Upgrade CSP to Perspective 3.0.3 #1
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Signed-off-by: Will Rieger <will.r.rieger@gmail.com>
Change Time::MIN to Time::MIN_VALUE to match DateTime
| self._limit = limit | ||
| self._localize = localize | ||
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| # TODO: we do not want 1 server per table, make a Client param? |
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It does "feel" wrong to me creating a new server per table, but I am curious how it would be a problem. Does a server have lots of overhead? Does a collection of tables in the same server share more resources than the same collection of tables on different servers? Are there any potential advantages to isolating a table per server?
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Signed-off-by: Davis Silverman <davis@thedav.is>
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Specific modifications done to CSP for this change:
csp/dataframe.py: Theto_perspectivefunction now must take aClientobject to construct theTable.csp/adapters/perspective.py: The use ofPerspectiveManagerwas changed to use of the newClient/Serverclasses.csp/impl/pandas_perspective.py: Perspective Table JSON update does not support directdateordatetimevalues now, as theses are not JSON serializable types. To work around this, the objects are directly translated to timestamp integers and sent to Perspective to be parsed into Perspective’s time types.to_dfwas rewritten to route throughPyArrow, but due to differences in how PyArrow chooses dtypes, there is some hackery around ordering of categories. We also removedto_dictandto_numpybecause they were removed in the Perspective 3.0 migration.csp/tests/impl/test_pandas_perspective.pymany tests were fixed to demonstrate changes that are needed to continue day-to-day use ofCspPerspectiveTable. Some tests were not fixed due to an outstanding Perspective bug (Unit test describing index behavior bug perspective-dev/perspective#2756).The use of PyArrow to underly Perspective’s DataFrame support leads to some semantic changes. PyArrow is much more eager to set columns to
CategoricalDtypeinstead ofStringDtype. It also has different behavior regarding category ordering, and perhaps others still not uncovered after fixing the tests.