Log dashboards strategies
powered by Grail
You can visualize data from your logs using dashboards. You can adjust dashboards to your observability needs, performance and consumption, by selecting one of the two strategies described below.
Pinning DQL queries
After selecting Advanced mode in Log and events viewer, you can use DQL queries to retrieve, explore, and analyze data, including patterns and trends over time. After running a query, you can pin the result to a dashboard as a data table, single value, bar chart or other visualization.
Note: You do not pin the static result of your query to a dashboard. Instead, your query fetches fresh results from Grail every time your dashboard is viewed or refreshed. This guarantees precise and up-to-date data in your dashboards.
When you use dashboards with pinned DQL queries, the following factors can impact your dashboard performance and consumption:
- The number of DQL queries pinned to a dashboard
- The number of users loading the dashboard
- Dashboard autorefresh
- Timeframe selected in the UI or defined in your pinned DQL query, for example 30 days
fetch logs, from:-30d
- Sampling ratio specified in your pinned DQL query, for example 1000
fetch logs, samplingRatio:1000
- Scanlimit parameter in Gigabytes in your pinned DQL query, for example 10,000
fetch logs, scanLimitGBytes:10000
See DQL Best practices for optimizing your queries.
Pinning log metrics
You have the possibility to create metrics based on log data and use them in your analyzes. For example, you can create a dashboard that combines log metrics with the chosen visualizations. See dashboards to learn more.
Metrics extraction from logs takes place during ingest. This means your log metrics are populated when new logs are ingested, and you are not able to use historical logs stored in Dynatrace to create your metrics. After metrics are extracted from logs, the original raw log data can be dropped. For details, see log buckets.
Note: In this scenario, the DDU consumption for log metrics is based on ingested log data and custom metrics. Viewing or analyzing metrics does not affect your consumption.
|Action||DQL query result||Log metric|
Show charts and pin to dashboard
Includes historical data
Yes, any timeframe
No, only after the metric is captured
Works without predefined schema
No, you have to set up a new metric
With log events, based on occurrences
Based on occurrences or attribute value
Works without retaining full log data
Yes, original logs can be dropped
DDUs for log ingest and processing,
DDUs for log ingest and processing, DDUs for custom metrics