* Add ISO duration to time grains
* Use ISO duration
* Remove debugging code
* Add module to yarn.lock
* Remove autolint
* Druid granularity as ISO
* Remove dangling comma
* [bugfix] convert metrics to numeric in dataframe
It appears sometimes the dbapi driver and pandas's read_sql fail at
returning the proper numeric types for metrics and they show up as
`object` in the dataframe. This results in "No numeric types to
aggregate" errors when trying to perform aggregations or pivoting in
pandas.
This PR looks for metrics in dataframes that are typed as "object"
and uses pandas' to_numeric to convert.
* Fix tests
* Remove all iteritems
* move access permissions methods to security manager
* consolidate all security methods into SupersetSecurityManager
* update security method calls
* update calls from tests
* move get_or_create_main_db to utils
* raise if supersetsecuritymanager is not extended
* rename sm to security_manager
* [sql lab] disable cross schema search
This is killing our metastore as people type it emits large
all-table-dump as they hit the keystroke. It never returns as it times
out and hammers the poor metastore.
Also some improvements around the disabling the table select on the left
panel and having the table name not be sticky.
* typo
* Expose hook to inject database connection logic on the fly
This environment configuration setting hook allows administrators to
alter the database connection parameters on the fly based on user
information. This can be use for a variety of purposes:
* rewire a subset of users to use different database user accounts
* pass user related information to the database for logging or QoS
purposes
* ...
* Fixes
* [Explore view] Use POST method for charting requests
* fix per code review comments
* more code review fixes
* code review fix: remove duplicated calls for getting values from request
* [Explore view] Use POST method for charting requests
* fix per code review comments
* more code review fixes
* code review fix: remove duplicated calls for getting values from request
Currently, even though `get_sqla_engine` calls get memoized, engines are
still short lived since they are attached to an models.Database ORM
object. All engines created through this method have the scope of a web
request.
Knowing that the SQLAlchemy objects are short lived means that
a related connection pool would also be short lived and mostly useless.
I think it's pretty rare that connections get reused within the context
of a view or Celery worker task.
We've noticed on Redshift that Superset was leaving many connections
opened (hundreds). This is probably due to a combination of the current
process not garbage collecting connections properly, and perhaps the
absence of connection timeout on the redshift side of things. This
could also be related to the fact that we experience web requests timeouts
(enforced by gunicorn) and that process-killing may not allow SQLAlchemy
to clean up connections as they occur (which this PR may not help
fixing...)
For all these reasons, it seems like the right thing to do to use
NullPool for external connection (but not for our connection to the metadata
db!).
Opening the PR for conversation. Putting this query into our staging
today to run some tests.
fixes https://github.com/apache/incubator-superset/issues/3507
This prevents malformed JSON from getting saved in a slice's params. It
also prevents the issue described in #3507 from happening though as a
result malformed slices will render using default control values.
When the database impersonation flag is on, a query using a template
fails. It has to do with templating using a database connection without
a username being specified by the caller, along with the fact that the
work is taking place on a worker, outside a web request, where
referencing g.user raises this exception.
* Add "Impersonate user" setting to Datasource
* Add tests
* Use g.user.username for all the sync cases
* use uri.username instead of uri.user
* Small refactoring
* [explore] fixed padding bug on filter section
* fix slice_id logging issue
* [superset-sqllab] fix slice_id population in appropriate column
* [explore-logging] test the slice_id logging fix
* fix travis errors
* fix nits pointed out in PR comments
* cleanup tests
* made python more beautiful
* made python even more beautiful
* made python even more more beautiful
* made python even more more more beautiful
* fix lint error
* make exception handling more specific
* fixed silly error
* fixed argument indentation
* [add] Save filters to dashboard
* format code
* fix CI error
* add semicolon semi
* fix none object
* add test data
optimize the js code
fix the compatibility issue
* fix urllib to urllib.parse
* add space
* update test case
* remove 'return'
* fix error
* update test case
* upgrade celery to 4.0.2
* using Redis for unit tests (sqla broker not supported in Celery 4)
* Setting Celery's soft_time_limit based on `SQLLAB_ASYNC_TIME_LIMIT_SEC` config
* Better error handling in async tasks
* Better statsd logging in async tasks
* show [pending/running] query status in Results tab
* systematically using sqla NullPool on worker (async) to limit number
of database connections
