I have a design issue regarding HASH function.
In my program I am using a hash table of size 2^13, where the slot is calculated based on the value of the node(the hash key) which I want to insert.
Now, say my each node has two value |A|B| however I am inserting value into hash table using A.
Later on, I want to search a particular node which B not A.
Is it possible to that way? Is yes, could you highlight some design approaches?
The constraint is that I have to use A as the hash key.
Sorry, I can't share the code. Small example:
Value[] = {Part1, Part2, Part3};
insert(value)
check_for_index(value.part1)
value.part1 to be used to calculate the index of the slot.
Once slot is found then insert the "value"
Later on,
search_in_hash(part2)
check_for_index("But here I need the value.part1 to check for slot index")
So, how can I relate the part1, part2 & part3 such that I later on I can find the slot by either part2 or part3
If the problem statement is vague kindly let me know.
Unless you intend to do a search element-by-element (in which case you don't need a hash, just a plain list), then what you basically ask is - can I have a hash such that hash(X) == hash(Y), but X!=Y, so that you could map to a location using part1 and then map to the same one using part2 or 3. That completely goes against what hashing stands for.
What you should do is (as viraptor also suggested), create 3 structures, each hashed using a different part of the value, and push the full value to all 3. Then when you need to search use the proper hash by the part you want to search by.
for e.g.:
value[] = {part1, part2, part3};
hash1.insert(part1, value)
hash2.insert(part2, value)
hash3.insert(part3, value)
then
hash2.search_in_hash(part2)
or
hash3.search_in_hash(part3)
The above 2 should produce the exact same values.
Also make sure that all data manipulations (removing values, changing them), is done on all 3 structures simultaneously. For e.g. -
value = hash2.search_in_hash(part2)
hash1.remove(value.part1)
hash2.remove(part2) // you can assert that part2 == value.part2
hash3.remove(value.part3)
Related
My question is theoretical,
I'm trying to make a design for a mapreduce example in Big data processing.
The case which I have requires a pair of keys to be mapped to a pair of values.
for example if we have below text:
"Bachelors in Engineering has experience of 5 years"
I am trying to count the words Engineering & Experience in a way where I would have a value for each word separately.
So my key would be (Engineering,Experience) and my value would be (1,1) as per the above given text example.
Note that there is a relationship between both key values in my homework, therefore I want them both in one set of a key-value to determine if both keys are mentioned in one text file, or only one key is mentioned, or none is mentioned.
Please let me know if above case is possible to do in map-reduce of big data or not..
Having a string key of "(Engineering,Experience)" is no different than just having a String of one of those words.
If you want to have some more custom type, then you will want to subclass the Writable and maybe the WritableComparable interfaces.
Simlarly, for the value, you could put the entire tuple as Text and parse it later, or you can create your own Writable subclass that can store two integers.
Thanks for the Answer, but I figured I could use "Engineering Experience" as a string for the key.
I'm a bit new to Redis, so please forgive if this is basic.
I'm working on an app that sends automatic replies to users for certain events. I would like to use Redis to store who has received what event.
Essentially, in ruby, the data structure could look like this where you have a map of users to events and the dates that each event was sent.
{
"mary#example.com" => {
"sent_comment_reply" => ["12/12/2014", "3/6/2015"],
"added_post_reply" => ["1/4/2006", "7/1/2016"]
}
}
What is the best way to represent this in a Redis data structure so you can ask, did Mary get a sent_comment_reply? and if so, when was the latest?
In short, the question is, how(if possible) can you have a Hash structure that holds an array in Redis.
The rationale as opposed to using a set or list with a compound key is that hashes have O(1) lookup time, whereas lookups on lists(lrange) and sets(smembers) will be O(s+n) and sets O(n), respectively.
One way of structuring it in Redis, depending on the idea that you know the events of the user and you want the latest to be fresh in memory :
A sorted set per user. the content of the sorted set will be event codes; sent_comment_reply, added_post_reply with the score of the latest event as the highest. you can use ZRANK to get the answer for the question :
Did Mary get a sent_comment_reply?
A hash also for the user, this time you will have the field as the event sent_comment_reply and the value is the content of it which should be updated with the latest value including the body, date, etc. this will answer the question:
and if so, when was the latest?
Note: Sorted sets are really fast , and in this example we are depending on the events as the data.
With sorted sets you can add, remove, or update elements in a very
fast way (in a time proportional to the logarithm of the number of
elements). Since elements are taken in order and not ordered
afterwards, you can also get ranges by score or by rank (position) in
a very fast way. Accessing the middle of a sorted set is also very
fast, so you can use Sorted Sets as a smart list of non repeating
elements where you can quickly access everything you need: elements in
order, fast existence test, fast access to elements in the middle!
A possible approach to use a hash to map an array is as follows:
add_element(key , value):
len := redis.hlen(key)
redis.hset(key , len , value)
this will map array[i] element to i field in a hash key.
this will work for some cases, but I would probably go with the answer suggested in https://stackoverflow.com/a/34886801/2868839
I am working on an untrained classifier model. I am working in Python 2.7. I have a loop. It looks like this:
features = [0 for i in xrange(len(dictionary))]
for bgrm in new_scored:
for i in xrange(len(dictionary)):
if bgrm[0] == dictionary[i]:
features[i] = int(bgrm[1])
break
I have a "dictionary" of bigrams that I have collected from a data set containing customer reviews and I would like to construct feature arrays of each review corresponding to the dictionary I have created. It would contain the frequencies of the bigrams found within the review of the features in the dictionary (I hope that makes sense). new_scored is a list of tuples which contains the bigrams found within a particular review paired with their relative frequency of occurrence in that review. The final feature arrays will be the same length as the original dictionary with few non zero entries.
The above works fine but I am looking at a data set of 13000 reviews, for each review to loop through this code is going to take for eeever (if my computer doesnt run out of RAM first). I have been sitting with it for a while and cannot see how I can condense it.
I am very new to python so I was hoping a more experienced could help with condensing it or perhaps point me in the right direction towards a library that will contain the function I need.
Thank you in advance!
Consider making dictionary an actual dict object (or some fancier subclass of dict if it better suits your needs), as opposed to an iterable (list or tuple seems like what it is now). dictionary could map bigrams as keys to an integer identifier that would identify a feature position.
If you refactor dictionary that way, then the loop can be rewritten as:
features = [0 for key in dictionary]
for bgram in new_scored:
try:
features[dictionary[bgram[0]]] = int(bgrm[1])
except KeyError:
# do something if the bigram is not in the dictionary for some reason
This should convert what was an O(n) traversal through dictionary into a hash lookup.
Hope this helps.
I'm trying to determine when to remove entries in the sorteddictionary, when a sequence is found, i.e. where the key is a sequence of 1,2,3,4,5,6,7,8,9,10... etc.
I have:
SortedDictionary<int, string>
Its hard to explain. I'm adding pairs where the key can be any integer value, generally on a random'ish basis. So, the program may add
<2,"jim"> <15,"Jack"> <62,"jill"> and so on.
So when it executes, the dictionary is going to filled with a sorted list which is not necessary in sequence, but I want to check, if say key values 1..10 are present, in a proper sequence, i.e 1,2,3,4,5,6,7,8,9,10.
The background is i've got stuff coming in from a messaging pipe, which is not in order. So it goes into this dictionary, and then on another thread I check the dictionary, and if return's success for the range I provide, then removes it from the dictionary and enques it, in order onto a concurrentqueue. Fundamentally an inorder to ordered exchange.
Any help is appreciated.
Bob.
If you get the highest and lowest keys, then the count would tell you if you've got a sequence.
My problem:
I'm looking for a way to represent a person's name and address as an encoded id. The id should contain only alpha-numeric characters, be collision-proof, and be represented in a smallest number of characters possible. My first thought was to simply use a cryptographic hash function like MD5 or SHA1, but this seems like overkill (security isn't important - doesn't need to be one-way) and I'd prefer to find something that would produce a shorter id. Does anyone know of an existing algorithm that fits this problem?
In other words, what is the best way to implement the following function so that the return value is the same consistently for the same input, collisions are unlikely, and ids are less than 20 characters?
>>> make_fake_id(fname = 'Oscar', lname = 'Grouch', stnum = '1', stname = 'Sesame', zip = '12345')
N1743123734
Application Context (for those that are interested):
This will be used for a record linkage app. Given an input name and address we search a very large database for the best match and return the database id and other data (how we do this is not important here). If there isn't a match I need to generate this psuedo/generated/derived id from the search input (entity's name and address data). Every search record should result in an output record with either a real (the actual database id resulting from a match/link) or this generated psuedo/generated/derived id. The psuedo id will be prefixed with a character (e.g. N) to differentiate it from a real id.
I know you said no to MD5 and SHA1, but I think you should consider them anyway. As well as being well studied hashing algorithms, the length gives you more protection against possible collisions. No hash is collision-proof, but the cryptographic ones generally are less collision-prone than something you couuld come up with yourself.
Use a cryptographic hash for its collision resistance, not its other qualities
Use as many bytes from the hash as you want (truncate)
convert to alpha-numeric characters
You can also truncate the alpha-numeric string instead of the hash
An easy way to do this: hash the data, encode in base64, remove all non-alpha-numeric characters, truncate.
N_HASH_CHARS = 11
import hashlib, re
def digest(name, address):
hash = hashlib.md5(name + "|" + address).digest().encode("base64")
alnum_hash = re.sub(r'[^a-zA-Z0-9]', "", hash)
return alnum_hash[:N_HASH_CHARS]
How many alpha-numeric characters should you keep? Each character gives you around 5.95 bits of entropy (log(62,2)). 11 characters give you 65.5 bits of entropy, which should be enough to avoid a collision for the first 2**32.7 users (about 7 billion).
A good solution is somewhat dependent on your application. Do you know how many users and what the set of all users is? If you provide more details you would get better help.
I agree with the other poster suggesting serial numbers. OTOH, if you really, really really want to do something else:
Create a SHA1 hash from the data, and store it in a table with a serial number field.
Then, when you get the data, calculate the hash, look it up on the table, get the serial, and that's your id. If it's not on the table, insert it.
I wonder whether you intend to "assign" these ids to the users? If so, I would expect your users to hate anything that you propose; who would want a user id of "AAAAA01"?
So, if these ids are visible to the user, then you should just let them pick what they like and check them for uniqueness (easy). If they are not visible to the user (e.g., internal primary key), then just generate them sequentially using an appropriate technique such as an Oracle Sequence or SQL Server AutoNumber (also easy).
If these ids are an attempt to detect a user that is registering more than once, then I would agree that you should consider a cryptographic hash followed by a full comparison of the registration data (name, address, etc.). However, to be usable, you will need to translate the data into a canonical form (standardized letter case, whitespace, canonical street address, etc.) before computing the hash or making the comparison. Otherwise, you will mismatch based on trivial differences.
EDIT: Now that I understand the problem space better based on your edits, I think that it is highly unlikely that your algorithm (so far) will catch most matches. Beyond my suggestion to canonicalize the inputs, I recommend that you consider an approach that results in a ranked list of a handful of possible matches (to be resolved by a human if possible) rather than an all-or-nothing attempt at a single match. In other words, I recommend a search approach rather than a lookup approach.
Is that feasible in your situation?
Well, if there's more than one person at the same address with the same name, you're toast here, (w/o adding code to detect this and add a discriminator of some kind).
but assuming that issue is not, then the street address and zip code portion of the full addresss is sufficient to guaranteee uniqueness there, so adding enough data from the name should take care of the issue...
Do you have access to a database, or other persistence mechanism, where you could generate and maintain key values for each address? Then keep the address and individual entities in two keyed dictionary structures, where the key is autogenerated for each new distinct address, person encountered... and then use the autogenerated alpha-numeric key...
You could use AAAAA01 for first person at first address,
AAAAA02 for second person at first address,
AAAAB07 for the seventh resident at the second adresss, etc.
If you donlt have any way to generate and maintain these entity-Key mappings then you need to use the full street address/Zip and fullNAme, or a hash value of the same, although the Hash value approach has a smnall chance of generating duplicates...