1
92%
master: 94%

Ran 19 Mar 2018 02:54PM UTC

Files 97

Run time 5s

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Committed 19 Mar 2018 02:15PM UTC coverage: 92.027%. Remained the same

Job # PYTHON=2.7 NUMPY=1.14.1 PANDAS=0.22.0 TEST='true' LINT='true' COVERAGE='true' PARALLEL='false' XTRATESTARGS= TEST_IMPORTS='false'

Build Type

push

travis-ci

Committed by

mrocklin

Commit Message

Avoid sorting large stacks in order

When performning task ordering we sort tasks based on the
number of dependents/dependencies they have.  This is critical to
low-memory processing.

However, sometimes individual tasks have millions of dependencies,
for which an n*log(n) sort adds significant overhead.  In these cases
we give up on sorting, and just hope that the tasks are well ordered
naturally (such as is often the case in Python 3.6+ due to sorted
dicts and the natural ordering that exists when constructing common
graphs)

See https://github.com/pangeo-data/pangeo/issues/150#issuecomment-373066066
for a real-world case

Run Details

14635 of 15903 relevant lines covered (92.03%)

0.92 hits per line

Source Files on job 2219.1 (PYTHON=2.7 NUMPY=1.14.1 PANDAS=0.22.0 TEST='true' LINT='true' COVERAGE='true' PARALLEL='false' XTRATESTARGS= TEST_IMPORTS='false')

mrocklin / dask / 2219 / 1
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master: 94%

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Source Files on job 2219.1 (PYTHON=2.7 NUMPY=1.14.1 PANDAS=0.22.0 TEST='true' LINT='true' COVERAGE='true' PARALLEL='false' XTRATESTARGS= TEST_IMPORTS='false')

mrocklin / dask / 2219 / 1 92% master: 94%

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Markdown

Textile

RDoc

HTML

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Source Files on job 2219.1 (PYTHON=2.7 NUMPY=1.14.1 PANDAS=0.22.0 TEST='true' LINT='true' COVERAGE='true' PARALLEL='false' XTRATESTARGS= TEST_IMPORTS='false')

mrocklin / dask / 2219 / 1
92%
master: 94%

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