
The workflow take two lanes of sequencing, demultiplexes them into 192 samples and then runs stacks over each sample. The only thing I changed between runs was the addition of an extra process at the end. For the StacksMapped:MapReadSingles process, nextflow used the cached version for 191 of the 192 samples that it ran last time, but for one of the samples, it decided that the cache was no good and needed to re-run the process (and everything downstream). I've used the default cache strategies for all processes.
Is there a sensible debugging approach to working out why the cached version wasn't used in this instance?
Thanks!
-Rob
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input:
tuple name, path(readPairs)
path(genomeIndex)
path(genome)ProcessRadtags.out.reads.flatten() \
| map {
def pattern = ~/([^(rem)]+)\.(rem)?\.?[12]/;
def matcher = it.getBaseName() =~ pattern;
matcher.find();
[ matcher[0][1], it, matcher[0][2] ? true : false ]
} \
| branch {
single: it[2]
paired: true
} \
| set { reads }
reads.paired \
| groupTuple() \
| map { name, paths, rems -> [name, paths] } \
| set { ReadsPaired }
reads.single \
| groupTuple() \
| map { name, paths, rems -> [name, paths] } \
| set { ReadsSingle }
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Hi Rob,
> The processes that are being unnecessarily restarted are being fed read pairs and read singletons fed from a demultiplexing step. Those processes have input "shape":Then it's not what I was supsecting. Try to use the `-dump-hashes` command-line options and compare the logs of two different executions. Beware is not a friendly log to ding into, use a good text diff tool.p
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Umm, this is weird. Any chance to create a test case that I can replicate?p
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