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Currently, there is no separate flow to process Lucene Logs on the backend. They are processed as time series in processAggregationDocs.
On the FE logs are processed in processResponseToDataFrames().
The output should be compared and separated out (from raw_data or in general) if necessary. We might be able to combine common logic with raw_data processing: For comparison, the ElasticSearch PR is here.
Some general guidelines are:
Create visualization to compare between frontend and backend flows, preferably in a shared Cloud dashboard e.g. clouddatasources.grafana.net
Unit tests in the frontend should be copied or updated in the backend to assert the same behavior and add unit tests when there are none.
Check Elasticsearch repo for related tests – helpful when there are no OpenSearch tests in the frontend or backend
We should match OpenSearch's backend behavior to the current frontend behavior, but be aware it may not always make sense. Elasticsearch's current behavior is also another resource for how OpenSearch should behave.
Let's be pragmatic about this – our foremost goal is the migration of OpenSearch's frontend behavior. We can aim for some Elasticsearch feature parity, but only within reason.
Migrate or implement: i.e. make any changes to code in backend
Use the dashboard created above to compare visualization results (data frame comparison)
Can also inspect data frame on a more detailed basis
Note: It seems like we have a problem, at least on the FE, with detecting field types, for example here the Query inspector shows AvgTicketPrice as string and not int, which isn't what I would expect. Just something to keep in mind when comparing Logs.
The text was updated successfully, but these errors were encountered:
Note: Blocked by #224
Currently, there is no separate flow to process Lucene Logs on the backend. They are processed as time series in processAggregationDocs.
On the FE logs are processed in processResponseToDataFrames().
The output should be compared and separated out (from raw_data or in general) if necessary. We might be able to combine common logic with raw_data processing: For comparison, the ElasticSearch PR is here.
Some general guidelines are:
Note: It seems like we have a problem, at least on the FE, with detecting field types, for example here the Query inspector shows AvgTicketPrice as string and not int, which isn't what I would expect. Just something to keep in mind when comparing Logs.
The text was updated successfully, but these errors were encountered: