python-caching | Python utils and decorators for cаching with TTL , maxsize | Caching library

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.

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: https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Cache-Control#response_directives

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.

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.

Illustrating:

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:

Now let's try a few examples, I will use human-readable hashes for easier readability:

• 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.

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.

Hypothetically:

• 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.}

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: