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* 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>
The open-source platform for monitoring and observability
Grafana allows you to query, visualize, alert on and understand your metrics no matter where they are stored. Create, explore, and share dashboards with your team and foster a data-driven culture:
- Visualizations: Fast and flexible client side graphs with a multitude of options. Panel plugins offer many different ways to visualize metrics and logs.
- Dynamic Dashboards: Create dynamic & reusable dashboards with template variables that appear as dropdowns at the top of the dashboard.
- Explore Metrics: Explore your data through ad-hoc queries and dynamic drilldown. Split view and compare different time ranges, queries and data sources side by side.
- Explore Logs: Experience the magic of switching from metrics to logs with preserved label filters. Quickly search through all your logs or streaming them live.
- Alerting: Visually define alert rules for your most important metrics. Grafana will continuously evaluate and send notifications to systems like Slack, PagerDuty, VictorOps, OpsGenie.
- Mixed Data Sources: Mix different data sources in the same graph! You can specify a data source on a per-query basis. This works for even custom datasources.
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Documentation
The Grafana documentation is available at grafana.com/docs.
Contributing
If you're interested in contributing to the Grafana project:
- Start by reading the Contributing guide.
- Learn how to set up your local environment, in our Developer guide.
- Explore our beginner-friendly issues.
- Look through our style guide and Storybook.
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- Follow @grafana on Twitter.
- Read and subscribe to the Grafana blog.
- If you have a specific question, check out our discussion forums.
- For general discussions, join us on the official Slack team.
License
Grafana is distributed under AGPL-3.0-only. For Apache-2.0 exceptions, see LICENSING.md.