One is able to group the number of page views by country, per this documentation. PFB KQL query.
pageViews | project client_CountryOrRegion, itemCount, client_City
However, visualizing by client_City doesn't work.
Is there a way one could group and visualize by the name of the city instead?
Workbooks itself doesn't currently have any built in mapping of city to lat/long, we only have that at the country/region level. (In the screenshots above, you told workbooks that a column of data has country information, but then you passed it cities, so it doesn't know of any countries named those things)
there are various ways to do it by using the externaldata operator in ADX/Log Analytics to have the database load, parse, and join with your other data. If you can get to lat/long, then you can tell workbooks to use that mode where you tell it which columns are lat and long and you'd have your points.
not the exact files you want, but in another example someone wanted to map from ip address to country, and in that example you'd add something like this to your query:
let geoData = externaldata
(network:string,geoname_id:string,continent_code:string,continent_name:string,
country_iso_code:string,country_name:string,is_anonymous_proxy:string,is_satellite_provider:string)
[#"https://raw.githubusercontent.com/datasets/geoip2-ipv4/master/data/geoip2-ipv4.csv"] with (ignoreFirstRecord=true, format="csv");
geoData
| limit 10
in your real query you'd not have the limit, you'd use the kql join operator to do an intersection and you'd get your lat/long that way.
Related
I'm trying to pull historical pricing data from CoinGecko's free API to use in a Google Sheet. It presents OHLC numbers in the following format:
[
[1589155200000,0.05129,0.05129,0.047632,0.047632],
[1589500800000,0.047784,0.052329,0.047784,0.052329],
[1589846400000,0.049656,0.053302,0.049656,0.053302],
...
]
As you can see, this isn't typical JSON format since there are no property names. So that everyone is on the same page, for this data the properties of each subarray in order are Time (in UNIX epoch format), Open Price, High Price, Low Price, and Close Price.
I'm using the ImportJSON code found here to try and pull this data, but it does not work. Instead of putting each subarray into a separate row, split into columns for the 5 properties, it prints everything out into a single cell like so:
1589155200000,0.05129,0.05129,0.047632,0.047632,1589500800000,0.047784,0.052329,0.047784,0.052329,15898 6400000,0.049656,0.053302,0.049656,0.053302,...
This is incredibly unhelpful. I'm trying to avoid using a paid API add-on since I really don't want to have to pay the frankly exorbitant fees they want to charge, but I can't figure out a way to get ImportJSON to play nicely with this data. Does anyone know of a solution?
It's simplier : your datas are in an array structure : I put
[
[1589155200000,0.05129,0.05129,0.047632,0.047632],
[1589500800000,0.047784,0.052329,0.047784,0.052329],
[1589846400000,0.049656,0.053302,0.049656,0.053302]
]
in A1, and I get the individual values by this simplier way :
function myArray(){
var f = SpreadsheetApp.getActiveSheet();
var result = eval(f.getRange('A1').getValue());
f.getRange(2,1,result.length,result[0].length).setValues(result)
}
I have 2 column in my table setting
with the following values
KEY VALUE
company ABC
phone 14344
address Somerset City
I need to display this like a single record or a flatten
array in the view/blade page
something like
{{$sett->company}}
{{$sett->phone}}
or an array with lookup
{{$myarray('company')}}
{{$myarray('phone')}}
The idea is if I add another settings like contact us email address
for my website I don't want to add another column.
I know this is achievable in controller by creating different variable
and executing different query but I'm kind of looking for some options here.
Thanks for the help really appreciated.
You can use $settings->pluck('value', 'key') to get your result. Read more here: https://laravel.com/docs/5.4/collections#method-pluck
I am working currently on telecom analytics project and newbie in query optimisation. To show result in browser it takes a full minute while just 45,000 records are to be accessed. Could you please suggest on ways to reduce time for showing results.
I wrote following query to find call-duration of a person of age-group:
sigma=0
popn=len(Demo.objects.filter(age_group=age))
card_list=[Demo.objects.filter(age_group=age)[i].card_no
for i in range(popn)]
for card in card_list:
dic=Fact_table.objects.filter(card_no=card.aggregate(Sum('duration'))
sigma+=dic['duration__sum']
avgDur=sigma/popn
Above code is within for loop to iterate over age-groups.
Model is as follows:
class Demo(models.Model):
card_no=models.CharField(max_length=20,primary_key=True)
gender=models.IntegerField()
age=models.IntegerField()
age_group=models.IntegerField()
class Fact_table(models.Model):
pri_key=models.BigIntegerField(primary_key=True)
card_no=models.CharField(max_length=20)
duration=models.IntegerField()
time_8bit=models.CharField(max_length=8)
time_of_day=models.IntegerField()
isBusinessHr=models.IntegerField()
Day_of_week=models.IntegerField()
Day=models.IntegerField()
Thanks
Try that:
sigma=0
demo_by_age = Demo.objects.filter(age_group=age);
popn=demo_by_age.count() #One
card_list = demo_by_age.values_list('card_no', flat=True) # Two
dic = Fact_table.objects.filter(card_no__in=card_list).aggregate(Sum('duration') #Three
sigma = dic['duration__sum']
avgDur=sigma/popn
A statement like card_list=[Demo.objects.filter(age_group=age)[i].card_no for i in range(popn)] will generate popn seperate queries and database hits. The query in the for-loop will also hit the database popn times. As a general rule, you should try to minimize the amount of queries you use, and you should only select the records you need.
With a few adjustments to your code this can be done in just one query.
There's generally no need to manually specify a primary_key, and in all but some very specific cases it's even better not to define any. Django automatically adds an indexed, auto-incremental primary key field. If you need the card_no field as a unique field, and you need to find rows based on this field, use this:
class Demo(models.Model):
card_no = models.SlugField(max_length=20, unique=True)
...
SlugField automatically adds a database index to the column, essentially making selections by this field as fast as when it is a primary key. This still allows other ways to access the table, e.g. foreign keys (as I'll explain in my next point), to use the (slightly) faster integer field specified by Django, and will ease the use of the model in Django.
If you need to relate an object to an object in another table, use models.ForeignKey. Django gives you a whole set of new functionality that not only makes it easier to use the models, it also makes a lot of queries faster by using JOIN clauses in the SQL query. So for you example:
class Fact_table(models.Model):
card = models.ForeignKey(Demo, related_name='facts')
...
The related_name fields allows you to access all Fact_table objects related to a Demo instance by using instance.facts in Django. (See https://docs.djangoproject.com/en/dev/ref/models/fields/#module-django.db.models.fields.related)
With these two changes, your query (including the loop over the different age_groups) can be changed into a blazing-fast one-hit query giving you the average duration of calls made by each age_group:
age_groups = Demo.objects.values('age_group').annotate(duration_avg=Avg('facts__duration'))
for group in age_groups:
print "Age group: %s - Average duration: %s" % group['age_group'], group['duration_avg']
.values('age_group') selects just the age_group field from the Demo's database table. .annotate(duration_avg=Avg('facts__duration')) takes every unique result from values (thus each unique age_group), and for each unique result will fetch all Fact_table objects related to any Demo object within that age_group, and calculate the average of all the duration fields - all in a single query.
I'm using an MDX query to pull information to support a set of reports. A high degree of detail is required for the reports so they take some time to generate. To speed up the access time we pull the data we need and store it in a flat Oracle table and then connect to the table in Excel. This makes the reports refresh in seconds instead of minutes.
Previously the MDX was generated and run by department for 100 departments and then for a number of other filters. All this was done in VB.Net. The requirements for filters have grown to the point where this method is not sustainable (and probably isn't the best approach regardless).
I've built the entire dataset into one MDX query that works perfectly. One of my sets that I cross join includes members from three different levels of hierarchy, it looks like this:
(
Descendants([Merch].[Merch CHQ].[All], 2),
Descendants([Merch].[Merch CHQ].[All], 3),
[Merch].[Merch CHQ].[Department].&[1].Children
)
The problem for me is in our hierarchy (which I can't change), each group (first item) and each department (second item) have the same structure to their naming, ie 15-DeptName and it's confusing to work with.
To address it I added a member:
MEMBER
[Measures].[Merch Level] AS
(
[Merch].[Merch CHQ].CurrentMember.Level.Name
)
Which returns what type the member is and it works perfectly.
The problem is that it updates for every member so none of the rows get filtered by NON BLANK, instead of 65k rows I have 130k rows which will hurt my access performance.
Can my query be altered to still filter out the non blanks short of using IIF to check each measurement for null?
You can specify Null for your member based on your main measure like:
MEMBER
[Measures].[Merch Level] AS
IIf(IsEmpty([Measures].[Normal Measure]),null,[Merch].[Merch CHQ].CurrentMember.Level.Name)
That way it will only generate when there is data. You can go further and add additional dimensions to the empty check if you need to get more precise.
For example you make a search for a hotel in London and get 250 hotels out of which 25 hotels are shown on first page. On each page user has an option to sort the hotels based on price, name, user-reviews etc. Now the intelligent thing to do will be to only get the first 25 hotels on the first page from the database. When user moves to page 2, make another database query for next 25 hotels and keep the previous results in cache.
Now consider this, user is on page 1 and sees 25 hotels sorted by price and now he sorts them based on user-ratings, in this case, we should keep the hotels we already got in cache and only request for additional hotels. How is that implemented? Is there something built in any language (preferably php) or we have to implement it from scratch using multiple queries?
This is usually done as follows:
The query is executed with order by the required field, and with a top (in some databases limit) set to (page_index + 1) * entries_per_page results. The query returns a random-access rowset (you might also hear of this referred to as a resultset or a recordset depending on the database library you are using) which supports methods such as MoveTo( row_index ) and MoveNext(). So, we execute MoveTo( page_index * entries_per_page ) and then we read and display entries_per_page results. The rowset generally also offers a Count property which we invoke to get the total number of rows that would be fetched by the query if we ever let it run to the end (which of course we don't) so that we can compute and show the user how many pages exist.