Log dashboards strategies

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. See the Strategy comparison section to help you choose a strategy.

Pinning DQL queries

Dashboards Classic

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.

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.

Keep an eye on costs

Don't turn on autorefresh if you don't need it. Each time a dashboard with DQL query tiles is refreshed manually or automatically, each of those tiles triggers a billed DQL query.
Queries pinned to a dashboard are stopped after 10 seconds to prevent generating excessive costs. If there are partial results within the first 10 seconds, they are displayed in the tile. Otherwise, the tile displays the message "Query exceeded maximum execution time."
To reduce query execution time, try changing the timeframe or adjusting the query.

See below for more about managing dashboard performance and consumption.

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 web UI or defined in your pinned DQL query (for example, 30 days)
```
dql
fetch logs, from:-30d
```
Sampling ratio specified in your pinned DQL query (for example, 1000)
```
dql
fetch logs, samplingRatio:1000
```
Scanlimit parameter in Gigabytes in your pinned DQL query (for example, 10,000)
```
dql
fetch logs, scanLimitGBytes:10000
```

See DQL Best practices for optimizing your queries.

Pinning log metrics

Dashboards Classic

You can 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.

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.

Strategy comparison

Action	DQL query result	Log metric
Show charts and pin to dashboard	Yes	Yes
Includes historical data	Yes, any timeframe	No, only after the metric is captured
Works without predefined schema	Yes	No
Easily modifiable	Yes	No, you have to set up a new metric
Alerting	With log events, based on occurrences	Based on occurrences or attribute value
Works without retaining full log data	No	Yes, original logs can be dropped
Consumption	DDUs for log ingest and processing, DDUs for log query, such as when a dashboard is refreshed	DDUs for log ingest and processing, DDUs for custom metrics