FASTER | Fast persistent recoverable log and key-value store | Key Value Database library

 by   microsoft C# Version: v2.5.6 License: MIT

kandi X-RAY | FASTER Summary

kandi X-RAY | FASTER Summary

FASTER is a C# library typically used in Database, Key Value Database applications. FASTER has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Managing large application state easily, resiliently, and with high performance is one of the hardest problems in the cloud today. The FASTER project offers two artifacts to help tackle this problem.

            kandi-support Support

              FASTER has a medium active ecosystem.
              It has 5571 star(s) with 524 fork(s). There are 179 watchers for this library.
              There were 10 major release(s) in the last 12 months.
              There are 0 open issues and 291 have been closed. On average issues are closed in 54 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of FASTER is v2.5.6

            kandi-Quality Quality

              FASTER has 0 bugs and 0 code smells.

            kandi-Security Security

              FASTER has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              FASTER code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              FASTER is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              FASTER releases are available to install and integrate.
              FASTER saves you 98 person hours of effort in developing the same functionality from scratch.
              It has 350 lines of code, 0 functions and 376 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of FASTER
            Get all kandi verified functions for this library.

            FASTER Key Features

            No Key Features are available at this moment for FASTER.

            FASTER Examples and Code Snippets

            Performs a faster approach using reflection .
            javadot img1Lines of Code : 8dot img1License : Permissive (MIT License)
            copy iconCopy
            public void processUsingApproachThree(Flux flux) {
          "starting approach three!");
                      .map(foo -> reportResult(foo, "THREE"))
                      .doOnError(error -> LOGG  
            This function will finish a faster task .
            pythondot img2Lines of Code : 4dot img2License : Permissive (MIT License)
            copy iconCopy
            def finish_faster():
                """Finish faster by sleeping less."""
                for _ in range(10):
            a bit faster
            javascriptdot img3Lines of Code : 1dot img3License : Permissive (MIT License)
            copy iconCopy
            function a(){return e>p&&f(m,function(t,e){return t&&l.hasOwnProperty(e)},!0)&&!l.hasOwnProperty(n)}  

            Community Discussions


            Why is it faster to compare strings that match than strings that do not?
            Asked 2022-Mar-30 at 11:58

            Here are two measurements:



            Answered 2022-Mar-30 at 11:57

            Combining my comment and the comment by @khelwood:

            When analysing the bytecode for the two comparisons, it reveals the 'time' and 'time' strings are assigned to the same object. Therefore, an up-front identity check (at C-level) is the reason for the increased comparison speed.

            The reason for the same object assignment is that, as an implementation detail, CPython interns strings which contain only 'name characters' (i.e. alpha and underscore characters). This enables the object's identity check.




            Why is `np.sum(range(N))` very slow?
            Asked 2022-Mar-29 at 14:31

            I saw a video about speed of loops in python, where it was explained that doing sum(range(N)) is much faster than manually looping through range and adding the variables together, since the former runs in C due to built-in functions being used, while in the latter the summation is done in (slow) python. I was curious what happens when adding numpy to the mix. As I expected np.sum(np.arange(N)) is the fastest, but sum(np.arange(N)) and np.sum(range(N)) are even slower than doing the naive for loop.

            Why is this?

            Here's the script I used to test, some comments about the supposed cause of slowing done where I know (taken mostly from the video) and the results I got on my machine (python 3.10.0, numpy 1.21.2):

            updated script:



            Answered 2021-Oct-16 at 17:42

            From the cpython source code for sum sum initially seems to attempt a fast path that assumes all inputs are the same type. If that fails it will just iterate:



            Next failed to load SWC binary
            Asked 2022-Mar-22 at 05:46

            When trying to run the command using nextjs npm run dev shows error - failed to load SWC binary see more info here:

            I've tried uninstalling node and reinstalling it again with version 16.13 but without success, on the vercel page, but unsuccessful so far. Any tips?

            Also, I noticed it's a current issue on NextJS discussion page and it has to do with the new Rust-base compiler which is faster than Babel.



            Answered 2021-Nov-20 at 13:57

            This worked as suggeted by nextJS docs but it takes away Rust compiler and all its benefits... Here is what I did for those who eventually get stuck...

            Step 1. add this line or edit next.json.js



            How do I calculate square root in Python?
            Asked 2022-Feb-17 at 03:40

            I need to calculate the square root of some numbers, for example √9 = 3 and √2 = 1.4142. How can I do it in Python?

            The inputs will probably be all positive integers, and relatively small (say less than a billion), but just in case they're not, is there anything that might break?


            Note: This is an attempt at a canonical question after a discussion on Meta about an existing question with the same title.



            Answered 2022-Feb-04 at 19:44
            Option 1: math.sqrt()

            The math module from the standard library has a sqrt function to calculate the square root of a number. It takes any type that can be converted to float (which includes int) as an argument and returns a float.



            Is if(A | B) always faster than if(A || B)?
            Asked 2022-Feb-11 at 05:03

            I am reading this book by Fedor Pikus and he has some very very interesting examples which for me were a surprise.
            Particularly this benchmark caught me, where the only difference is that in one of them we use || in if and in another we use |.



            Answered 2022-Feb-08 at 19:57

            Code readability, short-circuiting and it is not guaranteed that Ord will always outperform a || operand. Computer systems are more complicated than expected, even though they are man-made.

            There was a case where a for loop with a much more complicated condition ran faster on an IBM. The CPU didn't cool and thus instructions were executed faster, that was a possible reason. What I am trying to say, focus on other areas to improve code than fighting small-cases which will differ depending on the CPU and the boolean evaluation (compiler optimizations).



            Replacing whole string is faster than replacing only its first character
            Asked 2022-Jan-31 at 23:38

            I tried to replace a character a by b in a given large string. I did an experiment - first I replaced it in the whole string, then I replaced it only at its beginning.



            Answered 2022-Jan-31 at 23:38

            The functions provided in the Python re module do not optimize based on anchors. In particular, functions that try to apply a regex at every position - .search, .sub, .findall etc. - will do so even when the regex can only possibly match at the beginning. I.e., even without multi-line mode specified, such that ^ can only match at the beginning of the string, the call is not re-routed internally. Thus:



            Why is QuackSort 2x faster than Data.List's sort for random lists?
            Asked 2022-Jan-27 at 19:24

            I was looking for the canonical implementation of MergeSort on Haskell to port to HOVM, and I found this StackOverflow answer. When porting the algorithm, I realized something looked silly: the algorithm has a "halve" function that does nothing but split a list in two, using half of the length, before recursing and merging. So I thought: why not make a better use of this pass, and use a pivot, to make each half respectively smaller and bigger than that pivot? That would increase the odds that recursive merge calls are applied to already-sorted lists, which might speed up the algorithm!

            I've done this change, resulting in the following code:



            Answered 2022-Jan-27 at 19:15

            Your split splits the list in two ordered halves, so merge consumes its first argument first and then just produces the second half in full. In other words it is equivalent to ++, doing redundant comparisons on the first half which always turn out to be True.

            In the true mergesort the merge actually does twice the work on random data because the two parts are not ordered.

            The split though spends some work on the partitioning whereas an online bottom-up mergesort would spend no work there at all. But the built-in sort tries to detect ordered runs in the input, and apparently that extra work is not negligible.



            Bubble sort slower with -O3 than -O2 with GCC
            Asked 2022-Jan-21 at 02:41

            I made a bubble sort implementation in C, and was testing its performance when I noticed that the -O3 flag made it run even slower than no flags at all! Meanwhile -O2 was making it run a lot faster as expected.

            Without optimisations:



            Answered 2021-Oct-27 at 19:53

            It looks like GCC's naïveté about store-forwarding stalls is hurting its auto-vectorization strategy here. See also Store forwarding by example for some practical benchmarks on Intel with hardware performance counters, and What are the costs of failed store-to-load forwarding on x86? Also Agner Fog's x86 optimization guides.

            (gcc -O3 enables -ftree-vectorize and a few other options not included by -O2, e.g. if-conversion to branchless cmov, which is another way -O3 can hurt with data patterns GCC didn't expect. By comparison, Clang enables auto-vectorization even at -O2, although some of its optimizations are still only on at -O3.)

            It's doing 64-bit loads (and branching to store or not) on pairs of ints. This means, if we swapped the last iteration, this load comes half from that store, half from fresh memory, so we get a store-forwarding stall after every swap. But bubble sort often has long chains of swapping every iteration as an element bubbles far, so this is really bad.

            (Bubble sort is bad in general, especially if implemented naively without keeping the previous iteration's second element around in a register. It can be interesting to analyze the asm details of exactly why it sucks, so it is fair enough for wanting to try.)

            Anyway, this is pretty clearly an anti-optimization you should report on GCC Bugzilla with the "missed-optimization" keyword. Scalar loads are cheap, and store-forwarding stalls are costly. (Can modern x86 implementations store-forward from more than one prior store? no, nor can microarchitectures other than in-order Atom efficiently load when it partially overlaps with one previous store, and partially from data that has to come from the L1d cache.)

            Even better would be to keep buf[x+1] in a register and use it as buf[x] in the next iteration, avoiding a store and load. (Like good hand-written asm bubble sort examples, a few of which exist on Stack Overflow.)

            If it wasn't for the store-forwarding stalls (which AFAIK GCC doesn't know about in its cost model), this strategy might be about break-even. SSE 4.1 for a branchless pmind / pmaxd comparator might be interesting, but that would mean always storing and the C source doesn't do that.

            If this strategy of double-width load had any merit, it would be better implemented with pure integer on a 64-bit machine like x86-64, where you can operate on just the low 32 bits with garbage (or valuable data) in the upper half. E.g.,



            Problem with memory allocation in Julia code
            Asked 2022-Jan-19 at 09:34

            I used a function in Python/Numpy to solve a problem in combinatorial game theory.



            Answered 2022-Jan-19 at 09:34

            The original code can be re-written in the following way:



            Efficient summation in Python
            Asked 2022-Jan-16 at 12:49

            I am trying to efficiently compute a summation of a summation in Python:

            WolframAlpha is able to compute it too a high n value: sum of sum.

            I have two approaches: a for loop method and an np.sum method. I thought the np.sum approach would be faster. However, they are the same until a large n, after which the np.sum has overflow errors and gives the wrong result.

            I am trying to find the fastest way to compute this sum.



            Answered 2022-Jan-16 at 12:49

            (fastest methods, 3 and 4, are at the end)

            In a fast NumPy method you need to specify dtype=np.object so that NumPy does not convert Python int to its own dtypes (np.int64 or others). It will now give you correct results (checked it up to N=100000).


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install FASTER

            You can download it from GitHub.


            This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA. This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact with any additional questions or comments.
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