I was wondering if there is a way to get the functionality of SQL LAG() in my SPARQL query. More specifically, I have a set of states like
<http:\\stateA> p:start "2014-05-23T10:20:13+05:30"^^xsd:dateTime
<http:\\stateB> p:start "2014-06-23T10:20:13+05:30"^^xsd:dateTime
<http:\\stateC> p:start "2014-07-23T10:20:13+05:30"^^xsd:dateTime
And I want to find the duration of each state. The duration of each state, lets say state A, can be computed by subtracting the start of the next state (here it is state B) from the start of state A. So I need a way to find the next state with respect to each state.
These functions aren't yet part of SPARQL, but SEP-002 for proposed SPARQL 1.2 updates includes more XSD functions, including xsd:dateTime - xsd:dateTime. This is likely implemented in many implementations by now, although possibly with some setup required.
I've implemented it in my Ruby implementation.
Related
I require some more advanced MDX knowledge than mine.
I need to get the RepoRate_MAX for repo products, at book and instrument level, but also looking at the Java code I'm replacing that code always uses the max MurexId.
How can I perform the below (I've placed MAX in here on the dimension but this is wrong) and I need the combo of the dimensions and also the MAX MurexId:
[Measures].[RepoRate_VAL] = (([Deal].[ProductType].&[REPO],[Deal].[Book],[Deal].[Instrument],MAX([Deal].[MurexId])),[Measures].[RepoRate_MAX])
I'm sure it's a simple one but my mind is part way between the Java OO and MDX worlds currently haha :D
Thanks
Leigh
So after some experimenting I found out about the TAIL and Item MDX functions.
I think at one point I did get it working, but didn't make a note of what did work. I was playing around with this and variants of it..but most versions ended up in unusable query times:
[Measures].[RepoRate_VAL] = (([Deal].[ProductType].&[REPO],[Deal].[Book],[Deal].[Instrument],TAIL(EXISTING([Deal].[MurexId].[MurexId])).Item(0)),[Measures].[RepoRate_MAX])
So I then decided to push the RepoRate calculation back to the SQL data preparation script. Cleaner/smoother data is always better and then to have simple calculated members.
I used SQL to determine the RepoRate from tradelevel with MAX(MurexId) and GROUP BY on Book, Instrument to then update my main fact table to ensure that the correct RepoRate was set at Book, Instrument level.
Thus the calculated member is then:
[Measures].[RepoRate_VAL] = (([Deal].[Book],[Deal].[Instrument]),[Measures].[RepoRate_MAX])
Fast data prep and a fast calculated member on the Excel/Pivot/UI layer.
I need to group GTFS trips to human understandable "route variants". As one route can have run different trips based on day/time etc.
Is there any preferred way to group similar trips? Trip shape_id looks promising, but is there any guarantee that all similar trips has same shape_id?
My GTFS data is imported my sql database and the database structure is the same as GTFS txt files.
UPDATE
Im not looking sql query example, im looking high level example how to group similar trips to user friendly "route variants".
Many route planning apps (like Moovit) use GTFS data as source and they display different route variants to users.
There is no official way to do this. The best way is probably to group by the ordered list of stops on each trip, sometimes known as the "stopping pattern" of the trip. The idea is discussed at a conceptual level here by Mapzen.
In practice, I have created concatenated strings of all stops on a given trip (from stop_times), and grouped by that to define similar trips. E.g., if the stops on a given trip are A, B, C, D, and E, create a string A-B-C-D-E or A_B_C_D_E and group trips on that string. This functionality is not part of the SQL spec, although MySQL implements it as GROUP_CONCAT and PostgreSQL uses arrays and array_to_string. You may also want to add route_id and shape_id into the grouping as well, to handle some corner cases.
I am using log4javascript and want to be able to apply a Level to a group.
I currently have a PopupAppender that has a threshold of WARN. However, it is triggered whenever I create a group.
I am looking for a group function that takes a Level parameter like the time function does.
Is this implemented?
No, groups don't have a level. I may look into it for log4javascript 2.0.
We're developing an application based on neo4j and php with about 200k nodes, which every node has a property like type='user' or type='company' to denote a specific entity of our application. We need to get the count of all nodes of a specific type in the graph.
We created an index for every entity like users, companies which holds the nodes of that property. So inside users index resides 130K nodes, and the rest on companies.
With Cypher we quering like this.
START u=node:users('id:*')
RETURN count(u)
And the results are
Returned 1 row.Query took 4080ms
The Server is configured as default with a little tweaks, but 4 sec is too for our needs. Think that the database will grow in 1 month 20K, so we need this query performs very very much.
Is there any other way to do this, maybe with Gremlin, or with some other server plugin?
I'll cache those results, but I want to know if is possible to tweak this.
Thanks a lot and sorry for my poor english.
Finaly, using Gremlin instead of Cypher, I found the solution.
g.getRawGraph().index().forNodes('NAME_OF_USERS_INDEX').query(
new org.neo4j.index.lucene.QueryContext('*')
).size()
This method uses the lucene index to get "aproximate" rows.
Thanks again to all.
Mmh,
this is really about the performance of that Lucene index. If you just need this single query most of the time, why not update an integer with the total count on some node somewhere, and maybe update that together with the index insertions, for good measure run an update with the query above every night on it?
You could instead keep a property on a specific node up to date with the number of such nodes, where updates are done guarded by write locks:
Transaction tx = db.beginTx();
try {
...
...
tx.acquireWriteLock( countingNode );
countingNode.setProperty( "user_count",
((Integer)countingNode.getProperty( "user_count" ))+1 );
tx.success();
} finally {
tx.finish();
}
If you want the best performance, don't model your entity categories as properties on the node. In stead, do it like this :
company1-[:IS_ENTITY]->companyentity
Or if you are using 2.0
company1:COMPANY
The second would also allow you automatically update your index in a separate background thread by the way, imo one of the best new features of 2.0
The first method should also proof more efficient, since making a "hop" in general takes less time than reading a property from a node. It does however require you to create a separate index for the entities.
Your queries would look like this :
v2.0
MATCH company:COMPANY
RETURN count(company)
v1.9
START entity=node:entityindex(value='company')
MATCH company-[:IS_ENTITIY]->entity
RETURN count(company)
What I want to do is implement submission scoring for a site with users voting on the content, much like in e.g. reddit (see the 'hot' function in http://code.reddit.com/browser/sql/functions.sql). Edit: Ultimately I want to be able to retrieve an arbitrarily filtered list of arbitrary length of submissions ranked according to their score.
My submission model currently keeps track of up and down vote totals. Currently, when a user votes I create and save a related Vote object and then use F() expressions to update the Submission object's voting totals. The problem is that I want to update the score for the submission at the same time, but F() expressions are limited to only simple operations (it's missing support for log(), date_part(), sign() etc.)
From my limited experience with Django I can see 5 options here:
extend F() somehow (haven't looked at the code yet) to support the missing SQL functions; this is my preferred option and seems to fit within the Django framework the best
define a scoring function (much like reddit's 'hot' function) in my database, and have Django use the value of that function for the value of the score field; as far as I can tell, #2 is not possible
wrap my two step voting process in a suitably isolated transaction so that I can calculate the voting totals in Python and then update the Submission's voting totals without fear that another vote against the submission could be added/changed in the meantime; I'm hesitant to take this route because it seems overly complex - what is a "suitably isolated transaction" in this case anyway?
use raw SQL; I would prefer to avoid this entirely -- what's the point of an ORM if I have to revert to SQL for such a common use case as this! (Note that this coming from somebody who loves sprocs, but is using Django for ease of development.)
(edit: added this after further discussion) compute the score using an extra select parameter containing a call to my function; this would work but impose unnecessary load on the DB (would be forced to calculate the score for every submission ever made every time the query ran; caching could help here, but it still seems like a bit of lame workaround)
Before I embark on this mission to extend F() (which I'm not sure is even possible), am I about to reinvent the wheel? Is there a more standard way to do this? It seems like such a common use case and yet in an hour of searching I have yet to find a common solution...
EDIT: There is another option: set the default value of the field in the database script to be an expression containing my function. This is not as flexible as #1, but probably the quickest and cleanest approach to solving the problem (although my initial investigation into extending F() looks promising).
Why can't you just denormalize the score and reconstruct it with the Vote objects every once and a while?
If you can't do that, it is very easy to make a 'property' function that acts as an object attribute for scoring.
#property
def score(self):
... calculate score from Vote objects ...
return score
I've never used F() on a property like this, but it's Python, so I bet it works.
If you are using django-voting (which I recommend), you can put #3 in the manager's record_vote function since that's how all vote transactions take place.