Slow dashboard loading time when it got embedded

[nikhilkudavemnh]

I have facing 2/3 api which are very slow. Each takes 500 milliseconds which cause dashboard to load overall 5 sec
1st api is get metadata of perticular chart,
I have used metadata caching but still this takes time. Due to slow loading issue I'm not able use superset at enterprises level

[dosubot[bot]]

Hey @nikhilkudavemnh! I'm here to help you with any bugs or questions you have. Let's tackle this together!

To address slow loading times for embedded dashboards in Superset, especially when metadata caching is already in use, consider the following optimizations:

1. Optimize Configuration Settings: Adjust configuration settings to improve API performance. For instance, ensure that ROW_LIMIT is set appropriately to limit data processing, and configure SUPERSET_WEBSERVER_TIMEOUT to prevent long-running requests from consuming resources unnecessarily. Additionally, enable and configure rate limiting with RATELIMIT_ENABLED and RATELIMIT_APPLICATION to manage request loads ^[1].

2. Caching Layer: Ensure that the caching layer is properly configured and optimized. Using a robust caching solution like Redis can significantly reduce the load on the data warehouse and speed up data retrieval, which is crucial for embedded dashboards ^[2].

3. Logging and Monitoring: Utilize Superset's logging capabilities to diagnose slow API responses. The loggerMiddleware and LogUtils.ts provide detailed insights into user interactions and system performance, which can help identify and resolve performance issues ^[3][4].

By implementing these strategies, you can potentially improve the performance of embedded dashboards in Superset.

To continue talking to Dosu, mention @dosu.

---

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[nikhilkudavemnh]

this is the superset_config file I used caching on Redis and celery but it still wont work.

HTTP://domain.com/genreateguest token------------------------------------takes 2 sec
https://domain.com/embedded/3d0346ae-8dc0-4776-bf02-d87692a918f1uiConfig=10&show_filters=false&expand_filters=false----------this takes 3sec
https://domain.com/api/v1/dashboard/5/datasets---------------------------this takes 2 sec

ROW_LIMIT = 10000
SECRET_KEY = SUPERSET_SECRET_KEY

FEATURE FlAGS

FEATURE_FLAGS = {
"EMBEDDED_SUPERSET": True,
"TAGGING_SYSTEM": False,
"ENABLE_TEMPLATE_PROCESSING": True,
"HORIZONTAL_FILTER_BAR":True,
"FAB_ADD_SECURITY_API": True,
"DASHBOARD_RBAC":True,
"ALLOW_FULL_CSV_EXPORT":True,
"DRILL_TO_DETAIL":True,
"DASHBOARD_VIRTUALIZATION":False,
"SQL_VALIDATORS_BY_ENGINE": {
'postgresql': 'PostgresSQLValidator'
}
}

class CeleryConfig(object):
broker_url = "redis://localhost:6379/0"
imports = (
"superset.sql_lab",
"superset.tasks.scheduler",
)
result_backend = "redis://localhost:6379/0"
worker_prefetch_multiplier = 10
task_acks_late = True
task_annotations = {
"sql_lab.get_sql_results": {
"rate_limit": "100/s",
},
}

from flask_caching.backends.rediscache import RedisCache

RESULTS_BACKEND = RedisCache(
host='localhost', port=6379, key_prefix='superset_results')

FILTER_STATE_CACHE_CONFIG = {
'CACHE_TYPE': 'RedisCache',
'CACHE_DEFAULT_TIMEOUT': 86400,
'CACHE_KEY_PREFIX': 'superset_filter_cache',
'CACHE_REDIS_URL': 'redis://localhost:6379/0'
}

DATA_CACHE_CONFIG = {
"CACHE_TYPE": "SupersetMetastoreCache",
"CACHE_KEY_PREFIX": "superset_results",
"CACHE_DEFAULT_TIMEOUT": 86400,
}