The Log Explorer is your home base for log troubleshooting and exploration. Whether you start from scratch, from a Saved View, or land here from any other context like monitor notifications or dashboard widgets, the Log Explorer is designed to iteratively:
At any moment, Export your Log Explorer view to reuse it later or in different contexts.
The search filter consists of a timerange and a search query mixing
key:value and full-text search. Refer to our log search syntax and timerange documentation for details on advanced use cases. For example, the search query
service:payment status:error rejected over a
Past 5 minutes timerange:
Indexed Logs support both full-text search and
key:value search queries.
key:value queries require that you declare a facet beforehand.
Logs can be valuable as individual events, but sometimes valuable information lives in a subset of events. In order to expose this information, aggregate your logs.
Note: Aggregations are supported for indexed logs only. If you need to perform aggregation on non-indexed logs, consider temporary disabling exclusion filters, using logs to metrics and/or running a rehydration on your archives.
With fields aggregation, all logs matching the query filter are aggregated into groups based on the value of one or multiple log facets. On top of these aggregates, you can extract the following measures:
percentiles) on numerical values of a facet per group
Note: Individual logs having multiple values for a single facet belong to that many aggregates. For instance, a log having the
team:sre and the
team:marketplace tags are counted once in the
team:sre aggregate and once in the
Fields aggregation supports one dimension for the Toplist visualization, and up to three dimensions for the Timeseries and Table visualizations. When there are multiple dimensions, the top values are determined according to the first dimension, then according to the second dimension within the top values of the first dimension, then according to the third dimension within the top values of the second dimension.
With pattern aggregation, logs that have a
message with similar structures, belong to the same
service and have the same
status are grouped altogether. The patterns view is helpful for detecting and filtering noisy error patterns that could cause you to miss other issues:
Note: The pattern detection is based on 10,000 log samples. Refine the search to see patterns limited to a specific subset of logs.
Patterns support the List Aggregates visualization. Clicking a pattern in the list opens the pattern side panel from which you can:
Transactions aggregate indexed logs according to instances of a sequence of events, such as a user session or a request processed across multiple micro-services. For example, an e-commerce website groups log events across various user actions, such as catalog search, add to cart, and checkout, to build a transaction view using a common attribute such as
Note: The transaction aggregation differs from the natural group aggregation, in the sense that resulting aggregates not only include logs matching the query, but also all logs belonging to the related transactions.
facetwith string values, calculate specific log event information using the operations
measure, calculate statistical information using the operations
Transactions support the List Aggregates visualization. Clicking a pattern in the list opens the pattern side panel from which you can:
Visualizations define how the outcome of filter and aggregates are displayed.
Lists are paginated results of logs or aggregates. They are valuable when individual results matter, but you have no prior or clear knowledge on what defines a matching result. Lists allow you examine a group of results.
Lists displaying individual logs and lists displaying aggregates of logs have slightly different capabilities.
For a list of individual logs, choose which information of interest to display as columns. Manage the columns of the table using either:
With the Options button, control the number of lines displayed in the table per log event.
The default sort for logs in the list visualization is by timestamp, with the most recent logs on top. This is the fastest and therefore recommended sorting method for general purposes. Surface logs with lowest or highest value for a measure first, or sort your logs lexicographically for the unique value of facet, ordering a column according to that facet. Note that sorting your table according to a specific field requires that you declare a facet beforehand.
The configuration of the log table is stored alongside other elements of your troubleshooting context in Saved Views
The columns displayed in list of aggregates are columns derived from the aggregation.
Results are sorted according to:
The following Timeseries log analytics shows the evolution of the top 5 URL Paths according to the number of unique client IPs over the last month.
Choose additional display options for timeseries: the roll-up interval, whether you display results as bars (recommended for counts and unique counts), lines (recommended for statistical aggregations) or areas, and the colorset.
For example, the following Toplist shows the top 15 Customers on a merchant website according to the number of unique sessions they had over the last day.
Visualize the top values from a facet according to a chosen measure (the first measure you choose in the list), and display the value of additional measures for elements appearing in this table. Update a search query or drill through logs corresponding to either dimension.
Note: A table visualization used for one single measure and one single dimension is the same as a Toplist, just with a different display.
The following table log analytics show the evolution of the Top 10 Availability zones, and for each Availability Zone the Top 10 Versions according to their number or error logs, along with the number of unique count of Hosts and Container ID for each.
At any moment, and depending on your current aggregation, export your exploration as a:
Additional helpful documentation, links, and articles: