caching

The presence of an Age header field implies that the response was not generated or validated by the origin server for this request. However, lack of an Age header field does not imply the origin was contacted, since the response might have been received from an HTTP/1.0 cache that does not implement Age

Repeated references to local variables are good because the compiler cancache them in the register file (temporal locality).. Stride-1 reference patterns are good because caches at all levels of the memoryhierarchy store data as contiguous blocks (spatial locality).

we suggest adopting a mental model that assumeswrite-back, write-allocate caches.

fully associative caches are only appropriate for small caches

A copy of w is contained in the line if and only if the valid bit is setand the tag in the cache line matches the tag in the address of w.

The process that a cache goes through of determining whether a request is ahit or a miss and then extracting the requested word consists of three steps: (1) setselection, (2) line matching, and (3) word extraction.

Restrictive placement policies of this kind lead to a type of miss known asa conflict miss, in which the cache is large enough to hold the referenced dataobjects, but because they map to the same cache block, the cache keeps missing.

When the size of the working set exceedsthe size of the cache, the cache will experience what are known as capacity misses.

For caches high in the memory hierarchy (close tothe CPU) that are implemented in hardware and where speed is at a premium,this policy is usually too expensive to implement because randomly placed blocksare expensive to locate.

The decision about which block to replace is governed by the cache’s replacementpolicy.

a program needs a particular data object d from level k + 1, it first looksfor d in one of the blocks currently stored at level k. If d happens to be cachedat level k, then we have what is called a cache hit.

It is important to realize that while the block size is fixedbetween any particular pair of adjacent levels in the hierarchy, other pairs of levelscan have different block sizes.

The central idea of a memory hierarchy is that for each k, the faster and smallerstorage device at level k serves as a cache for the larger and slower storage device

The memory system must periodically refresh every bit of memory byreading it out and then rewriting it.

Org-roam caches most of the standard Org properties. The full list now includes: outline level todo state priority scheduled deadline tags

To limit the output to only the packages listed on the command line, set the APT::Cache::GivenOnly option.

:clean_gitlab_redis_cache which provides a clean Redis cache to the examples.

This is funny because it’s unexpected. Cache invalidation sounds like a hard thing, while naming sounds more straightforward. The joke works because it violates our expectation that hard things should be technical. It’s also funny because it’s true.

It's difficult because it's a case-by-case basis - there is no one right answer so it falls into subjective arguments.

Space: Suppose we had infinite memory, then cache all the data; but we don't so we have to decide what to cache that is meaningful to have the cache implemented (is a ??K cache size enough for your use case? Should you add more?) - It's the balance with the resources available.

Time: Suppose all your data was immutable, then cache all the data indefinitely. But this isn't always to case so you have to figure out what works for the given scenario (A person's mailing address doesn't change often, but their GPS position does).

The underlying data might get changed by another process and then your process that uses the cache will be working with incorrect data

You have to guess when the data is not likely to be needed in memory. It can't be too short that the cache is useless, and too long that you'll get a memory leak.

So the hard and unsolvable problem becomes: how up-to-date do you really need to be?

After considering the value we place, and the tradeoffs we make, when it comes to knowing anything of significance, I think it becomes much easier to understand why cache invalidation is one of the hard problems in computer science

The non-determinism is why cache invalidation — and that other hard problem, naming things — are uniquely and intractably hard problems in computer science. Computers can perfectly solve deterministic problems. But they can’t predict when to invalidate a cache because, ultimately, we, the humans who design and build computational processes, can’t agree on when a cache needs to be invalidated.

Cache invalidation is hard because: Everything in life we want to know, changes.Those changes are non-deterministic.

you began by first finding out if your crush was already in a relationship. If so, you then did what you could in your power to have the most most up-to-date information on their relationship status. The downside of outdated data is self-evident: you want to move in at the first sign of the current relationship dissolving.

Sometimes humorously extended as “cache invalidation, naming things, and off-by-one errors.”

There’s only one hard thing in Computer Science: human communication. The most complex part of cache invalidation is figuring out what the heck people mean with the word cache. Once you get that sorted out, the rest is not that complicated; the tools are out there, and they’re pretty good.

One reason Turbolinks sites seem faster than traditional web apps is because of its cache. However, the cache can be a source of great frustration. Many of the edge cases we're going to discuss involve the cache in some way.

They say that there are only two hard problems in computer science: cache invalidation, naming things, and off-by-one errors. Caching is what Service Workers do. It’s literally the #1 hard thing! … or maybe the #0 thing? Whatever. It’s hard.

My next move will be to further lower my visibility to 3rd party tracking with a combination of pi-hole caching DNS blocker and wireguard VPN .

Using the Git SHA in your image tag makes this less necessary since each job will be unique and you shouldn't ever have a stale image. However, it's still possible to have a stale image if you re-build a given commit after a dependency has changed.

In addition to strings, Redis supports lists, sets, sorted sets, hashes, bit arrays, and hyperloglogs. Applications can use these more advanced data structures to support a variety of use cases. For example, you can use Redis Sorted Sets to easily implement a game leaderboard that keeps a list of players sorted by their rank.

not cacheable, unless the response includes appropriate Cache-Control or Expires header fields

Purging by single-file through your Cloudflare dashboard

Wong and Gerras did a frightening study of the need for Army officers to lie routinely

INFERNO VIII↩⚓✪ The Fifth Circle. Intemperance in Indignation⚓✪ The Wrathful and Sullen. Styx. The City of Dis

// Download a json but don't reveal who is downloading it fetch("sneaky.json", {referrerPolicy: "no-referrer"}) .then(function(response) { /* consume the response */ }); // Download a json but pretend another page is downloading it fetch("sneaky.json", {referrer: "https://example.site/fake.html"}) .then(function(response) { /* consume the response */ }); // You can only set same-origin referrers. fetch("sneaky.json", {referrer: "https://cross.origin/page.html"}) .catch(function(exc) { // exc.name == "TypeError" // exc.message == "Referrer URL https://cross.origin/page.html cannot be cross-origin to the entry settings object (https://example.site)." }); // Download a potentially cross-origin json and don't reveal // the full referrer URL across origins fetch(jsonURL, {referrerPolicy: "origin-when-cross-origin"}) .then(function(response) { /* consume the response */ }); // Download a potentially cross-origin json and reveal a // fake referrer URL on your own origin only. fetch(jsonURL, {referrer: "https://example.site/fake.html", referrerPolicy: "origin-when-cross-origin"}) .then(function(response) { /* consume the response */ });

The purpose o this source is to encourage readers to have safer sex.

itative comparison analyses of the national coverage to help appraise their argument.

They

only

about Bobby Orr.

How are individuals who lived in the Eureka Valley, San Fransico (The Castro) embodied in the Aids Quilt?

flaw

using incidence, lethality, and morality coefficients

So maybe because he was a man and he also probably wasn’t protected is why he died from aids.

ould be anyone

a different environment, they can really be themsel

The purpose of this article is to see how participation in a friendly gay neighborhood contributes to the variability of gay youth.

connection of my artifact.

The intended audience for this article would be anyone who wants to know about gay men

There are several authors for this source but Stephen L. Eyre graduated from The University of California with a Ph.D.

oject preserves panels that ha

This letter was written by Bobby to

That was probably placed there to emphasize that is the name of the person who they lost

The subject of this quilt’s memorial is Bobby Orr is the name on quilt 1333.

els

s ther

1960’s.

orial “the quilt” so

co there mission

This made me that of ways I can promote or advocate something on social media.

vinyl is a text,

he primary text

Combing Haltman’s idea of Material culture and 3-d printing we can employ many ways to incorporate touch and serval other senses to our culture sense

The authors of the Haltman’s text taught me how to experience an object as space, right then and there. Not only how I would experience but how other people would experience the space of that object.

make the connection between the aids epidemic in the 1990’s

they are trying to fi

the national coverage to help appraise their argument.

 like bibliographies and tables

This source connects to my artifact because for Bobby Orr and article like would have been very helpful. Showing and explaining ways to prevent aids/HIV for people just like him.

safer sex. The intended audience of this article come be for researchers who are looking for ways to encourage safe sex or possibly college students.  Someone who’s family member who has recently died of aids would possibly be interested in help prevent others from getting aids.

he purpose o this source is to encourage readers to have safer sex.

evidence of developing materials

Next to the R on the right side of the name is a photo. I believe this photo was placed here because this is a photo of Bobby Orr. The photo is laminated, probably so that it can be preserved for a long time. The laminated photo was stitched on with a blue string. The photo is a bit creased, meaning that it been there for a while. The photo itself has a brown, grey color scheme meaning that it was taken a while ago. This is also assumed because he died in 1989

The 20th century is assumed because in most of the other surrounding panels the date was of the 20th century.

Probably there to emphasize that this is the name of the person they lost.

First I will describe the bold letters and numbers.

would be

Information about the quilt

HTTP/1.1定义的 Cache-Control 头用来区分对缓存机制的支持情况， 请求头和响应头都支持这个属性。通过它提供的不同的值来定义缓存策略。

通常定义Pragma以向后兼容基于HTTP/1.0的客户端

在过期时间前，资源缓存是有效的，反之则缓存失效。通过不停抛弃过期的缓存资源以保证资源的实时性。注意，旧的缓存不会被抛弃或者忽略；当发起一个针对旧缓存资源的请求时，会在请求头里带上If-None-Match用来判断缓存是否还有效。如果有效，服务端返回304(Not Modified)和空的body以节省一部分带宽。

当 web 缓存发现请求的资源已经被存储，它会拦截请求，返回该资源的拷贝，而不会去源服务器重新下载。

Optimum buffer size is related to a number of things: file system block size, CPU cache size and cache latency. Most file systems are configured to use block sizes of 4096 or 8192. In theory, if you configure your buffer size so you are reading a few bytes more than the disk block, the operations with the file system can be extremely inefficient (i.e. if you configured your buffer to read 4100 bytes at a time, each read would require 2 block reads by the file system). If the blocks are already in cache, then you wind up paying the price of RAM -> L3/L2 cache latency. If you are unlucky and the blocks are not in cache yet, the you pay the price of the disk->RAM latency as well. This is why you see most buffers sized as a power of 2, and generally larger than (or equal to) the disk block size. This means that one of your stream reads could result in multiple disk block reads - but those reads will always use a full block - no wasted reads. Now, this is offset quite a bit in a typical streaming scenario because the block that is read from disk is going to still be in memory when you hit the next read (we are doing sequential reads here, after all) - so you wind up paying the RAM -> L3/L2 cache latency price on the next read, but not the disk->RAM latency. In terms of order of magnitude, disk->RAM latency is so slow that it pretty much swamps any other latency you might be dealing with. So, I suspect that if you ran a test with different cache sizes (haven't done this myself), you will probably find a big impact of cache size up to the size of the file system block. Above that, I suspect that things would level out pretty quickly. There are a ton of conditions and exceptions here - the complexities of the system are actually quite staggering (just getting a handle on L3 -> L2 cache transfers is mind bogglingly complex, and it changes with every CPU type). This leads to the 'real world' answer: If your app is like 99% out there, set the cache size to 8192 and move on (even better, choose encapsulation over performance and use BufferedInputStream to hide the details). If you are in the 1% of apps that are highly dependent on disk throughput, craft your implementation so you can swap out different disk interaction strategies, and provide the knobs and dials to allow your users to test and optimize (or come up with some self optimizing system).