supported languages:

• Groovy
• JavaScript
• Python

Result "seko.kafka.connect.transformer.jmh.tests.TransformersTest.groovyTransformer": 442.675 ±(99.9%) 1.631 ns/op [Average] (min, avg, max) = (440.508, 442.675, 444.009), stdev = 1.079 CI (99.9%): [441.044, 444.306] (assumes normal distribution)

Result "seko.kafka.connect.transformer.jmh.tests.TransformersTest.jsTransformer": 1577617.798 ±(99.9%) 78341.136 ns/op [Average] (min, avg, max) = (1465677.763, 1577617.798, 1652716.223), stdev = 51817.811 CI (99.9%): [1499276.661, 1655958.934] (assumes normal distribution)

Result "seko.kafka.connect.transformer.jmh.tests.TransformersTest.pythonTransformer": 1321.100 ±(99.9%) 13.768 ns/op [Average] (min, avg, max) = (1309.414, 1321.100, 1332.275), stdev = 9.106 CI (99.9%): [1307.332, 1334.867] (assumes normal distribution)

The numbers below are just data. To gain reusable insights, you need to follow up on why the numbers are the way they are. Use profilers (see -prof, -lprof), design factorial experiments, perform baseline and negative tests that provide experimental control, make sure the benchmarking environment is safe on JVM/OS/HW level, ask for reviews from the domain experts. Do not assume the numbers tell you what you want them to tell.