promscale VS helm-charts

Now that Promscale has been deprecated, what are the other ideal means of self-hosted long term Prometheus storage?

Oh wow, I browsed the project just a few weeks ago, didn't see it then. I see the deprecation is recent (https://github.com/timescale/promscale/issues/1836)

They say:

> if you want to have a seamless experience between metrics and traces, then current experience of stitching together Prometheus & Jaeger is not great.

But I wonder if using Promscale https://github.com/timescale/promscale would make Prometheus & Jaeger not such a big problem as SigNoz imply.

Promscale readme:

> Promscale is a unified metric and trace observability backend for Prometheus, Jaeger and OpenTelemetry built on PostgreSQL and TimescaleDB.

Either way, SigNoz seems interesting indeed. And am glad to see that SigNoz supports OpenTelemetry.

Hi! So the team is over 100 at this point, but engineering effort is spread across multiple products at this point.

The core timescaledb repo [0] has 10-15 primary engineers (although we are aggressively hiring for database internal engineers), with a few others working on DB hyperfunctions and our function pipelining [1] in a separate extension [2]. I think generally the set of folks who contribute to low-level database internals in C is just smaller than other type of projects.

We also have our promscale product [3], which is our observability backend powered by SQL & TimescaleDB.

And then there is Timescale Cloud, which is obviously a large engineering effort (most of which does not happen in public repos).

And we are hiring. Fully remote & global.

https://www.timescale.com/careers

[0] https://github.com/timescale/timescaledb

[1] https://www.timescale.com/blog/function-pipelines-building-f...

[2] https://github.com/timescale/timescaledb-toolkit

[3] https://github.com/timescale/promscale ; https://github.com/timescale/tobs

Promscale is a connector for Prometheus, one of the leading open-source monitoring solutions. Promscale is developed by Timescale, a time series database with full compatibility to Postgres. Since logs are time series events, Timescale developed Promscale to ingest events from Prometheus and make them available in SQL. You can install Promscale in numerous ways.

In case it helps, Promscale (from Timescale) offers long-term storage for Prometheus data and supports both PromQL and SQL queries. Here's the project page: https://www.timescale.com/promscale/ and the repo is here https://github.com/timescale/promscale It also support OpenTelemetry tracing if that's of interest.

At first, let's give the definition of `time series`. This is a series of (timestamp, value) pairs ordered by timestamp. The `value` may contain arbitrary data - a floating-point value, a text, a json, a data structure with many columns, etc. Each time series is uniquely identified by its name plus an optional set of {label="value"} labels. For example, temperature{city="London",country="UK"} or log_stream{host="foobar",datacenter="abc",app="nginx"}.

ClickHouse is perfectly optimized for storing and querying of such time series, including metrics. That's true that ClickHouse isn't optimized for handling millions of tiny inserts per second. It prefers infrequent batches with big number of rows per each batch. But this isn't the real problem in practice, because:

1) ClickHouse provides Buffer table engine for frequent inserts.

2) It is easy to create a special proxy app or library for data buffering before sending it to ClickHouse.

TimescaleDB provides Promscale [1] - a service, which allows using TimescaleDB as a storage backend for Prometheus. Unfortunately, it doesn't show outstanding performance comparing to Prometheus itself and to other remote storage solutions for Prometheus. Promscale requires more disk space, disk IO, CPU and RAM according to production tests [2], [3].

[1] https://github.com/timescale/promscale

[2] https://abiosgaming.com/press/high-cardinality-aggregations/

[3] https://valyala.medium.com/promscale-vs-victoriametrics-reso...

Full disclosure: I'm CTO at VictoriaMetrics - competing solution for TimescaleDB. VictoriaMetrics is built on top of architecture ideas from ClickHouse.

Promscale + TimescaleDB

The official Kube-State-Metrics Helm chart is maintained as part of the Prometheus Community project. Installing Kube-State-Metrics in your cluster is generally the easiest way.

The go to solution for a k8s monitoring setup is https://github.com/prometheus-community/helm-charts/tree/mai...

That's why Kube-Prometheus-Stack was created. It installs a collection of Kubernetes manifests, Grafana dashboards, and Prometheus rules, providing an easy-to-operate, end-to-end Kubernetes cluster monitoring solution with Prometheus using the Prometheus Operator.

Step 1: Installing Kube-prometheus-stack

Kubernetes Documentation: https://kubernetes.io/docs/home/ Kubernetes Tutorials: https://kubernetes.io/docs/tutorials/ Kubernetes Community: https://kubernetes.io/community/ Prometheus: https://prometheus.io/ Grafana: https://grafana.com/ Elasticsearch: https://www.elastic.co/elasticsearch/ Kibana: https://www.elastic.co/kibana Helm: https://helm.sh/ Prometheus Helm Chart: https://github.com/prometheus-community/helm-charts/tree/main/prometheus Grafana Helm Chart: https://github.com/grafana/helm-charts/tree/main/grafana Elasticsearch Helm Chart: https://github.com/elastic/helm-charts/tree/main/elasticsearch Kibana Helm Chart: https://github.com/elastic/helm-charts/tree/main/kibana RBAC: https://kubernetes.io/docs/reference/access-authn-authz/rbac/ Network Policies: https://kubernetes.io/docs/concepts/services-networking/network-policies/ StatefulSets: https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/ DaemonSets: https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/ Taints and Tolerations: https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/ Custom Resource Definitions (CRDs): https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/ Operators: https://kubernetes.io/docs/concepts/extend-kubernetes/operator/

Installation example for prometheus:

Prometheus can be deployed using the Prometheus Helm Chart. This helm chart contains a lot of features such as the already mentioned Push Gateway, Alert Manager and so on. For simplicity reasons of this tutorial I will not show all the Helm chart configuration but you can see a real example used by me here.

Building upon Bartłomiej Płotka's insightful blog on Prometheus and its passthrough agent mode, this post dives into implementing multi-cluster Prometheus support. Notably, the official inclusion of support in the widely-used kube-prometheus-stack came with the release in July 2023, making it easier to extend Prometheus monitoring across clusters.