refinery | Powerful SQL migration toolkit for Rust | Database library

by rust-db Rust Version: 0.8.9 License: MIT

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kandi X-RAY | refinery Summary

refinery is a Rust library typically used in Database, PostgresSQL, MariaDB applications. refinery has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Powerful SQL migration toolkit for Rust. refinery makes running migrations for different databases as easy as possible. It works by running your migrations on a provided database connection, either by embedding them on your Rust code, or via refinery_cli. Currently postgres, tokio-postgres , mysql, mysql_async and rusqlite are supported. If you are using a driver that is not yet supported, namely SQLx you can run migrations providing a Config instead of the connection type, as Config impl's Migrate. You will still need to provide the postgres/mysql/rusqlite driver as a feature for Runner::run and tokio-postgres/mysql_async for Runner::run_async. refinery works best with Barrel but you can also have your migrations in .sql files or use any other Rust crate for schema generation.

Support

Quality

Security

License

Reuse

Support

refinery has a medium active ecosystem.

It has 1003 star(s) with 89 fork(s). There are 15 watchers for this library.

It had no major release in the last 12 months.

There are 17 open issues and 86 have been closed. On average issues are closed in 97 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of refinery is 0.8.9

Quality

refinery has no bugs reported.

Security

refinery has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

refinery is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

refinery releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

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refinery Key Features

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refinery Examples and Code Snippets

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Community Discussions

Trending Discussions on refinery

google apps script get range somehow not working what am i doing wrong?

Google Script -> how to change this part of code, to get all columns?

split row at specific time

Locust.io: RuntimeWarning: greenlet.greenlet size changed, may indicate binary incompatibility

Suffle dataframe

Validate the row data in one pyspark Dataframe matched in another Dataframe

LINQ Subquery Where Clause could not be translated

Playing a simulation in fast-forward

Classification of data in alphabetic order in r

Data Profiling - How to count nulls, NaNs and empty string values?

QUESTION

google apps script get range somehow not working what am i doing wrong?

Asked 2021-Jun-01 at 06:51

somehow .getRange not working ?? works this way in every other script why not here ? Log is attached...

Error is at line

**

"var days srcSheet.getRange("F" + i);"

**

...

ANSWER

Answered 2021-Jun-01 at 06:51

Modification points:

In the case of method getRange(a1Notation), the start number of A1Notation is 1. But in your script, the 1st number is 0. I think that this is the reason of your issue.
In your script, getValue and setValue are used in a loop. In this case, the process cost of the script becomes high. Ref

Modified script 1:

In this modification, your script is modified. In this case, please modify your script as follows.

From:

Source https://stackoverflow.com/questions/67783722

QUESTION

Google Script -> how to change this part of code, to get all columns?

Asked 2021-May-31 at 12:10

question is, how to change this part of the code, to get all columns from the array not only [0,1,2,3]

this is the codeline:

...

ANSWER

Answered 2021-May-31 at 12:10

Issue:

Object.keys, when applied to an array, will return an array with the indexes of this array. If you're using it on this_table, you'll only when an array with 4 items, since that's the length of this_table.

Because of this, you're only getting 4 rows for your "table".

Solution:

A easier and more efficient way to do this is to retrieve the values from your 4 desired columns at once, and use the different join on that:

Source https://stackoverflow.com/questions/67770205

QUESTION

split row at specific time

Asked 2021-Mar-13 at 21:58

So I have a table from our time series sensor data for the plant. One of the sensor deals with movement of raw product on the belt (voltage / weight scale) before its processed into refinery. Whenever there is a delta (voltage of the belt less or more than normal / weight on belt (derived to every second) less or more than target for the 24 hour period (target ÷ 86,400 seconds ~ rounded to closest ton without decimal ) we capture it as a new event trigger and row in our warehouse database and move into data lake We need to find efficiency by work shift (day shift / grave shift) for time periods that cut across shift time

Considering a 2400 tons target on a normal day and day shift between 5:00 AM to 5:00 PM and night shift vice versa, we want the following dataframe:

starting dataframe row # event_start event_end operation_status tons_actual tons_target comment 1 2021-02-01 7:00 AM 2021-02-01 9:00 AM normal_run 197 200 2 2021-02-01 9:00 AM 2021-02-01 7:00 PM curtailed 700 1004 shift split here 3 2021-02-01 7:00 PM 2021-02-01 11:00 PM down_for_maintenance 0 301 4 2021-02-01 11:00 PM 2021-02-02 3:00 AM curtailed 320 402 5 2021-02-02 3:00 AM 2021-02-02 8:00 AM over_producing 600 502 shift split here 6 2021-02-02 8:00 AM 2021-02-02 11:00 AM normal_run 280 301 7 2021-02-02 11:00 AM 2021-02-04 4:00 PM broken_belt_unscheduled_loss 0 5323 multiple shift splits here

to split rows at shift change hours like this:

target dataframe row # event_start event_end operation_status tons_actual tons_target -------- 1 2021-02-01 7:00 AM 2021-02-01 9:00 AM normal_run 197 200 2.1 2021-02-01 9:00 AM 2021-02-01 5:00 PM curtailed 560 804 grave shift split 2.2 2021-02-01 5:00 PM 2021-02-01 7:00 PM curtailed 140 201 grave shift split 3 2021-02-01 7:00 PM 2021-02-01 11:00 PM down_for_maintenance 0 302 4 2021-02-01 11:00 PM 2021-02-02 3:00 AM curtailed 320 402 5.1 2021-02-02 3:00 AM 2021-02-02 5:00 AM over_producing 240 200 day shift split 5.2 2021-02-02 5:00 AM 2021-02-02 8:00 AM over_producing 360 302 day shift split 6 2021-02-02 8:00 AM 2021-02-02 11:00 AM normal_run 280 301 7.1 2021-02-02 11:00 AM 2021-02-02 5:00 PM broken_belt_unscheduled_loss 0 602 shift split 7.2 2021-02-02 5:00 PM 2021-02-03 5:00 AM broken_belt_unscheduled_loss 0 1205 shift split 7.3 2021-02-03 5:00 AM 2021-02-03 5:00 PM broken_belt_unscheduled_loss 0 1205 shift split 7.4 2021-02-03 5:00 PM 2021-02-04 5:00 AM broken_belt_unscheduled_loss 0 1205 shift split 7.5 2021-02-03 5:00 AM 2021-02-04 4:00 PM broken_belt_unscheduled_loss 0 1105 shift split

so the end result can be then be df.groupby(sum : tons) per shift

for a start, I know it needs some kind of array creating UDF inside an F.explode() function

...

ANSWER

Answered 2021-Mar-13 at 21:58

You can use flatMap to transform a single row into multiple rows.

Step 1: parse the date columns (if necessary, depends on the data source):

Source https://stackoverflow.com/questions/66616834

QUESTION

Locust.io: RuntimeWarning: greenlet.greenlet size changed, may indicate binary incompatibility

Asked 2020-Nov-26 at 02:45

This is annoying, So I spent the past 15 hours trying to figure out why locust.io will not launch for me. On my mac machine, I am trying to get an example of locust running so I can begin my exploration of the package. I installed locust in my virtual env(python v3.7.7) using pip: pip install locust

All packages installed successfully.

here is the sample code:

...

ANSWER

Answered 2020-Oct-15 at 14:12

Greenlet runtime error and deployed app in docker keeps booting all the workers

Try pip install --upgrade gevent or pip install greenlet==0.14.6.

Source https://stackoverflow.com/questions/64362359

QUESTION

Suffle dataframe

Asked 2020-Oct-06 at 00:11

The columns present in the .ods file are: Fuel Name, Unit of Measure, Refinery, State, Year, January, February, March, April, May, June, July, August, September, October, November, December, Total. The columns for the months contain the corresponding sales figures for that month, and the Total column contains the sum of the values for each month of the corresponding row. However, in some file conversions, the month and total values shuffle n + k places to the right, starting from the first line, with k being incremented by 1 for each following line. More specifically, the first line suffers a shuffle of n squares, the second line suffers a shuffle of n + 1 squares, the third line suffers a shuffle of n + 2 squares ... the thirteenth line suffers a shuffle of 13 squares ( returning to the original configuration), and so on until the end of the file. Internally, you and your teammates have dubbed this problem "stair".

First line example:

Right: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

1000 2500 1200 3000 1234 700 300 1000 0 800 2400 3500 17634

With n = 4 shuffle:

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

800 2400 3500 17634 1000 2500 1200 3000 1234 700 300 1000 0

With n = 1 shuffle:

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

17634 1000 2500 1200 3000 1234 700 300 1000 0 800 2400 3500

Transforming the files into matrices, only with the columns above, we will have the following examples: Examples of desired patterns (correct matrix or step ladder):

...

ANSWER

Answered 2020-Oct-06 at 00:11


import numpy as np


matrizCorreta = np.array([ 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78],
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78]
 ])

matrizInutilizavel_1 =  np.array([ 
 [11, 12, 78, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 
 [10, 11, 12, 78, 1, 2, 3, 4, 5, 6, 7, 8, 9], 
 [9, 10, 11, 12, 78, 1, 2, 3, 4, 5, 6, 7, 8], 
 [8, 9, 10, 11, 12, 2, 1, 78, 3, 4, 5, 6, 7], 
 [7, 8, 9, 10, 11, 12, 78, 1, 2, 3, 4, 5, 6], 
 [6, 7, 8, 9, 10, 11, 12, 78, 1, 2, 3, 4, 5], 
 [5, 6, 7, 8, 9, 10, 11, 12, 78, 1, 2, 3, 4], 
 [4, 5, 6, 7, 8, 9, 10, 11, 12, 78, 1, 2, 3], 
 [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78, 1, 2], 
 [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78, 1], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [78, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] 
 ])


matrizInutilizavel_2 =  np.array([ 
 [11, 12, 1, 2, 78, 3, 4, 5, 6, 7, 8, 9, 10], 
 [10, 11, 12, 78, 1, 2, 3, 4, 5, 6, 7, 8, 9], 
 [9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 78],
 [8, 9, 10, 11, 12, 2, 1, 78, 3, 4, 5, 6, 7], 
 [7, 8, 9, 10, 11, 12, 78, 1, 2, 3, 4, 5, 6], 
 [6, 7, 8, 9, 10, 11, 12, 78, 1, 2, 3, 4, 5], 
 [5, 6, 7, 8, 9, 10, 11, 12, 78, 1, 2, 3, 4], 
 [4, 5, 6, 7, 8, 9, 10, 11, 12, 78, 1, 2, 3], 
 [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78, 1, 2], 
 [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78, 1], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78], 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 78] 
 ])



### função recebe uma array 2d qualquer e retorna uma nova array ordenada
def verificacaoMatriz (matriz):
    nova_matriz = []
    ## numero de linhas da matriz
    rows = matriz.shape[0]
    #Número de colunas da matriz
    cols = matriz.shape[1]
    #para todo X no range de 0 até o numero de itens - 1
    for x in range(0,rows-1):
         #para todo Y no range de 0 até o numero de itens - 1
        for y in range(0,cols-1):
            ## Se o valor atual da matriz (linha,coluna) for igual ao próximo valor da diagonal (linha+1,coluna + 1)
            ## Exemplo, valor (0,0) contra (1,1), faça:
            if(matriz[x,y] == matriz[x+1,y+1]):
                ### Ordena todas as linhas da matriz usando a função sort do numpy
                nova_matriz = np.sort(matriz)
    ### se a variavel nova matriz está vazia, significa que a matriz estava correta e não precisou de ordenação
    ### Então só atribuo a matriz original a esta
    if not len(nova_matriz):
        nova_matriz = matriz
    ### Verifica se a array está em ordem            
    is_sorted = lambda a: np.all(a[:-1] <= a[1:])
    if(is_sorted(nova_matriz)==True):
        print("Matriz Correta")
    else:
        print("Matriz Inutilizável")
    return nova_matriz        



#### Testando a matriz Correta
matriz1 = verificacaoMatriz(matrizCorreta)
print("Matriz1",matriz1)

#### Testando a matriz inutil1 
matriz2 = verificacaoMatriz(matrizInutilizavel_1)
print("Matriz2",matriz2)


#### Testando a matriz inutil2
matriz3 = verificacaoMatriz(matrizInutilizavel_2)
print("Matriz3",matriz3)

Source https://stackoverflow.com/questions/64178144

QUESTION

Validate the row data in one pyspark Dataframe matched in another Dataframe

Asked 2020-Feb-17 at 13:47

I have 2 Pyspark Dataframe df1,df2. Both df1 and df2 contains millions of records.

df1 is like:

...

ANSWER

Answered 2020-Feb-17 at 13:47

Using levenshtein-distance with left join you can do something like this:

Source https://stackoverflow.com/questions/60262477

QUESTION

LINQ Subquery Where Clause could not be translated

Asked 2020-Jan-27 at 11:31

I am converting this query to linq in EF Core 2.2 but I could not find any correct way:

...

ANSWER

Answered 2020-Jan-27 at 10:38

Seems to be one of the (many) EFC 2.2 query translator bugs/defects/shortcomings. Works as expected in EFC 3.1.

From all the "query patterns" (as they call them), the only one working I was able to found is to push the predicate into join clause, e.g. replace

Source https://stackoverflow.com/questions/59926956

QUESTION

Playing a simulation in fast-forward

Asked 2020-Jan-15 at 08:42

I am working a simple game where users create structures in a sector, which is a 10x10 grid. Some structures generate resources and some consume resources. The sector itself might contain some resources outside of any structure. The generators and consumers are related. For example, a well might be generating water, then an splitter be consuming water and making hydrogen and oxygen, while a refinery is consuming hydrogen and oxygen and making rocket fuel, etc.

The rate at which they generate or consume resources can vary by structure - I call this the tick rate. Each time a consumer ticks, it will first attempt to extract those resources from the structures that surround it in the sector. If there are not enough, it will try to get them from the sector's storage. If it is still not enough, the structure will stop. Structures hold the resources that they generate up to some maximum. Once they are full, they will not generate more until some are consumed. If a structure is stopped, it will also not generate more resources, but the resources it already has can still be used by another adjacent structure.

It is not uncommon that there are patterns. For example, if the well is very slow, the splitter will turn off when the well runs out of water, and then the refinery will turn off when the splitter runs out of gases. Then when the well generates again, everything will turn back on.

When the user is playing a sector, I tick the sector continuously at the resolution of shortest tick rate of the sector's structures. This works fine. The pseudo-code looks like this:

...

ANSWER

Answered 2020-Jan-15 at 08:42

Step 1: Convert the raw data into "graph of nodes" form, where each node represents a machine, and producers are at the bottom and consumers are at the top. For example it might look like:

Source https://stackoverflow.com/questions/59742268

QUESTION

Classification of data in alphabetic order in r

Asked 2020-Jan-07 at 12:24

I have a sample data below.

...

ANSWER

Answered 2020-Jan-07 at 11:49

In your data frame, the levels of the Fluid type are not in alphabetical order

Source https://stackoverflow.com/questions/59627791

QUESTION

Data Profiling - How to count nulls, NaNs and empty string values?

Asked 2019-Oct-28 at 12:52

I am new to pyspark and I have this example dataset:

...

ANSWER

Answered 2019-Oct-28 at 12:52

you could do the following :

first change all Null/None values into Panda NaN's

Source https://stackoverflow.com/questions/58591201

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install refinery

You can download it from GitHub.
Rust is installed and managed by the rustup tool. Rust has a 6-week rapid release process and supports a great number of platforms, so there are many builds of Rust available at any time. Please refer rust-lang.org for more information.

Support

:balloon: Thanks for your help improving the project! No contribution is too small and all contributions are valued, feel free to open Issues and submit Pull Requests.

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