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

## kandi X-RAY | FASTER Summary

## kandi X-RAY | FASTER Summary

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.

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### Top functions reviewed by kandi - BETA

Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of FASTER

## FASTER Key Features

## FASTER Examples and Code Snippets

```
public void processUsingApproachThree(Flux flux) {
LOGGER.info("starting approach three!");
flux.map(FooNameHelper::concatAndSubstringFooName)
.map(foo -> reportResult(foo, "THREE"))
.doOnError(error -> LOGG
```

```
def finish_faster():
"""Finish faster by sleeping less."""
for _ in range(10):
time.sleep(_SLEEP_DURATION)
```

`function a(){return e>p&&f(m,function(t,e){return t&&l.hasOwnProperty(e)},!0)&&!l.hasOwnProperty(n)} `

## Community Discussions

Trending Discussions on FASTER

QUESTION

Here are two measurements:

...ANSWER

Answered 2022-Mar-30 at 11:57Combining my comment and the comment by @khelwood:

**TL;DR:**

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.

**Bytecode:**

QUESTION

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:**

ANSWER

Answered 2021-Oct-16 at 17:42From 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:

QUESTION

When trying to run the command using nextjs npm run dev shows error - failed to load SWC binary see more info here: https://nextjs.org/docs/messages/failed-loading-swc.

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.

...ANSWER

Answered 2021-Nov-20 at 13:57This 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

QUESTION

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?

**Related**

- Integer square root in python
- Is there a short-hand for nth root of x in Python?
- Difference between **(1/2), math.sqrt and cmath.sqrt?
- Why is math.sqrt() incorrect for large numbers?
- Python sqrt limit for very large numbers?
- Which is faster in Python: x**.5 or math.sqrt(x)?
- Why does Python give the "wrong" answer for square root? (specific to Python 2)
- calculating n-th roots using Python 3's decimal module
- How can I take the square root of -1 using python? (focused on NumPy)
- Arbitrary precision of square roots

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

ANSWER

Answered 2022-Feb-04 at 19:44`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`

.

QUESTION

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 |.

ANSWER

Answered 2022-Feb-08 at 19:57Code 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).

QUESTION

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.

ANSWER

Answered 2022-Jan-31 at 23:38The 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:

QUESTION

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:

...ANSWER

Answered 2022-Jan-27 at 19:15Your `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.

QUESTION

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:

...ANSWER

Answered 2021-Oct-27 at 19:53It 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.,

QUESTION

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

...ANSWER

Answered 2022-Jan-19 at 09:34The original code can be re-written in the following way:

QUESTION

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.

...ANSWER

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).

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