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Top Answer
Answered Dec 28, 2022 · 1875 votes
Cross-Site Request Forgery (CSRF) in simple words
- Assume you are currently logged into your online banking at www.mybank.com
- Assume a money transfer from mybank.com will result in a request of (conceptually) the form http://www.mybank.com/transfer?to=<SomeAccountnumber>;amount=<SomeAmount>. (Your account number is not needed, because it is implied by your login.)
- You visit www.cute-cat-pictures.org, not knowing that it is a malicious site.
- If the owner of that site knows the form of the above request (easy!) and correctly guesses you are logged into mybank.com (requires some luck!), they could include on their page a request like http://www.mybank.com/transfer?to=123456;amount=10000 (where 123456 is the number of their Cayman Islands account and 10000 is an amount that you previously thought you were glad to possess).
- You retrieved that www.cute-cat-pictures.org page, so your browser will make that request.
- Your bank cannot recognize this origin of the request: Your web browser will send the request along with your www.mybank.com cookie and it will look perfectly legitimate. There goes your money!
This is the world without CSRF tokens.
Now for the better one with CSRF tokens:
- The transfer request is extended with a third argument: http://www.mybank.com/transfer?to=123456;amount=10000;token=31415926535897932384626433832795028841971.
- That token is a huge, impossible-to-guess random number that mybank.com will include on their own web page when they serve it to you. It is different each time they serve any page to anybody.
- The attacker is not able to guess the token, is not able to convince your web browser to surrender it (if the browser works correctly...), and so the attacker will not be able to create a valid request, because requests with the wrong token (or no token) will be refused by www.mybank.com.
Result: You keep your 10000 monetary units.
(Your mileage may vary.)
EDIT from comment worth reading by SOFe:
It would be worthy to note that script from www.cute-cat-pictures.org normally does not have access to your anti-CSRF token from www.mybank.com because of HTTP access control. This note is important for some people who unreasonably send a header Access-Control-Allow-Origin: * for every website response without knowing what it is for, just because they can't use the API from another website.
1/5
Top Answer
Answered Jul 20, 2018 · 182 votes
The article mentioned by sgbj in the comments written by Google's Paul Turner explains the following in much more detail, but I'll give it a shot:
As far as I can piece this together from the limited information at the moment, a retpoline is a return trampoline that uses an infinite loop that is never executed to prevent the CPU from speculating on the target of an indirect jump.
The basic approach can be seen in Andi Kleen's kernel branch addressing this issue:
It introduces the new __x86.indirect_thunk
call that loads the call target whose memory address (which I'll call ADDR) is stored on top of the stack and executes the jump using a the RET instruction. The thunk itself is then called using the NOSPEC_JMP/CALL macro, which was used to replace many (if not all) indirect calls and jumps. The macro simply places the call target on the stack and sets the return address correctly, if necessary (note the non-linear control flow):
.macro NOSPEC_CALL target jmp 1221f /* jumps to the end of the macro */1222: push \target /* pushes ADDR to the stack */ jmp __x86.indirect_thunk /* executes the indirect jump */1221: call 1222b /* pushes the return address to the stack */.endm-
The placement of call in the end is necessary so that when the indirect call is finished, the control flow continues behind the use of the NOSPEC_CALL macro, so it can be used in place of a regular call
The thunk itself looks as follows:
call retpoline_call_target2: lfence /* stop speculation */ jmp 2bretpoline_call_target: lea 8(%rsp), %rsp ret-
The control flow can get a bit confusing here, so let me clarify:
- call pushes the current instruction pointer (label 2) to the stack.
- lea adds 8 to the stack pointer, effectively discarding the most recently pushed quadword, which is the last return address (to label 2). After this, the top of the stack points at the real return address ADDR again.
- ret jumps to *ADDR and resets the stack pointer to the beginning of the call stack.
In the end, this whole behaviour is practically equivalent to jumping directly to *ADDR. The one benefit we get is that the branch predictor used for return statements (Return Stack Buffer, RSB), when executing the call instruction, assumes that the corresponding ret statement will jump to the label 2.
The part after the label 2 actually never gets executed, it's simply an infinite loop that would in theory fill the instruction pipeline with JMP instructions. By using LFENCE,PAUSE or more generally an instruction causing the instruction pipeline to be stall stops the CPU from wasting any power and time on this speculative execution. This is because in case the call to retpoline_call_target would return normally, the LFENCE would be the next instruction to be executed. This is also what the branch predictor will predict based on the original return address (the label 2)
To quote from Intel's architecture manual:
Instructions following an LFENCE may be fetched from memory before the LFENCE, but they will not execute until the LFENCE completes.
Note however that the specification never mentions that LFENCE and PAUSE cause the pipeline to stall, so I'm reading a bit between the lines here.
Now back to your original question: The kernel memory information disclosure is possible because of the combination of two ideas:
- Even though speculative execution should be side-effect free when the speculation was wrong, speculative execution still affects the cache hierarchy. This means that when a memory load is executed speculatively, it may still have caused a cache line to be evicted. This change in the cache hierarchy can be identified by carefully measuring the access time to memory that is mapped onto the same cache set.You can even leak some bits of arbitrary memory when the source address of the memory read was itself read from kernel memory.
- The indirect branch predictor of Intel CPUs only uses the lowermost 12 bits of the source instruction, thus it is easy to poison all 2^12 possible prediction histories with user-controlled memory addresses. These can then, when the indirect jump is predicted within the kernel, be speculatively executed with kernel privileges. Using the cache-timing side-channel, you can thus leak arbitrary kernel memory.
UPDATE: On the kernel mailing list, there is an ongoing discussion that leads me to believe retpolines don't fully mitigate the branch prediction issues, as when the Return Stack Buffer (RSB) runs empty, more recent Intel architectures (Skylake+) fall back to the vulnerable Branch Target Buffer (BTB):
Retpoline as a mitigation strategy swaps indirect branches for returns, to avoid using predictions which come from the BTB, as they can be poisoned by an attacker. The problem with Skylake+ is that an RSB underflow falls back to using a BTB prediction, which allows the attacker to take control of speculation.
2/5
A predicate is a word that represents the action but not the subject. A subject can be a Noun or Pronoun. A predicate is a clause that may be dependent or independent. The predicate defines what the subject does in a given sentence. It means a predicate includes all other words like verbs, prepositions, and adverbs but not the subject who does the action. Predicates are the building blocks of a given sentence. Predicate defines the action itself.
For example, She ate something.
She is the subject and the rest is the predicate.
What is a Predicate?
What are the Types of Predicate?
The types of predicates are defined below :
a) Simple Predicate
A simple predicate is one that consists of only a verb. It can be a helping verb or a main verb. It doesn’t contain other words like adverbs, prepositions, or objects. These are the examples given below
1. She is sleeping
Here she is the subject and is sleeping is the predicate which has only the helping verb and the main verb
2. He started his own business
He is the subject and...
3/5
Top Answer
Answered Sep 25, 2023 · 42 votes
At a high level, a zip tree is a
- randomized balanced binary search tree,
- that is a way of encoding a skiplist as a BST, and
- that uses a pair of operations called zipping and unzipping rather than tree rotations.
The first bullet point - that zip trees are randomized, balanced BSTs - gives a feel for what a zip tree achieves at a high level. It's a type of balanced binary search tree that, like treaps and unlike red/black trees, uses randomization to balance the tree. In that sense, a zip tree isn't guaranteed to be a balanced tree, but rather has a very high probability of being balanced.
The second bullet point - that zip trees are encodings of skiplists - shows where zip trees come from and why, intuitively, they're balanced. You can think of a zip tree as a way of taking the randomized skiplist data structure, which supports all major operations in expected time O(log n), and representing it as a binary search tree. This provides the intuition for where zip trees come from and why we'd expect them to be so fast.
The third bullet point - zip trees use zipping and unzipping rather than tree rotations - accounts for the name of the zip tree and what it feels like to code one up. Zip trees differ from other types of balanced trees (say, red/black trees or AVL trees) in that nodes are moved around the tree not through rotations, but through a pair of operations that convert a larger chain of nodes into two smaller chains or vice-versa.
The rest of this answer dives deeper into where zip trees come from, how they work, and how they're structured.
Review: Skip Lists
To understand where zip trees come from, let's begin with a review of another data structure, the skiplist. A skiplist is a data structure that, like a binary search tree, stores a collection of elements in sorted order. Skiplists, however, aren't tree structures. Rather, a skiplist works by storing elements in sorted order through several layers of linked lists. A sample skiplist is shown here:
As you can see, the elements are represented in sorted order. Each element has an associated height, and is part of a number of linked lists equal to its height. All of the elements of the skiplist participate in the bottom layer. Ideally, roughly half of the nodes will be in the layer above that, roughly a quarter of the nodes will be in the layer above that, roughly an eighth of the nodes will be in the layer above that, etc. (More on how this works later on.)
To do a lookup in a skiplist, we begin in the topmost layer. We walk forward in the skiplist until either (1) we find the element we're looking for, (2) we find an element bigger than the one we're looking for, or (3) we hit the end of the list. In the first case, we uncork the champagne and celebrate because we discovered the item we were searching for and there's nothing more to do. In the second case or third cases, we've "overshot" the element that we're looking for. But that's nothing to worry about - in fact, that's helpful because it means that what we're looking for must be between the node we hit that "overshot" and the node that comes before it. So we'll go to the previous node, drop down one layer, and pick up our search from there.
For example, here's how we'd do a search for 47:
Here, the blue edges indicate the links followed where we moved forward, and the red edges indicate where we overshot and decided to descend down a layer.
A powerful intuition for how skiplists work - which we'll need later on as we transition to zip trees - is that the topmost layer of the skiplist partitions the remaining elements of the skiplists into different ranges. You can see this here:
Intuitively, a skiplist search will be "fast" if we're able to skip looking at most of the elements. Imagine, for example, that the second-to-last layer of the skiplist only stores every other element of the skiplist. In that case, traversing the second-to-last layer is twice as fast as traversing the bottom layer, so we'd expect a lookup starting in the second-to-last layer to take half as much time as a lookup starting in the bottom layer. Similarly, imagine that the layer above that one only stores every other element from the layer below it. Then searching in that layer will take roughly half as much time as searching the layer below it. More generally, if each layer only stores roughly half the elements of the layer below it, then we could skip past huge amounts of the elements in the skiplist during a search, giving us good performance.
The skiplist accomplishes this by using the following rule: whenever we insert an element into the skiplist, we flip a coin until we get heads. We then set the height of the newly-inserted node to be the number of coins that we ended up tossing. This means it has a 50% chance to stay in its current layer and a 50% chance to move to the layer above it, which means, in aggregate, that roughly half the nodes will only be in the bottom layer, roughly half of what's left will be one layer above that, roughly half of what's left will be one layer above that, etc.
(For those of you with a math background, you could also say that the height of each node in the skiplist is a Geom(1/2) random variable.)
Here's an example of inserting 42 into the skiplist shown above, using a height of 1:
Deletion from a skiplist is also a fairly simple operation: we simply splice it out of whatever linked lists it happens to be in. That means that if we were to delete the 42 we just inserted from the above list, we'd end up with the same skiplist that we started with.
It can be shown that the expected cost of an insertion, deletion, or lookup in a skiplist is O(log n), based on the fact that the number of items in each list is roughly half the number of items in the one below it. (That means we'd expect to see O(log n) layers, and only take a constant number of steps in each layer.)
From Skiplists to Zip Trees
Now that we've reviewed skiplists, let's talk about where the zip tree comes from.
Let's imagine that you're looking at the skiplist data structure. You really like the expected O(log n) performance of each operation, and you like how conceptually simple it is. There's just one problem - you really don't like linked lists, and the idea of building something with layers upon layers of linked lists doesn't excite you. On the other hand, you really love binary search trees. They've got a really simple structure - each node has just two pointers leaving it, and there's a simple rule about where everything gets placed. This question then naturally arises: could you get all the benefits of a skiplist, except in BST form?
It turns out that there's a really nice way to do this. Let's imagine that you have the skiplist shown here:
Now, imagine you perform a lookup in this skiplist. How would that search work? Well, you'd always begin by scanning across the top layer of the skiplist, moving forward until you found a key that was bigger than the one you were looking for, or until you hit the end of the list and found that there were no more nodes at the top level. From there, you'd then "descend" one level into a sub-skiplist containing only the keys between the last node you visited and the one that overshot.
It's possible to model this exact same search as a BST traversal. Specifically, here's how we might represent the top layer of that skiplist as a BST:
Notice that all these nodes chain to the right, with the idea being that "scanning forward in the skiplist" corresponds to "visiting larger and larger keys." In a BST, moving from one node to a larger node corresponds to moving right, hence the chain of nodes to the right.
Now, each node in a BST can have up to two children, and in the picture shown above each node has either zero children or one child. If we fill in the missing children by marking what ranges they correspond to, we get this.
And hey, wait a minute! It sure looks like the BST is partitioning the space of keys the same way that the skiplist is. That's promising, since it suggests that we're on to something here. Plus, it gives us a way to fill in the rest of the tree: we can recursively convert the subranges of the skiplist into their own BSTs and glue the whole thing together. If we do that, we get this tree encoding the skiplist:
We now have a way of representing a skiplist as a binary search tree. Very cool!
Now, could we go the other way around? That is, could we go from a BST to a skiplist? In general, there's no one unique way to do this. After all, when we converted the skiplist to a BST, we did lose some information. Specifically, each node in the skiplist has an associated height, and while each node in our BST has a height as well it's not closely connected to the skiplist node heights. To address this, let's tag each BST node with the height of the skiplist node that it came from. This is shown here:
Now, some nice patterns emerge. For starters, notice that each node's associated number is bigger than its left child's number. That makes sense, since each step to the left corresponds to descending into a subrange of the skiplist, where nodes will have lower heights. Similarly, each node's associated number is greater than or equal to the number of its right child. And that again makes sense - moving to the right either means
- continuing forward at the same level that we were already on, in which case the height remains the same, or
- hitting the end of a range and descending into a subrange, in which case the height decreases.
Can we say more about the shape of the tree? Sure we can! For example, in a skiplist, each node's height is picked by flipping coins until we get heads, then counting how many total coins we flipped. (Or, as before, it's geometrically distributed with probability 1/2). So if we were to imagine building a BST that corresponded to a skiplist, we'd want the numbers assigned to the nodes to work out the same way.
Putting these three rules together, we get the following, which defines the shape of our tree, the zip tree!
A zip tree is a binary search tree where
- Each node has an associated number called its rank. Ranks are assigned randomly to each node by flipping coins until heads is flipped, then counting how many total coins were tossed.
- Each node's rank is strictly greater than its left child's rank.
- Each node's rank is greater than or equal to its right child's rank.
It's amazing how something like a skiplist can be represented as a BST by writing out such simple rules!
Inserting Elements: Unzipping
Let's suppose you have a zip tree. How would you insert a new element into it?
We could in principle answer this question by looking purely at the rules given above, but I think it's a lot easier to figure this out by remembering that zip trees are skiplists in disguise. For example, here's the above zip tree, with its associated skiplist:
Now, suppose we want to insert 18 into this zip tree. To see how this might play out, imagine that we decide to give 18 a rank of 2. Rather than looking at the zip tree, let's look at what would happen if we did the insertion into the skiplist. That would give rise to this skiplist:
If we were to take this skiplist and encode it as a zip tree, we'd get the following result:
What's interesting about this is that we can see what the tree needs to look like after the insertion, even if we don't know how to perform the insertion. We can then try to figure out what the insertion logic needs to look like by reverse-engineering it from these "before" and "after" pictures.
Let's think about what change this insertion made to our zip tree. To begin with, let's think back to our intuition for how we encode skiplists as zip trees. Specifically, chains of nodes at the same level in a skiplist with no intervening "higher" elements map to chains of nodes in the zip tree that lean to the right. Inserting an element into the skiplist corresponds to adding some new element into one of the levels, which has the effect of (1) adding in something new into some level of the skiplist, and (2) taking chains of elements in the skiplist that previously were adjacent at some level, then breaking those connections.
For example, when we inserted 18 into the skiplist shown here, we added something new into the blue chain highlighted here, and we broke all of the red chains shown here:
What is that going to translate into in our zip tree? Well, we can highlight the blue link where our item was inserted here, as well as the red links that were cut:
Let's see if we can work out what's going on here. The blue link here is, fortunately, pretty easy to find. Imagine we do a regular BST insertion to add 18 into our tree. As we're doing so, we'll pause when we reach this point:
Notice that we've hit a key with the same rank as us. That means that, if we were to keep moving to the right, we'd trace out this region of the skiplist:
To find the blue edge - the place where we go - we just need to walk down through this chain of nodes until we find one bigger than us. The blue edge - our insertion point - is then given by the edge between that node and the one above it.
We can identify this location in a different way: we've found the blue edge - our insertion point - when we've reached a point where the node to insert (1) has a bigger rank than the node to the left, (2) has a rank that's greater than or equal to the node on the right, and (3) if the node to the right has the same rank, our new item to insert is less than the item to the right. The first two rules ensure that we're inserting into the right level of the skiplist, and the last rule ensures that we insert into the right place in that level of the skiplist.
Now, where are our red edges? Intuitively, these are the edges that were "cut" because 18 has been added into the skiplist. Those would be items that previously were between the two nodes on opposite ends of the blue edge, but which node need to get partitioned into the new ranges defined by the split version of that blue edge.
Fortunately, those edges appear in really nice places. Here's where they map to:
(In this picture, I've placed the new node 18 in the middle of the blue edge that we identified in the skiplist. This causes the result not to remain a BST, but we'll fix that in a minute.)
Notice that these are the exact same edges that we'd encounter if we were to finish doing our regular BST insertion - it's the path traced out by looking for 18! And something really nice happens here. Notice that
- each time we move to the right, the node, when cut, goes to the right of 18, and
- each time we move to the left, the node, when cut, goes to the left of 18.
In other words, once we find the blue edge where we get inserted, we keep walking as though we were doing our insertion as usual, keeping track of the nodes where we went left and the nodes where we went right. We can then chain together all the nodes where we went left and chain together all the nodes where we went right, gluing the results together under our new node. That's shown here:
This operation is called unzipping, and it's where we get the name "zip tree" from. The name kinda make sense - we're taking two interleaved structures (the left and right chains) and splitting them apart into two simpler linear chains.
To summarize:
Inserting x into a zip tree works as follows:
- Assign a random rank to x by flipping coins and counting how many flips were needed to get heads.
- Do a search for x. Stop the search once you reach a node where
- the node's left child has a lower rank than x,
- the node's right child has a rank less than or equal to x, and
- the node's right child, if it has the same rank as x, has a larger key than x.
- Perform a unzip. Specifically:
- Continue the search for x as before, recording when we move left and when we move right.
- Chain all the nodes together where we went left by making each the left child of the previously-visited left-moving node.
- Chain all the nodes together where we went right by making each the right child of the previously-visited right-moving node.
- Make those two chains the children of the node x.
You might notice that this "unzipping" procedure is equivalent to what you'd get if you performed a different operation. You could achieve the same result by inserting x as usual, then using tree rotations to pull x higher and higher in the tree until it came to rest in the right place. This is a perfectly valid alternative strategy for doing insertions, though it's a bit slower because two passes over the tree are required (a top-down pass to insert at a leaf, then a bottom-up pass to do the rotations).
Removing Elements: Zipping
Now that we've seen how to insert elements, how do we remove them?
Let's begin with a helpful observation: if we insert an item into a zip tree and then remove it, we should end up with the exact same tree that we started with. To see why this is, we can point back to a skiplist. If you add and then remove something from a skiplist, then you end up with the same skiplist that you would have had before. So that means that the zip tree needs to end up looking identical to how it started after we add and then remove an element.
To see how to do this, we'd need to perform two steps:
- Undo the unzip operation, converting the two chains of nodes formed back into a linear chain of nodes.
- Undo the break of the blue edge, restoring the insertion point of x.
Let's begin with how to undo an unzip operation. This, fortunately, isn't too bad. We can identify the chains of nodes that we made with the unzip operation when we inserted x into the zip tree fairly easily - we simply look at the left and right children of x, then move, respectively, purely to the left and purely to the right.
Now, we know that these nodes used to be linked together in a chain. What order do we reassemble them into? As an example, take a look a this part of a zip tree, where we want to remove 53. The chains to the left and right of 53 are highlighted:
If we look at the nodes making up the left and right chains, we can see that there's only one way to reassemble them. The topmost node of the reassembled chain must be 67, since it has rank 3 and will outrank all other items. After that, the next node must be 41, because it's the smaller of the rank-2 elements and elements with the same rank have smaller items on top. By repeating this process, we can reconstruct the chain of nodes, as shown here, simply by using the rules for how zip trees have to be structured:
This operation, which interleaves two chains together into one, is called zipping.
To summarize, here's how a deletion works:
Deleting a node x from a zip tree works as follows:
- Find the node x in the tree.
- Perform a zip of its left and right subtrees. Specifically: Maintain "lhs" and "rhs" pointers, initially to the left and right subtrees.While both those pointers aren't null:3. If lhs has a higher rank than rhs, make lhs's right child rhs, then advance lhs to what used to be lhs's right child.4. Otherwise, make rhs's left child lhs, then advance rhs to point to what used to be rhs's left child.
More to Explore
To recap our main points: we saw how to represent a skiplist as a BST by using the idea of ranks. That gave rise to the zip tree, which uses ranking rules to determine parent/child relationships. Those rules are maintained using the zip and unzip operations, hence the name.
Doing a full analysis of a zip list is basically done by reasoning by analogy to a skiplist. We can show, for example, that the expected runtime of an insertion or deletion is O(log n) by pointing at the equivalent skiplist and noting that the time complexity of the equivalent operations there are O(log n). And we can similary show that these aren't just expected time bounds, but expected time bounds with a high probability of occurring.
There's a question of how to actually store the information needed to maintain a zip tree. One option would be to simply write the rank of each item down in the nodes themselves. That works, though since ranks are very unlikely to exceed O(log n) due to the nature of geometric random variables, that would waste a lot of space. Another alternative would be to use a hash function on node addresses to generate a random, uniformly-distributed integer in some range, then find the position of the most least-significant 1 bit to simulate our coin tosses. That increases the costs of insertions and deletions due to the overhead of computing the hash codes, but also decreases the space usage.
Zip trees aren't the first data structure to map skiplists and BSTs together. Dean and Jones developed an alternative presentation of this idea in 2007. There's also another way to exploit this connection. Here, we started with a randomized skiplist, and used it to derive a randomized BST. But we can run this in reverse as well - we can start with a deterministic balanced BST and use that to derive a deterministic skiplist. Munro, Papadakis, and Sedgewick found a way to do this by connecting 2-3-4 trees and skiplists.
And zip trees aren't the only randomized balanced BST. The treap was the first structure to do this, and with a little math you can show that treaps tend to have slightly lower expected heights than zip trees. The tradeoff, though, is that you need more random bits per node than in a zip tree. (However, in 2023, the “zip zip tree” was invented that only uses O(log log n) bits per node and matches the height bounds from treaps.)
Hope this helps!
4/5
Top Answer
Answered Sep 21, 2016 · 7 votes
What is a multi-threading program and how does it work exactly?
Interesting part about this question is complete books are written on the topic, but still it is elusive to lot of people. I will try to explain in the order detailed underneath.
Please note this is just to provide a gist, an answer like this can never do justice to the depth and detail required. Regarding videos, best that I have come across are part of paid subscriptions (Wintellect and Pluralsight), check out if you can listen to them on trial basis, assuming you don't already have the subscription:
- Wintellect by Jeffery Ritcher (from his Book, CLR via C#, has same chapter on Thread Fundamentals)
- CLR Threading by Mike Woodring
Explanation Order
- What is a thread ?
- Why were threads introduced, main purpose ?
- Pitfalls and how to avoid them, using Synchronization constructs ?
- Thread Vs ThreadPool ?
- Evolution of Multi threaded programming API, like Parallel API, Task API
- Concurrent Collections, usage ?
- Async-Await, thread but no thread, why they are best for IO
What is a thread ?
It is software implementation, which is purely a Windows OS concept (multi-threaded architecture), it is bare minimum unit of work. Every process on windows OS has at least one thread, every method call is done on the thread. Each process can have multiple threads, to do multiple things in parallel (provided hardware support). Other Unix based OS are multi process architecture, in fact in Windows, even the most complex piece of software like Oracle.exe have single process with multiple threads for different critical background operations.
Why were threads introduced, main purpose ?
Contrary to the perception that concurrency is the main purpose, it was robustness that lead to the introduction of threads, imagine every process on Windows is running using same thread (in the initial 16 bit version) and out of them one process crash, that simply means system restart to recover in most of the cases. Usage of threads for concurrent operations, as multiple of them can be invoked in each process, came in picture down the line. In fact it is even important to utilize the processor with multiple cores to its full ability.
Pitfalls and how to avoid using Synchronization constructs ?
More threads means, more work completed concurrently, but issue comes, when same memory is accessed, especially for Write, as that's when it can lead to:
- Memory corruption
- Race condition
Also, another issue is thread is a very costly resource, each thread has a thread environment block, Kernel memory allocation. Also for scheduling each thread on a processor core, time is spent for context switching. It is quite possible that misuse can cause huge performance penalty, instead of improvement. To avoid Thread related corruption issues, its important to use the Synchronization constructs, like lock, mutex, semaphore, based on requirement. Read is always thread safe, but Write needs appropriate Synchronization.
Thread Vs ThreadPool ?
Real threads are not the ones, we use in C#.Net, that's just the managed wrapper to invoke Win32 threads. Challenge remain in user's ability to grossly misuse, like invoking lot more than required number of threads, assigning the processor affinity, so isn't it better that we request a standard pool to queue the work item and its windows which decide when the new thread is required, when an already existing thread can schedule the work item. Thread is a costly resource, which needs to be optimized in usage, else it can be bane not boon.
Evolution of Multi threaded programming, like Parallel API, Task API
From .Net 4.0 onward, variety of new APIs Parallel.For, Parallel.ForEach for data paralellization and Task Parallelization, have made it very simple to introduce concurrency in the system. These APIs again work using a Thread pool internally. Task is more like scheduling a work for sometime in the future. Now introducing concurrency is like a breeze, though still synchronization constructs are required to avoid memory corruption, race condition or thread safe collections can be used.
Concurrent Collections, usage ?
Implementations like ConcurrentBag, ConcurrentQueue, ConcurrentDictionary, part of System.Collections.Concurrent are inherent thread safe, using spin-wait and much easier and quicker than explicit Synchronization. Also much easier to manage and work. There's another set API like ImmutableList System.Collections.Immutable, available via nuget, which are thread safe by virtue of creating another copy of data structure internally.
Async-Await, thread but no thread, why they are best for IO
This is an important aspect of concurrency meant for IO calls (disk, network), other APIs discussed till now, are meant for compute based concurrency so threads are important and make it faster, but for IO calls thread has no use except waiting for the call to return, IO calls are processed on hardware based queue IO Completion ports
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www.languagehumanities.org › what-is-a-dictionaryWhat is a Dictionary? (with pictures) - Language Humanities
www.languagehumanities.org › what-is-a-dictionaryMay 23, 2024 · A dictionary is a reference book that focuses on defining words and phrases, including multiple meanings. The most frequently used type is a language dictionary that includes the majority of frequently used words in a language. These books are made for different types of users: scholars, office workers, schools, and second language learners.
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libguides.anu.edu.au › cDictionaries - English Literature - LibGuides at The ...
libguides.anu.edu.au › cMay 13, 2024 · English Literature. A guide to English Literature resources available from the ANU Library. Dictionaries. The print and electronic dictionaries listed here will assist you in finding definitions and concise information on the English language and a broad variety of literature topics and subject areas.
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www.geeksforgeeks.org › append-a-value-to-aAppend a Value to a Dictionary Python - GeeksforGeeks
www.geeksforgeeks.org › append-a-value-to-aMay 10, 2024 · While dictionaries are mutable, meaning they can be modified after creation, adding new values to a dictionary requires some specific methods. In this article, we’ll explore four commonly used methods to append a value to a dictionary in Python.
mediadesignschool.libguides.com › apa › dictionariesDictionary & Encyclopedia - APA 7th ed. - Referencing Tool ...
mediadesignschool.libguides.com › apa › dictionariesMay 2, 2024 · Online Dictionary - In general, include the same elements, in the same order, that you would for a reference to a non-electronic source. - If there is no author for the entry, put the title in place of the author. - If the online version refers to a print edition, include the edition number after the title of the reference work.
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What is a dictionary in English?
- What is a Dictionary? A dictionary is a reference book that focuses on defining words and phrases, including multiple meanings. The most frequently used type is a language dictionary that includes the majority of frequently used words in a language.
What is a Dictionary? (with pictures) - Language Humanities
www.languagehumanities.org/what-is-a-dictionary.htmWhat is a language dictionary?
- A dictionary is a reference book that focuses on defining words and phrases, including multiple meanings. The most frequently used type is a language dictionary that includes the majority of frequently used words in a language. These books are made for different types of users: scholars, office workers, schools, and second language learners.
What is a Dictionary? (with pictures) - Language Humanities
www.languagehumanities.org/what-is-a-dictionary.htmWhat do dictionaries include?
- Many dictionaries include their experts' guidance on grammar, usage, and the history of the English language. Definitions differ slightly, and sample sentences may be the work of the editorial staff or taken from real-world usage, perhaps the first known written use, when possible.
What is a Dictionary? (with pictures) - Language Humanities
www.languagehumanities.org/what-is-a-dictionary.htmWhat are dictionaries in Python?
- Dictionaries in Python are versatile data structures that allow you to store and retrieve key-value pairs efficiently. While dictionaries are mutable, meaning they can be modified after creation, adding new values to a dictionary requires some specific methods.
Append a Value to a Dictionary Python - GeeksforGeeks
www.geeksforgeeks.org/append-a-value-to-a-dictionary-python/camosun.libguides.com › apa7 › dictionaryDictionary/Encyclopedia - APA Citation Style (7th edition ...
camosun.libguides.com › apa7 › dictionaryMay 24, 2024 · Dictionary definition (electronic with author, no date or continuously updated) (Sec. 10.3 #47, #48 APA Publication Manual, pp 326-329) When an online reference work is continuously updated and not archived use "n.d" and include retrieval date (10.3 #47, also see 9.16 for retrieval dates).
www.geeksforgeeks.org › lzw-lempel-ziv-welchLZW (Lempel–Ziv–Welch) Compression technique - GeeksforGeeks
www.geeksforgeeks.org › lzw-lempel-ziv-welchMay 21, 2024 · The idea of the compression algorithm is the following: as the input data is being processed, a dictionary keeps a correspondence between the longest encountered words and a list of code values. The words are replaced by their corresponding codes and so the input file is compressed.
www.lifewire.com › what-is-apple-tv-1994193What Is Apple TV? How Does It Work? - Lifewire
www.lifewire.com › what-is-apple-tv-1994193May 6, 2024 · Apple TV 4K vs Roku Ultra. Essentially, Apple TV turns your television into a "smart" TV. You can rent movies or stream your collection from iTunes, stream movies and TV shows from apps like Netflix and Hulu, stream music through Apple Music and Pandora, listen to podcasts, and even use it to replace your traditional cable TV subscription with ...
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