Smartsheet-Data-Tracker | command line application | CSV Processing library
kandi X-RAY | Smartsheet-Data-Tracker Summary
kandi X-RAY | Smartsheet-Data-Tracker Summary
A command line application that updates an existing sheet with data from external sources.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Main entry point for the client .
- Validate mapping configuration .
- return a record matching the given lookup value
- Initializes the LDAP connection
- Searches the source record for the given lookup value
- Validate the source config
- Recursive function to recursively parse Jira fields
- Tries to send an update attempt .
- send update to smartsheet
Smartsheet-Data-Tracker Key Features
Smartsheet-Data-Tracker Examples and Code Snippets
Community Discussions
Trending Discussions on CSV Processing
QUESTION
I am currently working on a spring based API which has to transform csv data and to expose them as json. it has to read big CSV files which will contain more than 500 columns and 2.5 millions lines each. I am not guaranteed to have the same header between files (each file can have a completly different header than another), so I have no way to create a dedicated class which would provide mapping with the CSV headers. Currently the api controller is calling a csv service which reads the CSV data using a BufferReader.
The code works fine on my local machine but it is very slow : it takes about 20 seconds to process 450 columns and 40 000 lines. To improve speed processing, I tried to implement multithreading with Callable(s) but I am not familiar with that kind of concept, so the implementation might be wrong.
Other than that the api is running out of heap memory when running on the server, I know that a solution would be to enhance the amount of available memory but I suspect that the replace() and split() operations on strings made in the Callable(s) are responsible for consuming a large amout of heap memory.
So I actually have several questions :
#1. How could I improve the speed of the CSV reading ?
#2. Is the multithread implementation with Callable correct ?
#3. How could I reduce the amount of heap memory used in the process ?
#4. Do you know of a different approach to split at comas and replace the double quotes in each CSV line ? Would StringBuilder be of any healp here ? What about StringTokenizer ?
Here below the CSV method
...ANSWER
Answered 2022-Jan-29 at 02:56I don't think that splitting this work onto multiple threads is going to provide much improvement, and may in fact make the problem worse by consuming even more memory. The main problem is using too much heap memory, and the performance problem is likely to be due to excessive garbage collection when the remaining available heap is very small (but it's best to measure and profile to determine the exact cause of performance problems).
The memory consumption would be less from the replace and split operations, and more from the fact that the entire contents of the file need to be read into memory in this approach. Each line may not consume much memory, but multiplied by millions of lines, it all adds up.
If you have enough memory available on the machine to assign a heap size large enough to hold the entire contents, that will be the simplest solution, as it won't require changing the code.
Otherwise, the best way to deal with large amounts of data in a bounded amount of memory is to use a streaming approach. This means that each line of the file is processed and then passed directly to the output, without collecting all of the lines in memory in between. This will require changing the method signature to use a return type other than List. Assuming you are using Java 8 or later, the Stream API can be very helpful. You could rewrite the method like this:
QUESTION
I have a JSON that I want to insert into BQ. The column data type is STRING. Here is the sample JSON value.
...ANSWER
Answered 2021-Jun-02 at 06:55I think there is an issue with how you escape the double quotes.
I could reproduce the issue you describe, and fixed it by escaping the double quotes with " instead of a backslash \:
QUESTION
I'm sorry if this has been asked before. It probably has, but I just have not been able to find it. On with the question:
I often have loops which are initialized with certain conditions that affect or (de)activate certain behaviors inside them, but do not drastically change the general loop logic. These conditions do not change through the loop's operation, but have to be checked every iteration anyways. Is there a way to optimized said loop in a pythonic way to avoid doing the same check every single time? I understand this would be a compiler's job in any compiled language, but there ain't no compiler here.
Now, for a specific example, imagine I have a function that parses a CSV file with a format somewhat like this, where you do not know in advance the columns that will be present on it:
...ANSWER
Answered 2021-Apr-23 at 11:36Your code seems right to me, performance-wise.
You are doing your checks at the beginning of the loop:
QUESTION
I am attempting to create an program to scrape xml files. I'm experimenting with go because of it's goroutines. I have several thousand files, so some type of multiprocessing is almost a necessity...
I got a program to successfully run, and convert xml to csv(as a test, not quite the end result), on a test set of files, but when run with the full set of files, it gives this:
...ANSWER
Answered 2021-Apr-21 at 15:25I apologize for not including the correct error. as the comments pointed out i was doing something dumb and creating a routine for every file. Thanks to JimB for correcting me, and torek for providing a solution and this link. https://gobyexample.com/worker-pools
QUESTION
I am processing CSV and using the following code to process a single line.
...ANSWER
Answered 2020-Jul-31 at 21:54The fastest way to do something is to not do it at all.
If you can ensure that your source string s will outlive the use of the returned vector, you could replace your std::vector with std::vector which would point to the beginning of each substring. You then replace your identified delimiters with zeroes.
[EDIT] I have not moved up to C++17, so no string_view for me :)
NOTE: typical CSV is different from what you imply; it doesn't use escape for the comma, but surrounds entries with comma in it with double quotes. But I assume you know your data.
Implementation:
QUESTION
I am using regex for CSV processing where data can be in Quotes, or no quotes. But if there is just a comma at the starting column, it skips it.
Here is the regex I am using:
(?:,"|^")(""|[\w\W]*?)(?=",|"$)|(?:,(?!")|^(?!"))([^,]*?|)(?=$|,)
Now the example data I am using is:
,"data",moredata,"Data"
Which should have 4 matches ["","data","moredata","Data"], but it always skips the first comma. It is fine if there is quotes on the first column, or it is not blank, but if it is empty with no quotes, it ignores it.
Here is a sample code I am using for testing purposes, it is written in Dart:
...ANSWER
Answered 2020-May-11 at 22:02Investigating your expression
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Smartsheet-Data-Tracker
main.py -- primary application script. This is the main file that runs the application.
settings directory app.json -- configuration settings for the whole application mapping.json -- configuration file that maps values in the external source to the sheet sources.json -- configuration file that holds information about each source that the application queries.
connectors directory CSVCon.py -- Python class file that houses CSV external connector MySQLCon.py -- Python class file that houses MySQL external connector OpenLDAPCon.py -- Python class file that houses OpenLDAP external connector RestGETCon.py -- Python class file that houses generic REST GET external connector RestGETDeskCon.py -- Python class file that houses Desk.com REST GET external connector RestGETJiraCon.py -- Python class file that houses Jira REST GET external connector
utils directory config.py -- a utility class that deals with app configurations match.py -- a utility class that processes matches and prepares them to send to Smartsheet API
sampleData directory employees.csv -- example CSV source file issues.csv -- example CSV source file
The Data Tracker application can be configured to automatically run on a schedule,. Please refer to your system documentation for details on how to setup a scheduled job. Here is how to add Data Tracker as a scheduled cron job on a UNIX/Linux system:.
minute ( 0-59 ) -- the minute the job runs every hour
hour ( 0-23 ) -- the hour the job runs every day
day of the month ( 1-31 ) -- the day of the month the job runs every month
month ( 0-12) -- the month the job runs every year
day of week ( 0-6 ) ( 0 to 6 are Sunday to Saturday, or use names ) -- day the job runs each week
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
