recompute | Selector library for Redux | State Container library
kandi X-RAY | recompute Summary
kandi X-RAY | recompute Summary
Alternative “selector” library (for Redux and others) inspired by Reselect and Computed properties from MobX, Aurelia and Angular. Recompute is based on Observers and Selectors. Observers are simple non memoized functions used to read specific state properties. Selectors are memoized functions that compute results based on the values returned by one or more observers.
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QUESTION
When a particular task fails that causes RDD to be recomputed from lineage (maybe by reading input file again), how does Spark ensure that there is no duplicate processing of data? What if the task that failed had written half of the data to some output like HDFS or Kafka ? Will it re-write that part of the data again? Is this related to exactly once processing?
...ANSWER
Answered 2021-Jun-12 at 18:37Output operation by default has at-least-once semantics. The foreachRDD function will execute more than once if there’s worker failure, thus writing same data to external storage multiple times. There’re two approaches to solve this issue, idempotent updates, and transactional updates. They are further discussed in the following sections
Further reading
http://shzhangji.com/blog/2017/07/31/how-to-achieve-exactly-once-semantics-in-spark-streaming/
QUESTION
I have a function which looks like the following:
...ANSWER
Answered 2021-Jun-09 at 07:21This looks like a good case for a try-catch approach. You could throw an exception in either of the methods e.g. StatusAbortedException and catch that to return the appropriate Status. It could look like this
QUESTION
library(ggplot2)
library(dplyr)
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)
my.data <- data.frame(x = x, y = y,
group = c("A", "B"),
facet = c("C", "D", "E", "F", "G"),
y2 = y * c(0.5,2),
w = sqrt(x))
formula <- y ~ poly(x, 3, raw = TRUE)
ggplot(my.data %>% group_by(facet, group) %>% mutate(n = n()), aes(x, y, n = n, color = facet)) +
geom_point() +
geom_smooth(method = "lm", formula = formula) +
facet_grid(vars(group)) +
ggpmisc::stat_poly_eq(aes(label = paste(stat(rr.label), paste("N ~`=`~", n), sep = "*\", \"*")),
formula = formula, parse=T)
ANSWER
Answered 2021-Jun-03 at 11:54Yes, they will fit it twice. I do not know of a way of avoiding this within 'ggplot2' without relying on writing a new pair of stat + geom. It could be done I think, and in fact would be a good enhancement to the package.
QUESTION
There are 3 objects, v1, df1 and df2 as below:
...ANSWER
Answered 2021-May-26 at 08:55This is not possible out of the box. You will have to write your own function doing so or use some external library, in R objects are a copy, so that means that df2is unaware of what v1 contains or how v1 changes after df2 is created. See also this similar question
QUESTION
The Spark docs state:
a Spark executor exits either on failure or when the associated application has also exited. In both scenarios, all state associated with the executor is no longer needed and can be safely discarded.
However, in the scenario where the Spark cluster configuration and dataset are such that occasional executor failure OOM occurs deep into a job, it is far more preferable for the shuffle files written by the dead executor to continue to be available to the job rather than have them recomputed.
In such a scenario with the External Shuffle Service enabled, I have appeared to observe Spark continuing to fetch the afore mentioned shuffle files and only rerunning the tasks that were active at the time when the executor died. In contrast, with the External Shuffle Service disabled I have seen Spark rerun a proportion of previously completed stages to recompute lost shuffle files as expected.
So can Spark with the External Shuffle Service enabled use saved shuffle files in the event of Executor failure as I have appeared to observe? I think so, but the documentation makes me doubt it.
I am running Spark 3.0.1 with Yarn on EMR 6.2 with dynamic allocation disabled.
Also, pre-empting comments, of course it is preferable to configure the cluster so that executor OOM never occurs. However, when initially aiming to complete an expensive Spark job the optimal cluster configuration is not yet achieved. It is at this time that shuffle reuse in the face of executor failure is valuable.
...ANSWER
Answered 2021-May-12 at 16:21The sentence you quoted:
a Spark executor exits either on failure or when the associated application has also exited. In both scenarios, all state associated with the executor is no longer needed and can be safely discarded.
is from the "Graceful Decommission of Executors" section.
That feature main intention is to provide a solution when Kubernetes is used as a resource manager. Where external shuffle service is not available. It is migrating the disk persisted RDD blocks and shuffle blocks to the remaining executors.
In case of Yarn when external shuffle service is enabled the blocks will be fetched from the external shuffle service which is running as an auxiliary service of the Yarn (within the node manager). That service knows the executors internal directory structure and able to serve the blocks (as it is on the same host). This way when the node survives and just the executor dies the blocks won't be lost.
QUESTION
I'm trying to store some data in an associative array in php for access in javascript later. I want to process the data in such a way that I can access it in multiple ways, say both by name and by type?
...ANSWER
Answered 2021-May-05 at 07:21I think formatting the datas in php that way to be parsed by js later from a json is not the best way to do.
Basically you have objects Fruit that have two properties name and color. I'd just encode a json with an array of Fruit and in js map it the way I want to use those objects.
I don't think mapping the objects is the responsability of the server, it's responsability is to give the client the datas.
In JS i would even not store them in multiple maps.
QUESTION
I have a simple use case where I am trying to recompute a provider providing a list of items on watching an item provider as follows :
...ANSWER
Answered 2021-Apr-20 at 19:03This can be accomplished handily with a single StateNotifierProvider.
QUESTION
I am writing low level 2D/3D rendering engine as a part of display driver for MCU platform in C++ and I hit a wall with perspective projection in 3D and face culling.
Lets assume my 3D engine is using M,V,P matrices (in the same manner as Model,View and Projection matrices in OpenGL fixed pipeline).
The idea is to convert rasterized face normal into view local coordinates (using MV) and test the sign of coordinate corresponding to view direction. The same can be done with dot product between camera view direction and the normal itself. And according to sign either rasterize or skip face. This works well for parallel rays projections ... however with perspective projections this leads to false positives (you can see faces "visually" tilted away up to some angle).
For filled surfaces this poses only performance hit as depth buffer remedies the artifacts so render looks like it should. However wire frame is a problem:
The remedy is to transform vertexes of face by MVP and do the perspective divide. And then re-compute the normal from the result and use that for face culling:
However that is a lot of more operations which on slow platforms like MCU could pose a performance problem. So my question is:
If it is possible How to use face normal for back face culling safely?
I have tried to transform the normal locally by transforming face center and its small displacement in normal direction by MVP with perspective divide and then recomputing the normal from these 2 points. Still twice the operations then using directly normal but better than 3x. However result was not correct (looked almost identical to using normal directly).
I am thinking of somehow computing the tilt angle for given projection / location and test the:
...ANSWER
Answered 2021-Apr-20 at 14:22Usually, face normal is not used for backface culling. Instead of it, rasterizers use screen positions of triangle vertices. Basically, if vertices are in clockwise order on screen this face is considered to be facing away.
Moreover, it is possible to have a triangle with normal pointed away from view direction and yet faced to the camera.
QUESTION
Let's say A1&A2 are Two 3D points that make a line Segment.
T1,T2,T3 are Three 3D points that make a Triangle Polygon in 3D space.
Let P1 be a point on the Line segment, Let P2 be a point on the Triangle Polygon
P1 And P2 Are Closest To each other
Now, how can I Calculate P1 and P2 which method shall I use?
The question is now Solved Here Is the Answer
Right now I know how to find the closest point on a line segment from a point.
and Closest two Points between Two Line Segment.
I use this Below Function to find the closest line segment between two line segment
...ANSWER
Answered 2021-Apr-09 at 17:37I guess you could spend a lot of time writing and debugging quite a lot of code if you do this 'by hand'. A better approach would be to formulate it as an instance of a general problem and then look for libraries that can solve this problem.
In this case the problem is 'constrained linear least squares' which is quite common.
The first thing to do is to introduce parameters, for example:
A point P on the line is given by
QUESTION
I am working with the orings data set in the faraway package in R. I have written the following grouped binomial model:
ANSWER
Answered 2021-Mar-31 at 15:27It is not completely clear what you're looking to do here, but we can at least show some quick principles of how we can achieve this, and then hopefully you can get to your goal.
1) Simulating the null modelIt is not entirely clear that you would like to simulate the null model here. It seems more like you're interested in simulating the actual model fit. Note that the null model is the model with form cbind(damage, 6-damage) ~ 1, and the null deviance and df are from this model. Either way, we can simulate data from the model using the simulate function in base R.
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