* improved text
* prevent all labels removed
* use `structuredClone` instead of lodash
* update test
* get `structuredClone` in test?
* fix using structuredClone
* add prom query var editor with tests and migrations
* fix migration, now query not expr
* fix label_values migration
* remove comments
* fix label_values() variables order
* update UI and use more clear language
* fix tests
* use null coalescing operators
* allow users to query label values with label and metric if they have not set there flavor and version
* use enums instead of numbers for readability
* fix label&metrics switch
* update type in qv editor
* reuse datasource function to get all label names, getLabelNames(), prev named getTagKeys()
* use getLabelNames in the query var editor
* make label_values() variables label and metric more readable in the migration
* fix tooltip for label_values to remove API reference
* clean up tooltips and allow newlines in query_result function
* change function wording and exprType to query type/qryType for readability
* update prometheus query variable docs
* Update public/app/plugins/datasource/prometheus/components/VariableQueryEditor.tsx
Co-authored-by: Galen Kistler <109082771+gtk-grafana@users.noreply.github.com>
---------
Co-authored-by: Galen Kistler <109082771+gtk-grafana@users.noreply.github.com>
* Range splitting: range splitting function
* Range splitting: experiment with 1 hour splits
* Range splitting: reorganize code
* Range splitting: improve code readability and meaning
* Range splitting: add partition limit to prevent infinite loops
* Range splitting: add error handling
* Range splitting: disable for logs queries
* Range splitting: support any arbitrary time splitting + respect original from/to in the partition
* Chore: remove console logs
* Chore: delete unused import
* Range splitting: actually send requests in sequence
* Range splitting: do not split when > 1 query
* Range splitting: combine frames
* Chore: rename function
* split in reverse
* polished reversing
* keep reference to the right frame in the result
* Range splitting: change request state to Streaming
* Range splitting: fix moving only 1 unit of time instead of the provided one
* Chore: change default parameter to timeShift = 1
* Range splitting: do not split for range queqries
* Range splitting: add initial support for log queries
* Range splitting: do not use MutableDataFrame
It has bad performance and it's not required
* Chore: remove unused export
* Query Splitting: move to module
* loki: split: fix off-by-one error (#62966)
loki: split: fix off-by-one loop
* Range splitting: disable for logs volume queries
* Range splitting: combine any number of fields, not just hardcoded 2
* Range splitting: optimize frame-combining function
* Range splitting: further optimize
* Range splitting: combine frame length
* Range splitting: combine stats
* Range splitting: combine stats without assuming the same order
* Query splitting: catch and raise errors
* Range splitting: create feature flag
* Range splitting: implement feature flag
* Range splitting: add unit test for datasource query
* Range splitting: add basic test for runPartitionedQuery
* Range splitting: add unit test for resultLimitReached
* Range splitting: test frame merging
* Chore: fix unit test
---------
Co-authored-by: Sven Grossmann <svennergr@gmail.com>
Co-authored-by: Gábor Farkas <gabor.farkas@gmail.com>
* drag files to dashboard
* use file name as panel title
* add file size limitation, file type limitation and error handling
* Refactor file parsing for code sharing
move accepted types and max size to file-import constants
show which file types are allowed in file type error
* update codeowners
* Adjust max size to 1mb
* fix: add placeholder to loki query editor
* fix: use original placeholder
* refactor: extract set placeholder into seperate function
* fix: use correct monaco types
* revert: unwanted change from merge
* New pfs impl
* Reached codegen parity with old system
* Update all models.cue inputs
* Rename all models.cue files
* Remove unused prefixfs
* Changes Queries->DataQuery schema interface
* Recodegen
* All tests passing, nearly good now
* Add SchemaInterface to kindsys props
* Add pascal name deriver
* Relocate plugin cue files again
* Clarify use of injected fields
* Remove unnecessary aliasing
* Move DataQuery into mudball
* Allow forcing ExpandReferences on go type generation
* Move DataQuery def into kindsys, add generator to copy it to common
* Fix copy generator to replace package name correctly
* Setup Cloudwatch data query schema
* Add go file
* Re-generate types
* Re-generate types
* Add missing files
* Re-generate docs
* Add MetricStat
* Add CloudWatchAnnotationQuery
* Remove DataQuery extension
* Fixes
* Sync with main
* Update report
---------
Co-authored-by: sam boyer <sdboyer@grafana.com>
* Initial schema
- Add types based off of current frontend
* Rename and field-level comments
* Update report and regenerate files
* Rename frontend Azure folder
- Doing this for consistency and to ensure code-generation works
- Update betterer results due to file renames
* Remove default and add back enum vals that I deleted
* Set workspace prop as optional
* Replace template variable types
* Connect frontend query types
- Keep properties optional for now to avoid major changes
- Rename AzureMetricResource
- Correctly use ResultFormat
* Add TSVeneer decorator
* Update schema
* Update type
* Update CODEOWNERS
* Fix gen-cue issue
* Fix backend test
* Fix e2e test
* Update code coverage
* Remove references to old Azure Monitor path
* Review
* Regen files
* feat(debugpanel): schematizing debug panel plugin
* delete old types and update imports to new generated schema
* update to experimental
* derived counters, fix options key name
* num to int
* chore(debugpanel): update ts imports post type rename
* chore(kindsys): refresh report.json
* migrate UpdateCounters back to runtime
* merge with main
---------
Co-authored-by: Will Browne <will.browne@grafana.com>
Co-authored-by: sam boyer <sdboyer@grafana.com>
* Elasticsearch: Implement schema for query
* Comment out types I am not sure how to do
* Manually fix typing for PipelineMetricAggregationWithMultipleBucketPaths and BasePipelineMetricAggregation
* Import types to types.ts to have single source of truth
* Cleanup, reorder
* Remove unnecesary Schema.
* Fix test
* Refactor
* fix: label filter expression treats int as string
* refactor: labelFilterRenderer
* test: add tests to labelFilterRenderer
* refactor: use backticks based on the operator not the value
* test: labelFilterRenderer uses the correct value type based on the operator
* test: use it.every rather than having multiple repetitive tests
* QueryHistory: Improve handling of mixed datasource entries
* remove todo
* remove todo
* fix comment submit test
* disable running queries if at least one doesn't have a datasource
* remove unnecessary code
* add tests for diabled buttons state
* Create cue file and gen ts/go types
* Use generated schema in ts/go
* Run make den-cue to update report
* Manually extend Phlare query
* Updates
* Add default queryType
* Run make gen-cue to Update report.json
* Kindsys: Replace DeclForGen with kindsys.Kind
DeclForGen was always unnecessary - it just wasn't obvious on initial
implementation, when we were focused on generating unique types for each
core kind. This removes it, considerably simplifying interactions with
kindsys - virtually everything now just relies on kindsys.Kind and its
derived interfaces.
* Removed unused jenny
* Rename params in jennies
* SQL: toRawSQL required and escape table
* Fix autocomplete for MySQL
* Change the way we escape for builder
* Rework escape ident to be smart instead
* Fix A11y for alias
* Add first e2e test
* Add test for code editor
* Add doc
* Review comments
* Move functions to sqlUtil
* Use dbName in jsonData instead of database
* Use dbName in instead of database
* Remove database fields and define dbName instead
* Fix tests
* set database field as empty string
* Tempo data query wip
* Replace TempoQuery with new type from schema
* Added some documentation for each DataQuery field
* Change limit type from number to int64
* Use TempoDataQuery instead of local model in the backend
* Update report.json
