simple-image-loader | 🖼️ Modern image loading library for Android Simple by design, powerful under the hood | Caching library

 by   solkin Kotlin Version: v0.9.2 License: MIT

kandi X-RAY | simple-image-loader Summary

kandi X-RAY | simple-image-loader Summary

simple-image-loader is a Kotlin library typically used in Server, Caching applications. simple-image-loader has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Modern image loading library for Android. Simple by design, powerful under the hood.

            kandi-support Support

              simple-image-loader has a low active ecosystem.
              It has 8 star(s) with 0 fork(s). There are 2 watchers for this library.
              It had no major release in the last 12 months.
              simple-image-loader has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of simple-image-loader is v0.9.2

            kandi-Quality Quality

              simple-image-loader has no bugs reported.

            kandi-Security Security

              simple-image-loader has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              simple-image-loader 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

              simple-image-loader releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.

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            simple-image-loader Key Features

            No Key Features are available at this moment for simple-image-loader.

            simple-image-loader Examples and Code Snippets

            No Code Snippets are available at this moment for simple-image-loader.

            Community Discussions


            How to invalidate a view cache using django-cacheops
            Asked 2022-Mar-19 at 15:05

            I have a view and I cached it in using django-cacheops (



            Answered 2022-Mar-19 at 14:37

            Since you used a named group usr in your regex, Django passes it as a keyword argument:



            Is there a way to align objects in C# same way as in C++ to avoid false sharing?
            Asked 2022-Mar-02 at 17:45

            I am a C++ habitat working on a C# project.
            I have encountered the following situation.

            I have 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#?



            Answered 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.



            Background images in css are not getting cached
            Asked 2022-Feb-25 at 03:48

            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 -



            Answered 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.

            Cache-Control: no-cache

            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.

            See here:

            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.



            Nginx cache size not growing above 344GB
            Asked 2022-Feb-06 at 08:14

            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



            Answered 2022-Jan-27 at 02:15

            You can try to configure the temporary cache directory



            Why is Python list slower when sorted?
            Asked 2021-Dec-02 at 05:34

            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.



            Answered 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.

            At r==1, and 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.




            Implementation of QueryCache
            Asked 2021-Nov-12 at 07:04

            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:



            Answered 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:

            1. Start by substituting any udf's or cte's. The query itself needs to be self-contained.
            2. Normalize whitespaces and capitalization.
            3. Hash the entire query. This will be our starting place.
            4. Remove the select fields and hash the rest of the query. Now store a hash of all the individual items in the select list.
            5. 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:



            Using GitHub cache action with multiple cache paths?
            Asked 2021-Nov-10 at 23:06

            I'm trying to use the official GitHub cache action ( 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:



            Answered 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:

            • /d/a/so-foobar-cache/so-foobar-cache/cache_test/cache_test/subdir

            Where so-foobar-cache is the name of the repository.




            How to create the str "1" at two different memory locations?
            Asked 2021-Oct-14 at 20:35

            We are able to defeat the small integer intern in this way (a calculation allows us to avoid the caching layer):



            Answered 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:



            Is Thread.yield guaranteed to flush reads/writes to memory?
            Asked 2021-Sep-20 at 20:38

            Lets save I have this code which exhibits stale cache reads by a thread, which prevent it from exiting its while loop.



            Answered 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 yield() and 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 Thread.yield() was implemented as a no-op. (In the same way that System.gc() can be a no-op.)

            • 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.



            How to get axios-cache-adapter to cache file downloads with responseType blob?
            Asked 2021-Sep-13 at 12:09

            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([])),

            My adapter setup is as follows:



            Answered 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 responseType blob or 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:


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


            No vulnerabilities reported

            Install simple-image-loader

            To load an image into an ImageView, use the load extension function:.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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