* add extraction fn support for Druid queries
* bump pydruid version to get extraction fn commits
* update and add tests for druid for filters with extraction fns
* conform to flake8 rules
* fix flake8 issues
* bump pyruid version for extraction function features
It appears the officially maintained fork of flask-cache is
flask-caching https://github.com/sh4nks/flask-caching . It is fully
compatible with flask-cache.
fixes https://github.com/apache/incubator-superset/issues/4926
In rare cases where the query is stopped before it is started, SQL Lab
returns an unexpected string payload instead of a normal dictionary.
This aligns the process to handle the error in a homogeneous fashion.
if a filter is created on a chart, and the column is removed from the
dataset, you get a "'NoneType' object has no attribute
'is_num'" or something to that
effect. This fix disregards the filter.
Also error messages were HTML escaped which React does already anyways
so that's not necessary [anymore] here.
* [bugfix] temporal columns with expression fail
error msg: "local variable 'literal' referenced before assignment"
Error occurs [only] when using temporal column defined as a SQL
expression.
Also noticed that examples were using `granularity` instead of using
`granularity_sqla` as they should. Fixed that here.
* Add tests
* [formats] add better defaults for time + number formatting
* [formatDate] add tests for concise formatDate
* [nvd3] use verbose time format in tooltips
* [number format] improve number format description
* [formats] revert to .3s defaults, tweak number format preview
* [formats] default number vis to .3s
* [WiP] [explore] proper filtering of NULLs and ''
TODO: handling of Druid equivalents
* Unit tests
* Some refactoring
* [druid] fix 'Unorderable types' when col has nuls
Error "unorderable types: str() < int()" occurs when grouping by a
numerical Druid colummn that contains null values.
* druid/pydruid returns strings in the datafram with NAs for nulls
* Superset has custom logic around get_fillna_for_col that fills in the
NULLs based on declared column type (FLOAT here), so now we have a mixed
bag of type in the series
* pandas chokes on pivot_table or groupby operations as it cannot sorts
mixed types
The approach here is to stringify and fillna('<NULL>') to get a
consistent series.
* typo
* Fix druid_func tests
* Addressing more comments
* last touches
* fixed RSTs so images will show up on github
* fresh screenshots on main page
* removing irrelevant portions
* moved a set of sections under 'Misc'
* rebuilt the Gallery with all screenshots
* Filtering out SQLLab views out of table list view by default
This adds a `is_sqllab_view` flag in the "tables" table, and makes the
filters those out in the Tables list view.
The filter showing on top may be a bit confusing, but certainly less
than seeing lots of user generated views.
* flake
* Addressing comments
