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Analytics cookies We use analytics cookies to understand how you use our websites so we can make them better, e. Save preferences. AnnotationQueryRequest. CoreApp. DataFrame. DataQueryResponse. FieldCache. TimeRange. Explore. QueryResultMeta. ScopedVars .LogQL can be considered a distributed grep that aggregates log sources. LogQL uses labels and operators for filtering. The log stream selector determines how many log streams unique sources of log content, such as files will be searched.
A more granular log stream selector then reduces the number of searched streams to a manageable volume. The filter expression is then used to do a distributed grep over the aggregated logs from the matching log streams.
Using Loki in Grafana
This is specially useful when writing a regular expression which contains multiple backslashes that require escaping. The log stream selector determines which log streams should be included in your query results. In this example, all log streams that have a label of app whose value is mysql and a label of name whose value is mysql-backup will be included in the query results.
Note that this will match any log stream whose labels at least contain mysql-backup for their name label; if there are multiple streams that contain that label, logs from all of the matching streams will be shown in the results.
The following label matching operators are supported:. The same rules that apply for Prometheus Label Selectors apply for Loki log stream selectors. After writing the log stream selector, the resulting set of logs can be further filtered with a search expression.
The search expression can be just text or regex:. The following filter operators are supported:. Filter operators can be chained and will sequentially filter down the expression - resulting log lines must satisfy every filter:.
The matching is case-sensitive by default and can be switched to case-insensitive prefixing the regex with? LogQL also supports wrapping a log query with functions that allows for counting entries per stream.
Metric queries can be used to calculate things such as the rate of error messages, or the top N log sources with the most amount of logs over the last 3 hours. LogQL shares the same range vector concept from Prometheus, except the selected range of samples include a value of 1 for each log entry. An aggregation can be applied over the selected range to transform it into an instance vector. This example demonstrates that a fully LogQL query can be wrapped in the aggregation syntax, including filter expressions.
This example gets the per-second rate of all non-timeout errors within the last ten seconds for the MySQL job. It should be noted that the range notation [5m] can be placed at end of the log stream filter or right after the log stream matcher. For example, the two syntaxes below are equivalent:. Like PromQLLogQL supports a subset of built-in aggregation operators that can be used to aggregate the element of a single vector, resulting in a new vector of fewer elements but with aggregated values:.
The aggregation operators can either be used to aggregate over all label values or a set of distinct label values by including a without or a by clause:. The without clause removes the listed labels from the resulting vector, keeping all others. The by clause does the opposite, dropping labels that are not listed in the clause, even if their label values are identical between all elements of the vector.Alert on your Loki logs with Grafana
Binary arithmetic operators are defined between two literals scalarsa literal and a vector, and two vectors. Between a vector and a literal, the operator is applied to the value of every data sample in the vector, e.
Between two vectors, a binary arithmetic operator is applied to each entry in the left-hand side vector and its matching element in the right-hand vector. The result is propagated into the result vector with the grouping labels becoming the output label set. Entries for which no matching entry in the right-hand vector can be found are not part of the result.
Pay special attention to operator order when chaining arithmetic operators. Other elements are dropped. All matching elements in both vectors are dropped. By default they filter. Their behavior can be modified by providing bool after the operator, which will return 0 or 1 for the value rather than filtering. Between two scalars, these operators result in another scalar that is either 0 false or 1 truedepending on the comparison result.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus. It is designed to be very cost effective and easy to operate. It does not index the contents of the logs, but rather a set of labels for each log stream.
Loki is like Prometheus, but for logs: we prefer a multidimensional label-based approach to indexing, and want a single-binary, easy to operate system with no dependencies.
Loki differs from Prometheus by focusing on logs instead of metrics, and delivering logs via push, instead of pull. You need gowe recommend using the version found in our build Dockerfile.
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Like Prometheus, but for logs. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 1, commits. Failed to load latest commit information.
Update Loki build image Aug 4, Added logcli docker image Sep 1, Aug 31, Oct 5, Oct 8, Update docs for redis Oct 7, Jul 30, Lambda-Promtail Jul 29, Promtail: Fix deadlock on tailer shutdown. Oct 4, Promtail is an agent which ships the contents of local logs to a private Loki instance or Grafana Cloud. It is usually deployed to every machine that has applications needed to be monitored.
Loki Dashboard quick search
Currently, Promtail can tail logs from two sources: local log files and the systemd journal on AMD64 machines only. Before Promtail can ship any data from log files to Loki, it needs to find out information about its environment. Specifically, this means discovering applications emitting log lines to files that need to be monitored. Promtail borrows the same service discovery mechanism from Prometheusalthough it currently only supports static and kubernetes service discovery.
This limitation is due to the fact that promtail is deployed as a daemon to every local machine and, as such, does not discover label from other machines. Refer to the docs for configuring Promtail for more details. When the Syslog Target is being used, logs can be written with the syslog protocol to the configured port. During service discovery, metadata is determined pod name, filename, etc. To allow more sophisticated filtering afterwards, Promtail allows to set labels not only from service discovery, but also based on the contents of each log line.
Refer to the documentation for pipelines for more details. Once Promtail has a set of targets i.
Once enough data is read into memory or after a configurable timeout, it is flushed as a single batch to Loki. As Promtail reads data from sources files and systemd journal, if configuredit will track the last offset it read in a positions file. The positions file helps Promtail continue reading from where it left off in the case of the Promtail instance restarting. This endpoint returns Promtail metrics for Prometheus. The web server exposed by Promtail can be configured in the Promtail.
Register for free. Legal and Security. Terms of Service.Grafana v6. In Grafana v5. Viewing Loki data in dashboard panels is supported in Grafana v6. Just add it as a data source and you are ready to query your log data in Explore.
You can use this functionality to link to your tracing backend directly from your logs, or link to a user profile page if a userId is present in the log line. These links appear in the log details. Each derived field consists of:. You can use a debug section to see what your fields extract and how the URL is interpolated. Click Show example log message to show the text area where you can enter a log message.
The new field with the link shown in log details:. Querying and displaying log data from Loki is available via Exploreand with the logs panel in dashboards. Select the Loki data source, and then enter a LogQL query to display your logs. A log query consists of two parts: log stream selectorand a search expression. For performance reasons you need to start by choosing a log stream by selecting a log label. The Logs Explorer the Log labels button next to the query field shows a list of labels of available log streams.
Press the Enter key to execute the query. Multiple label expressions are separated by a comma:. Another way to add a label selector is in the table section. Click Filter beside a label to add the label to the query expression. This even works for multiple queries and will add the label selector to each query.
After writing the Log Stream Selector, you can filter the results further by writing a search expression. The search expression can be just text or a regex expression. Filter operators can be chained and will sequentially filter down the expression. The resulting log lines will satisfy every filter. Loki supports Live tailing which displays logs in real-time. This feature is supported in Explore. Note that Live Tailing relies on two Websocket connections: one between the browser and the Grafana server, and another between the Grafana server and the Loki server.
If you run any reverse proxies, please configure them accordingly. The following example for Apache2 can be used for proxying between the browser and the Grafana server:. When using a search expression as detailed above, you now have the ability to retrieve the context surrounding your filtered results. Instead of hard-coding things like server, application and sensor name in your metric queries, you can use variables in their place.
Variables are shown as drop-down select boxes at the top of the dashboard. These drop-down boxes make it easy to change the data being displayed in your dashboard. Check out the Templating documentation for an introduction to the templating feature and the different types of template variables. You can use any non-metric Loki query as a source for annotations. Log content will be used as annotation text and your log stream labels as tags, so there is no need for additional mapping.
You can read more about how it works and all the settings you can set for data sources on the provisioning docs page.A detailed look at how to set up Promtail to process your log lines, including extracting metrics and labels. A pipeline is used to transform a single log line, its labels, and its timestamp. A pipeline is comprised of a set of stages. There are 4 types of stages:.
Typical pipelines will start with a parsing stage such as a regex or json stage to extract data from the log line. Then, a series of action stages will be present to do something with that extracted data. The most common action stage will be a labels stage to turn extracted data into a label. A common stage will also be the match stage to selectively apply stages or drop entries based on a LogQL stream selector and filter expressions. Note that pipelines can not currently be used to deduplicate logs; Loki will receive the same log line multiple times if, for example:.
However, Loki will perform some deduplication at query time for logs that have the exact same nanosecond timestamp, labels, and log contents. The following sections further describe the types that are accessible to each stage although not all may be used :.
The current set of labels for the log line. Initialized to be the set of labels that were scraped along with the log line. The label set is only modified by an action stage, but filtering stages read from it.
A collection of key-value pairs extracted during a parsing stage. Subsequent stages operate on the extracted map, either transforming them or taking action with them. At the end of a pipeline, the extracted map is discarded; for a parsing stage to be useful, it must always be paired with at least one action stage. The extracted map is initialized with the same set of initial labels that were scraped along with the log line. This initial data allows for taking action on the values of labels inside pipeline stages that only manipulate the extracted map.
For example, log entries tailed from files have the label filename whose value is the file path that was tailed. When a pipeline executes for that log entry, the initial extracted map would contain filename using the same value as the label.
Expert-led sessions on Prometheus, Grafana, Loki logs and more.Make sure you have Helm installed and deployed to your cluster. We recommend Promtail to ship your logs to Loki as the configuration is very similar to Prometheus.
When using Grafana having the same labels will allows you to pivot from Metrics to Logs verify easily by simply switching datasource. The chart loki-stack contains a pre-configured Grafana, simply use --set grafana. If Loki and Promtail are deployed on different clusters you can add an Ingress in front of Loki.
By adding a certificate you create an https endpoint. For extra security enable basic authentication on the Ingress. In order to receive and process syslog message into promtail, the following changes will be necessary:. Review the promtail syslog-receiver configuration documentation. Configure the promtail helm chart with the syslog configuration added to the extraScrapeConfigs section and associated service definition to listen for syslog messages.
For example:. Review the promtail systemd-journal configuration documentation. Configure the promtail helm chart with the systemd-journal configuration added to the extraScrapeConfigs section and volume mounts for the promtail pods to access the log files. After adding your new feature to the appropriate chart, you can build and deploy it locally to test:.
After verifying your changes, you need to bump the chart version following semantic versioning rules. For example, if you update the loki chart, you need to bump the versions as follows:. You can use the make helm-debug to test and print out all chart templates. If you want to install helm tiller in your cluster use make helm-installto install the current build in your Kubernetes cluster run make helm-upgrade. This site is open source. Improve this page.