kandi X-RAY | cache-example Summary
kandi X-RAY | cache-example Summary
Example cache implementation (Guava)
Top functions reviewed by kandi - BETA
- Main method for testing purposes
- Returns the score of the given id
cache-example Key Features
cache-example Examples and Code Snippets
Trending Discussions on Caching
I have a view and I cached it in views.py using django-cacheops (https://github.com/Suor/django-cacheops):...
ANSWERAnswered 2022-Mar-19 at 14:37
Since you used a named group
usr in your regex, Django passes it as a keyword argument:
I am a C++ habitat working on a C# project.
I have encountered the following situation.
class MyClass and want to avoid any 2 objects of type MyClass ever to share a cache line even if I have an array or any sequential collection of type MyClass.
In C++ we can declare
class alignas(hardware_destructive_interference_size) Myclass and this will make sure that any 2 objects never share a cache line.
Is there any equivalent method in C#?...
ANSWERAnswered 2022-Feb-28 at 13:23
No, you can't control the alignment or memory location of classes (reference types). You can't even get the size of a class instance in memory.
It is possible to control the size and alignment of structs (and of the fields within them). Structs are value types and work pretty much the same as in C++. If you create an array of a struct, each entry has the size of the struct, and if that is large enough, you could get what you want. But there's no guarantee that the individual entries are really distributed over cache lines. That will also depend on the size and organisation of the cache.
Note also that the address of a managed instance (whether a class or a struct) can change at runtime. The garbage collector is allowed to move instances around to compact the heap, and it will do this quite often. So there is also no guarantee that the same instance will always end up in the same cache line. It is possible to "pin" an instance while a certain block executes, but this is mostly intended when interfacing with native functions and not in a context of performance optimization.
I have some react code that is rendering content dynamically via
React.createElement. As such, css is applied via an object. Elements in that dynamic generation can have background image, pointing to a public aws S3 bucket.
It seems that every time my components re-render, the background images are being fetched again from S3. This is delaying the page render. I have S3 meta-data for Cache-Control set on all the objects . Here are request and response headers for background image load -
Response header -...
ANSWERAnswered 2022-Feb-23 at 20:53
The reason you're seeing a network request is probably because you're using the
Cache-Control: no-cache header in your request.
As seen here:
The no-cache response directive indicates that the response can be stored in caches, but the response must be validated with the origin server before each reuse, even when the cache is disconnected from the origin server.
If you want caches to always check for content updates while reusing stored content, no-cache is the directive to use. It does this by requiring caches to revalidate each request with the origin server.
Note that no-cache does not mean "don't cache". no-cache allows caches to store a response but requires them to revalidate it before reuse. If the sense of "don't cache" that you want is actually "don't store", then no-store is the directive to use.
Here is what a full request for a cached asset looks like on my network tab, when the asset returns 304 Not Modified from the validation request. (from S3) This is in a
background: url context.
I have Nginx cache server built on Ubuntu 18 and with docker image nginx:1.19.10-alpine.
Ubuntu 18 disk usage details given below for reference...
ANSWERAnswered 2022-Jan-27 at 02:15
You can try to configure the temporary cache directory
In the following code, I create two lists with the same values: one list unsorted (s_not), the other sorted (s_yes). The values are created by randint(). I run some loop for each list and time it....
ANSWERAnswered 2021-Nov-15 at 21:05
Cache misses. When
N int objects are allocated back-to-back, the memory reserved to hold them tends to be in a contiguous chunk. So crawling over the list in allocation order tends to access the memory holding the ints' values in sequential, contiguous, increasing order too.
Shuffle it, and the access pattern when crawling over the list is randomized too. Cache misses abound, provided there are enough different int objects that they don't all fit in cache.
r==2, CPython happens to treat such small ints as singletons, so, e.g., despite that you have 10 million elements in the list, at
r==2 it contains only (at most) 100 distinct int objects. All the data for those fit in cache simultaneously.
Beyond that, though, you're likely to get more, and more, and more distinct int objects. Hardware caches become increasingly useless then when the access pattern is random.
Aside from doing a direct match on something like a whitespace normalized hash of a query, what might be a useful (but-not-necessarily-perfect) way to handle query cache in a partial manner? For example, let's take the following basic case:...
ANSWERAnswered 2021-Nov-05 at 00:03
I think the following might be a good starting place for a basic cache implementation that allows the usage of a cache that can be further queried for refinements:
- Start by substituting any udf's or cte's. The query itself needs to be self-contained.
- Normalize whitespaces and capitalization.
- Hash the entire query. This will be our starting place.
- Remove the select fields and hash the rest of the query. Now store a hash of all the individual items in the select list.
- For partial cache, generate a hash minus select fields, where, sort, and limit+offset. Hash the where's list (separated by AND), making sure no filter is contained in the cache that is not contained in the current query, the orderby, seeing if the data needs to be re-sorted, and the limit+offset number, making sure the limit+offset in the initial query is null or greater than the current query.
Here would be an example of how the data might look saved:Hash 673c0185c6a580d51266e78608e8e9b2 HashMinusFields 41257d239fb19ec0ccf34c36eba1948e HashOfFields [dc99e4006c8a77025c0407c1fdebeed3, …] HashMinusFieldsWhereOrderLimit d50961b6ca0afe05120a0196a93726f5 HashOfWheres [0519669bae709d2efdc4dc8db2d171aa, ...] HashOfOrder 81961d1ff6063ed9d7515a3cefb0c2a5 LimitOffset null
Now let's try a few examples, I will use human-readable hashes for easier readability:
I'm trying to use the official GitHub cache action (https://github.com/actions/cache) to cache some binary files to speed up some of my workflows, however I've been unable to get it working when specifying multiple cache paths.
Here's a simple, working test I've set up using a single cache path: There is one action for writing the cache, and one for reading it (both executed in separate workflows, but on the same repository and branch). The write-action is executed first, and creates a file "subdir/a.txt", and then caches it with the "actions/cache@v2" action:...
ANSWERAnswered 2021-Nov-10 at 23:06
I was able to make it work with a few modifications;
- use relative paths instead of absolute
- use a hash of the content for the key
It looks like with at least bash the absolute paths look like this:
Where so-foobar-cache is the name of the repository..github/workflows/foobar.yml
We are able to defeat the small integer intern in this way (a calculation allows us to avoid the caching layer):...
ANSWERAnswered 2021-Oct-14 at 20:35
Unicode consisting of only one character (with value smaller than 128 or more precisely from
latin1) is the most complicated case, because those strings aren't really interned but (more similar to the integer pool or identically to the behavior for
bytes) are created at the start and are stored in an array as long as the interpreter is alive:
Lets save I have this code which exhibits stale cache reads by a thread, which prevent it from exiting its while loop....
ANSWERAnswered 2021-Sep-15 at 14:35
No. It is not guaranteed, by either the JLS or the javadocs for the classes or methods you are using there.
In current implementations, there are in practice memory barriers in
println. (If you were to dig deeply into the implementation code, you should be able to figure out how they come about and what purpose they serve.)
However, there is no guarantee that these memory barriers will exist for all implementations of Java1 on all platforms. The specs do not specify that the happens before relations exist2, and therefore they do not require3 memory barriers to be inserted.
Suppose that the output stream stack was optimized in a way that it synchronization was no longer needed under the hood. For example, suppose that the JVM could deduce that an particular output stream was thread-confined, and there was no need for a memory barrier when writing to its buffer.
Now I don't personally think that those changes are likely to happen. (And they may not even be feasible.) But if they did happen, quite a few "broken" applications that currently depended on those serendipitous memory barriers would most likely stop working.
The point is: if you want guarantees, rely on what the specs say. The specs are the only real guarantee ... if your code needs to be portable.
1 - In particular, future ones.
2 - Indeed as Holger's answer explains, the javadocs for
Thread clearly state that you cannot assume or rely on any synchronizing behavior happening for a
yield(). That clearly means that there is no happens before between the
yield() and any action on any other thread.
3 - The memory barriers are in fact an implementation detail. They are used by a typical compiler to implement the JMM's visibility guarantees. It is the guarantees that are the key, not the strategy used to implement them. Thus, any discussion of memory barriers, caches, registers, and so on is beside the point when you are trying to work out if multi-threaded code is correct.
For some reason
axios-cache-adapter is not caching
GET requests for file downloads which I believe is due to setting
responseType: 'blob' (as I don't have caching issues on other requests that don't require this field be set as such) which is required for axios to generate the src url(as per this answer):
src: URL.createObjectURL(new Blob([response.data])),
My adapter setup is as follows:...
ANSWERAnswered 2021-Sep-13 at 12:09
@D-Money pointed me in the right direction. So basically
axios-cache-adapter v3 fixes the issue of not caching requests with
arraybuffers, however it's currently only available in beta so you'll have to install that as follows in the interim:
npm install axios-cache-adapter@beta
Then you'll have to make a slight adjustment by setting
readHeaders: false, in the
axios-cache-adapter options in
setup and additionally move the axios default config directly into
setup, which in my case results in the following net changes:
No vulnerabilities reported
You can use cache-example like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the cache-example component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
Reuse Trending Solutions
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page