【BI看板】Superset时间过滤控件二次开发

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_dataThis endpoint evolved to be the entry point of many differentrequests that GETs or POSTs a form_data.`self.generate_json` receives this input and returns differentpayloads based on the request args in the first blockTODO: break into one endpoint for each return shape"""response_type = ChartDataResultFormat.JSON.valueresponses: 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_optionbreak# Verify user has permission to export CSV fileif (response_type == ChartDataResultFormat.CSVand 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 typesif (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 = Noneif json_body is None:return self.response_400(message=_("Chart has no query context saved. Please save the chart again."))# override saved query contextjson_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 typesif (is_feature_enabled("GLOBAL_ASYNC_QUERIES")and query_context.result_format == ChartDataResultFormat.JSONand 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_dttmkwargs["to_dttm"] = to_dttmreturn 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。