In jenkins-kubernetes-plugin, how to generate labels in Pod template that are based on a pattern

Question

Set-Up

I am using jenkins-kubernetes-plugin to run our QE jobs. The QE jobs are executed over multiple PODs and each POD has a static set of labels like testing chrome

Issue:

In these QE jobs, there is one port say 7900 that I want to expose through Kubernetes Ingress Controller.

The issue is we have multiple PODs running from the same Pod Template and they all have the same set of labels. For Ingress Controller to work, I want these PODs to have some labels that come from a pattern.

Like POD1 has a label chrome-1 and POD2 has a label called chrome-2 and so on...

Is this possible?

Answer 1

This is not currently possible directly, but you could use groovy in the pipeline to customize it, ie. add the build id as a label

Answer 2

You attempt to compute interquartile as a boolean mask based on the & operator, but its components are Series containing values from the ranges. While the two series are likely to be similar sizes, & will not give you an intersection of their indices. If they were boolean masks, in your subsequent usage, you'd be taking the mean of a bunch of zeros and ones, which is going to be 0.5 (the ratio of data that falls within the IQR as a matter of fact).

First, compute interquartile as a proper mask. Pandas has its own quantile method, which, like np.percentile and siblings, accepts multiple percentiles simultaneously. You can combine that with between to get your mask more efficiently:

interquartile = df['order_amount'].between(*df['order_amount'].quantile([0.25, 0.75]))

You can apply the mask to the column and take the mean like this:

df.loc[interquartile, 'order_amount'].mean()

Answer 3

Try:

above25percentile = df["order_amount"]>np.percentile(df['order_amount'],25)
below75percentile = df['order_amount']<np.percentile(df['order_amount'],75)
print(df.loc[above25percentile & below75percentile, 'order_amount'].mean())

Or you can use between:

df.loc[df['order_amount'].between(np.percentile(df['order_amount'], 25),
                                  np.percentile(df['order_amount'], 75),
                                  inclusive='neither'), 'order_amount'].mean()

Suppose the following dataframe:

df = pd.DataFrame({'order_amount': range(0, 10)})
print(df)

# Output
   order_amount
0             0  # Excluded
1             1  # "
2             2  # "
3             3
4             4  # mean <- (3 + 4 + 5 + 6) / 4 = 4.5
5             5
6             6
7             7  # Excluded
8             8  # "
9             9  # "

Output:

>>> df.loc[df['order_amount'].between(np.percentile(df['order_amount'], 25),
                                  np.percentile(df['order_amount'], 75),
                                  inclusive='neither'), 'order_amount'].mean()
4.5