The Logs Explorer is your home base for troubleshooting and exploration:
In this view you can:
Build up a context to explore your logs in your log explorer view first by selecting the proper time range then by using the search bar to filter your Logstream and Log Analytics.
The time range feature allows you to display logs in the Logstream or Log Analytics within a given time period. It appears directly under the search bar as a timeline. The timeline can be displayed or wrapped up with the Show timeline check box in the Logstream option panel.
Quickly change the time range by selecting a preset range from the dropdown:
Use facets, measures, tags, or even free text search to filter your Logstream and Log Analytics with dedicated context. The search bar and url automatically reflect your selections.
Follow the guide to search your logs for a detailed explanation of all the Log Explorer search features, including use of wildcards and queries of numerical values.
Use Saved Views to automatically configure your Log Explorer with a preselected set of facets, measures, searches, time ranges, and visualizations.
Check the dedicated saved views documentation to learn more.
Switch between the Logstream and the Log Analytics modes by clicking on the Log Mode button in the upper left corner of the page:
The logstream is displayed in the logs table.
Configure the logs table content according to your needs and preferences with the “Options” button. Among your custom attributes, only faceted or measures attributes are available for columns.
Log results are sorted by date—the most recent on top by default. You can also inverse-sort by date, with the least recent (within the limits of the time range) on top.
Click on any log line to open the log panel and see more details about it: raw message, extracted attributes, and tags (with host, service, and source tags on top).
Some standard attributes—for instance,
duration—have specific highlighted displays in the Log Panel for better readability. Make sure you extract corresponding information from your logs and remap your attributes with standard attribute remappers.
Interact with the attributes names and values in the lower JSON section to:
Interact with the upper reserved attributes section:
hostof the log.
trace_idattribute in the log: refer to trace injection in logs) or append search request with the
serviceof the log.
sourceof the log.
The View in context button updates the search request in order to show you the log lines dated just before and after a selected log—even if they don’t match your filter. This context is different according to the situation, as Datadog uses the
container_id attributes, along with tags, in order find the appropriate context for your logs.
Copy the JSON log content to the clipboard through the Export button or keyboard interaction (Ctrl+C/Cmd+C).
After having gone through Datadog processing, log parsing, and having facets and measures over the important attributes, you can graph log queries and see maximums, averages, percentiles, unique counts, and more.
Follow the log graphing guide to learn more about all the graphing options.
Investigating large volumes of log data can be time consuming: you can spend hours on them and still understand only a fraction of them. However, applicative logs often look the same with some fraction of them varying. These what we call patterns.
In the Log Explorer, patterns can be surfaced automatically to bring structure to the problem and help you quickly see what matters—exclude what’s irrelevant.
Find out more in the Log Patterns section
Note: To leverage the most out of your Log explorer view, make sure your logs attributes follow Datadog attribute naming convention.
A facet displays all the distinct members of an attribute or a tag as well as provides some basic analytics, such as the number of logs represented. This is also a switch to easily filter your data.
Facets allow you to pivot or filter your datasets based on a given attribute. Examples facets may include users, services, etc…
Create a Facet:
To start using an attribute as a facet or in the search, click on it and add it as a facet:
A measure is a attribute with a numerical value contained in your logs. Think of it as a “log metric”.
Create a Measure:
To start using an attribute as a measure, click on a numerical attribute of your log:
Select the Measure Unit:
Each measure has its own unit that is then used for display in the Log Explorer columns, Log stream widgets in dashboards, and Log Analytics.
Export your current Log Visualization with the Export functionality:
|Export to Monitor||Export the query applied to your Logstream in order to create the log monitor query for a new log monitor|
|Export to CSV||Export your current Logstream view with its selected column into a CSV file. You can export up to 5000 logs at once.|
Additional helpful documentation, links, and articles: