client_python
statsd_exporter
client_python | statsd_exporter | |
---|---|---|
15 | 3 | |
3,778 | 895 | |
1.3% | 0.9% | |
7.2 | 7.7 | |
6 days ago | 16 days ago | |
Python | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
client_python
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Show HN: Hatchet – Open-source distributed task queue
Here you go: https://stackoverflow.com/questions/75652326/celery-spawn-si...
Plus some adjacent discussion on GitHub: https://github.com/prometheus/client_python/issues/902
Hope that helps!
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How to monitor Python application performance
Prometheus, which is also a CNCF open source project, collects metrics data by scraping HTTP endpoints and then stores that data in a time series database that uses a multidimensional model. It’s a powerful tool for gathering metrics about your application and it also includes alerting functionality that you can use to notify your teams when issues come up. Prometheus includes a client library for Python.
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Kafka-Python metric reporters
We have a java one but the principle is the same. Install the Prometheus client ( https://github.com/prometheus/client_python) ,create the metrics you want, then push jmx settings to Prometheus.
- Observabilidade com Prometheus
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Setup Grafana with Prometheus for Python projects using Docker
The code above is copied from the official documentation of prometheus_client which simply creates a new metric named request_processing_seconds that measures the time spent on that particular request. We'll cover other types of metrics later in this post.
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Prometheus histogram with python
Just use the client? https://github.com/prometheus/client_python
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Monitoring Latency with Python
I've experimented with the official Prometheus python client, i really really like the way they use decorators to instrument. I've tried to measure latency with multiple types of metrics (histogram, & summary), i see the value in both of them, but the one that between fits my objective is the histogram metric type. Great!
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Best way to handle several python script plugins for a service? Create an image + container for each one? Create one for them all? Running them as microservices?
Now is a good time to expand your event loop by adding metrics collection of the event handler functions and also use that endpoint as a liveness probe. E.g. https://github.com/prometheus/client_python just add the event handled, success/error and the duration as a histogram (look for examples of tracking http requests served)
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Why is Prometheus generating duplicate data (while using python client)?
I've spent along time trying to figure out a bug that I'm facing while using Prometheus from its python client.
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Python node exporter *Help
The official Prometheus Python client library makes this easy, no need to worry about the export file format.
statsd_exporter
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Help: UDP proxy/relay to multiple destinations
It's part of the statsd_exporter.
- Ask HN: Prometheus vs. StatsD / Telegraf
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Statsd + Prometheus on Grafana Cloud
package metrics import ( "fmt" "strings" "time" "github.com/alexcesaro/statsd" ) // Hybrid metrics type Hybrid struct { Statsd StatsdConfig Client *statsd.Client AttachLabels bool } // StatsdConfig ... type StatsdConfig struct { Host string Prefix string } // Init ... func (m *Hybrid) Init() error { // Set up the statsd client client, err := statsd.New( statsd.Address(m.Statsd.Host), statsd.Prefix(m.Statsd.Prefix), statsd.FlushPeriod(time.Second*1), ) if err != nil { return fmt.Errorf("Hybrid: Could not connect to %s: %s", m.Statsd.Host, err.Error()) } m.Client = client return nil } // Format key with labels func (m *Hybrid) formatKeyWithLabels(key string, labels map[string]string) string { // Do we want to attach labels? if m.AttachLabels == false { return key } // Use librato-style tags // metrim.name#tagName=val,tag2Name=val2:0|c // https://github.com/prometheus/statsd_exporter#tagging-extensions if len(key) == 0 { return key } formattedLabels := []string{} for labelKey, labelVal := range labels { formattedLabels = append(formattedLabels, fmt.Sprintf("%s=%s", labelKey, labelVal)) } joinedFormattedLabels := strings.TrimSpace(strings.Join(formattedLabels, ",")) if joinedFormattedLabels != "" { key = fmt.Sprintf("%s#%s", key, joinedFormattedLabels) } return strings.Trim(key, "#") } // Increment ... func (m *Hybrid) Increment(key string, labels map[string]string) { m.IncrementBy(key, 1, labels) } // IncrementBy ... func (m *Hybrid) IncrementBy(key string, by int, labels map[string]string) { updatedKey := m.formatKeyWithLabels(key, labels) if m.Client != nil { m.Client.Count(updatedKey, by) } } // Gauge ... func (m *Hybrid) Gauge(key string, value int, labels map[string]string) { updatedKey := m.formatKeyWithLabels(key, labels) if m.Client != nil { m.Client.Gauge(updatedKey, value) } }
What are some alternatives?
prometheus-fastapi-instrumentator - Instrument your FastAPI with Prometheus metrics.
pushgateway - Push acceptor for ephemeral and batch jobs.
django-prometheus - Export Django monitoring metrics for Prometheus.io
netbox-plugin-prometheus-sd - Provide Prometheus url_sd compatible API Endpoint with data from Netbox
windows_exporter - Prometheus exporter for Windows machines
process-exporter - Prometheus exporter that mines /proc to report on selected processes
node_exporter - Exporter for machine metrics
haproxy_exporter - Simple server that scrapes HAProxy stats and exports them via HTTP for Prometheus consumption
homelab
script_exporter - Prometheus exporter to execute scripts and collect metrics from the output or the exit status.