* Add support for msgpack results_backend serialization
* Serialize DataFrame with PyArrow rather than JSON
* Adjust dependencies, de-lint
* Add tests for (de)serialization methods
* Add MessagePack config info to Installation docs
* Enable msgpack/arrow serialization by default
* [Fix] Prevent msgpack serialization on synchronous queries
* Add type annotations
* feat: rough check in for Presto rows and arrays
* fix: presto arrays
* fix: return selected and expanded columns
* fix: add helper methods and unit tests
* fix: only allow exploration of selected columns
* fix: address Beto's comments and add more unit tests
* [scheduled reports] Add support for scheduled reports
* Scheduled email reports for slice and dashboard visualization
(attachment or inline)
* Scheduled email reports for slice data (CSV attachment on inline table)
* Each schedule has a list of recipients (all of them can receive a single mail,
or separate mails)
* All outgoing mails can have a mandatory bcc - for audit purposes.
* Each dashboard/slice can have multiple schedules.
In addition, this PR also makes a few minor improvements to the celery
infrastructure.
* Create a common celery app
* Added more celery annotations for the tasks
* Introduced celery beat
* Update docs about concurrency / pools
* [scheduled reports] - Debug mode for scheduled emails
* [scheduled reports] - Ability to send test mails
* [scheduled reports] - Test email functionality - minor improvements
* [scheduled reports] - Rebase with master. Minor fixes
* [scheduled reports] - Add warning messages
* [scheduled reports] - flake8
* [scheduled reports] - fix rebase
* [scheduled reports] - fix rebase
* [scheduled reports] - fix flake8
* [scheduled reports] Rebase in prep for merge
* Fixed alembic tree after rebase
* Updated requirements to latest version of packages (and tested)
* Removed py2 stuff
* [scheduled reports] - fix flake8
* [scheduled reports] - address review comments
* [scheduled reports] - rebase with master
* Add separate limit setting for SqlLab
Use separate param for wrap sql
Get query limit from config
unit tests for limit control rendering in sql editor
py unit test
pg tests
Add max rows limit
Remove concept of infinity, always require defined limits
consistency
Assert on validation errors instead of tooltip
fix unit tests
attempt persist state
pr comments and linting
* load configs in via common param
* default to 1k
* [utils] gathering/refactoring into a "utils/" folder
Moving current utils.py into utils/core.py and moving other *util*
modules under this new "utils/" as well.
Following steps include eroding at "utils/core.py" and breaking it down
into smaller modules.
* Improve tests
* Make loading examples in scope for tests
* Remove test class attrs examples_loaded and requires_examples
* Bug: fixing async syntax for python 3.7
Rename async to async_ so superset installs for python 3.7.
* Addressing PR comments. Use kwargs instead of explicitly specifying async_ so downstream engines (e.g. PyHive) that supports async can choose to use the async_ in pythonwq3.7 and async in <=python3.6
* addressing additional pr comments
I think that the only place where we want db connection pooling would be
to talk to the metadata database. SQL Lab should close its connections
and never pool them.
Given that each Gunicorn worker will create its own pool that can lead
to way too many connections opened.
closes https://github.com/apache/incubator-superset/issues/4666
* Improve database type inference
Python's DBAPI isn't super clear and homogeneous on the
cursor.description specification, and this PR attempts to improve
inferring the datatypes returned in the cursor.
This work started around Presto's TIMESTAMP type being mishandled as
string as the database driver (pyhive) returns it as a string. The work
here fixes this bug and does a better job at inferring MySQL and Presto types.
It also creates a new method in db_engine_specs allowing for other
databases engines to implement and become more precise on type-inference
as needed.
* Fixing tests
* Adressing comments
* Using infer_objects
* Removing faulty line
* Addressing PrestoSpec redundant method comment
* Fix rebase issue
* Fix tests
* [sql lab] a better approach at limiting queries
Currently there are two mechanisms that we use to enforce the row
limiting constraints, depending on the database engine:
1. use dbapi's `cursor.fetchmany()`
2. wrap the SQL into a limiting subquery
Method 1 isn't great as it can result in the database server storing
larger than required result sets in memory expecting another fetch
command while we know we don't need that.
Method 2 has a positive side of working with all database engines,
whether they use LIMIT, ROWNUM, TOP or whatever else since sqlalchemy
does the work as specified for the dialect. On the downside though
the query optimizer might not be able to optimize this as much as an
approach that doesn't use a subquery.
Since most modern DBs use the LIMIT syntax, this adds a regex approach
to modify the query and force a LIMIT clause without using a subquery
for the database that support this syntax and uses method 2 for all
others.
* Fixing build
* Fix lint
* Added more tests
* Fix tests
* Force lowercase column names for Snowflake and Oracle
* Force lowercase column names for Snowflake and Oracle
* Remove lowercasing of DB2 columns
* Remove DB2 lowercasing
* Fix test cases
* use session context manager
* contextlib2 added to requirements.txt
* Fixing error: Import statements are in the wrong order. from contextlib2 import contextmanager should be before import sqlalchemy
* Fixing return inside generator
* fixed C812 missing trailing comma
* E501 line too long
* fixed E127 continuation line over-indented for visual indent
* E722 do not use bare except
* reorganized imports
* added context manager contextlib2.contextmanager
* fixed import ordering
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.
* 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
* conditional check on datatype of results before converting to df
fix type checking
fix conditional checks
remove trailing whitespace and fix df_data fallback def
actually remove trailing whitespace
generalized type check to check all columns for dict
refactor dict col check
* move df conversion to helper and add unit test
add missing newlines
another missing newline
fix quotes
more quote fixes
