Is there any difference in performance if I lower case the query before going to use __contains or directly using __icontains. In code:
This
def search(request):
query = (request.GET.get("q")).lower()
if query:
users = User.objects.filter(location__contains=query)
VS
def search(request):
query = request.GET.get("q")
if query:
users = User.objects.filter(location__icontains=query)
I lowercased the location while inserting it into database. And, query is the query which can be in any cases.
Feel free to ask!!!
Usually, the case-insensitive search (or the LIKE operation) is carried out by converting the LHS and RHS into same cases, either to lower case or to upper case.
Something like this,
SELECT *
FROM YourTable
WHERE UPPER(YourColumn) = UPPER('VALUE')
If you are sure that your DB column location only contains lowercase characters, the first option is better.
Note: You may not see any performance difference in small databases(10k entries), but you will see it on bigger DBs.
Related
Basically what I want to do is the Sequelize's equivalent of this question:
More efficient way of querying for this data?
My use case is a bit different from the above question though, much more troublesome. In particular:
Unlike the original question, I use MySQL.
My case could potentially have not just a pair of values, but a set of up to 4 different values (number of values in each set are not fixed), all thanks to my company's immaculate database
The maximum amount of sets is not just limited to ~100 sets. I can see this easily exceeds 2000 sets. (this is my main concern)
This query is a part of a already rather complex function. I tried to trim the thing down as much as possible already, but it still take quite a while to do. This query would be triggered, in my estimation, 5 to 7 times throughout the runtime of the function. I have tried the following:
The conventional way of just stuffing the processed search set inside of [Op.or] would fire up a really long query, which could exceed MySQL's query line limit (I'm not allowed to change this).
Querying item by item is reliable but slower.
The main function right now runs in approximately 1 minute (note that this is me using a smaller set of data for the purpose of testing, actual runtime can easily be 4-5 times this), which I don't think is acceptable as it is called multiple times a day. I also can't heavily modify the database itself, as it is a legacy database which is also used by other applications. If the original database had been designed properly, we wouldn't have gone to this, but alas, I can only try my best.
Any help would be very appreciated.
In MySQL, you can use tuple in WHERE clause and you can fill the missing value with ANY_VALUE(attribute name) to match anything.
SELECT * FROM Employees
WHERE (name, age, dept, salary) IN (
('Alice', 40, ANY_VALUE(dept), ANY_VALUE(salary)),
('Bob', ANY_VALUE(age), 'Tech', 120),
('Mike', 25, 'HR', ANY_VALUE(salary))
)
I tested with 100k data with 1k criterion and the query returns with 2.954s on my laptop.
========================================================
UPDATE
If you always have 4 values and no needs of ANY_VALUE, it can write in Sequelize with least literal.
const criteria = [
['Alice', 40, 'Tech', 120],
['Bob', 30, 'Tech', 120],
['Mike', 25, 'HR', 120]
];
const result = await db.Employee.findAll({
where: Sequelize.where(Sequelize.literal('(name, age, dept, salary)'), Op.in, [criteria])
});
However, in your case, the set doesn't guarantee to have all 4 values, thus needs ANY_VALUE. Unfortunately, I cannot use Sequelize.fn('ANY_VALUE', 'name') in Sequelize.where as it tries to escape it and it cannot be escaped.
Therefore, the 3rd argument for Sequelize.where also need to be replaced with literal. At this point, the code is mostly literal and I don't see any differences to just using Sequelize.query unless if you are using many other options such as offset, limit, attributes... that can still benefit the Sequelize's query generator.
const result = await db.sequelize.query(`
SELECT * FROM Employees WHERE (name, age, dept, salary) IN (${constructedCriteria})`,
{ type: Sequelize.QueryTypes.SELECT } // This will let Sequelize to format the response as in `findAll` function.
);
Some thoughts.
What is the use case of >1k criterion? Are all 1k criterion distinct?
Maybe this scenario is more suited with search engine like ElasticSearch? (if your situation is flexible)
In my application, I have a collection of 50 million data. I am using like search and then count the results on a particular field(i.e Patientfirstname). I also created an index on the Patientfirstname field it improved the performance but still it is taking a lot of time.
db.patients.find({"Patientfirstname":{"$regex":"Testuser"}}).count() without index 40 sec
db.patients.find({"Patientfirstname":{"$regex":"Testuser"}}).count() after adding index on the Patientfirstname field 31 sec
db.patients.find({"Patientfirstname":{"$regex":"Testuser"}}).count()
I tried with a different approach (aggregate) but still, response is very slow
db.patients.aggregate.([{$match:{"Patientfirstname":{"$regex":"Testuser"}}},
{$project:{"Patientfirstname":1,"_id":1}},
{$group : {_id:"$Patientfirstname", count:{$sum:1}}},
{$sort:{"count":-1}} ])
this query also takes the same to time fetch the results 31 sec
another approach was tried but the results are not correct
select only the field from the entire collection and then apply like search and count and result.
db.patients.find({},{Patientfirstname:1,_id:1}).count({"Patientfirstname":{"$regex":"Testuser"}})
applying a filter in the count is not working, entire collection count is displayed
Please help in this query to fetch results faster.Thanks in advance
So here is the deal:
As rightly pointed in the comments, $regex is an operator that would not perform well with or without indexes. Here is the reason why:
Queries without indexes are slow because they executed using COLLSCAN - which is essentially iteration of the whole 50 Million documents on the disk one-by-one, filtering data and returning only the ones that match. Disks being an inherently slow piece of hardware does not help the situation either.
Now, When indexed - MongoDB creates a B-Tree in the RAM. And $regex operator being not very selective in nature, it forces a complete Tree Scan (as compared to a reduced / partial tree scan in case of equalities or ranges) in the index b-tree - which is as bad as a Collection Scan itself. The only reason you get a benefit on 9 seconds is because this Tree Scan occurs in the RAM and not the disk.
Having said that, there are a few alternatives to it:
Optimize your $regex. From the MongoDB Documentation itself:
For case sensitive regular expression queries, if an index exists for the field, then MongoDB matches the regular expression against the values in the index, which can be faster than a collection scan. Further optimization can occur if the regular expression is a "prefix expression", which means that all potential matches start with the same string. This allows MongoDB to construct a "range" from that prefix and only match against those values from the index that fall within that range.
A regular expression is a "prefix expression" if it starts with a caret (^) or a left anchor (\A), followed by a string of simple symbols. For example, the regex /^abc.*/ will be optimized by matching only against the values from the index that start with abc.
Additionally, while /^a/, /^a./, and /^a.$/ match equivalent strings, they have different performance characteristics. All of these expressions use an index if an appropriate index exists; however, /^a./, and /^a.$/ are slower. /^a/ can stop scanning after matching the prefix.
Case insensitive regular expression queries generally cannot use indexes effectively. The $regex implementation is not collation-aware and is unable to utilize case-insensitive indexes.
Create a Text Index - This would tokenize your text string and enable faster text based searches
If you are deployed on MongoDB Atlas - Then you can use Atlas Search which is a Lucene based Text Search Engine (Works almost like elasticsearch on steroids). This offers significantly greater performance and functionalities like fuzzy text search, text automcomplete etc.
I have inherited a slow query in Informix. I suspect part of the slowness is due to the use of subqueries to do left outer joins. Here is a sample of the code:
FROM intide_rec AS IDE
LEFT OUTER JOIN (SELECT idp_cmpy_id, idp_idc_ctl_no, idp_itm_ctl_no, idp_brh, idp_invt_typ, idp_frm, idp_grd, idp_size, idp_fnsh, idp_whs, idp_mill, idp_heat, idp_tag_no, idp_num_size1, idp_num_size2, idp_num_size3, idp_num_size4, idp_num_size5, idp_wdth, idp_lgth, idp_idia, idp_odia, idp_ga_size, idp_ohd_mat_val, idp_ohd_pcs, idp_ohd_wgt, idp_invt_sts, idp_invt_qlty, idp_bgt_for, idp_ownr_id FROM intidp_rec) AS IDP ON (IDE.ide_cmpy_id = IDP.idp_cmpy_id AND IDE.ide_idc_ctl_no = IDP.idp_idc_ctl_no)
LEFT OUTER JOIN (SELECT prm_pep, prm_frm, prm_grd, prm_size, prm_fnsh FROM inrprm_rec) AS PRM ON
(IDP.idp_frm = PRM.prm_frm AND IDP.idp_grd = PRM.prm_grd AND IDP.idp_size = PRM.prm_size AND IDP.idp_fnsh = PRM.prm_fnsh)
Notice that the subqueries are simply retrieving columns. There is no manipulation of the columns. What is odd to me is why there are SELECT statements, i.e. subqueries, here.
Why not just remove the subqueries, move the columns out of the subqueries and into the main SELECT statement since there is no manipulation of columns and write the joins like this:
FROM intide_rec AS IDE
LEFT OUTER JOIN intidp_rec AS IDP ON (IDE.ide_cmpy_id = IDP.idp_cmpy_id AND IDE.ide_idc_ctl_no = IDP.idp_idc_ctl_no)
LEFT OUTER JOIN inrprm_rec AS PRM ON (IDP.idp_frm = PRM.prm_frm AND IDP.idp_grd = PRM.prm_grd AND IDP.idp_size = PRM.prm_size AND IDP.idp_fnsh = PRM.prm_fnsh)
What are your thoughts on the original code and subqueries vs the way I have rewritten the code? Is it inefficient from a performance perspective? Or is it acceptable from a performance perspective?
Thanks for any thoughts.
One way to provide some answer is to analyze the output from SET EXPLAIN ON for the two queries. Ideally, there shouldn't be a difference between the query plans. If the query plans are demonstrably 'the same' or 'equivalent', then the optimizer is doing its stuff well. Determining that they are equivalent may be harder than either of us would like. However, if there is a major difference in the query plans, the subqueries probably are slower and your rewrite should be at least as fast as the original and probably faster. Also, remember that query plans are only indicative of what the optimizer thinks will happen — time the different queries on production data as well.
You don't mention which version of Informix you're using or which platform you're using it on. It probably doesn't matter and it must be a relatively recent version to support the LEFT OUTER JOIN notation (this millennium rather than the last, at any rate). However, it is beneficial to state that. Note that only versions 12.10 and 14.10 are under support unless you've made special arrangements with IBM or HCL.
I am trying to search the results for the negation of particular id in solr. It have found that this can be done in two ways:
(1) fq=userid:(-750376)
(2) fq=-userid:750376
Both are working fine and both are giving correct results. But I can one tell me which is the better way of either two. Which one should I prefer?
You can find out what query the fq parameter's value is parsed into by turning on debugQuery (add the parameter debug=true). Then, in the Solr response, there should be an entry "parsed_filter_queries" under "debug", and the entry should show the string representation of the parsed filter query (or queries) being used.
In your case, both forms of fq should be parsed into the same query, i.e. a boolean query with a single clause stating that the term userid:750376 must not occur. Therefore, which form you use does not matter, at least in terms of correctness or performance.
For us the query looks little different. But for Solr, both are same.
First, Solr parse the query provided by you. Then search for the result. In your case, for both the queries Solr's "parsed_filter_queries" is fq=-userid:750376 only.
fq=userid:(-750376)
fq=-userid:750376
You can check this by enabling debugQuery from Admin window. You can also pass debugQuery=true with query. Hope this will help.
I have a sparql performance issue with DBpedia. I'd like to extract ordered information from DBpedia sparql endpoint page by page. My first example query looked like this:
select distinct ?objProperty ?label where {
?x ?objProperty <http://dbpedia.org/resource/United_States>.
?objProperty a owl:ObjectProperty.
OPTIONAL{?objProperty rdfs:label ?label}
}order by ?label limit 10 offset 3
It was executed about 2s for me on avg(please, if you try it yourself and you see timing less than a second - increment 'offset', because it seems that DBpedia's Virtuoso is caching request results).
However the result returned is not suitable for pagination, because it is a mess of lines with labels from different languages. I want English language for labels and for precise pagination I want exactly 10 different object properties to be returned as a result. Also they have to be ordered by label. Ok. Another try:
select distinct ?objProperty ?label where {
?a ?objProperty <http://dbpedia.org/resource/United_States>.
?objProperty a owl:ObjectProperty.
OPTIONAL{?objProperty rdfs:label ?label}
FILTER ( LANGMATCHES(lang(?label),"EN") || LANG(?label) = "")
}order by ?label limit 10 offset 3
For me this request returned what I expected,.. but it was executed about 7 seconds on avg!!! So sloooow!!! Without order by and langmatch, query works about 1s on avg. Without order by but with langmatch, it takes about 6s, so it seems that langmatch eats ~ 5s on avg for this query.
I do not understand (these are questions by the way):
Am I doing something wrong? :)
Why langmatch slows query SOOO much? I wish langmatch is not regex based? If this performance issue is unavoidable using langmatch, is there a faster way to work with languages? If no, I can't imagine how semantic technologies would conquer the world in nearest future as people expect :))
Is there a better (faster) way to build pagination based requests than using limit/offset? If no, what is the best way to avoid performance issues like mentioned above with limit/offset?
1. Am I doing something wrong? :)
I think there's a slight issue that could make your query a bit faster. You've got the ?label as optional, but I think that the filter will only succeed when ?label is bound, effectively making ?label non-optional. My reasoning is as follows: in the case where ?label is not bound, the expression lang(?label) will be an error (unless an implementation extends lang()), and both langMatches and = expect non-error values, so we'd have this reduction:
langMatches(lang(?label),"en") || lang(?label) = "en"
langMatches(error, "en") || error = "en"
error || error
false
I'm basing this on section 17.2 of the SPARQL 1.1 recommendation, which says:
17.2 Filter Evaluation
Functions invoked with an argument of the wrong type will produce a type error. Effective boolean value arguments (labeled "xsd:boolean
(EBV)" in the operator mapping table below), are coerced to
xsd:boolean using the EBV rules in section 17.2.2.
Apart from BOUND, COALESCE, NOT EXISTS and EXISTS, all functions and operators operate on RDF Terms and will produce a type error if any
arguments are unbound.
Any expression other than logical-or (||) or logical-and (&&) that encounters an error will produce that error.
Based on that, I'd rewrite the query as the following. My impression is that it's a little bit faster, but that might just be confirmation bias. It's not much faster, though.
select distinct ?p ?label where {
?x ?p dbpedia:United_States .
?p a owl:ObjectProperty ;
rdfs:label ?label .
filter( langMatches(lang(?label),"en") || lang(?label) = "" )
}
order by ?label
limit 10
offset 3
SPARQL results
2. Why langmatch slows query SOOO much? I wish langmatch is not regex based? If this performance issue is unavoidable using langmatch, is there a faster way to work with languages?
The public DBpedia SPARQL endpoint can be a bit slow at times, but that doesn't seem to be the issue here. When I run your original query, or the new one above, query, it takes six or seven seconds to get the results. Two things to note though:
langMatch isn't regular expression based. The docs for langMatches say that "Returns true if language-tag (first argument) matches language-range (second argument) per the basic filtering scheme defined in RFC4647 section 3.3.1. language-range is a basic language range per Matching of Language Tags RFC4647 section 2.1. A language-range of "*" matches any non-empty language-tag string." The basic filtering is case insensitive, but it's not regex.
langMatches isn't the only thing that might be causing some slower results. Note that to find the first 10 of something (or, in general, the mth through the _n_th), you have to visit all the elements. You don't have to sort all of them, but you have to visit all of them, which means that there's no way to get just the results from the desired page (unless there's some special indexing going on; keep making this query and maybe it will speed up overtime :)). This leads us into the next point, though.
3. Is there a better (faster) way to build pagination based requests than using limit/offset? If no, what is the best way to avoid performance issues like mentioned above with limit/offset?
While the original and updated queries take six or seven seconds to retrieve the 10 results with limit 10, asking for limit 1000, or limit 5000, also only take about six or seven seconds. Using limit/offset is the correct way to do pagination, but ordering the results can be expensive, since to find the elements in some particular range, you have to look at all the elements (though you don't necessarily have to order all the elements). It probably makes sense, then, to make those pages as big as possible, and to do any presentation paging locally. E.g., instead of running 100 queries for 10 results each (100 queries × 7 seconds = 700 seconds = 11 minutes and 40 seconds), you can run 1 query for 1000 results (1 query × 7 seconds = 7 seconds), and do any important paged presentation locally.
Handling of language filter is up to SPARQL engine. How it stores literals? Whether it can use indexes or another technique to avoid full text scan to get literal for desired language?
You can store literal as "chat"#en string, but selecting all literals for english for a given property would require all property literals scan for #en match.
In some SPARQL engines, you can get actual execution plan. For example, here is the way to do it in Virtuoso: Virtuoso execution plan, however, you can't use it on public endpoint.
Query optimization, execution, query hints are very well documented for RDBMS, you can easily find out what database really does to answer your query and how to modify schema or query to get best results. IMHO, SPARQL engines are not that mature for this.