I have a collection of strings that I want to filter. They'll be in this pattern:
xxx_xxx_xxx_xxx
so always a sequence of letters or numbers separated by three underscores. The max length of each string will be 60 characters. I might have a few million of these in my collection.
What data structure could I use to efficiently do something like this:
Get all strings starts with: "abc_123_456"
Get all strings starts with: "def_999_888"
etc..
for example, I could do this:
List<String> matched = new ArrayList<String>();
for (String it : strings) {
if (it.startsWith(match)) {
matched.add(it);
}
}
but that would take a long time if my collection is on the order of millions of strings, and worse yet if the number of matched strings is also high.
The high-level problem is that I want to answer the following question for an app I'm writing: "which of my friends have recommended product A for product B?". I could store this information in a sql table and run the following statement:
select recommender from recs where username='me' and prodIdA='a' and prodIdB='b';
I'm curious if something custom in java/C/C++ could run faster, using encoded flat strings like I have above:
myusername_prodIdA_prodIdB_recommenderusername
The idea being that you could do a starts-with operation on the whole collection of encoded strings to get your answer.
I know trying to implement a custom solution like this is most likely not usable in a production environment, so some sql db would be better, just curious though,
Thanks
To do that in Java, you can use a Trie structure.
That being said, I don't think it's a good idea. Dumping "a few million" records in the memory won't always work.
That's what databases are for; with the right design and proper indexing you can have very good performance with the DB alone.
I think you are looking for a SortedMap.
"headMap(K toKey)
Returns a view of the portion of this map whose keys are strictly less than toKey."
I know trying to implement a custom solution like this is most likely not usable in a production environment, so some sql db would be better, just curious though
If only for the sake of curiosity, you can put all existing different "myusername_prodIdA_prodIdB" combinations in hashtable. And for each combination store a list of relevant results.
So, the structure would look like Map<String, List<String>> and used like hash.get("def_999_888"). Constant time (O(1))
You can get rid of inner list and optimize it in many ways, but this is the idea.
The first thing that comes to mind for me is pre-processing the strings into some sort of data structure so that they could be searched for efficiently. If you're going to be calling the search function many times, I think it'd be good for you to put all of the strings into a hash table for a constant-time look up. It'd take more processing power to construct your array of strings, but it'd trivialize the task of searching for them.
Related
I have a List<String[]> of customer records in Java (from a database). I know from manually eyeballing the data that 25%+ are duplicates.
The duplicates are far from exact though. Sometimes they have different zips, but the same name and address. Other times the address is missing completely, etc...
After a day of research; I'm still really stumped as to how to even begin to attack this problem?
What are the "terms" that I should be googling for that describe this area (from a solve this in Java perspective)? And I don't suppose there is fuzzymatch.jar out there that makes it all just to easy?
I've done similar systems before for matching place information and people information. These are complex objects with many features and figuring out whether two different objects describe the same place or person is tricky. The way to do it is to break it down to the essentials.
Here's a few things that you can do:
0) If this is a oneoff, load the data into openrefine and fix things interactively. Maximum this solves your problem, minimum it will show you where your possible matches are.
1) there are several ways you can compare strings. Basically they differ in how reliable they are in producing negative and false matches. A negative match is when it matches when it shouldn't have. A positive match is when it should match and does. String equals will not produce negative matches but will miss a lot of potential matches due to slight variations. Levenstein with a small factor is a slightly better. Ngrams produce a lot of matches, but many of them will be false. There are a few more algorithms, take a look at e.g. the openrefine code to find various ways of comparing and clustering strings. Lucene implements a lot of this stuff in its analyzer framework but is a bit of a beast to work with if you are not very familiar with its design.
2) Separate the process of comparing stuff from the process of deciding whether you have a match. What I did in the past was qualify my comparisons, using a simple numeric score e.g. this field matched exactly (100) but that field was a partial match (75) and that field did not match at all. The resulting vector of qualified comparisons, e.g. (100, 75,0,25) can be compared to a reference vector that defines your perfect or partial match criteria. For example if first name, last name, and street match, the two records are the same regardless of the rest of the fields. Or if phonenumbers and last names match, that's a valid match too. You can encode such perfect matches as a vector and then simply compare it with your comparison vectors to determine whether it was a match, not a match, or a partial match. This is sort of a manual version of what machine learning does which is to extract vectors of features and then build up a probability model of which vectors mean what from reference data. Doing it manually, can work for simple problems.
3) Build up a reference data set with test cases that you know to match or not match and evaluate your algorithm against that reference set. That way you will know when you are improving things or making things worse when you tweak e.g. the factor that goes into Levinstein or whatever.
Jilles' answer is great and comes from experience. I've also had to work on cleaning up large messy tables and sadly didn't know much about my options at that time (I ended up using Excel and a lot of autofilters). Wish I'd known about OpenRefine.
But if you get to the point where you have to write custom code to do this, I want to make a suggestion as to how: The columns are always the same, right? For instance, the first String is always the key, the second is the First name, the sixth is the ZIP code, tenth is the fax number, etc.?
Assuming there's not an unreasonable number of fields, I would start with a custom Record type which has each DB field as member rather than a position in an array. Something like
class CustomerRow {
public final String id;
public final String firstName;
// ...
public CustomerRow(String[] data) {
id = data[0];
// ...
}
You could also include some validation code in the constructor, if you knew there to be garbage values you always want to filter out.
(Note that you're basically doing what an ORM would do automatically, but getting started with one would probably be more work than just writing the Record type.)
Then you'd implement some Comparator<CustomerRow>s which only look at particular fields, or define equality in fuzzy terms (there's where the edit distance algorithms would come in handy), or do special sorts.
Java uses a stable sort for objects, so to sort by e.g. name, then address, then key, you would just do each sort, but choose your comparators in the reverse order.
Also if you have access to the actual database, and it's a real relational database, I'd recommend doing some of your searches as queries where possible. And if you need to go back and forth between your Java objects and the DB, then using an ORM may end up being a good option.
I'm running some experiments over a large dataset and would like to optimize a particular part. Currently, I have 5-6 Models each of which stores a mapping from Topics to List of Strings. The set of Topics is large and the same between each Model, so there must be a better way. Ultimately the query I need to perform is: what is the String in position x of the List for some Model-Topic combination.
One of the problems with using the mapping method is that if there are say 500k-5M topics, each has a list of 20 strings. Then my Map<Model, Map<Topic, List<String>>> is going to be massive.
Have you tried SortedSet / Maps? Sounds like you need to optimize your search, sorted collections (like TreeMap) should be log(n) while regular list is O(1). Of course, this kind of thing is something at which databases excel...
Not clear where/how you want to achieve "memory efficiency". First one needs to look at the particulars of your detailed data to see how much storage that consumes, then examine various ways of organizing it and analyze their efficiency in terms of % overhead vs your "real" data.
A brief glance shows that a HashMap, when you consider the associated tables, has about 80 bytes of overhead per entry. An ArrayList looks to average out around 10-12. Without looking, I would guess that a TreeMap would be more than a HashMap -- maybe 100.
Generally speaking, links within your own objects will be "cheaper", both in storage and speed to access, than links using these aggregating objects. But the aggregating objects are convenient to use, and have been "optimized" to a degree.
(But looking at your update, you probably should be looking at a DB application, rather than holding everything in heap.)
You could use Topic and Model to construct a composite key in a single Map, e.g.
map.put(topic1_id + model1_id, list1_1);
map.put(topic1_id + model2_id, list1_2);
...
map.get(topic_id + model_id)
where the IDs are Strings (or a similar scheme could be used with numeric identifiers).
A similar approach is to assign each topic and model a unique number, then store the lists of strings in arrays, so looking up the list for a given combination is a matter of looking up two indexes, then accessing a given location in a 2D array. (however, this is easier when you know the number of topics and models in advance of constructing the data structure)
For memory efficiency, also consider the small details. In general, you want to minimise the number of Objects - each Object carries an overhead. ArrayLists can have a lot of wasted space as they grow dynamically, doubling in size when they exceed their current capacity. If you can pre-size them to the required capacity (or use an array instead) then you can save a lot of memory. The same applies when using large numbers of small HashMaps.
One possible data structure is a hierarchy of maps, leading to an array of Strings. E.g.:
HashMap<Model, HashMap<Topic, String[]>> map;
A query function would then look like:
public String query(Model model, Topic topic, int x) {
HashMap<Topic, String[]> childMap = map.get(model);
if (childMap == null) {
return null;
}
String[] list = childMap.get(topic);
if (list == null) {
return null;
}
return list[x];
}
Presuming your Model and Topic structures implement hashCode() and equals() reasonably, the query performance should be quite good.
One potential weakness: I'm assuming you need to index a large number of Model/Topic combinations, and related lists of Strings (if not, you presumably wouldn't be asking about optimization). My guess is that the child String[] arrays will consume a large amount of memory. Each array is a Java object (about 20 bytes) + a pointer at each array location.
2 suggestions there:
1) If many Model/Topic combinations share the same set of Strings, you could gain quite a lot by sharing those String[] instances.
2) If you're using a 64-bit VM, be sure to use compressed ordinary object pointers (-XX:+UseCompressedOops). That will at least keep most of the pointers to 4 bytes instead of 8. Compressed OOPs is the default since 1.6.0_23, so a relatively recent VM will save you some memory here.
One other possibility not mentioned is store the strings using String[][][] and models and topics in a List such as ArrayList and then at query time:
public String query(Model model, Topic topic, int x) {
return strings[models.indexOf(model)][topics.indexOf(topic)][x];
}
It could be further improved for speed if the topics and models were sorted, then binary search rather than indexOf could be used.
Suppose I have a large list (around 10,000 entries) of string triples as such:
car noun yes
dog noun no
effect noun yes
effect verb no
Suppose I am presented with a string double - for example, (effect, verb) - and I need to quickly look in the list to see if the pair appears and, if it does, whether its value is yes or no. (For this example the double does appear and the value is "no".)
What is the best data structure in Java to store the list and the most efficient way to perform the search? I am running hundreds of thousands of these searches so speed is of the essence.
Thanks!
You might consider using a HashMap<YourDouble, String>. Searches will be O(1).
You could either create an object, YourDouble which holds the first two values, or else append one to the other -- if values will still be unique -- and use HashMap<String, String>.
I would create a HashMultimap for each type of search you want, e.g. "all three", "each pair", and "each single field". When you build the list, populate all the different maps, then you can fetch from whichever map is appropriate for your query.
(The downside is that you'll need a type for at least each arity, e.g. use just String for the "single field" maps, but a Pair for the two-field maps, and a Triple for the three-field map.)
You could use a HashMap where the key is the concatenation of the first two strings, the ones which you'll use for lookups, and the value is a Boolean, representing the yes and no strings.
Alternatively, it seems the words in the second column would be fewer, since they represent categories. You could have a HashMap<String, HashMap<String, Boolean>> where you first index by e.g. "noun", "verb" etc. and then you index by e.g. "car", "dog", "effect", to get to your boolean. This would probably be more space-efficient.
10k doesn't seem that large to me. Have you tried a DB?
The place to look for information like this is the Semantic Web. A number of projects work on Triple Stores of just this type. There's a list at the bottom of the Triple Store page of implementations.
As far as java is concerned your algorithms are almost certainly going to be language dependent and if you find a good algorithm implemented in C its java port will also be fast.
Also, what's your data set look like? Are there a lot of 2 matches such that subject and verb are often the same? How many matches are you expecting to get? MapReduce will work work well for finding one match in 10k but won't work as well doing a query that returns a 8k of 10k where the query can't be easily partitioned.
There's a query language made just for this problem too: SPARQL. The bigdata blog has some good insights, though again 10k doesn't seem that large.
Is it a good idea to store words of a dictionary with 100.000 words in a static array of string. I'm working on spellchecker and I thought that way would be faster.
You should generally prefer a Java Collections Framework class to a native Java array for anything non-trivial. In this particular case, what you have is a Set<String> (since no words should appear more than once in the dictionary).
A HashSet<String> offers constant time performance for the basic operations add, remove, and contains, and should work very well with String hashcode formula.
For larger dictionaries, you'd want to use more sophisticated data structures specialized for storing a set of strings (e.g. a trie), but for 100K words, a HashSet should suffice.
See also
Java Tutorials/Collections Framework
Effective Java 2nd Edition, Item 25: Prefer lists to arrays
Definitely its not a good idea to store so many strings as an array especially if you are using it for spell check which means you will have to search for and compare strings. It would make it inefficient to search or compare a string through the array as it would always be a linear search
How about an approach with in memory database technology like for example sqlite inmemory This allows you to use efficient querying without disk overhead
I think 100 000 is not so large amount that search wolud be inefficent. Of course it depends ... It would work nice if you are checking if a word exists in array - it's a linear complexity algorithm. You can keep table ordered so you can use quicksort search algoritm and make it more efficent.
On the other hand - if you wold like to find, 5 most likely words (using N-gram method or something) you should consider using Lucene or other text database.
Perhaps using an SQLite database would be more efficient ? I think that's what firefox/thunderbird does for spell checking but I'm not entirely sure.
You won't be able to store that amount of strings in a static variable. Java has a size limit for static code and even method bodies. Simply use a flatfile and read it upon class instanciation - Java is faster than most people think with those things.
See Enum exeeding the 65535 bytes limit of static initializer... what's best to do?.
Hellow Stack Overflow people. I'd like some suggestions regarding the following problem. I am using Java.
I have an array #1 with a number of Strings. For example, two of the strings might be: "An apple fell on Newton's head" and "Apples grow on trees".
On the other side, I have another array #2 with terms like (Fruits => Apple, Orange, Peach; Items => Pen, Book; ...). I'd call this array my "dictionary".
By comparing items from one array to the other, I need to see in which "category" the items from #1 fall into from #2. E.g. Both from #1 would fall under "Fruits".
My most important consideration is speed. I need to do those operations fast. A structure allowing constant time retrieval would be good.
I considered a Hashset with the contains() method, but it doesn't allow substrings. I also tried running regex like (apple|orange|peach|...etc) with case insensitive flag on, but I read that it will not be fast when the terms increase in number (minimum 200 to be expected). Finally, I searched, and am considering using an ArrayList with indexOf() but I don't know about its performance. I also need to know which of the terms actually matched, so in this case, it would be "Apple".
Please provide your views, ideas and suggestions on this problem.
I saw Aho-Corasick algorithm, but the keywords/terms are very likely to change often. So I don't think I can use that. Oh, I'm no expert in text mining and maths, so please elaborate on complex concepts.
Thank you, Stack Overflow people, for your time! :)
If you use a multimap from Google Collections, they have a function to invert the map (so you can start with a map like {"Fruits" => [Apple]}, and produce a map with {"Apple" => ["Fruits"]}. So you can lookup the word and find a list of categories for it, in one call to the map.
I would expect I'd want to split the strings myself and lookup the words in the map one at a time, so that I could do stemming (adjusting for different word endings) and stopword-filtering. Using the map should get good lookup times, plus it's easy to try out.
Would a suffix tree or similar data structure work for your application? It offers O(m) string lookup, where m is the length of the search string, after an O(n2)--or better with some trickery--initial setup, and, with some extra effort, you can associate arbitrary data, such as a reference to a category, with complete words in your dictionary. If you don't want to code it yourself, I believe the BioJava library includes an implementation.
You can also add strings to a suffix tree after initial setup, although the cost will still be around O(n2). That's probably not a big deal if you're adding short words.
If you have only 200 terms to look for, regexps might actually work for you. Of course the regular expression is large, but if you compile it once and just use this compiled Pattern the lookup time is probably linear in the combined length of all the strings in array#1 and I don't see how you can hope for being better than that.
So the algorithm would be: concatenate the words of array#2 which you want to look for into the regular expression, compile it, and then find the matches in array#1 .
(Regular expressions are compiled into a state machine - that is on each character of the string it just does a table lookup for the next state. If the regular expression is complicated you might have backtracking that increases the time, but your regular expression has a very simple structure.)