Frequently Asked Questions
Why job takes more time?
- User cannot run multiple jobs at a time. Until current jobs completes other jobs will be in accepted (awaited) state in cluster.
- Multiple joins with the large tables takes more time to execute. If the requirement is for the most recent stats then try to use time filters on tables.
- Using Cartesian joins takes more time.
- If cluster is running on full capacity it takes more time to execute.
Why job got failed?
- Job fails due to compilation errors like invalid column reference, table not found etc.
Why still showing old data?
- Auto run is not enabled to data source and new data not ingested in data source then it shows old data in all the dependencies.
- Until the source (parent) schedule completes new data can’t be seen in the dependencies. Example: Until Flow schedule completes new data can’t be seen in segments and dashboards.
- If source auto run scheduled for every night and their dependencies scheduled for every two hours then new data will not be reflected.
Why schedule got failed?
- Schedule fails due to compilation errors like invalid column reference, table not found etc.
Why schedule didn’t run?
- Until source schedule completes their dependencies schedule will not run.
- It also depends on the capacity of cluster. For example under one flow there are 20 dependencies (cubes, segments, dashboards) then it takes time to run all the dependency schedules. If cluster capacity is good then it runs in time.
Why ingestion fails?
- System or Data Base is not available.
- Timeout waiting for connection from pool.
Why ingestion takes more time?
- Ingesting large data takes more time. By adding many tables to the single data source it takes more time to ingest complete load.
- Tables might be ingesting in complete loading mode. Change it to incremental loading mode.
- If there is no indexes on the table ingestion takes more time.
- Depends on response of the Data Base server. Data Base should respond faster for the select queries.