nikolay_sivko

5 ms·

1.
Show HN: RCA-lab – test observability tools on real failures(github.com)
3 points by nikolay_sivko 2mo ago|0 comments
2.
Andy Pavlo joins ClickHouse to establish ClickHouse Labs(clickhouse.com)
340 points by nikolay_sivko 2mo ago|76 comments
3.
It was surprisingly hard to break CloudNativePG replication(coroot.com)
4 points by nikolay_sivko 2mo ago|0 comments
4.
Understand Pressure Stall Information (Psi) Metrics(kubernetes.io)
2 points by nikolay_sivko 2mo ago|0 comments
5.
PostgreSQL Autovacuum Internals and Benchmark(percona.community)
5 points by nikolay_sivko 2mo ago|0 comments
6.
What matters for performance: lessons from a year of benchmarks(clickhouse.com)
2 points by nikolay_sivko 2mo ago|0 comments
7.
Let's break autovacuum in Postgres: reproducing failures to make it observable(coroot.com)
4 points by nikolay_sivko 3mo ago|0 comments
8.
Meta's Root Cause Analysis Platform at Scale(engineering.fb.com)
2 points by nikolay_sivko 3mo ago|0 comments
9.
The hard part of AI root cause analysis is no longer the model(coroot.com)
2 points by nikolay_sivko 3mo ago|0 comments
10.
Tracing PostgreSQL Using eBPF and Hardware Breakpoints(jnidzwetzki.github.io)
3 points by nikolay_sivko 5mo ago|0 comments
11.
PgBackRest is archived, what now?(percona.community)
4 points by nikolay_sivko 5mo ago|1 comments
12.
Golang heap profiling without pprof enabled or eBPF(coroot.com)
2 points by nikolay_sivko 5mo ago|0 comments
13.
Making encrypted Java traffic observable with eBPF(coroot.com)
3 points by nikolay_sivko 6mo ago|0 comments
14.
Native Threading and Multiprocessing in Go(antonz.org)
2 points by nikolay_sivko 1y ago|0 comments
15.
by nikolay_sivko 1y ago
As suggested, I measured the overhead at various sampling rates: No instrumentation (otel is not initialized): CPU=2.0 cores SAMPLING 0% (otel initialized): CPU=2.2 cores SAMPLING 10%: CPU=2.5 cores SAMPLING 50%: CPU=2.6 cores SAMPLING 100%
16.
by nikolay_sivko 1y ago
I'm the author. I wouldn’t say the post is critical of OTEL. I just wanted to measure the overhead, that’s all. Benchmarks shouldn’t be seen as critique. Quite the opposite, we can only improve things if we’ve measured them first.
17.
From Utilization to Psi: Rethinking Resource Starvation Monitoring in Kubernetes(blog.zmalik.dev)
2 points by nikolay_sivko 1y ago|0 comments
18.
by nikolay_sivko 1y ago
We could totally add that, but no one's asked for it so far
19.
by nikolay_sivko 1y ago
1. Regarding overhead — we ran a benchmark focused on performance impact rather than raw overhead [1]. TL;DR: we didn’t observe any noticeable impact at 10K RPS. CPU usage stayed around 200 millicores (about 20% of a single core). 2. Coroot
20.
by nikolay_sivko 1y ago
From a user’s perspective, it doesn’t really matter how the data is collected. What actually matters is whether the tool helps you answer questions about your system and figure out what’s going wrong. At Coroot, we use eBPF for a couple of
21.
by nikolay_sivko 1y ago
Enterprise Edition = Community Edition + Support + AI-based Root Cause Analysis + SSO + RBAC
22.
by nikolay_sivko 1y ago
Yes, it captures traffic before encryption and after decryption using eBPF uprobes on OpenSSL and Go’s TLS library calls.
23.
by nikolay_sivko 1y ago
Currently, you can define custom SLIs (Service Level Indicators, such as service latency or error rate) for each service using PromQL queries. In the future, you'll be able to define custom metrics for each application, including expla
24.
by nikolay_sivko 1y ago
Initially, we relied on the ClickHouse OTEL exporter and its schema, but for performance optimization, we decided to modify our ClickHouse schema, and they are no longer compatible :(
25.
by nikolay_sivko 1y ago
At Coroot, we solve the same problem, but in a slightly different way. The traffic source is always a container (Kubernetes pod, systemd slice, etc.). The destination is initially identified as an IP:PORT pair, which, in the case of Kuberne
26.
by nikolay_sivko 1y ago
Coroot builds a model of each system, allowing it to traverse the dependency graph and identify correlations between metrics. On top of that, we're experimenting with LLMs for summarization — here are a few examples: https:/&#x2F
27.
by nikolay_sivko 1y ago
It only requires a modern Linux kernel. Note: The agent does not support Docker-in-Docker environments, such as KinD or Minikube (D-in-D plugin).
28.
by nikolay_sivko 1y ago
(I'm a co-founder). At Coroot, we're strong believers in open source, especially when it comes to observability. Agents often require significant privileges, and the cost of switching solutions is high, so being open source is the
29.
by nikolay_sivko 1y ago
In addition to raw logs, Coroot can extract recurring patterns to generate log-based metrics [1]. We also plan to convert structured logs into OpenTelemetry attributes [2]. [1] https://demo.coroot.com/p/tbuzvelk/ap
30.
Show HN: OopsDB – failure scenarios for testing observability tools(oopsdb.coroot.com)
2 points by nikolay_sivko 2y ago|0 comments

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