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This is documentation for the next version of Loki. For the latest stable release, go to the latest version.

Open source

Quickstart to run Loki locally

If you want to experiment with Loki, you can run Loki locally using the Docker Compose file that ships with Loki. It runs Loki in a monolithic deployment mode and includes a sample application to generate logs.

The Docker Compose configuration instantiates the following components, each in its own container:

  • flog a sample application which generates log lines. flog is a log generator for common log formats.
  • Promtail which scrapes the log lines from flog, and pushes them to Loki through the gateway.
  • Gateway (NGINX) which receives requests and redirects them to the appropriate container based on the request’s URL.
  • One Loki read component.
  • One Loki write component.
  • Minio an S3-compatible object store which Loki uses to store its index and chunks.
  • Grafana which provides visualization of the log lines captured within Loki.
Getting started sample application
Getting started sample application

Installing Loki and collecting sample logs

Prerequisites

Note

This quickstart assumes you are running Linux.

To install Loki locally, follow these steps:

  1. Create a directory called evaluate-loki for the demo environment. Make evaluate-loki your current working directory:

    bash
    mkdir evaluate-loki
    cd evaluate-loki
  2. Download loki-config.yaml, promtail-local-config.yaml, and docker-compose.yaml:

    bash
    wget https://raw.githubusercontent.com/grafana/loki/main/examples/getting-started/loki-config.yaml -O loki-config.yaml
    wget https://raw.githubusercontent.com/grafana/loki/main/examples/getting-started/promtail-local-config.yaml -O promtail-local-config.yaml
    wget https://raw.githubusercontent.com/grafana/loki/main/examples/getting-started/docker-compose.yaml -O docker-compose.yaml
  3. Deploy the sample Docker image.

    With evaluate-loki as the current working directory, start the demo environment using docker compose:

    bash
    docker compose up -d

    You should see something similar to the following:

    bash
    ✔ Network evaluate-loki_loki          Created      0.1s 
    ✔ Container evaluate-loki-minio-1     Started      0.6s 
    ✔ Container evaluate-loki-flog-1      Started      0.6s 
    ✔ Container evaluate-loki-write-1     Started      0.8s 
    ✔ Container evaluate-loki-read-1      Started      0.8s 
    ✔ Container evaluate-loki-gateway-1   Started      1.1s 
    ✔ Container evaluate-loki-grafana-1   Started      1.4s 
    ✔ Container evaluate-loki-promtail-1  Started      1.4s 
  4. (Optional) Verify that the Loki cluster is up and running.

    • The read component returns ready when you point a web browser at http://localhost:3101/ready. The message Query Frontend not ready: not ready: number of schedulers this worker is connected to is 0 will show prior to the read component being ready.
    • The write component returns ready when you point a web browser at http://localhost:3102/ready. The message Ingester not ready: waiting for 15s after being ready will show prior to the write component being ready.

Viewing your logs in Grafana

Once you have collected logs, you will want to view them. You can view your logs using the command line interface, LogCLI, but the easiest way to view your logs is with Grafana.

  1. Use Grafana to query the Loki data source.

    The test environment includes Grafana, which you can use to query and observe the sample logs generated by the flog application. You can access the Grafana cluster by navigating to http://localhost:3000. The Grafana instance provided with this demo has a Loki datasource already configured.

    Grafana Explore
    Grafana Explore
  2. From the Grafana main menu, click the Explore icon (1) to launch the Explore tab. To learn more about Explore, refer the Explore documentation.

  3. From the menu in the dashboard header, select the Loki data source (2). This displays the Loki query editor. In the query editor you use the Loki query language, LogQL, to query your logs. To learn more about the query editor, refer to the query editor documentation.

  4. The Loki query editor has two modes (3):

    • Builder mode, which provides a visual query designer.
    • Code mode, which provides a feature-rich editor for writing LogQL queries.

    Next we’ll walk through a few simple queries using both the builder and code views.

  5. Click Code (3) to work in Code mode in the query editor.

    Here are some basic sample queries to get you started using LogQL. Note that these queries assume that you followed the instructions to create a directory called evaluate-loki. If you installed in a different directory, you’ll need to modify these queries to match your installation directory. After copying any of these queries into the query editor, click Run Query (4) to execute the query.

    1. View all the log lines which have the container label “flog”:

      bash
      {container="evaluate-loki-flog-1"}

      In Loki, this is called a log stream. Loki uses labels as metadata to describe log streams. Loki queries always start with a label selector. In the query above, the label selector is container.

    2. To view all the log lines which have the container label “grafana”:

      bash
      {container="evaluate-loki-grafana-1"}
    3. Find all the log lines in the container=flog stream that contain the string “status”:

      bash
      {container="evaluate-loki-flog-1"} |= `status`
    4. Find all the log lines in the container=flog stream where the JSON field “status” is “404”:

      bash
      {container="evaluate-loki-flog-1"} | json | status=`404`
    5. Calculate the number of logs per second where the JSON field “status” is “404”:

      bash
      sum by(container) (rate({container="evaluate-loki-flog-1"} | json | status=`404` [$__auto]))        

    The final query above is a metric query which returns a time series. This will trigger Grafana to draw a graph of the results. You can change the type of graph for a different view of the data. Click Bars to view a bar graph of the data.

  6. Click the Builder tab (3) to return to Builder mode in the query editor.

    1. In Builder view, click Kick start your query(5).
    2. Expand the Log query starters section.
    3. Select the first choice, Parse log lines with logfmt parser, by clicking Use this query.
    4. On the Explore tab, click Label browser, in the dialog select a container and click Show logs.

For a thorough introduction to LogQL, refer to the LogQL reference.

Sample queries (code view)

Here are some more sample queries that you can run using the Flog sample data.

To see all the log lines that flog has generated, enter the LogQL query:

bash
{container="evaluate-loki-flog-1"}|= ``

The flog app generates log lines for simulated HTTP requests.

To see all GET log lines, enter the LogQL query:

bash
{container="evaluate-loki-flog-1"} |= "GET"

To see all POST methods, enter the LogQL query:

bash
{container="evaluate-loki-flog-1"} |= "POST"

To see every log line with a 401 status (unauthorized error), enter the LogQL query:

bash
{container="evaluate-loki-flog-1"} | json | status="401"

To see every log line that does not contain the value 401:

bash
{container="evaluate-loki-flog-1"} != "401"

For more examples, refer to the query documentation.

Complete metrics, logs, traces, and profiling example

If you would like to use a demo that includes Mimir, Loki, Tempo, and Grafana, you can use Introduction to Metrics, Logs, Traces, and Profiling in Grafana. Intro-to-mltp provides a self-contained environment for learning about Mimir, Loki, Tempo, and Grafana.

The project includes detailed explanations of each component and annotated configurations for a single-instance deployment. Data from intro-to-mltp can also be pushed to Grafana Cloud.