I was able to confirm from the documentation that bpf_map_update_elem is an atomic operation if done on HASH_MAPs. Source (https://man7.org/linux/man-pages/man2/bpf.2.html). [Cite: map_update_elem() replaces existing elements atomically]
My question is 2 folds.
What if the element does not exist, is the map_update_elem still atomic?
Is the XDP operation bpf_map_delete_elem thread safe from User space program?
The map is a HASH_MAP.
Atomic ops, race conditions and thread safety are sort of complex in eBPF, so I will make a broad answer since it is hard to judge from your question what your goals are.
Yes, both the bpf_map_update_elem command via the syscall and the helper function update the maps 'atmomically', which in this case means that if we go from value 'A' to value 'B' that the program always sees either 'A' or 'B' not some combination of the two(first bytes of 'B' and last bytes of 'A' for example). This is true for all map types. This holds true for all map modifying syscall commands(including bpf_map_delete_elem).
This however doesn't make race conditions impossible since the value of the map may have changed between a map_lookup_elem and the moment you update it.
What is also good to keep in mind is that the map_lookup_elem syscall command(userspace) works differently from the helper function(kernelspace). The syscall will always return a copy of the data which isn't mutable. But the helper function will return a pointer to the location in kernel memory where the map value is stored, and you can directly update the map value this way without using the map_update_elem helper. That is why you often see hash maps used like:
value = bpf_map_lookup_elem(&hash_map, &key);
if (value) {
__sync_fetch_and_add(&value->packets, 1);
__sync_fetch_and_add(&value->bytes, skb->len);
} else {
struct pair val = {1, skb->len};
bpf_map_update_elem(&hash_map, &key, &val, BPF_ANY);
}
Note that in this example, __sync_fetch_and_add is used to update parts of the map value. We need to do this since updating it like value->packets++; or value->packets += 1 would result in a race condition. The __sync_fetch_and_add emits a atomic CPU instruction which in this case fetches, adds and writes back all in one instruction.
Also, in this example, the two struct fields are atomically updated, but not together, it is still possible for the packets to have incremented but bytes not yet. If you want to avoid this you need to use a spinlock(using the bpf_spin_lock and bpf_spin_unlock helpers).
Another way to sidestep the issue entirely is to use the _PER_CPU variants of maps, where you trade-off congestion/speed and memory use.
I am currently working on a script where within a function, key-value pairs are being added to a dictionary x - consider x as a single dictionary of different inputs used to query data, and different key-values are appended to this depending on certain conditions being fulfilled.
However, when I load in the script into my session with some new assignment logic added, I am hitting a 'constants error. This is despite all assignments being kept to this dictionary x. When these two new assignments within x are commented out, the script will load in successfully.
I know the 'constants error usually refers to the max number of constants within a certain scope being exceeded, but surely this shouldn't be happening when all assignment is happening within this dictionary x. Is there a way to get around this? What is causing this issue?
I think you are trying to do too much in one function. I think you are indexing or assigning values to the dictionary with too many constants. Below code will return the constants error:
dict:(10 + til 100)!til 100
value (raze -1_"{","dict[",/:(string[10+til 97],\:"];")),"}"
// with til 96
{dict[10];dict[11] ... dict[104]}
It's the code that is indexing the dictionary is causing the issue rather than the dictionary itself.
Perl 6 has lazy lists, but it also has unbounded Range objects. Which one should you choose for counting up by whole numbers?
And there's unbounded Range with two dots:
0 .. *
There's the Seq (sequence) with three dots:
0 ... *
A Range generates lists of consecutives thingys using their natural order. It inherits from Iterable, but also Positional so you can index a range. You can check if something is within a Range, but that's not part of the task.
A Seq can generate just about anything you like as long as it knows how to get to the next element. It inherits from Iterable, but also PositionalBindFailover which fakes the Positional stuff through a cache and list conversion. I don't think that a big deal if you're only moving from one element to the next.
I'm going back and forth on this. At the moment I'm thinking it's Range.
Both 0 .. * and 0 ... * are fine.
Iterating over them, for example with a for loop, has exactly the same effect in both cases. (Neither will leak memory by keeping around already iterated elements.)
Assigning them to a # variable produces the same lazy Array.
So as long as you only want to count up numbers to infinity by a step of 1, I don't see a downside to either.
The ... sequence construction operator is more generic though, in that it can also be used to
count with a different step (1, 3 ... *)
count downwards (10 ... -Inf)
follow a geometric sequence (2, 4, 8 ... *)
follow a custom iteration formula (1, 1, *+* ... *)
so when I need to do something like that, then I'd consider using ... for any nearby and related "count up by one" as well, for consistency.
On the other hand:
A Range can be indexed efficiently without having to generate and cache all preceding elements, so if you want to index your counter in addition to iterating over it, it is preferable. The same goes for other list operations that deal with element positions, like reverse: Range has efficient overloads for them, whereas using them on a Seq has to iterate and cache its elements first.
If you want to count upwards to a variable end-point (as in 1 .. $n), it's safer to use a Range because you can be sure it'll never count downwards, no matter what $n is. (If the endpoint is less than the startpoint, as in 1 .. 0, it will behave as an empty sequence when iterated, which tends to get edge-cases right in practice.)
Conversely, if you want to safely count downwards ensuring it will never unexpectedly count upwards, you can use reverse 1 .. $n.
Lastly, a Range is a more specific/high-level representation of the concept of "numbers from x to y", whereas a Seq represents the more generic concept of "a sequence of values". A Seq is, in general, driven by arbitrary generator code (see gather/take) - the ... operator is just semantic sugar for creating some common types of sequences. So it may feel more declarative to use a Range when "numbers from x to y" is the concept you want to express. But I suppose that's a purely psychological concern... :P
Semantically speaking, a Range is a static thing (a bounded set of values), a Seq is a dynamic thing (a value generator) and a lazy List a static view of a dynamic thing (an immutable cache for generated values).
Rule of thumb: Prefer static over dynamic, but simple over complex.
In addition, a Seq is an iterable thing, a List is an iterable positional thing, and a Range is an ordered iterable positional thing.
Rule of thumb: Go with the most generic or most specific depending on context.
As we're dealing with iteration only and are not interested in positional access or bounds, using a Seq (which is essentially a boxed Iterator) seems like a natural choice. However, ordered sets of consecutive integers are exactly what an integer Range represents, and personally that's what I would see as most appropriate for your particular use case.
When there is no clear choice, I tend to prefer ranges for their simplicity anyway (and try to avoid lazy lists, the heavy-weight).
Note that the language syntax also nudges you in the direction of Range, which are rather heavily Huffman-coded (two-char infix .., one-char prefix ^).
There is a difference between ".." (Range) and "..." (Seq):
$ perl6
> 1..10
1..10
> 1...10
(1 2 3 4 5 6 7 8 9 10)
> 2,4...10
(2 4 6 8 10)
> (3,6...*)[^5]
(3 6 9 12 15)
The "..." operator can intuit patterns!
https://docs.perl6.org/language/operators#index-entry-..._operators
As I understand, you can traverse a Seq only once. It's meant for streaming where you don't need to go back (e.g., a file). I would think a Range should be a fine choice.
I'm trying to implement a simple cryptocurrency similar to bitcoin, just to understand it deeply down to the code level.
I understand that a bitcoin block contains a hash of the previous block, many transactions and an reward transaction for the solver.
the miner basically runs SHA256 on this candidate block combined with an random number. As long as the first certain digits of a hash result are zeros, we say this block is solved, and we broadcast the result to the entire network to claim the reward.
but I have never seen anyone proving that a block is solvable at all. I guess this is guaranteed by SHA256? because the solution size is fixed, after trying enough inputs, you are guaranteed to hit every hash result? but how can you prove that the solution distribution of a block is even (uniform), so that you can indeed cover all hash results?
now, suppose a block is indeed always solvable, can I assume that using 64bit for the random integer is enough to solve it? how about 32bit? or I have to use an infinite bit integer?
for example, in the basiccoin project:
the code for proof of work is the following:
def POW(block, hashes):
halfHash = tools.det_hash(block)
block[u'nonce'] = random.randint(0, 10000000000000000000000000000000000000000)
count = 0
while tools.det_hash({u'nonce': block['nonce'],
u'halfHash': halfHash}) > block['target']:
count += 1
block[u'nonce'] += 1
if count > hashes:
return {'error': False}
if restart_signal.is_set():
restart_signal.clear()
return {'solution_found': True}
''' for testing sudden loss in hashpower from miners.
if block[u'length']>150:
else: time.sleep(0.01)
'''
return block
this code randoms a number between [0, 10000000000000000000000000000000000000000] as a start point, and then it just increases the value one by one:
block[u'nonce'] += 1
I'm not a python programmer, I don't know how python handles the type of the integer. there is no handling of integer overflow.
I'm trying to implement similar thing with c++, I don't know what kind of integer can guarantee a solution.
but how can you prove that the solution distribution of a block is even (uniform), so that you can indeed cover all hash results?
SHA256 is deterministic so if you rehash the txns it will always provide the same 256 hash.
The client nodes keep all the txn and the hashes in the merkle tree for the network clients to propagate and verify the longest possible block chain.
The merkle tree is the essential data structure for recording the hashes of previous blocks.
From there the chain of hash confirmations can be tracked from the origin (genesis) block.
Consider a lookup function with the following signature, which needs to return an integer for a given string key:
int GetValue(string key) { ... }
Consider furthermore that the key-value mappings, numbering N, are known in advance when the source code for function is being written, e.g.:
// N=3
{ "foo", 1 },
{ "bar", 42 },
{ "bazz", 314159 }
So a valid (but not perfect!) implementation for the function for the input above would be:
int GetValue(string key)
{
switch (key)
{
case "foo": return 1;
case "bar": return 42;
case "bazz": return 314159;
}
// Doesn't matter what we do here, control will never come to this point
throw new Exception();
}
It is also known in advance exactly how many times (C>=1) the function will be called at run-time for every given key. For example:
C["foo"] = 1;
C["bar"] = 1;
C["bazz"] = 2;
The order of such calls is not known, however. E.g. the above could describe the following sequence of calls at run-time:
GetValue("foo");
GetValue("bazz");
GetValue("bar");
GetValue("bazz");
or any other sequence, provided the call counts match.
There is also a restriction M, specified in whatever units is most convenient, defining the upper memory bound of any lookup tables and other helper structures that can be used by the GetValue (the structures are initialized in advance; that initialization is not counted against the complexity of the function). For example, M=100 chars, or M=256 sizeof(object reference).
The question is, how to write the body of GetValue such that it is as fast as possible - in other words, the aggregate time of all GetValue calls (note that we know the total count, per everything above) is minimal, for given N, C and M?
The algorithm may require a reasonable minimal value for M, e.g. M >= char.MaxValue. It may also require that M be aligned to some reasonable boundary - for example, that it may only be a power of two. It may also require that M must be a function of N of a certain kind (for example, it may allow valid M=N, or M=2N, ...; or valid M=N, or M=N^2, ...; etc).
The algorithm can be expressed in any suitable language or other form. For runtime performance constrains for generated code, assume that the generated code for GetValue will be in C#, VB or Java (really, any language will do, so long as strings are treated as immutable arrays of characters - i.e. O(1) length and O(1) indexing, and no other data computed for them in advance). Also, to simplify this a bit, answers which assume that C=1 for all keys are considered valid, though those answers which cover the more general case are preferred.
Some musings on possible approaches
The obvious first answer to the above is using a perfect hash, but generic approaches to finding one seem to be imperfect. For example, one can easily generate a table for a minimal perfect hash using Pearson hashing for the sample data above, but then the input key would have to be hashed for every call to GetValue, and Pearson hash necessarily scans the entire input string. But all sample keys actually differ in their third character, so only that can be used as the input for the hash instead of the entire string. Furthermore, if M is required to be at least char.MaxValue, then the third character itself becomes a perfect hash.
For a different set of keys this may no longer be true, but it may still be possible to reduce the amount of characters considered before the precise answer can be given. Furthermore, in some cases where a minimal perfect hash would require inspecting the entire string, it may be possible to reduce the lookup to a subset, or otherwise make it faster (e.g. a less complex hashing function?) by making the hash non-minimal (i.e. M > N) - effectively sacrificing space for the sake of speed.
It may also be that traditional hashing is not such a good idea to begin with, and it's easier to structure the body of GetValue as a series of conditionals, arranged such that the first checks for the "most variable" character (the one that varies across most keys), with further nested checks as needed to determine the correct answer. Note that "variance" here can be influenced by the number of times each key is going to be looked up (C). Furthermore, it is not always readily obvious what the best structure of branches should be - it may be, for example, that the "most variable" character only lets you distinguish 10 keys out of 100, but for the remaining 90 that one extra check is unnecessary to distinguish between them, and on average (considering C) there are more checks per key than in a different solution which does not start with the "most variable" character. The goal then is to determine the perfect sequence of checks.
You could use the Boyer search, but I think that the Trie would be a much more effiecent method. You can modify the Trie to collapse the words as you make the hit count for a key zero, thus reducing the number of searches you would have to do the farther down the line you get. The biggest benefit you would get is that you are doing array lookups for the indexes, which is much faster than a comparison.
You've talked about a memory limitation when it comes to precomputation - is there also a time limitation?
I would consider a trie, but one where you didn't necessarily start with the first character. Instead, find the index which will cut down the search space most, and consider that first. So in your sample case ("foo", "bar", "bazz") you'd take the third character, which would immediately tell you which string it was. (If we know we'll always be given one of the input words, we can return as soon as we've found a unique potential match.)
Now assuming that there isn't a single index which will get you down to a unique string, you need to determine the character to look at after that. In theory you precompute the trie to work out for each branch what the optimal character to look at next is (e.g. "if the third character was 'a', we need to look at the second character next; if it was 'o' we need to look at the first character next) but that potentially takes a lot more time and space. On the other hand, it could save a lot of time - because having gone down one character, each of the branches may have an index to pick which will uniquely identify the final string, but be a different index each time. The amount of space required by this approach would depend on how similar the strings were, and might be hard to predict in advance. It would be nice to be able to dynamically do this for all the trie nodes you can, but then when you find you're running out of construction space, determine a single order for "everything under this node". (So you don't end up storing a "next character index" on each node underneath that node, just the single sequence.) Let me know if this isn't clear, and I can try to elaborate...
How you represent the trie will depend on the range of input characters. If they're all in the range 'a'-'z' then a simple array would be incredibly fast to navigate, and reasonably efficient for trie nodes where there are possibilities for most of the available options. Later on, when there are only two or three possible branches, that becomes wasteful in memory. I would suggest a polymorphic Trie node class, such that you can build the most appropriate type of node depending on how many sub-branches there are.
None of this performs any culling - it's not clear how much can be achieved by culling quickly. One situation where I can see it helping is when the number of branches from one trie node drops to 1 (because of the removal of a branch which is exhausted), that branch can be eliminated completely. Over time this could make a big difference, and shouldn't be too hard to compute. Basically as you build the trie you can predict how many times each branch will be taken, and as you navigate the trie you can subtract one from that count per branch when you navigate it.
That's all I've come up with so far, and it's not exactly a full implementation - but I hope it helps...
Is a binary search of the table really so awful? I would take the list of potential strings and "minimize" them, the sort them, and finally do a binary search upon the block of them.
By minimize I mean reducing them to the minimum they need to be, kind of a custom stemming.
For example if you had the strings: "alfred", "bob", "bill", "joe", I'd knock them down to "a", "bi", "bo", "j".
Then put those in to a contiguous block of memory, for example:
char *table = "a\0bi\0bo\0j\0"; // last 0 is really redundant..but
char *keys[4];
keys[0] = table;
keys[1] = table + 2;
keys[2] = table + 5;
keys[3] = table + 8;
Ideally the compiler would do all this for you if you simply go:
keys[0] = "a";
keys[1] = "bi";
keys[2] = "bo";
keys[3] = "j";
But I can't say if that's true or not.
Now you can bsearch that table, and the keys are as short as possible. If you hit the end of the key, you match. If not, then follow the standard bsearch algorithm.
The goal is to get all of the data close together and keep the code itty bitty so that it all fits in to the CPU cache. You can process the key from the program directly, no pre-processing or adding anything up.
For a reasonably large number of keys that are reasonably distributed, I think this would be quite fast. It really depends on the number of strings involved. For smaller numbers, the overhead of computing hash values etc is more than search something like this. For larger values, it's worth it. Just what those number are all depends on the algorithms etc.
This, however, is likely the smallest solution in terms of memory, if that's important.
This also has the benefit of simplicity.
Addenda:
You don't have any specifications on the inputs beyond 'strings'. There's also no discussion about how many strings you expect to use, their length, their commonality or their frequency of use. These can perhaps all be derived from the "source", but not planned upon by the algorithm designer. You're asking for an algorithm that creates something like this:
inline int GetValue(char *key) {
return 1234;
}
For a small program that happens to use only one key all the time, all the way up to something that creates a perfect hash algorithm for millions of strings. That's a pretty tall order.
Any design going after "squeezing every single bit of performance possible" needs to know more about the inputs than "any and all strings". That problem space is simply too large if you want it the fastest possible for any condition.
An algorithm that handles strings with extremely long identical prefixes might be quite different than one that works on completely random strings. The algorithm could say "if the key starts with "a", skip the next 100 chars, since they're all a's".
But if these strings are sourced by human beings, and they're using long strings of the same letters, and not going insane trying to maintain that data, then when they complain that the algorithm is performing badly, you reply that "you're doing silly things, don't do that". But we don't know the source of these strings either.
So, you need to pick a problem space to target the algorithm. We have all sorts of algorithms that ostensibly do the same thing because they address different constraints and work better in different situations.
Hashing is expensive, laying out hashmaps is expensive. If there's not enough data involved, there are better techniques than hashing. If you have large memory budget, you could make an enormous state machine, based upon N states per node (N being your character set size -- which you don't specify -- BAUDOT? 7-bit ASCII? UTF-32?). That will run very quickly, unless the amount of memory consumed by the states smashes the CPU cache or squeezes out other things.
You could possibly generate code for all of this, but you may run in to code size limits (you don't say what language either -- Java has a 64K method byte code limit for example).
But you don't specify any of these constraints. So, it's kind of hard to get the most performant solution for your needs.
What you want is a look-up table of look-up tables.
If memory cost is not an issue you can go all out.
const int POSSIBLE_CHARCODES = 256; //256 for ascii //65536 for unicode 16bit
struct LutMap {
int value;
LutMap[POSSIBLE_CHARCODES] next;
}
int GetValue(string key) {
LutMap root = Global.AlreadyCreatedLutMap;
for(int x=0; x<key.length; x++) {
int c = key.charCodeAt(x);
if(root.next[c] == null) {
return root.value;
}
root = root.next[c];
}
}
I reckon that it's all about finding the right hash function. As long as you know what the key-value relationship is in advance, you can do an analysis to try and find a hash function to meet your requrements. Taking the example you've provided, treat the input strings as binary integers:
foo = 0x666F6F (hex value)
bar = 0x626172
bazz = 0x62617A7A
The last column present in all of them is different in each. Analyse further:
foo = 0xF = 1111
bar = 0x2 = 0010
bazz = 0xA = 1010
Bit-shift to the right twice, discarding overflow, you get a distinct value for each of them:
foo = 0011
bar = 0000
bazz = 0010
Bit-shift to the right twice again, adding the overflow to a new buffer:
foo = 0010
bar = 0000
bazz = 0001
You can use those to query a static 3-entry lookup table. I reckon this highly personal hash function would take 9 very basic operations to get the nibble (2), bit-shift (2), bit-shift and add (4) and query (1), and a lot of these operations can be compressed further through clever assembly usage. This might well be faster than taking run-time infomation into account.
Have you looked at TCB . Perhaps the algorithm used there can be used to retrieve your values. It sounds a lot like the problem you are trying to solve. And from experience I can say tcb is one of the fastest key store lookups I have used. It is a constant lookup time, regardless of the number of keys stored.
Consider using Knuth–Morris–Pratt algorithm.
Pre-process given map to a large string like below
String string = "{foo:1}{bar:42}{bazz:314159}";
int length = string.length();
According KMP preprocessing time for the string will take O(length).
For searching with any word/key will take O(w) complexity, where w is length of the word/key.
You will be needed to make 2 modification to KMP algorithm:
key should be appear ordered in the joined string
instead of returning true/false it should parse the number and return it
Wish it can give a good hints.
Here's a feasible approach to determine the smallest subset of chars to target for your hash routine:
let:
k be the amount of distinct chars across all your keywords
c be the max keyword length
n be the number of keywords
in your example (padded shorter keywords w/spaces):
"foo "
"bar "
"bazz"
k = 7 (f,o,b,a,r,z, ), c = 4, n = 3
We can use this to compute a lower bound for our search. We need at least log_k(n) chars to uniquely identify a keyword, if log_k(n) >= c then you'll need to use the whole keyword and there's no reason to proceed.
Next, eliminate one column at a time and check if there are still n distinct values remaining. Use the distinct chars in each column as a heuristic to optimize our search:
2 2 3 2
f o o .
b a r .
b a z z
Eliminate columns with the lowest distinct chars first. If you have <= log_k(n) columns remaining you can stop. Optionally you could randomize a bit and eliminate the 2nd lowest distinct col or try to recover if the eliminated col results in less than n distinct words. This algorithm is roughly O(n!) depending on how much you try to recover. It's not guaranteed to find an optimal solution but it's a good tradeoff.
Once you have your subset of chars, proceed with the usual routines for generating a perfect hash. The result should be an optimal perfect hash.