Browsing through examples all over the web, I can see that people generate the path using something like "parent_id.node_id". Examples:-
uid | name | tree_id
--------------------
1 | Ali | 1.
2 | Abu | 2.
3 | Ita | 1.3.
4 | Ira | 1.3.
5 | Yui | 1.3.4
But as explained in this question - Sorting tree with a materialized path?, using zero padding to the tree_id make it easy to sort it by the creation order.
uid | name | tree_id
--------------------
1 | Ali | 0001.
2 | Abu | 0002.
3 | Ita | 0001.0003.
4 | Ira | 0001.0003.
5 | Yui | 0001.0003.0004
Using fix length string like this also make it easy for me to calculate the level - length(tree_id)/5. What I'm worried is it would limit me to maximum 9999 users rather than 9999 per branch. Am I right here ?
9999 | Tar | 0001.9999
10000 | Tor | 0001.??
You are correct -- zero-padding each node ID would allow you to sort the entire tree quite simply. However, you have to make the padding width match the upper limit of digits of the ID field, as you have pointed out in your last example. E.g., if you're using an int unsigned field for your ID, the highest value would be 4,294,967,295. This is ten digits, meaning that the record set from your last example might look like:
uid | name | tree_id
9999 | Tar | 0000000001.0000009999
10000 | Tor | 0000000001.0000010000
As long as you know you're not going to need to change your ID field to bigint unsigned in the future, this will continue work, though it might be a bit data-hungry depending on how huge your tables get. You could shave off two bytes per node ID by storing the values in hexadecimal, which would still be sorted correctly in a string sort:
uid | name | tree_id
9999 | Tar | 00000001.0000270F
10000 | Tor | 00000001.00002710
I can imagine this would make things a real headache when trying to update the paths (pruning nodes, etc) though.
You can also create extra fields for sorting, e.g.:
uid | name | tree_id | name_sort
9999 | Tar | 00000001.0000270F | Ali.Tar
10000 | Tor | 00000001.00002710 | Ali.Tor
There are limitations, however, as laid out by this guy's answer to a similar materialized path sorting question. The name field would have to be padded to a set length (fortunately, in your example, each name seems to be three characters long), and it would take up a lot of space.
In conclusion, given the above issues, I've found that the most versatile way to do sorting like this is to simply do it in your application logic -- say, using a recursive function that builds a nested array, sorting the children of each node as it goes.
Related
I have a column in Google Sheets where each cell contains pre-defined logic. For example, something like the second column in this table:
| 1 | =A1*-1 |
| 2 | =B2*-1 |
| -3 | =C2*-1 |
Let's say later I want to add the same logic to each cell in column B. For example, make it such that it looks like:
| 1 | =MAX(A1*-1,0) |
| 2 | =MAX(B2*-1,0) |
| -3 | =MAX(C2*-1,0) |
What is the fastest way to do this, besides manually typing MAX(...,0) in each cell? Normal Sheets functions act on the value of the cell, not the logic, so I'm a bit lost.
To my knowledge there isn't a function that pipes in the logic from one cell to another ...
try:
=ARRAYFORMULA(IF(A1:A="",,IF(SIGN(A1:A)<0, A1:A*-1, 0)))
=ARRAYFORMULA(IF(A1:A="",,IF(SIGN(A1:A)>0, A1:A, 0)))
I have a group of people who started receiving a specific type of social benefit called benefitA, I am interested in knowing what(if any) social benefits the people in the group might have received immediately before they started receiving BenefitA.
My optimal result would be a table with the number people who was receiving respectively BenefitB, BenefitC and not receiving any benefit “BenefitNon” immediately before they started receiving BenefitA.
My data is organized as a relation database with a Facttabel containing an ID for each person in my data and several dimension tables connected to the facttabel. The important ones here at DimDreamYdelse(showing type of benefit received), DimDreamTid(showing week and year). Here is an example of the raw data.
Data Example
I'm not sure how to approach this in PowerBi as I am fairly new to this program. Any advice is most welcome.
I have tried to solve the problem in SQL but as I need this as part of a running report i need to do it in PowerBi. This bit of code might however give some context to what I want to do.
USE FLISDATA_Beskaeftigelse;
SELECT dbo.FactDream.DimDreamTid , dbo.FactDream.DimDreamBenefit , dbo.DimDreamTid.Aar, dbo.DimDreamTid.UgeIAar, dbo.DimDreamBenefit.Benefit,
FROM dbo.FactDream INNER JOIN
dbo.DimDreamTid ON dbo.FactDream.DimDreamTid = dbo.DimDreamTid.DimDreamTidID INNER JOIN
dbo.DimDreamYdelse ON dbo.FactDream.DimDreamBenefit = dbo.DimDreamYdelse.DimDreamBenefitID
WHERE (dbo.DimDreamYdelse.Ydelse LIKE 'Benefit%') AND (dbo.DimDreamTid.Aar = '2019')
ORDER BY dbo.DimDreamTid.Aar, dbo.DimDreamTid.UgeIAar
I suggest to use PowerQuery to transform your table into more suitable form for your analysis. Things would be much easier if each row of the table represents the "change" of benefit plan like this.
| Person ID | Benefit From | Benefit To | Date |
|-----------|--------------|------------|------------|
| 15 | BenefitNon | BenefitA | 2019-07-01 |
| 15 | BenefitA | BenefitNon | 2019-12-01 |
| 17 | BenefitC | BenefitA | 2019-06-01 |
| 17 | BenefitA | BenefitB | 2019-08-01 |
| 17 | BenefitB | BenefitA | 2019-09-01 |
| ...
Then you can simply count the numbers by COUNTROWS(BenefitChanges) filtering/slicing with both Benefit From and Benefit To.
I figured out how to read files into my pyspark shell (and script) from an S3 directory, e.g. by using:
rdd = sc.wholeTextFiles('s3n://bucketname/dir/*')
But, while that's great in letting me read all the files in ONE directory, I want to read every single file from all of the directories.
I don't want to flatten them or load everything at once, because I will have memory issues.
Instead, I need it to automatically go load all the files from each sub-directory in a batched manner. Is that possible?
Here's my directory structure:
S3_bucket_name -> year (2016 or 2017) -> month (max 12 folders) -> day (max 31 folders) -> sub-day folders (max 30; basically just partitioned the collecting each day).
Something like this, except it'll go for all 12 months and up to 31 days...
BucketName
|
|
|---Year(2016)
| |
| |---Month(11)
| | |
| | |---Day(01)
| | | |
| | | |---Sub-folder(01)
| | | |
| | | |---Sub-folder(02)
| | | |
| | |---Day(02)
| | | |
| | | |---Sub-folder(01)
| | | |
| | | |---Sub-folder(02)
| | | |
| |---Month(12)
|
|---Year(2017)
| |
| |---Month(1)
| | |
| | |---Day(01)
| | | |
| | | |---Sub-folder(01)
| | | |
| | | |---Sub-folder(02)
| | | |
| | |---Day(02)
| | | |
| | | |---Sub-folder(01)
| | | |
| | | |---Sub-folder(02)
| | | |
| |---Month(2)
Each arrow above represents a fork. e.g. I've been collecting data for 2 years, so there are 2 years in the "year" fork. Then for each year, up to 12 months max, and then for each month, up to 31 possible day folders. And in each day, there will be up to 30 folders just because I split it up that way...
I hope that makes sense...
I was looking at another post (read files recursively from sub directories with spark from s3 or local filesystem) where I believe they suggested using wildcards, so something like:
rdd = sc.wholeTextFiles('s3n://bucketname/*/data/*/*')
But the problem with that is it tries to find a common folder among the various subdirectories - in this case there are no guarantees and I would just need everything.
However, on that line of reasoning, I thought what if I did..:
rdd = sc.wholeTextFiles("s3n://bucketname/*/*/*/*/*')
But the issue is that now I get OutOfMemory errors, probably because it's loading everything at once and freaking out.
Ideally, what I would be able to do is this:
Go to the sub-directory level of the day and read those in, so e.g.
First read in 2016/12/01, then 2016/12/02, up until 2012/12/31, and then 2017/01/01, then 2017/01/02, ... 2017/01/31 and so on.
That way, instead of using five wildcards (*) as I did above, I would somehow have it know to look trough each sub-directory at the level of "day".
I thought of using a python dictionary to specify the file path to each of the days, but that seems like a rather cumbersome approach. What I mean by that is as follows:
file_dict = {
0:'2016/12/01/*/*',
1:'2016/12/02/*/*',
...
30:'2016/12/31/*/*',
}
basically for all the folders, and then iterating through them and loading them in using something like this:
sc.wholeTextFiles('s3n://bucketname/' + file_dict[i])
But I don't want to manually type out all those paths. I hope this made sense...
EDIT:
Another way of asking the question is, how do I read the files from a nested sub-directory structure in a batched way? How can I enumerate all the possible folder names in my s3 bucket in python? Maybe that would help...
EDIT2:
The structure of the data in each of my files is as follows:
{json object 1},
{json object 2},
{json object 3},
...
{json object n},
For it to be "true json", it either just needed to be like the above without a trailing comma at the end, or something like this (note square brackets, and lack of the final trailing comma:
[
{json object 1},
{json object 2},
{json object 3},
...
{json object n}
]
The reason I did it entirely in PySpark as a script I submit is because I forced myself to handle this formatting quirk manually. If I use Hive/Athena, I am not sure how to deal with it.
Why dont you use Hive, or even better, Athena? These will both deploy tables ontop of file systems, to give you access to all the data. Then you can capture this in to Spark
Alternatively, I believe you can also use HiveQL in Spark to set up a tempTable ontop of your file system location, and it'll register it all as a Hive table which you can execute SQL against. It's been a while since I've done that, but it is definitely do-able
I am learing cucumber and trying to write a feature file.
Following is my feature file.
Feature: Doctors handover Notes Module
Scenario: Search for patients on the bases of filter criteria
Given I am on website login page
When I put username, password and select database:
| Field | Value |
| username | test |
| password | pass |
| database | test|
Then I login to eoasis
Then I click on doctors hand over notes link
And I am on doctors handover notes page
Then I select sites, wards, onCallTeam, grades,potential Discharge, outstanding task,High priority:
| siteList | wardsList | onCallTeamList | gradesList | potentialDischargeCB | outstandingTasksCB | highPriorityCB |
| THE INFIRMARY | INFIRMARY WARD 9 - ASSESSMENT | null | null | null | null | null |
| THE INFIRMARY | INFIRMARY WARD 9 - ASSESSMENT | GENERAL MEDICINE | null | null | null | null |
| THE INFIRMARY | INFIRMARY WARD 9 - ASSESSMENT | GENERAL MEDICINE | CONSULTANT | null | null | null |
| THE INFIRMARY | INFIRMARY WARD 9 - ASSESSMENT | GENERAL MEDICINE | CONSULTANT | true | null | null |
| THE INFIRMARY | INFIRMARY WARD 9 - ASSESSMENT | GENERAL MEDICINE | CONSULTANT | true | true | null |
| THE INFIRMARY | INFIRMARY WARD 9 - ASSESSMENT | GENERAL MEDICINE | CONSULTANT | true | true | true |
Then I click on search button
Then I should see search results
I want to repeat last three steps like I select the search criteria then click on search button and then check search result. So how should I break this feature file. if I use scenario outline then there would be two different scenarios One for login and one for search criteria. Is that fine? Will the session will maintain in that case? Whats the best way to write such feature file.
Or is this a right way to write?
I don't think we can have multiple example sets in a Scenario Outline.
Most of the scenario steps in the example is too procedural to have its own step.
The first three steps could be reduced to something like.
Given I am logged into eoasis as a <user>
Code in the step definition, which could make calls to a separate login method that could take care of updating entering the username, password and selecting database.
Another rule is to avoid statements like "When I click the doctor's handover link". The keyword to avoid here being click. Today its a click, tomorrow it could be drop down or a button. So the focus should be on the functional expectation of the user, which is viewing the handover notes. So we modify this to
When I view the doctor's handover notes link
To summarize, this is how I would write this test.
Scenario Outline: Search for patients on the basis of filter criteria
Given I am logged into eoasis as a <user>
When I view the doctor's handover notes link
And I select sites, wards, onCallTeam, grades, potential Discharge, outstanding task, High priority
And perform a search
Then I should see the search results
Examples:
|sites |wards |onCallTeam |grades |potential Discharge |outstanding task |High priority|
| THE INFIRMARY | INFIRMARY WARD 9 - ASSESSMENT | null | null | null | null | null |
This really is the wrong way to write features. This feature is very declarative, its all about HOW you do something. What a feature should do is explain WHY you are doing something.
Another bad thing this feature does is mix up the details of two different operations, signing in, and searching for patients. Write a feature for each one e.g.
Feature: Signing in
As a doctor
I want my patients data to only be available if I sign in
So I ensure their confidentiality
Scenario: Sign in
Given I am a doctor
When I sign in
Then I should be signed in
Feature: Search for patients
Explain why searching for patients gives value to the doctor
...
You should focus on the name of the feature and the bit at the top that explains why this has value first. If you do that well then the scenarios are much easier to write (look how simple my sign in scenario is).
The art of writing features is doing this bit well, so that you end up with simple scenarios.
The SqlServer datetime data type is used to hold timestamps and it is 64 bits long - http://msdn.microsoft.com/en-us/library/ms187819.aspx
I am looking for a sane way to work with it in C++, something in the boost library, probably?
Thanks.
EDIT
I would settle for being able to do these two operations:
Display the timestamp in some human readable format, like 2012-01-15 16:54:13.123
Parse a string like 2012-01-15 16:54:13.123 into the respective SqlServer datetime value.
EDIT2
Here is what I know until now. I have a table with a datetime column. When I select rows from it, I get this column back with the data type of DBTYPE_DBTIMESTAMP. According to http://msdn.microsoft.com/en-us/library/ms187819.aspx it should be an 8 byte value, however, I get back a 16 byte value, for instance:
00070015000c07db 00000000001f0007
I could not find any description of this format, but examining it reveals the following structure:
0007 0015 000c 07db 00000000 001f 0007
^ ^ ^ ^ ^ ^
| | | | | |
| | | | | +--- minutes (7)
| | | | +----+--- seconds (31)
| | | +-------------+----+--- year (2011)
| | +----+-------------+----+--- month (12)
| +----+----+-------------+----+--- day (21)
+----+----+----+-------------+----+--- hour (7)
Which corresponds to 2011-12-21 07:07:31. So, this appears to be easy, but where is the documentation? Are DBTYPE_DBTIMESTAMP values always reported in this format? Is it SqlSever CE specific or whether the Express and other flavours work the same? Can it contain milliseconds?
BTW, I am using OLEDB to access the database.
Why don't you handle the thing on the side of the sql-server?
You could write a view, which gives you the DBTYPE_DBTIMESTAMP as datetime or varchar back. You could just define, which format you want.
Use the cast()- or convert()- Function
To write back values, you could wirte a little function, also at the sql-server.
You could use the function:
Convert(datetime, [value], [format])