Metadata-Version: 2.4
Name: reaction-metrics-exporter
Version: 1.0.1
Summary: A daemon that scans reaction outputs, and serves an HTTP OpenMetrics endpoint
License-Expression: AGPL-3.0-or-later
License-File: LICENSE
Keywords: reaction,prometheus,openmetrics
Author: Lilou
Author-email: lilou@chosto.me
Requires-Python: >=3.10
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: System :: Monitoring
Requires-Dist: aiofiles (>=24.1.0,<25.0.0)
Requires-Dist: aionotify (>=0.3.1,<0.4.0)
Requires-Dist: asyncio-simple-http-server (>=0.0.8,<0.0.9)
Requires-Dist: jinja2 (>=3.1.6,<4.0.0)
Requires-Dist: prometheus-client (>=0.22.1,<0.23.0)
Requires-Dist: pytest (>=8.4.1,<9.0.0)
Requires-Dist: python-dateutil (>=2.9.0.post0,<3.0.0)
Requires-Dist: python-systemd (>=0.0.9,<0.0.10)
Requires-Dist: ruamel-yaml (>=0.18.14,<0.19.0)
Requires-Dist: schema (>=0.7.7,<0.8.0)
Requires-Dist: structlog (>=25.4.0,<26.0.0)
Requires-Dist: systemd-python (>=235,<236)
Description-Content-Type: text/markdown


<!-- TOC ignore:true -->
# reaction-metrics-exporter

> [!note] 💚 A lot of inspiration has been drawn from [`dmarc-metrics-exporter`](https://github.com/jgosmann/dmarc-metrics-exporter).

Export [OpenMetrics](https://prometheus.io/docs/specs/om/open_metrics_spec/) for [reaction](https://reaction.ppom.me/). The exporter continuously monitors and parses reaction's logs and state.

<!-- TOC ignore:true -->
## Main metrics

- `reaction_match_count`: number of matches since last collection;
- `reaction_action_count`: number of actions since last collection;
- `reaction_pending_count`: current number of pending actions.

All metrics are labelled with `stream`, `filter` and matched patterns.
Action-related metrics have an additional `action` label.

For example, matches exported from [the SSH filter](https://reaction.ppom.me/filters/ssh.html) look like:

```
reaction_match_count{stream="ssh",filter="failedlogin",ip="X.X.X.X"}: N
```

`N` being the number of matches since last collection for this unique combination of labels.

<!-- TOC ignore:true -->
## Secondary metrics

- `reaction_exporter_build_info`: value always 1, labelled with the version of the exporter;
- `reaction_status_info`: value always 1, labelled with version of reaction and its status;
- `reaction_commands_count`: count of start/stop commands run by reaction, labelled with command name, status and exit code, since the last collection;
- `reaction_stream_event_count`: count of stream events since the last collection, labelled with stream name and event type, which is one of:
  - `start`: stream started successfully;
  - `exit`: stream exited;
  - `exec`: stream is unexecutable;
  - `read`: stream is unreadable;
  - `timeout`: stream didn't terminate on time on exit.

<!-- TOC ignore:true -->
## Side notes

> ℹ️ In the long-term, `reaction` probably will expose builtin metrics. Whether the exporter's ones will be adopted depends on long-term relevance and performance.

> ⚠️ For very large numbers of matches and actions, your TSDB may grow too much. Read ["the patterns dilemma"](#the-patterns-dilemma) if in doubt.

<!-- TOC ignore:true -->
## Table of contents

<!-- TOC -->

- [Quick start](#quick-start)
    - [Prerequisites](#prerequisites)
    - [Install](#install)
    - [Configure](#configure)
    - [Run](#run)
- [Real-world setups](#real-world-setups)
    - [Create an unprivileged user](#create-an-unprivileged-user)
    - [Running with systemd](#running-with-systemd)
    - [Running with Docker](#running-with-docker)
- [Usage details](#usage-details)
    - [Configuration](#configuration)
    - [Commands](#commands)
    - [Pre-treating matches](#pre-treating-matches)
    - [Enabling internals metrics](#enabling-internals-metrics)
- [The patterns dilemma](#the-patterns-dilemma)
    - [How matches could become a problem](#how-matches-could-become-a-problem)
    - [Disable metrics](#disable-metrics)
    - [Disable patterns](#disable-patterns)
- [Exploitation](#exploitation)
    - [Scraping](#scraping)
    - [Visualisation](#visualisation)
    - [Alerts](#alerts)
- [Details about design and usage of metrics](#details-about-design-and-usage-of-metrics)
    - [Using OpenMetrics is wrong but useful](#using-openmetrics-is-wrong-but-useful)
    - [Emulate monotonic counters from events](#emulate-monotonic-counters-from-events)
- [Development setup](#development-setup)

<!-- /TOC -->

# Quick start

> [!caution] ⚠️ Do not use in production as-is; see [real-world setup](#real-world-setup).

## Prerequisites

### To run the exporter

- `python>=3.10` and `pip`;
- `reaction==2` (tested up to `2.2.0`);
- [`libsystemd`](https://www.freedesktop.org/software/systemd/man/latest/libsystemd.html);
- [`pkg-config`](https://www.freedesktop.org/wiki/Software/pkg-config/).

### To exploit the metrics

- A [Prometheus](https://prometheus.io/)-compatible TSBD ([VictoriaMetrics](https://victoriametrics.com/) is recommended, see [below](#details-about-design-and-usage-of-metrics));
- [Grafana](https://grafana.com/oss/grafana/).

## Install

```bash
python3 -m pip install reaction-metrics-exporter
```

## Configure

Create a configuration file, *e.g.* `config.yml`:

```yaml
reaction:
  # as you would pass to `reaction test-config`
  config: /etc/reaction
  logs:
    # monitor logs for `reaction.service`
    systemd:
```

>>> [!tip] Using a log file ?
```yaml
reaction:
  # ...
  logs:
    file: /var/log/reaction.log
```
>>>

## Run

```bash
python3 -m reaction_metrics_exporter -c config.yml start
```

Metrics are exposed at http://localhost:8080/metrics.

> 💡 The exporter supports an arbitrary 15-minute window maintenance, *i.e.* it saves the last read log and the unexported metrics so it can restart at this point.

# Real-world setups

## Create an unprivileged user

For security reasons, the exporter should run as an unprivileged, system user.

This user should be able to read [journald](https://www.freedesktop.org/software/systemd/man/latest/systemd-journald.service.html) logs and to communicate with `reaction`'s socket.

To do so, first create a user and a group, then add the user to the `systemd-journal` group.

```bash
# creates group automatically
/sbin/adduser reaction --no-create-home --system
usermod -aG systemd-journal reaction
```

Then, open an editor to configure `reaction.service` :

```bash
systemctl edit reaction.service
```

Paste the following and save:

```systemd
[Service]
Group=reaction
# Files will be created with rwxrwxr_x so the exporter
# can communicate with reaction through the socket
UMask=0002
```

Restart reaction:

```bash
systemctl daemon-reload
systemctl restart reaction
```

>>> [!tip] Check that the user is able to work with reaction
```bash
sudo su reaction
reaction show
journalctl -feu reaction
```
>>>

## Running with systemd

> ✅ It is recommended to install the exporter in a [virtualenv](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/), *e.g.* in `/usr/share/reaction-metrics-exporter/venv`.

Save the [service file](./reaction-metrics.service) in `/etc/systemd/systemd`.

> 💡 Add your config file and adjust the `venv` path in the `ExecStart=` directive.

Enable and start the exporter:

```bash
systemctl daemon-reload
systemctl enable --now reaction-metrics.service
```

Follow the logs with:

```bash
journalctl -feu reaction-metrics.service
```

## Running with Docker

> ⚠️ Running with Docker adds complexity for the exporter; prefer `systemd`.

Start inside the [docker](./docker) directory.

>>> [!tip] Homemade is better
It is recommended to build you own image so that the version of `reaction` inside the container matches yours. In [`compose.yml`](./docker/compose.yml), adjust `REACTION_VERSION` variable, and then:
```bash
docker compose build
```
>>>

Create a `.env` file:

```ini
UID=
GID=
JOURNAL_GID=
```

Values can be found out of command `id reaction`.

You may need to adjust the default mounts in [`compose.yml`](./docker/compose.yml). Expectations are:
- `reaction`'s configuration is mounted on `/etc/reaction`;
- `reaction`'s socket is mounted on `/run/reaction/reaction.sock`;
- `journald` file is mounted on `/var/log/journal`.

Use the [sample configuration file](./docker/config.yml) and tweak it to your needs and run the exporter:

```bash
docker compose up -d && docker compose logs -f
```

The exporter is mapped to the host's `8080` port by default.

> ℹ️ The container will restart automatically until logs and socket are accessible.

# Usage details

## Configuration

You can either provide a YAML file or a JSON file. Albeit not recommended, you can run the exporter without a configuration file.

The default configuration is as follows:

```yaml
# only stdout is supported atm
loglevel: INFO
listen:
  address: 127.0.0.1
  port: 8080
reaction:
  config: /etc/reaction
  logs:
    systemd: reaction.service
  # same default as reaction
  socket: /run/reaction/reaction.sock
  # ignore actions for 60 seconds (in logtime, not realtime) after reaction start
  hold: 60
metrics:
  all: {}
  for: {}
  # all metrics with labels are exported by default
  export:
    matches:
      extra: true
    actions:
      extra: true
    pending:
      extra: true
    commands:
    streams:
```

## Commands

```
usage: python -m reaction_metrics_exporter [-h] [-c CONFIG] [-f {yaml,json}] {start,defaults,test-config,version}

positional arguments:
  {start,defaults,test-config,version}
                        mode of operation; see below

options:
  -h, --help            show this help message and exit
  -c, --config CONFIG   path to the configuration file (JSON or YAML)
  -f, --format {yaml,json}
                        format for dumping default configuration (defautl yaml)

command:
    start: continuously read logs, compute metrics and serve HTTP endpoint
    defaults: print the default configuration in json and exit
    test-config: validate and output configuration in json and exit
    version: print the exporter's version and exit
```

## Pre-treating matches

You may want to transform matches prior to exporting instead of relabelling. You can do so with [Jinja2](https://jinja.palletsprojects.com/en/stable/) expressions.

For example, for an `email` pattern, you could keep only the domain part in metrics.s

```yaml
metrics:
  all:
    email: "{{ email.split('@') | last }}
```

You can also differentiate by stream and filter:

```yaml
metrics:
  for:
    ssh:
      failedlogin:
        ip: TEMPLATE_A
    traefik:
      aiBots:
        ip: TEMPLATE_B
```

## Enabling internals metrics

The Prometheus client library has some defauts metrics about Python, Garbage Collector and so on, that I found useless. You can nevertheless enable them:

```yaml
metrics:
  export:
    internals:
```

# The patterns dilemma

`reaction` matches often contains valuable information, such as IP addresses.

## How matches could become a problem

Quoting the [Prometheus docs](https://prometheus.io/docs/practices/naming/):

> CAUTION: Remember that every unique combination of key-value label pairs represents a new time series, which can dramatically increase the amount of data stored. Do not use labels to store dimensions with high cardinality (many different label values), such as user IDs, email addresses, or other unbounded sets of values.

In other words, each new IP address will **create a new time serie**.


Prometheus used to claim being able of handling [millions of time series](https://prometheus.io/docs/prometheus/1.8/storage/), and VictoriaMetrics claims being able of handling [100 million active time series](https://docs.victoriametrics.com/victoriametrics/faq/#what-are-scalability-limits-of-victoriametrics). Besides, each time series have very few data points (each one necessitating a match). In the end we use OpenMetrics in a kind of hackish way, so that recommandations tailored for active and dense time series do not apply.

You should just test and check after a few months. In most cases, the retention period will kick before you run into troubles.

You can still disable the export of some metrics or patterns.

## Disable metrics

```yaml
metrics:
  export:
    actions: false
    matches: false
    pending: false
```

## Disable patterns

For example, to remove `ip` from export for the `failedlogin` filter from the `openssh` stream:

```yaml
metrics:
  for:
    ssh:
      failedlogin:
        ip: false
```

If you use the pattern `ip` in multiple streams, you can avoid repetition by removing it globally:

```yaml
metrics:
  all:
    ip: false
```

# Exploitation

## Scraping

Sample Prometheus scrape config:

```yaml
  # must start with `reaction`
  - job_name: "reaction"
    scrape_interval: 60s
    # if behind HTTP basic auth
    basic_auth:
      username: "${REACTION_METRICS_USER}"
      password: "${REACTION_METRICS_PASSWORD}"
    static_configs:
      - targets:
        - subdomain1.<domain>
        - subdomain2.<domain>
        - [...]
    # only keep subdomains
    # recommended if <domain> is always the same,
    # to make tables more readable.
    relabel_configs:
      - source_labels: [__address__]
        regex: "(.*).<domain>"
        target_label: instance
        replacement: "$1"
```

## Visualisation

Two dashboards are provided:
- One for [Prometheus](./grafana/reaction-metrics-prometheus.json);
- One for [VictoriaMetrics](./grafana/reaction-metrics-victoriametrics.json).

The later only have a few more panels.

Dashboards have been built with multi-instances and `ip` matching, but can be easily adapted for other setups.

### Variables

All the queries are templated with the following variables:

![](./grafana/img/param.png)

It can be useful to narrow down a research.

### Status

![](./grafana/img/status.png)

Health and versions of the exporter and of `reaction`.

### Stats

![](./grafana/img/stats.png)

Cumulative sums of events for selected range and for the last year.
You will find this pattern everywhere in the panels.

### Actions

![](./grafana/img/pies.png)

Insights for actions triggered within the selected range. "Pending actions" are the ones printed by `reaction show`.

### Evolution of events

![](./grafana/img/global.png)

Basic plots, by instances. Note that they will always start from 0, so that evolution within the range is clear.

### Filters

![](./grafana/img/filters.png)

Matches are aggregated by filter and per instance, hopefuly giving insights about which machine is targeted by what.

### Reaction's events

<div align="center">

![](./grafana/img/stream.png)
</div>

Boring plots which look like this. If everything goes well, they should be almost empty. They are probably more useful for alerts (*e.g.* when a stream exited, when a stop command failed, and so on).

### IP blame

![](./grafana/img/ip.png)

Then you have those kind of tables, each time for range and for year, highlighting *e.g.* IP that have been matched a lot.

Another example is IP matched in different filters, which can ask further investigation. In that case you can template every panel with that specific IP:

<div align="center">

![](./grafana/img/ip_filter.png)
</div>

That allow for basic information gathering (which instance(s), which filter(s), time of presence and so on).

### Lifetime

![](./grafana/img/lifetime.png)

Real usefulness yet to be shown, I feel it lacks something.
Lifetime is the time between the last time and the first time a unique combination of labels has been seen.
In other words, it tells us whether an IP did a oneshot match or a long run.

On the left is the evolution of average lifetime per instance **for the range**. It could help to determine if certain instances are targeted with insistance.

On the right is the distribution of lifetime for the range, per instance.

## Alerts

Basic queries that can be used in Grafana, Prometheus or [vmalert](https://docs.victoriametrics.com/victoriametrics/vmalert/) to warn Alertmanager. Given in Prometheus format.

```yaml
# Reaction is down
- alert: ReactionDown
  expr: reaction_status_info{status="0"} == 1
  for: 5m
# More than one match per second on an instance
- alert: ReactionHighMatches
  expr: rate(sum(reaction_match_count) by (instance)[5m]) >= 1
  for: 5m
# A stream exited or cannot be read
- alert: ReactionStreamError
  expr: sum(reaction_stream_event_count{type!="start"}) by (instance, type) > 0
# A start/stop command failed
- alert: ReactionCommandError
  expr: sum(reaction_commands_count{status!="True"}) by (instance) > 0
# Detects an IP matching on one or several machines without
# being banned (adjust X and T to your needs)
- alert: ReactionIPUnbanned
  expr: sum(reaction_match_count[T]) by (ip) > X
```

# Details about design and usage of metrics

## Using OpenMetrics is wrong but useful

The exporter does not quite follow Prometheus' philosophy. For example, it uses [gauges](https://prometheus.io/docs/concepts/metric_types/#gauge) for counting matches, actions and so on.

Let's take the example of matches. Normally, gauge "represents a single numerical value that can arbitrarily go up and down". In our case, this single numerical value will give **the number of matches since the last collection**. Without extra labels, such as IPs, it would fit the model.

But when exporting pattern's values, such as IPs, the result is a multitude of short-lived time series. As an example:

```
reaction_match_count{filter="aiBots",ip="121.229.156.18",stream="traefik"} 1.0
reaction_match_count{filter="aiBots",ip="23.21.179.120",stream="traefik"} 1.0
reaction_match_count{filter="failedlogin",ip="77.201.74.44",stream="ssh"} 3.0
```

These metrics will be ingested as three distinct timeseries and discarded on the exported side. They will get a new data point only on the next hypothetical identical match.

Semantically, **we are exporting events**, which is [discouraged](https://prometheus.io/docs/introduction/faq/#how-to-feed-logs-into-prometheus). Events are usually processed with a log aggregator, *e.g.* [Grafana Loki](https://grafana.com/oss/loki/), or could be aggregated by a Prometheus middleware, *e.g.* [prom-aggregation-gateway](https://github.com/zapier/prom-aggregation-gateway). We made the choice to stick to OpenMetrics because the TSBD/Grafana setup is **simple and widespread**.

In the early development, aggregation was made locally, *i.e.* the total count for each unique combination of stream, filter and matches was exported continuously, with restarts handled by a persistence file, but it made no sense: the HTTP body would grow indefinitly and each timeserie would get a new datapoint at each export while no event has happened.

Above all considerations, the exported metrics had to be exploitable through a comprehensive Grafana dashboard, and they are, minus we don't have yet high-volume tests.

## Emulate monotonic counters from events

A natural expectation would be to visualize the evolution of matches, actions and so on. With our event-based export, the only way would be to do a cumulative sum of timeseries for the current time window, **starting from 0**.

In theory, **this is not possible with Prometheus**. [VictoriaMetrics](https://victoriametrics.com/), beside way higher performance, has a lot of useful built-in functions, including running sum.

We suppose the query is executed in Grafana, which sets start, stop, range (stop - start) and interval (range / max data points, also known as step).

### Using VictoriaMetrics

If you're unfamiliar we MetricsQL, you can [read the beggining of the docs](https://docs.victoriametrics.com/victoriametrics/metricsql/). It has a lot in common with PromQL, with additional functions and sugar-syntax.

A naive approach would be:

```
running_sum(reaction_match_count)
```

That request will calculate, for each time serie (*i.e.* each unique combination of stream, filter and matches), the cumulative sum for the range. As the number of timeseries is huge when exporting extra labels, is it useful to aggregate results *e.g.* per instance:

```
sum(running_sum(reaction_match_count)) by (instance)
```

However, this request will give wrong results for large time windows. The above query is in fact converted (more or less) by VictoriaMetrics to:

```
sum(running_sum(default_rollup(reaction_match_count[step]))) by (instance)
```

`default_rollup` simply takes the last value of the [range vector](https://prometheus.io/docs/prometheus/latest/querying/basics/#range-vector-selectors), *i.e.* the result is equivalent to `reaction_match_count` quantized every 5 minutes. In other words, every event occuring during that time is discarded.

To fix the query, we need to compute the cumulative sum **inside the step**. The final query will look like this:

```
sum(running_sum(sum_over_time(reaction_match_count))) by (instance)
```

`sum_over_time` will [automatically](https://docs.victoriametrics.com/victoriametrics/metricsql/#rollup-functions) use the step as a lookbehind window.

> ⚠️ We cannot nest `running_sum`, because it needs rollup results; the inner `running_sum` would therefore apply on `default_rollup`, calculate rollups, and the outer `running_sum` would give crazy results, calculating the cumulative sum of the initial wrong cumulative sum.

Wrapping up, this is an efficient way to emulate Prometheus [counters](https://prometheus.io/docs/concepts/metric_types/#counter), in a given time window.

### Using Prometheus

Doing the same in Prometheus is a whole different ballgame. A question that may arise is: why don't we simply `sum_over_time` over the whole window (`$__range` in Grafana)?

```
sum_over_time(reaction_match_count[$__range])
```

Let's say our time window is 2 days (`2d`). The problem is that `sum_over_time` being a rollup function, or a moving function, the first value shown on the graph will be the cumulative sum of `[-4d, -2d]`. Values for the last 2 days will be kind of smoothed by old values.

Ideally, we would like to ask Prometheus to have a fixed starting point (`-2d`), and a moving end point. This is really against the way Prometheus works, because the query range is made to be static, not dynamic.

After a lot of infructuous trials, I found a way to do it. It takes advantage of:

- Grafana exposing the `$__from` variable, which is the timestamp of the start of the range;
- The `time()` function which returns the timestamp currently being evaluated (it ranges from `start` to `end` with `step` increments).

The trick is as follows: first, build a timeserie made of timestamps:

```
vector(time()) > $__from/1000
```

Second, build "*rollup-resistant*" range-vectors:

```
(reaction_match_count and on() (vector(time()) > $__from/1000))[$__range:]
```

The [`and` operator](https://prometheus.io/docs/prometheus/latest/querying/operators/#logicalset-binary-operators) will drop every data point in the left part which is not in the right part. The `on()` clause has to do with [vector matching](https://prometheus.io/docs/prometheus/latest/querying/operators/#vector-matching); here it means that no label is required to match, *i.e.* every point with the same timestamp will match.


Finally we can compute the cumulative sum over `$__range`:

```
sum(sum_over_time((reaction_match_count and on() (vector(time()) > $__from/1000))[$__range:]))
```

The first point will be 0, exactly what we want. This is because to compute the first point, Prometheus will start from `-2d`. Therefore, the outer sum will be, in theory, calculated with data from `]-4d, -2d]`. But our fixed timeserie has data within `]-2d, now]`. In other words, Prometheus find no values, giving a 0. Then, the window will slide by `step`, and the cumulative sum from `[-2d, -2d+step]` will give the second value, and so on.

The drawback is these requests perform poorly on large ranges (like several weeks).


# Development setup

In addition of the prerequisites, you need [Poetry](https://python-poetry.org/).

```bash
# inside the cloned repository
poetry install
# run app
poetry run python -m reaction_metrics_exporter [...]
# run tests
poetry run pytest
```
