I am using Elastic Search with Titan. How can I do pagination in ES with titan?
I saw THIS and so was trying this:
Iterable<Result<Vertex>> vertices = g.indexQuery("search","v.testTitle:(mytext)")
.addParameter(new Parameter("from", 0))
.addParameter(new Parameter("size", 2)).vertices();
for (Result<Vertex> result : vertices) {
Vertex tv = result.getElement();
System.out.println(tv.getProperty("testTitle")+ ": " + result.getScore());
}
The thing is it return all 4-5 records not in the size of 2
parameters are not yet supported. The method only exists for future implementations.
However, you can currently limit your result. The following code should work:
Iterable<Result<Vertex>> vertices = g.indexQuery("search","v.testTitle:(mytext)")
.limit(2).vertices();
for (Result<Vertex> result : vertices) {
Vertex tv = result.getElement();
System.out.println(tv.getProperty("testTitle")+ ": " + result.getScore());
}
...but you can't specify an offset.
Cheers,
Daniel
I don know anyrthing about titan.But for implementing pagination concept in Elasticsearch ,you can use scroll concept.It will help a lot and Its like db cursor.. it reduces CPU usage a lot.
Refer http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-request-scroll.html
Related
The following query can't run on a dataset with ~2M nodes. What should i do to make it run faster?
MATCH (cc:ConComp)-[r1:IN_CONCOMP]-(p1:Person)-[r2:SAME_CLUSTER]-(p2:Person)
WHERE cc.cluster_type = "household"
MERGE (cluster:Cluster {CLUSTER_TMP_ID:cc.CONCOMP_ID + '|' + r2.root_id, cluster_type:cc.cluster_type })
MERGE (cluster)-[r3:IN_CLUSTER]-(p1)
A number of suggestions:
adding directions to your relationships will decrease the number of paths in the MATCH
make sure that you have indexes on all properties that you MERGE on
in the second MERGE , also add direction.
I finally found a solution by using the following query (and by indexing cc.cluster_type and cc.CONCOMP_ID):
CALL apoc.periodic.iterate('MATCH (cc:ConComp)<-[r1:IN_CONCOMP]-(p1:Person)-[r2:SAME_CLUSTER]-(p2:Person) WHERE cc.cluster_type = "household" WITH DISTINCT cc.CONCOMP_ID + "|" + r2.root_id as id_name, cc.cluster_type as cluster_type_name, p1 RETURN id_name, cluster_type_name, p1', '
MERGE (cluster:Cluster {CLUSTER_TMP_ID: id_name, cluster_type: cluster_type_name})
MERGE (cluster)-[r3:IN_CLUSTER]->(p1)', {batchSize:10000, parallel:false})
I precise that I had previously ran my initial question query with apoc.periodic.iterate without success.
When benchmarking Apache Lucene v7.5 I noticed a strange behavior:
I indexed the English Wikipedia dump (5,677,776 docs) using Lucene with the SimpleAnalyzer (No stopwords, no stemming)
Then I searched the index with the following queries:
the totalHits=5,382,873
who totalHits=1,687,254
the who totalHits=5,411,305
"the who" totalHits=8,827
The result number for the Boolean query the who is both larger than the result number for the single term the and the result number for the single term who, when it should be smaller than both.
Is there an explanation for that?
Code snippet:
analyzer = new SimpleAnalyzer();
MultiFieldQueryParser parser = new MultiFieldQueryParser(new String[]{"title", "content","domain","url"},analyzer);
// Parse
Query q = parser.parse(querystr);
// top-10 results
int hitsPerPage = 10;
IndexReader indexReader = DirectoryReader.open(index);
IndexSearcher searcher = new IndexSearcher(indexReader);
// Ranker
TopScoreDocCollector collector = TopScoreDocCollector.create(hitsPerPage);
// Search
searcher.search(q, collector);
// Retrieve the top-10 documents
TopDocs topDocs=collector.topDocs();
ScoreDoc[] hits = topDocs.scoreDocs;
totalHits=topDocs.totalHits;
System.out.println("query: "+querystr + " " + hits.length+" "+String.format("%,d",totalHits));
The explanation is that the default operator is OR and not AND as you assume. Searching for the who returns documents that have either the or who or both.
the - 5,382,873
who - 1,687,254
the OR who - 5,411,305
I.e. most documents that contain who also contains the, except for 28 432 documents which are added to the result set when you retrieve both.
You can change this behavior by changing the default operator:
parser.setDefaultOperator(QueryParserBase.AND_OPERATOR)
I am new to Azure Search so I just want to run this by before I try to implement it. We have a search setup on items and we want to score/rank the results based on its initial score and how many times the item has been used/downloaded. We want the items downloaded the most to appear at the top of the result list.
We have a separate field in the search index that contains the used/download count (itemCount).
I know I have to set up a Magnitude profile but I am not sure what to use for the range as the itemCount can contain 0 - N So do I just set the range to be some large number i.e. 100,000,000 or what is the best practice?
var functionRankByDownload = new MagnitudeFunction()
{
Boost = 1000,
BoostingRangeStart = 0,
BoostingRangeEnd = 100000000,
ConstantBoostBeyondRange = true,
FieldName = "itemCount",
Interpolation = InterpolationTypes.Linear
};
scoringProfile1.Functions = new List() { functionRankByDownload };
I found the score calculation is as follows:
((initialScore * boost * itemCount) - min) / (max-min)
So it seems like it should work ok having a large value for the max but again just wanting to know the best practice.
Thanks!
That seems reasonable. The BoostingRangeEnd can be any reasonable bound to your range depending on the scenario. Since, you are using ConstantBoostBeyondRange, it would also take care of boosting values outside ranges appropriately.
You might also want to experiment with the boost value for a large range like this and see if a bigger boost value is more helpful for your scenario.
i am a beginner & developing my very first project with lucene.net i.e. an address search utility, lucene.net 3.0.3
using standard analyzer, query parser, (suppose i have a single field, Stored & Analyzed as well)
- sample data : (every row is a document with a single field)
(Postcode and street column concatenated)
UB6 9AH Greenford Road something
UB6 9AP Greenford Road something
UB1 3EB Greenford Road something
PR8 3JT Greenford Road something
HA1 3QD something Greenford Road
SM1 1JY something Greenford Road something
Searching
StringBuilder customQuery = new StringBuilder();
customQuery.Append(_searchFieldName + ":\"" + searchTerm + "\"^" + (wordsCount));
// this is for phrase matching
foreach (var word in words.Where(word => !string.IsNullOrEmpty(word)))
{
customQuery.Append(" +" + _searchFieldName + ":" + word + "*");
}
// this is prefix match for each word
Query query = _parser.Parse(customQuery.ToString());
_searcher.Search(query, collector);
all above (searching) working fine
Question
if i search for "Greenford road" ,
i may want that row that has 'SM1' should come up (means i want to priorities result as per postcode)
i have tested Query-Time-Boost and it works fine
but i may have a long list of priority postcodes sometimes (so i don't want to loop over each postcode and set its priority at query time
I WANT DOCUMENT TIME BOOSTING
but whatever document boost i set (at the time of indexing), it doesn't effect my search results
doc.Add(new Field(SearchFieldName, SearchField, Field.Store.YES, Field.Index.ANALYZED));
if (condition == true)
{
doc.Boost = 2; // or 5 or 200 etc (nothing works)
}
please HELP
i tried to understand similarity and scoring, but its too much mathematics there...
please help....
I recently had this problem myself and I think it might be due to wildcard queries (It was in my case at least). There is another post here that explains the issue better, and provides a possible solution:
Lucene .net Boost not working when using * wildcard
Imagine the following problem:
You have a database containing about 20,000 texts in a table called "articles"
You want to connect the related ones using a clustering algorithm in order to display related articles together
The algorithm should do flat clustering (not hierarchical)
The related articles should be inserted into the table "related"
The clustering algorithm should decide whether two or more articles are related or not based on the texts
I want to code in PHP but examples with pseudo code or other programming languages are ok, too
I've coded a first draft with a function check() which gives "true" if the two input articles are related and "false" if not. The rest of the code (selecting the articles from the database, selecting articles to compare with, inserting the related ones) is complete, too. Maybe you can improve the rest, too. But the main point which is important to me is the function check(). So it would be great if you could post some improvements or completely different approaches.
APPROACH 1
<?php
$zeit = time();
function check($str1, $str2){
$minprozent = 60;
similar_text($str1, $str2, $prozent);
$prozent = sprintf("%01.2f", $prozent);
if ($prozent > $minprozent) {
return TRUE;
}
else {
return FALSE;
}
}
$sql1 = "SELECT id, text FROM articles ORDER BY RAND() LIMIT 0, 20";
$sql2 = mysql_query($sql1);
while ($sql3 = mysql_fetch_assoc($sql2)) {
$rel1 = "SELECT id, text, MATCH (text) AGAINST ('".$sql3['text']."') AS score FROM articles WHERE MATCH (text) AGAINST ('".$sql3['text']."') AND id NOT LIKE ".$sql3['id']." LIMIT 0, 20";
$rel2 = mysql_query($rel1);
$rel2a = mysql_num_rows($rel2);
if ($rel2a > 0) {
while ($rel3 = mysql_fetch_assoc($rel2)) {
if (check($sql3['text'], $rel3['text']) == TRUE) {
$id_a = $sql3['id'];
$id_b = $rel3['id'];
$rein1 = "INSERT INTO related (article1, article2) VALUES ('".$id_a."', '".$id_b."')";
$rein2 = mysql_query($rein1);
$rein3 = "INSERT INTO related (article1, article2) VALUES ('".$id_b."', '".$id_a."')";
$rein4 = mysql_query($rein3);
}
}
}
}
?>
APPROACH 2 [only check()]
<?php
function square($number) {
$square = pow($number, 2);
return $square;
}
function check($text1, $text2) {
$words_sub = text_splitter($text2); // splits the text into single words
$words = text_splitter($text1); // splits the text into single words
// document 1 start
$document1 = array();
foreach ($words as $word) {
if (in_array($word, $words)) {
if (isset($document1[$word])) { $document1[$word]++; } else { $document1[$word] = 1; }
}
}
$rating1 = 0;
foreach ($document1 as $temp) {
$rating1 = $rating1+square($temp);
}
$rating1 = sqrt($rating1);
// document 1 end
// document 2 start
$document2 = array();
foreach ($words_sub as $word_sub) {
if (in_array($word_sub, $words)) {
if (isset($document2[$word_sub])) { $document2[$word_sub]++; } else { $document2[$word_sub] = 1; }
}
}
$rating2 = 0;
foreach ($document2 as $temp) {
$rating2 = $rating2+square($temp);
}
$rating2 = sqrt($rating2);
// document 2 end
$skalarprodukt = 0;
for ($m=0; $m<count($words)-1; $m++) {
$skalarprodukt = $skalarprodukt+(array_shift($document1)*array_shift($document2));
}
if (($rating1*$rating2) == 0) { continue; }
$kosinusmass = $skalarprodukt/($rating1*$rating2);
if ($kosinusmass < 0.7) {
return FALSE;
}
else {
return TRUE;
}
}
?>
I would also like to say that I know that there are lots of algorithms for clustering but on every site there is only the mathematical description which is a bit difficult to understand for me. So coding examples in (pseudo) code would be great.
I hope you can help me. Thanks in advance!
The most standard way I know of to do this on text data like you have, is to use the 'bag of words' technique.
First, create a 'histogram' of words for each article. Lets say between all your articles, you only have 500 unique words between them. Then this histogram is going to be a vector(Array, List, Whatever) of size 500, where the data is the number of times each word appears in the article. So if the first spot in the vector represented the word 'asked', and that word appeared 5 times in the article, vector[0] would be 5:
for word in article.text
article.histogram[indexLookup[word]]++
Now, to compare any two articles, it is pretty straightforward. We simply multiply the two vectors:
def check(articleA, articleB)
rtn = 0
for a,b in zip(articleA.histogram, articleB.histogram)
rtn += a*b
return rtn > threshold
(Sorry for using python instead of PHP, my PHP is rusty and the use of zip makes that bit easier)
This is the basic idea. Notice the threshold value is semi-arbitrary; you'll probably want to find a good way to normalize the dot product of your histograms (this will almost have to factor in the article length somewhere) and decide what you consider 'related'.
Also, you should not just put every word into your histogram. You'll, in general, want to include the ones that are used semi-frequently: Not in every article nor in only one article. This saves you a bit of overhead on your histogram, and increases the value of your relations.
By the way, this technique is described in more detail here
Maybe clustering is the wrong strategy here?
If you want to display similar articles, use similarity search instead.
For text articles, this is well understood. Just insert your articles in a text search database like Lucene, and use your current article as search query. In Lucene, there exists a query called MoreLikeThis that performs exactly this: find similar articles.
Clustering is the wrong tool, because (in particular with your requirements), every article must be put into some cluster; and the related items would be the same for every object in the cluster. If there are outliers in the database - a very likely case - they could ruin your clustering. Furthermore, clusters may be very big. There is no size constraint, the clustering algorithm may decide to put half of your data set into the same cluster. So you have 10000 related articles for each article in your database. With similarity search, you can just get the top-10 similar items for each document!
Last but not least: forget PHP for clustering. It's not designed for this, and not performant enough. But you can probably access a lucene index from PHP well enough.
I believe you need to make some design decisions about clustering, and continue from there:
Why are you clustering texts? Do you want to display related documents together? Do you want to explore your document corpus via clusters?
As a result, do you want flat or hierarchical clustering?
Now we have the complexity issue, in two dimensions: first, the number and type of features you create from the text - individual words may number in the tens of thousands. You may want to try some feature selection - such as taking the N most informative words, or the N words appearing the most times, after ignoring stop words.
Second, you want to minimize the number of times you measure similarity between documents. As bubaker correctly points out, checking similarity between all pairs of documents may be too much. If clustering into a small number of clusters is enough, you may consider K-means clustering, which is basically: choose an initial K documents as cluster centers, assign every document to the closest cluster, recalculate cluster centers by finding document vector means, and iterate. This only costs K*number of documents per iteration. I believe there are also heuristics for reducing the needed number of computations for hierarchical clustering as well.
What does the similar_text function called in Approach #1 look like? I think what you're referring to isn't clustering, but a similarity metric. I can't really improve on the White Walloun's :-) histogram approach - an interesting problem to do some reading on.
However you implement check(), you've got to use it to make at least 200M comparisons (half of 20000^2). The cutoff for "related" articles may limit what you store in the database, but seems too arbitrary to catch all useful clustering of texts,
My approach would be to modify check() to return the "similarity" metric ($prozent or rtn). Write the 20K x 20K matrix to a file and use an external program to perform a clustering to identify nearest neighbors for each article, which you could load into the related table. I would do the clustering in R - there's a nice tutorial for clustering data in a file running R from php.