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Workshop – Accessing log data with Loki

LogQL

Loki supports complex queries through (potentially) terabytes worth of logs.

You can compose your own queries using the LogQL query language, which is a modified version of the Prometheus language (PromQL). LogQL might look complicated at first, but you'll soon discover that it is basically a glorified grep.

LogQL queries consist of two parts:

a log stream selector
a log pipeline

Start by selecting one or more streams, and then apply a pipeline operator specifying the string you're looking for. For example, you might want to look for all accesses coming from a specific IP address:

{job="apache"} |= "172.17.0.1"

Or you might be interested in entries related to a specific page:

{job="apache"} |= "cron.php"

The following query defines the exact file you wish to search, loosely looking for Firefox accesses through regexp syntax:

{job="apache",filename="/somelogsdir/access.log"} |~ "Firefox.*"

You can also elect not to filter specific strings. For instance, if you wish to exclude entries with an http 200 status code from the result, use the ` character to delimit the search string:

{job="apache"} != `HTTP/1.1\" 200`

LogQL also provides a way to parse specific log formats (like JSON and logfmt) using a log parser. This option won't be useful in the case of Apache logs, but you'll find great documentation on the topic in the Loki wiki [3].

Grafana and Visualization

You can display the log data in a visually meaningful form using Grafana. To speed things up, I'll deploy Grafana through the official Docker image. I will make sure to run it in host network mode so a connection to Loki is possible:

docker run -d --network host grafana/grafana

Once Grafana is up, use your browser to land on http://localhost:3000. Set up the default admin credentials, click on Configuration | Data Sources, and finally, select Add Data Source.

Select Loki as data source type and enter http://localhost:3100 as the HTTP URL parameter (Figure 1) .

Figure 1: Connecting the Grafana instance to Loki.

The Save & Test button will make sure settings are validated. You can finally move to the Explore tab, where you can freely query any data source (Figure 2). Once the query is entered and verified, share a short link with coworkers by clicking on the Share icon in the upper part of the page (again Figure 2).

Figure 2: Comfortable querying and result sharing with Grafana.

Conclusions

Loki lets you set up a complete log aggregation infrastructure in a very short time span, without having to write a single line of code (see the box entitled "Loki vs Elasticsearch"). All components can run inside a Docker container or in a Kubernetes cluster when it's time to deploy Loki as a production application.

Loki vs Elasticsearch

In the log aggregation ecosystem, Elasticsearch (and, in general, the ELK stack) is certainly a popular choice, but in my opinion, Loki might be better for certain use cases. I give the advantage to Loki for:

Scalability: Although Elasticsearch indexes all elements of log entries beforehand, Loki specializes in brute force text searches. Loki data is stored unstructured, meaning that Loki can handle a larger amount of data compared to Elasticsearch.
Metrics format: Loki stores logs with the same structure logic as Prometheus TSDB (streams). This approach means that an application stack (Grafana, Prometheus, and Loki) can pinpoint an application issue starting from a metric or the other way around.

The Loki project has a comparison page for your consideration [4]. Always choose the best tool for your use case.

Loki also supports third-party storage for its logs collection (such as AWS S3 or Apache Cassandra). Next time you deploy a machine or a service, install a Promtail agent and give Loki a try. You'll be surprised by how quickly you can get productive.

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