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Netflix
vector
JavaScript2015–2016
Netflix / vector · Archived

vector

Vector is an on-host performance monitoring framework which exposes hand picked high resolution metrics to every engineer’s browser.

Web DevelopmentObservability
Explore on GitHubRead Hacker News discussions
5Distinct people
5Mentions
0Explicit recommenders
3.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Vector is an on-host performance monitoring framework which exposes hand picked high resolution metrics to every engineer’s browser.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month5 dated mentions

Observed mentions: 2015-04-08 20:31:34 UTC to 2016-10-16 18:26:26 UTC.

0122015-04-01 00:00:00 UTC to 2015-05-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsApr 20152015-05-01 00:00:00 UTC to 2015-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-06-01 00:00:00 UTC to 2015-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-07-01 00:00:00 UTC to 2015-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-08-01 00:00:00 UTC to 2015-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-09-01 00:00:00 UTC to 2015-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-10-01 00:00:00 UTC to 2015-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-11-01 00:00:00 UTC to 2015-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-12-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2016-01-01 00:00:00 UTC to 2016-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJan 20162016-02-01 00:00:00 UTC to 2016-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-03-01 00:00:00 UTC to 2016-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2016-04-01 00:00:00 UTC to 2016-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-05-01 00:00:00 UTC to 2016-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-06-01 00:00:00 UTC to 2016-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-07-01 00:00:00 UTC to 2016-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-08-01 00:00:00 UTC to 2016-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-09-01 00:00:00 UTC to 2016-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-10-01 00:00:00 UTC to 2016-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsOct 2016

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Apr 20152015-04-01 00:00:00 UTC2015-05-01 00:00:00 UTC211
May 20152015-05-01 00:00:00 UTC2015-06-01 00:00:00 UTC000
Jun 20152015-06-01 00:00:00 UTC2015-07-01 00:00:00 UTC000
Jul 20152015-07-01 00:00:00 UTC2015-08-01 00:00:00 UTC000
Aug 20152015-08-01 00:00:00 UTC2015-09-01 00:00:00 UTC000
Sep 20152015-09-01 00:00:00 UTC2015-10-01 00:00:00 UTC000
Oct 20152015-10-01 00:00:00 UTC2015-11-01 00:00:00 UTC000
Nov 20152015-11-01 00:00:00 UTC2015-12-01 00:00:00 UTC000
Dec 20152015-12-01 00:00:00 UTC2016-01-01 00:00:00 UTC101
Jan 20162016-01-01 00:00:00 UTC2016-02-01 00:00:00 UTC000
Feb 20162016-02-01 00:00:00 UTC2016-03-01 00:00:00 UTC000
Mar 20162016-03-01 00:00:00 UTC2016-04-01 00:00:00 UTC101
Apr 20162016-04-01 00:00:00 UTC2016-05-01 00:00:00 UTC000
May 20162016-05-01 00:00:00 UTC2016-06-01 00:00:00 UTC000
Jun 20162016-06-01 00:00:00 UTC2016-07-01 00:00:00 UTC000
Jul 20162016-07-01 00:00:00 UTC2016-08-01 00:00:00 UTC000
Aug 20162016-08-01 00:00:00 UTC2016-09-01 00:00:00 UTC000
Sep 20162016-09-01 00:00:00 UTC2016-10-01 00:00:00 UTC000
Oct 20162016-10-01 00:00:00 UTC2016-11-01 00:00:00 UTC101

What Hacker News says

5 sources
mholt·Oct 16, 2016
I know of several companies/organizations using Caddy in production; one instance in particular is serving tens of thousands of domains and managing their certificates without difficulty. Netflix uses it for Vector (https://github.com/Netflix/vector/blob/6d013993fc1a5b2ae296f...), ChannelMeter uses it (https://channelmeter.com/), and there are others. It seems that the biggest trouble that people have using Caddy in production is that there aren't official packages for installing/updating Caddy. Fortunately, it's a single, statically-compiled binary no matter how many plugins you choose, and downloading Caddy is as simple as a GET request. But I anticipate we will have some packages by version 1.0. (There's also this Docker image by abiosoft that has 100k+ pulls: https://hub.docker.com/r/abiosoft/caddy/) Caddy is generally _easiest_ to us
Read the rest
e standalone and does fine for most people. Granted, it's not the right tool for everyone, but it's pretty good most of the time. Most of the troubles I've seen reported are in combination with Docker, faulty init system configurations, behind other proxies and load balancers, or trying to serve sites that don't have proper DNS resolution yet (which is required for auto HTTPS, which is on by default).
In Caddy – HTTP/2 Web Server with Automatic HTTPS
seanp2k2·Mar 11, 2016
https://speakerdeck.com/niteshkant/distributed-tracing-at-ne... http://techblog.netflix.com/2012/06/scalable-logging-and-tra... https://github.com/Netflix/vector Not terribly easy, but possible. I agree that "never have to SSH" is a bad target because there will always be edge cases, but for the majority of things it's possible to troubleshoot at the cluster level given immutable infrastructure; all the systems will be doing the exact same thing given an input. Reality shows that this is a bit idealistic, but if you start finding that e.g. A certain set of requests cause weird behavior, you can look at what makes those requests different, then set up some routing logic to send them to a debug farm (using something like https://github.com/Netflix/zuul ).
In Lessons from 10 Years of Amazon Web Services
ucsdrake·Dec 1, 2015
Agreed, considering they've built Vector (mentioned by bgregg in the second sentence - https://github.com/Netflix/vector) on top of Performance Co-Pilot. While PCP doesn't yet have all the wrappers to mimic each sysstat output in a fully compat manner. The underlying mechanisms to remotely fetch that data (using the tools Vector is already built on), is already there.
In Linux Performance Analysis

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