runtime guarantees like the built-in regexp package, but is compatible with Perl5 and .NET; regexp2 Go (1.0.0) - provides support for compatible regular expressions using or . JIT compilation is available, making this fork much faster than its counterparts. On the downside, you'll need the dependency; go-pcre Perl libpcre libpcre++ libpcre3-dev (regex 1.9.3) - uses the regex engine with bindings. The downside is a library dependency that needs to be compiled; rure-go Rust CGo Rust (1.2.2 + hs5.4.1) - regex engine designed for high performance. It is implemented as a library that provides a simple . It also requires compilation and linking of third-party dependencies; gohs C-API - A tool for identifying and classifying malware samples. Although has functionality for templating and regular expressions, it is very limited, so I will not include this library in the upcoming tests. go-yara YARA Existing benchmarks Before we start comparing the aforementioned solutions, it is worth to show how bad things are with the standard regex library in . I found the where the author compares the performance of standard regex engines of various languages. The point of this benchmark is to repeatedly run 3 regular expressions over a predefined text. came in in this benchmark! Go project Go 3rd place From the end... Language Email(ms) URI(ms) IP(ms) Total(ms) Nim Regex 1.32 26.92 7.84 36.09 Nim 22.70 21.49 6.75 50.94 Rust 26.66 25.70 5.28 57.63 ... ... ... ... ... Python PyPy3 258.78 221.89 257.35 738.03 Python 3 273.86 190.79 319.13 783.78 Go 248.14 241.28 360.90 850.32 C++ STL 433.09 344.74 245.66 1023.49 C# Mono 2859.05 2431.87 145.82 5436.75 . From other benchmarks I can highlight the following: As far as I can tell, benchmarking regex engines is not the easiest topic, because you need to know implementation and algorithm details for a correct comparison - comparison of engines with optimized versions for each language. For example, is no longer at the bottom, but it is still far from ideal there... And of course it does not use a native library, but a wrapper over PCRE - , which was specified in analogues. Benchmarks Game Go go-pcre - Comparison of different regex engines (PCRE, PCRE-DFA, TRE, Oniguruma, RE2, PCRE-JIT). Performance comparison of regular expression engines - here the author tries to compare engines using different regular expressions, which can complicate things depending on the engine implementation. In this benchmark, the author's top three engines are: Hyperscan, PCRE (with JIT compilation) and Rust regex ( uses it): A comparison of regex engines rure Benchmark #1 Now let's try to compare the analogues with default regex engine libraries of other languages. And also see how much faster they are compared to the default . To do this, I updated the above project by adding new libraries code. Here is what I got after running it on my machine: Go regex Language Email(ms) URI(ms) IP(ms) Total(ms) Go Rure 2.61 2.11 3.33 8.05 C++ SRELL 1.74 3.03 10.67 15.45 Rust 11.66 1.70 5.13 18.48 Nim 13.98 14.35 3.13 31.46 PHP 14.43 14.63 4.87 33.93 Go PCRE 14.18 14.98 6.21 35.37 C# .Net Core 10.83 5.10 26.71 42.64 Javascript 42.78 30.17 0.92 73.87 Go Re2 35.81 37.86 33.79 107.46 Go Hyperscan 90.17 31.64 8.68 130.49 Perl 92.51 66.42 22.51 181.44 Dart 73.48 63.60 80.52 217.59 C PCRE2 110.87 100.57 10.50 221.94 Kotlin 120.31 163.53 293.69 577.53 Java 205.14 201.55 295.68 702.36 Python 3 246.26 176.01 303.08 725.34 C++ STL 328.87 273.43 230.35 832.64 Go 270.19 275.73 504.79 1050.71 Go Regexp2 1703.51 1482.60 64.46 3250.57 C# Mono 2543.82 2139.44 110.37 4793.64 . Ps. Shortened the table a bit, so please check the to see all the results. project fork Pss. In this case, virtualization might have affected the results (the tests were performed in a Docker environment on Windows 11). Still, you can see how much faster regexps can be with some libraries! We even outperformed Rust with a Go library that uses the Rust library 🥴🙆🏼‍♂️. Perhaps this is what the author of this solution is trying to explain to us in his . repository As a result, almost all alternative solutions give us a speedup of times! Except for , which turns out to be slower than the standard library. 8-130 Regexp2 Benchmark #2 Questions While studying the existing benchmarks and the results of , I was lacking the answers to the following questions: Benchmark#1 How fast do the above libraries process large files? How much faster is the processing when grouping regular expressions? How fast will regular expressions with no matches in the text be processed? How much memory do the different libraries use? How many regexps will I be able to compile using grouping? Benchmarker differences To answer these questions, I wrote a small benchmarking program that can be used to compare different regex engines in terms of speed and memory usage. If you want to test it yourself or evaluate the correctness of the used methods, here is the . code The following features of this benchmark are worth mentioning: In the tests below, I used different regular expressions: 5 allRegexps["email"] = `(?P<name>[-\w\d\.]+?)(?:\s+at\s+|\s*@\s*|\s*(?:[\[\]@]){3}\s*)(?P<host>[-\w\d\.]*?)\s*(?:dot|\.|(?:[\[\]dot\.]){3,5})\s*(?P<domain>\w+)`
allRegexps["bitcoin"] = `\b([13][a-km-zA-HJ-NP-Z1-9]{25,34}|bc1[ac-hj-np-zAC-HJ-NP-Z02-9]{11,71})`
allRegexps["ssn"] = `\d{3}-\d{2}-\d{4}`
allRegexps["uri"] = `[\w]+://[^/\s?#]+[^\s?#]+(?:\?[^\s#]*)?(?:#[^\s]*)?`
allRegexps["tel"] = `\+\d{1,4}?[-.\s]?\(?\d{1,3}?\)?[-.\s]?\d{1,4}[-.\s]?\d{1,4}[-.\s]?\d{1,9}` In a good way, I should have used tricky regular expressions, like other benchmark authors do - to check the "weaknesses" of the algorithms. But I don't have much insight into the under-the-hood details of engines, so I used general-purpose regexps. That's why I think it should be possible to evaluate different parameters of libraries from a practical point of view. Instead of a static file, we will use a string containing our matches, repeated many times in memory to simulate files of different sizes: var data = bytes.Repeat([]byte("123@mail.co nümbr=+71112223334 SSN:123-45-6789 http://1.1.1.1 3FZbgi29cpjq2GjdwV8eyHuJJnkLtktZc5 Й"), config.repeatScanTimes) In addition to running the regexps sequentially over the data, there will be a separate run with named grouping of regular expressions, where they will take the following form: `(?P<name_1>regexp_1)|(?P<name_2>regexp_2)|...|(?P<name_N>regexp_N)` By the way, Hyperscan has a special functionality where we can build a database of regexps and use it against data. In the benchmarks I will use this method. Unlike in , for each regexp I will measure the time to find the result, without taking into account the compilation time; Benchmark#1 In the end, we will get the results for each library and each regexp in the following form: Generate data...
Test data size: 100.00MB

Run RURE:
  [bitcoin] count=1000000, mem=16007.26KB, time=2.11075s 
  [ssn] count=1000000, mem=16007.26KB, time=62.074ms 
  [uri] count=1000000, mem=16007.26KB, time=69.186ms 
  [tel] count=1000000, mem=16007.26KB, time=83.101ms 
  [email] count=1000000, mem=16007.26KB, time=172.915ms 
Total. Counted: 5000000, Memory: 80.04MB, Duration: 2.498027s
... 1-2. Processing large files and grouping regular expressions By "large file" I mean the amount of data generated from our predefined string in memory equal to . Of course it's hardly a big file, but I didn't want to wait hours for results in some cases 😅. 100MB Also, it makes sense to try grouping all regular expressions into one and see how much it affects performance. The hypothesis is that a sequential run of regexps over the data should take longer than a single run with a grouped regexp. Well, let's run the benchmark and see how fast each library processes the data. You can do this with my tool as follows: # Bench all libs, repeat the line 1000000 times (100MB)
regexcmp 1000000 
# Bench individual lib
regexcmp 1000000 rure
# See all options
regexcmp -h As a result, we have the following data: Lib Email Tel URI SSN Bitcoin Total Total-group Rure 0.173s 0.083s 0.069s 0.062s 2.11s 2.49s 10.13s PCRE 49.5s 0.367s 2.92s 2.07s 2.19s 57.07s 9.94s Re2 0.954s 0.63s 0.956s 0.945s 1.05s 4.54s 3.24s Hyperscan 0.469s 1.09s 0.669s 0.174s 1.97s 4.38s 4.97s Regexp2 126.4s 1.02s 21.51s 2.63s 3.38s 154.9s 20.65s Go regexp 11.22s 1.52s 3.62s 2.66s 3.32s 22.36s 26.49s The plot below shows the processing time of 100MB of data by all regexps in sequential mode and using grouping: : Conclusions Grouping can indeed significantly improve execution speed, but in some cases it can make it worse :); The fastest in sequential processing turned out to be - , with grouping - ; Rure Re2 Certain regexps may cause problems with some libraries (time to find in and ); email Regexp2 PCRE Now it is hard to say that some solutions are 180 times faster than the standard library, the maximum gain is . x8-9 3. Non-matching regexps In the previous case, we simulated the ideal situation where there are always matches in the data. But what if there are no hits for regexp in the text, how much will that affect performance? In this test, I additionally added modified regexps for SSN that do not match the data. In this case, means regexp, and - . Not a big difference? But let's see how it affects the time it takes to find all the matches: 5 SSN \d{3}-\d{2}-\d{4} Non-matching \d{3}-\d{2}-\d{4}1 Lib SSN Non-matching Total Total-group Rure 0.06s 0.083s 2.93s 15.68s PCRE 2.06s 4.21s 81.02s 13.52s Re2 0.960s 0.449s 6.75s 3.26s Hyperscan 0.175s 0.043s 4.61s 4.94s Regexp2 2.73s 3.09s 171.1s 15.72s Go regexp 2.66s 2.57s 35.13s 37.66s The plot below shows the time taken to process all regexps, sorted by processing time: 10 Non-matching : Conclusions This time is the same: the fastest in sequential processing was - , with grouped expressions - ; Rure Re2 is once again different, with time to process regexps in sequential mode; PCRE 2x non-matching Some algorithms are much faster when there are no matches ( , ); Re2 Hyperscan 4. Memory consumption Now let's see how much memory the different solutions consume when processing a 100MB file. Below, I have provided the results for each individual regexp and the total amount of memory consumed: Lib Email, MB Tel, MB URI, MB SSN, MB Bitcoin, MB Non-matching, KB Total, GB Total-group, GB Rure 16 16 16 16 16 0.0001 0.08 0.08 PCRE 186 186 186 186 186 0.38 0.93 0.9 Re2 254 254 254 255 254 100473 1.77 0.97 Hyperscan 314 314 314 314 314 0.68 1.57 1.7 Regexp2 997 854 854 854 902 396006 6.44 5.23 Go regex 191 144 144 160 208 3.62 0.78 1.8 The graph below shows the memory used by the libraries to process regexps (as in the last test), sorted by the time of `non-mathcing': 10 Conclusions: surprises with its practically zero memory consumption; Rure is resource hungry, consuming significantly more memory than its competitors; Regexp2 , despite its speed, is not the most memory-efficient solution; Re2 is on the good side, with relatively low costs in sequential mode. Go regex 5. Maximum number of regexps The main questions seem to be answered. Now let's see the maximum number of regexps we can compile with different solutions. In this case we will take a single regexp and repeat it many times in groups. For this purpose I will use the regexp: URI `[\w]+://[^/\s?#]+[^\s?#]+(?:\?[^\s#]*)?(?:#[^\s]*)?` Next, I've listed the results of compiling the regexps, along with the memory they use. The numbers in the first line are the number of expressions in the group: URI Lib 100 250 500 1000 5000 10000 Rure 0.38MB ❌ ❌ ❌ ❌ ❌ PCRE 0.40MB 2.37MB ❌ ❌ ❌ ❌ Re2 7.10MB 15.51MB 38.35MB 92.79MB 1181MB ❌ Hyperscan 0.03MB 0.06MB 0.12MB 0.25MB 1.21MB 2.52MB Regexp2 1.06MB 3.96MB 12.56MB 43.93MB 926MB 3604MB Go regex 1.29MB 4.52MB 14.02MB 47.18MB 942MB 3636MB : Conclusions As we can see, some solutions have limitations on the size of compiled regexps; not only allows to use a large number of regexps, but also uses minimal memory for compiled regexps; Hyperscan and have comparable memory consumption and also allow to compile a large number of regexps; Regexp2 Go Regex consumes the most memory at compile time. Re2 Conclusion I hope it was useful for you to learn about alternative solutions for regexps in , and based on the data I presented, everyone will be able to draw some conclusions for themselves, which will allow you to choose the most suitable regex solution for your situation. Go We can compare these libraries, the algorithms they use, their best/worst sides in details and for a long time. I just wanted to show the difference between them in the most general cases. Thus, I would advise you to look at for maximum speedup of regexps, but if you need the easiest library installation without dependencies, that is . And in the case of handling a large number of regexps would be a good choice. Also, don't forget the cost of using in some libraries. rure-go go-re2 hyperscan CGo As for me, I will definitely use these "findings" in my future :). project

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