有没有人发觉Superset时间过滤组件非常高级，😟但又有点复杂，没有选择时间区间的快捷方式。
Superset的时间过滤控件可以通过在代码中进行二次开发来进行定制。以下是一些可能有用的提示：
-
查找源代码：可以在Superset的源代码中找到时间过滤器的相关代码，在
superset/assets/src/explore/components/controls目录下搜索时间控件的名称，比如DateFilterControl.jsx。 -
深入理解时间控件：了解时间控件是如何工作的非常重要，可以通过阅读Superset的源代码和官方文档来学习。
-
编写自定义时间控件：可以使用React等工具编写自定义时间控件，然后将其与Superset集成。可以在前端开发人员手册中找到相关的信息。
-
集成自定义时间控件：集成自定义时间控件的过程取决于它的实现方式和您的Superset部署环境。可以参考官方文档中的信息来集成您的自定义时间控件。
-
测试：一旦自定义时间控件被集成，需要对其进行测试以确保它正常工作。这包括测试常见的时间过滤器用例，以及测试它在不同的浏览器和设备上是否正常显示。
过滤参数，入参time_range
图表有两种接口
/superset/explore_json/?form_data
目录：\superset-2.0\superset\views\core.py
@api
@has_access_api
@handle_api_exception
@event_logger.log_this
@expose(
"/explore_json/<datasource_type>/<int:datasource_id>/",
methods=EXPLORE_JSON_METHODS,
)
@expose("/explore_json/", methods=EXPLORE_JSON_METHODS)
@etag_cache()
@check_resource_permissions(check_datasource_perms)
def explore_json(
self, datasource_type: Optional[str] = None, datasource_id: Optional[int] = None
) -> FlaskResponse:
"""Serves all request that GET or POST form_data
This endpoint evolved to be the entry point of many different
requests that GETs or POSTs a form_data.
`self.generate_json` receives this input and returns different
payloads based on the request args in the first block
TODO: break into one endpoint for each return shape"""
response_type = ChartDataResultFormat.JSON.value
responses: List[Union[ChartDataResultFormat, ChartDataResultType]] = list(
ChartDataResultFormat
)
responses.extend(list(ChartDataResultType))
for response_option in responses:
if request.args.get(response_option) == "true":
response_type = response_option
break
# Verify user has permission to export CSV file
if (
response_type == ChartDataResultFormat.CSV
and not security_manager.can_access("can_csv", "Superset")
):
return json_error_response(
_("You don't have the rights to ") + _("download as csv"),
status=403,
)
form_data = get_form_data()[0]
try:
datasource_id, datasource_type = get_datasource_info(
datasource_id, datasource_type, form_data
)
force = request.args.get("force") == "true"
# TODO: support CSV, SQL query and other non-JSON types
if (
is_feature_enabled("GLOBAL_ASYNC_QUERIES")
and response_type == ChartDataResultFormat.JSON
):
# First, look for the chart query results in the cache.
try:
viz_obj = get_viz(
datasource_type=cast(str, datasource_type),
datasource_id=datasource_id,
form_data=form_data,
force_cached=True,
force=force,
)
payload = viz_obj.get_payload()
# If the chart query has already been cached, return it immediately.
if payload is not None:
return self.send_data_payload_response(viz_obj, payload)
except CacheLoadError:
pass
# Otherwise, kick off a background job to run the chart query.
# Clients will either poll or be notified of query completion,
# at which point they will call the /explore_json/data/<cache_key>
# endpoint to retrieve the results.
try:
async_channel_id = async_query_manager.parse_jwt_from_request(
request
)["channel"]
job_metadata = async_query_manager.init_job(
async_channel_id, g.user.get_id()
)
load_explore_json_into_cache.delay(
job_metadata, form_data, response_type, force
)
except AsyncQueryTokenException:
return json_error_response("Not authorized", 401)
return json_success(json.dumps(job_metadata), status=202)
viz_obj = get_viz(
datasource_type=cast(str, datasource_type),
datasource_id=datasource_id,
form_data=form_data,
force=force,
)
return self.generate_json(viz_obj, response_type)
except SupersetException as ex:
return json_error_response(utils.error_msg_from_exception(ex), 400)/api/v1/chart/data?form_data=
目录：\superset\charts\data\api.py
该接口还比较难找哈
class ChartDataRestApi(ChartRestApi):
include_route_methods = {"get_data", "data", "data_from_cache"}
@expose("/<int:pk>/data/", methods=["GET"])
@protect()
@statsd_metrics
@event_logger.log_this_with_context(
action=lambda self, *args, **kwargs: f"{self.__class__.__name__}.data",
log_to_statsd=False,
)
def get_data(self, pk: int) -> Response:
chart = self.datamodel.get(pk, self._base_filters)
if not chart:
return self.response_404()
try:
json_body = json.loads(chart.query_context)
except (TypeError, json.decoder.JSONDecodeError):
json_body = None
if json_body is None:
return self.response_400(
message=_(
"Chart has no query context saved. Please save the chart again."
)
)
# override saved query context
json_body["result_format"] = request.args.get(
"format", ChartDataResultFormat.JSON
)
json_body["result_type"] = request.args.get("type", ChartDataResultType.FULL)
。。。。。
。。。。。
。。。。。
# TODO: support CSV, SQL query and other non-JSON types
if (
is_feature_enabled("GLOBAL_ASYNC_QUERIES")
and query_context.result_format == ChartDataResultFormat.JSON
and query_context.result_type == ChartDataResultType.FULL
):
return self._run_async(json_body, command)
try:
form_data = json.loads(chart.params)
except (TypeError, json.decoder.JSONDecodeError):
form_data = {}
return self._get_data_response(
command=command, form_data=form_data, datasource=query_context.datasource
)时间过滤控件
计算时间范围
venv38\Lib\site-packages\marshmallow\schema.py
再往下，计算日期地方
data = processor(data, many=many, **kwargs)
再往下在superset\common\query_object_factory.py，可以看到from_dttm和to_dttm
processed_extras = self._process_extras(extras)
result_type = kwargs.setdefault("result_type", parent_result_type)
row_limit = self._process_row_limit(row_limit, result_type)
from_dttm, to_dttm = self._get_dttms(time_range, time_shift, processed_extras)
kwargs["from_dttm"] = from_dttm
kwargs["to_dttm"] = to_dttm
return QueryObject(
datasource=datasource_model_instance,
extras=extras,
row_limit=row_limit,
time_range=time_range,
time_shift=time_shift,
**kwargs,
)总之，Superset的时间过滤器可以通过在代码中进行二次开发来实现定制化，这需要一些前端编程技能和对Superset源代码的深入理解。
