Get the best route between two dirrent paths in wp8 - algorithm

I have created a bus transit application for a city in windows phone 8. I have stored all my routes and bus stops in a separate table in the database.
My bus stop table contains:
stop_id | stop_name | latitude | longitude | status | alias_name
---------------------------------------------------------------------------------------
1736 | Atlas Company | 18.629243 | 73.833814 | Active | Centurenca Corner
1737 | Atlas company | 18.629243 | 73.833814 | Active |
681 | Atma Anand Dhyan Kendra | 18.600349 | 73.926251 | InActive |
My Routes Table contains
bus_id | bus_no | bus_source | bus_destination | days_of_week | total_distance estimated_time | source_stop_names | destination_stop_names | total_stops | source_trip_time | destination_trip_time | bus_status
source_stop_names contains
-----------------
1 | Swargate
2 | Parvati payatha
3 | Dandekar pul
4 | Pan mala Sinhgad Road
5 | Jal Shuddhikarn Kendra Sinhgad Road
6 | Ganesh mala
7 | Vitbhatti Sinhgad Road
8 | Vitthalwadi jakat naka
9 | Jaydeo nagar
10 | Rajaram pul
11 | Vitthalwadi Mandir Hingne
12 | Hingne rasta
13 | Anand nagar singhgad rd
14 | Manik Bag
15 | Indian hum company
16 | Wadgaon phata
17 | Patil colony
18 | Dhayari phata
19 | Sanas Vidyalaya
20 | Dangat wasti
21 | Gar mala
22 | Dhayarai gaon
23 | Raykar wasti
24 | Poultry farm singhgad road
25 | Dhayarigaon shala
26 | Chavan mala
27 | DSK Vishwa
I want to find the shortest path between two bus stops not connected by a bus route, and show the number of routes and buses the user has to take while travelling from one point to another, like google maps does.
I have used default map control in windows phone 8.

Related

Converting Column headings into Row data

I have a table in an Access Database that has columns that I would like to convert to be Row data.
I found a code in here
converting database columns into associated row data
I am new to VBA and I just don't know how to use this code.
I have attached some sample data
How the table currently is set up it is 14 columns long.
+-------+--------+-------------+-------------+-------------+----------------+
| ID | Name | 2019-10-31 | 2019-11-30 | 2019-12-31 | ... etc ... |
+-------+--------+-------------+-------------+-------------+----------------+
| 555 | Fred | 1 | 4 | 12 | |
| 556 | Barney| 5 | 33 | 24 | |
| 557 | Betty | 4 | 11 | 76 | |
+-------+--------+-------------+-------------+-------------+----------------+
I would like the output to be
+-------+------------+-------------+
| ID | Date | HOLB |
+-------+------------+-------------+
| 555 | 2019-10-31| 1 |
| 555 | 2019-11-30| 4 |
| 555 | 2019-12-31| 12 |
| 556 | 2019-10-31| 5 |
| 556 | 2019-11-30| 33 |
| 556 | 2019-12-31| 24 |
+-------+--------+-------------+---+
How can I modify this code into a Module and call the module in a query?
Or any other idea you may have.

sqlite: wide v. long performance

I'm considering whether I should format a table in my sqlite database in "wide or "long" format. Examples of these formats are included at the end of the question.
I anticipate that the majority of my requests will be of the form:
SELECT * FROM table
WHERE
series in (series1, series100);
or the analog for selecting by columns in wide format.
I also anticipate that there will be a large number of columns, even enough to need to increase the column limit.
Are there any general guidelines for selecting a table layout that will optimize query performance for this sort of case?
(Examples of each)
"Wide" format:
| date | series1 | series2 | ... | seriesN |
| ---------- | ------- | ------- | ---- | ------- |
| "1/1/1900" | 15 | 24 | 43 | 23 |
| "1/2/1900" | 15 | null | null | 23 |
| ... | 15 | null | null | 23 |
| "1/2/2019" | 12 | 12 | 4 | null |
"Long" format:
| date | series | value |
| ---------- | ------- | ----- |
| "1/1/1900" | series1 | 15 |
| "1/2/1900" | series1 | 15 |
| ... | series1 | 43 |
| "1/2/2019" | series1 | 12 |
| "1/1/1900" | series2 | 15 |
| "1/2/1900" | series2 | 15 |
| ... | series2 | 43 |
| "1/2/2019" | series2 | 12 |
| ... | ... | ... |
| "1/1/1900" | seriesN | 15 |
| "1/2/1900" | seriesN | 15 |
| ... | seriesN | 43 |
| "1/2/2019" | seriesN | 12 |

The "long" format is the preferred way to go here, for so many reasons. First, if you use the "wide" format and there is ever a need to add more series, then you would have to add new columns to the database table. While this is not too much of a hassle, in general once you put a schema into production, you want to avoid further schema changes.
Second, the "long" format makes reporting and querying much easier. For example, suppose you wanted to get a count of rows/data points for each series. Then you would only need something like:
SELECT series, COUNT(*) AS cnt
FROM yourTable
GROUP BY series;
To get this report with the "wide" format, you would need a lot more code, and it would be as verbose as your sample data above.
The thing to keep in mind here is that SQL databases are built to operate on sets of records (read: across rows). They can also process things column wise, but they are not generally setup to do this.

data with specific length and characters in select query oracle

My table look like this in my oracle db;
ID | NI | NT | MB | ETC
-------------------------------------------
1 |1234567 | | | comments //valid
2 |9654875 | | | jhdsd //valid
3 |43gf543 | | | dd //in-valid
4 |123 | | | dfds //in-valid
5 |12654332 | | | dffd //in-valid
6 | |542 | | comments //valid
7 | |362 | | jhdsd //valid
8 | |9631 | | dd //invlaid
9 | |r45 | | dfds //in-valid
10 | |56 | | dffd // in-valid
11 | | |03121234567 | comments //valid
12 | | |03874514414 | jhdsd //valid
13 | | |05764544444 | dd //in-valid as not starts with 03
14 | | |30010101019 | dfds //in-valid
15 | | |038f5678543 | dffd //in-valid
I like select only valid records with select query
where
NI length should be fix 7 and all, starts with any digit
NT length should be fix 3 and all, starts with any digit
digits MB length should be fix 11, starts with 03 and all digits.
result should look like this;
1 |1234567 | | | comments
2 |9654875 | | | jhdsd
3 | |542 | | comments
4 | |362 | | jhdsd
5 | | |03121234567 | comments
6 | | |03874514414 | jhdsd

Try this:
NI length should be fix 7 and all, starts with any digit
REGEXP_LIKE(NI, '^\d{7}$')
NT length should be fix 3 and all, starts with any digit
REGEXP_LIKE(NT, '^\d{3}$')
digits MB length should be fix 11, starts with 03 and all digits.
REGEXP_LIKE(MB, '^03\d{9}$')

you could use a substr and length
select ID, NI, NT, MB, ETC
from my_table
where length(NI) = 7
and length(NT) = 3
and substr(MB,1,2) ='03'
AND REGEXP_LIKE(NI, '^[[:digit:]]+$')
AND REGEXP_LIKE(NT, '^[[:digit:]]+$')

Sort DataFrame Columns individually

I have seen many examples about how to sort a DataFrame based on some specific columns.
What I want to achieve is to sort Columns DataFrame individually, independently of each other. See the example below.
Input
+-----------+-----------+----------------+
| Column1 | Column2 | Column 3 |
+-----------+-----------+----------------+
| 61 | 5 | 9 |
| 14 | 16 | 8 |
| 26 | 27 | 7 |
+-----------+-----------+----------------+
Output
+-----------+-----------+----------------+
| Column1 | Column2 | Column 3 |
+-----------+-----------+----------------+
| 14 | 5 | 7 |
| 26 | 16 | 8 |
| 61 | 27 | 9 |
+-----------+-----------+----------------+
Any clue how can I achieve this?

create a hive table without specifying the column names and column types

I have huge dataset with 1000 columns stored on HDFS. I want to create a hive table to filter and work on the data.
CREATE EXTERNAL TABLE IF NOT EXISTS tablename(
var1 INT,var2 STRING, var2 STRING)
COMMENT 'testbykasa'
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION '/folder1/';
For smaller no. of columns(~ 5-10), I manually specify the column name and column type. Is there a way to get hive create the table by inferring the column name and datatype, without manually specifying it.

Demo
mydata.csv
2,2,8,1,5,1,8,1,4,1,3,4,9,2,8,2,6,5,3,1,5,5,8,0,1,6,0,7,1,4
2,6,8,7,7,9,9,3,8,7,3,1,9,1,7,5,9,7,1,2,5,7,0,5,1,2,6,4,0,4
0,0,1,3,6,5,6,2,4,2,4,9,0,4,9,8,1,0,2,8,4,7,8,3,9,7,8,9,5,5
3,4,9,1,8,7,4,2,1,0,4,3,1,4,6,6,7,4,9,9,6,7,9,5,2,2,8,0,2,9
3,4,8,9,9,1,5,2,7,4,7,1,4,9,8,9,3,3,2,3,3,5,4,8,6,5,8,8,6,4
4,0,6,9,3,2,4,2,9,4,6,8,8,2,6,7,1,7,3,1,6,6,5,2,9,9,4,6,9,7
7,0,9,3,7,6,5,5,7,2,4,2,7,4,6,1,0,9,8,2,5,7,1,4,0,4,3,9,4,3
2,8,3,7,7,3,3,6,9,3,5,5,0,7,5,3,6,2,9,0,8,2,3,0,6,2,4,3,2,6
3,2,0,8,8,8,1,8,4,0,5,2,5,0,2,0,4,1,2,2,1,0,2,8,6,7,2,2,7,0
0,5,9,1,0,3,1,9,3,6,2,1,5,0,6,6,3,8,2,8,0,0,1,9,1,5,5,2,4,8
create external table mycsv (rec string)
row format delimited
stored as textfile
tblproperties ('serialization.last.column.takes.rest'='true')
;
select pe.pos + 1 as col
,count(distinct pe.val) as count_distinct_val
from mycsv
lateral view posexplode(split(rec,',')) pe
group by pe.pos
;
+------+---------------------+
| col | count_distinct_val |
+------+---------------------+
| 1 | 5 |
| 2 | 6 |
| 3 | 6 |
| 4 | 5 |
| 5 | 7 |
| 6 | 8 |
| 7 | 7 |
| 8 | 7 |
| 9 | 6 |
| 10 | 7 |
| 11 | 6 |
| 12 | 7 |
| 13 | 7 |
| 14 | 6 |
| 15 | 6 |
| 16 | 9 |
| 17 | 7 |
| 18 | 9 |
| 19 | 5 |
| 20 | 6 |
| 21 | 7 |
| 22 | 5 |
| 23 | 8 |
| 24 | 7 |
| 25 | 5 |
| 26 | 6 |
| 27 | 7 |
| 28 | 8 |
| 29 | 8 |
| 30 | 8 |
+------+---------------------+

Yes, it is possible, but not with SQL script. To do this I use a Python script that read the first line of the csv file and create a script dynamically sending to Hive using the pyhive library (and erasing the first line of the csv). To identify the types, is just use Python functions to discovery if is a String, a Number etc.
The problem with Python is that it just work on Python 2.7, so I recommend you considere try to do the same code on Scala.

Resources