refinery | aware tail-based sampling proxy | Analytics library
kandi X-RAY | refinery Summary
kandi X-RAY | refinery Summary
Refinery is a trace-aware sampling proxy. It collects spans emitted by your application, gathers them into traces, and examines them as a whole. This enables Refinery to make an intelligent sampling decision (whether to keep or discard) based on the entire trace.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Main entry point
- compare compares two values .
- NewConfig returns a new config object .
- publicAddr returns the public version of the given configuration .
- scan is used to scan all keys in a Redis
- new redis peers
- getEventTime extracts the time . Time from an etHeader
- processTraceRequest processes trace requests
- buildOptions builds redis . DialOption from the given config .
- ConvertNumeric converts numeric value to a float64
refinery Key Features
refinery Examples and Code Snippets
Community Discussions
Trending Discussions on refinery
QUESTION
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- In the case of method
getRange(a1Notation)
, the start number of A1Notation is1
. But in your script, the 1st number is0
. I think that this is the reason of your issue. - In your script,
getValue
andsetValue
are used in a loop. In this case, the process cost of the script becomes high. Ref
In this modification, your script is modified. In this case, please modify your script as follows.
From:QUESTION
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 12:10Object.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:
QUESTION
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 hereto 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 splitso 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 21:58You can use flatMap to transform a single row into multiple rows.
Step 1: parse the date columns (if necessary, depends on the data source):
QUESTION
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 14:12Greenlet runtime error and deployed app in docker keeps booting all the workers
Try pip install --upgrade gevent
or pip install greenlet==0.14.6
.
QUESTION
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)
QUESTION
I have 2 Pyspark Dataframe df1,df2. Both df1 and df2 contains millions of records.
df1 is like:
...ANSWER
Answered 2020-Feb-17 at 13:47Using levenshtein-distance
with left join you can do something like this:
QUESTION
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 10:38Seems 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
QUESTION
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 08:42Step 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:
QUESTION
I have a sample data below.
...ANSWER
Answered 2020-Jan-07 at 11:49In your data frame, the levels of the Fluid type are not in alphabetical order
QUESTION
I am new to pyspark and I have this example dataset:
...ANSWER
Answered 2019-Oct-28 at 12:52you could do the following :
first change all Null/None values into Panda NaN's
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install refinery
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