2018/Data Project: Difference between revisions

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=Project Overview=
=Project Overview=


The 2018 data project is an ongoing effort to figure out how to set up "painless" dashboards.
The 2018 data project is an ongoing effort to figure out how to set up "painless" dashboards for monitoring metrics.


==Phase 1a: Netdata (Done)==
==Stage 1: Collecting System Data==


First, we set up [[Netdata]].
See [[2018/Data Project/Stage 1]]
* '''Pros:''' Netdata has a fantastic dashboard with all kinds of stuff all ready to go.
* '''Cons:''' Netdata is custom-built for monitoring compute nodes, and not for general visualization.
* [[Netdata]]
* Link to Netdata scripts: https://charlesreid1.com:3000/data/netdata


Netdata is a useful tool for monitoring an individual machine instance remotely and it works excellent.
==Stage 2: Spy==


==Phase 1b: Prometheus (Nope)==
dahak-spy project:
* lightweight server (may want larger disk, okay if non-free)
* running mongodb
* running mongoexpress
* running prometheus
* running grafana
* running netdata


<s>Second, we set up [[Netdata]] to dump to a [[Prometheus]] database.  
additional components before real world testing:
* '''Pros:''' Prometheus was fairly easy to integrate with Netdata.
* netdata on the build node
* '''Cons:''' Prometheus was not a particularly outstanding tool, don't know much about how to use it.
* netdata python plugin from another process, monitoring.....???
* [[Prometheus]]
* metrics:
** is snakemake running (binary yes/no)
** current stage of snakemake (adjust snakemake file to write into a dotfile)
** cpu/memory/network/disk io


Need to get more involved with Prometheus and/or Grafana to monitor more than one machine.</s>
netdata python plugin workflow?
* does it need to be installed and netdata restarted, or can it push data into netdata?


==Phase 2: MongoDB and MongoExpress (Done)==
real yeti:
* get a yeti node
* debug the snakemake file one step at a time using already-downloaded files (faster step)
* let the snakemake file run with netdata and friends running


We then set up [[MongoDB]] and [[MongoExpress]] in Docker containers. MongoDB listens for incoming data on the VPN. MongoExpress is connected to MongoDB and exposes a web interface to interact with MongoDB. We used MongoDB to store edit history and page graph data from the charlesreid1 wiki.
=Flags=
* '''Pros:''' MongoDB is a containerized solution with persistent data. MongoDB had (has?) a high setup barrier, but a low usage barrier. Very easy to do basic CRUD operations, make new databases as needed, etc.
* '''Cons:''' No visualization tools baked in, need to define own tools. Collectd cannot dump to MongoDB because of a bunch of installation stupidity.
* [[MongoDB]]
* [[MongoExpress]]
* [[Pywikibot]]
* Link to MongoDB docker files: https://charlesreid1.com:3000/docker/d-mongodb
* Link to MongoExpress docker files: https://charlesreid1.com:3000/docker/d-mongoexpress
* Link to wiki scraping scripts: https://charlesreid1.com:3000/wiki/charlesreid1-wiki-data


==Phase 2b: Collectd (Nope)==
[[Category:Data Engineering]]
[[Category:Data Project]]


<s>We struggled a LOT with [[Collectd]], mainly because we wanted to use the collectd plugin to write to MongoDB. Unfortunately, this was the only plugin that seemed impossible to install.
[[Category:MongoDB]]
[[Category:MongoExpress]]
[[Category:Graphite]]
[[Category:Grafana]]
[[Category:Netdata]]
[[Category:Collectd]]
[[Category:Prometheus]]


See [[Collectd]] page.
[[Category:2018]]
 
[[Category:January 2018]]
(This is all installation stupidity. I tried installing collectd with aptitude, no plugins. Then the core, no plugins. Then installing from source, and MongoDB plugin did not work. Struggling to get collectd to link to MongoDB. Needed custom config or something. Then I just gave up, and re-installed collectd core, and the library was there, but it was complaining it couldn't find it. In the end, I totally abandoned the attempt to get collectd to talk to mongodb. Could probably use a collectd docker and fix this whole issue.)</s>
[[Category:February 2018]]
 
==Phase 3: Graphite (Nope)==
 
<s>Next, we deployed a [[Graphite]] container to hold time series from [[Collectd]].
* '''Pros:''' Containerized solution. Collectd graphite plugin worked fine.
* '''Cons:''' Graphite comes with Carbon (web interface), which is utter crap. It provides the absolute bare minimum, but it looks like it's trapped in a miserable 1998 computer prison.
* [[Graphite]]
* Link to Graphite docker files: https://charlesreid1.com:3000/docker/d-graphite</s>


==Phase 3b: Visualizing Graphite (Nope)==


A few years back we explored [[Cubism]] and Cube (difference?) as a way of visualizing time series from Graphite. It took some effort to get a basic dashboard, and Cubism is (ultimately) D3, the most frustratingly stupidly over-designed and over-complicated library ever, implemented in a totally irrational programming language.


So, no.
<!--


We're going to focus on Mongo, which is more transparent and more flexible for all purposes.
==Stage 2: Finalized Data Collection System==


==Conclusion: Netdata, Not Collectd==
===Phase 4: Netdata and Mongo===
 
All of the struggle to get collectd working with mongo was a waste of effort, and led to the graphite distraction in the first place. A broken build procedure (collectd) led to an unknown, mediocre tool (graphite).
 
Ultimately, if we need to run collectd, interface with the collectd API via Python: https://collectd.org/wiki/index.php/Plugin:Python
 
==Phase 4: Netdata and Mongo==


Netdata provides a backend API that can be called to extract data from Netdata. MongoDB listens for API calls to store data in the database. All we need is software that will poll various Netdata instances using the Netdata API and dump that data into MongoDB. This gives much more fine-grained control over the process, schema, and storage format of the data.
Netdata provides a backend API that can be called to extract data from Netdata. MongoDB listens for API calls to store data in the database. All we need is software that will poll various Netdata instances using the Netdata API and dump that data into MongoDB. This gives much more fine-grained control over the process, schema, and storage format of the data.
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See [[Netdata/MongoDB/API]] for script that calls APIs of Netdata and MongoDB to construct the time series database in MongoDB.
See [[Netdata/MongoDB/API]] for script that calls APIs of Netdata and MongoDB to construct the time series database in MongoDB.


The following repository contains code to process Netdata data and insert it into a MongoDB database.
Link to script: https://charlesreid1.com:3000/data/netdata/src/master/netdata_mongo.py


[[Image:NetdataMongodb.png|500px]]
[[Image:NetdataMongodb.png|500px]]
In the figure above, the application coordinates the data transfer (creates the arrow pipelines).


This is a (micro)service design pattern - small, lightweight, standalone daemons act as instruments that continuously read whatever they read, available to be queried but otherwise not saving or doing anything with the data themselves. The data is handled by an application that queries each service it manages to collect data about those services (and coordinate if necessary).
This is a (micro)service design pattern - small, lightweight, standalone daemons act as instruments that continuously read whatever they read, available to be queried but otherwise not saving or doing anything with the data themselves. The data is handled by an application that queries each service it manages to collect data about those services (and coordinate if necessary).


==Phase 5: Grafana==
-->


[[Grafana]] container to create dashboards from it.
<!--


Link to Grafana docker files: https://charlesreid1.com:3000/docker/d-graphite
==Stage 3: Visualizing Data==


Need to fix grafana user on jupiter.
===Phase 5: Grafana===


We're basically after something like this, except MongoDB instead of Prometheus: https://github.com/firehol/netdata/wiki/Netdata,-Prometheus,-and-Grafana-Stack
[[Grafana]] container to create dashboards from it.


==Next Steps==
Link to Grafana docker files: https://charlesreid1.com:3000/docker/d-grafana


Bioconda and biocontainers - start to adopt their approach to doing things, and be able to replicate workflows
Need to fix grafana user on jupiter.
 
Slang some cluster workflows around
 
=Flags=
 
[[Category:Data Engineering]]
[[Category:Data Project]]


[[Category:MongoDB]]
We're basically after something like this: https://github.com/firehol/netdata/wiki/Netdata,-Prometheus,-and-Grafana-Stack
[[Category:MongoExpress]]
[[Category:Graphite]]
[[Category:Grafana]]
[[Category:Netdata]]
[[Category:Collectd]]
[[Category:Prometheus]]


[[Category:2018]]
-->
[[Category:January 2018]]
[[Category:February 2018]]

Latest revision as of 08:18, 3 March 2018

Project Overview

The 2018 data project is an ongoing effort to figure out how to set up "painless" dashboards for monitoring metrics.

Stage 1: Collecting System Data

See 2018/Data Project/Stage 1

Stage 2: Spy

dahak-spy project:

  • lightweight server (may want larger disk, okay if non-free)
  • running mongodb
  • running mongoexpress
  • running prometheus
  • running grafana
  • running netdata

additional components before real world testing:

  • netdata on the build node
  • netdata python plugin from another process, monitoring.....???
  • metrics:
    • is snakemake running (binary yes/no)
    • current stage of snakemake (adjust snakemake file to write into a dotfile)
    • cpu/memory/network/disk io

netdata python plugin workflow?

  • does it need to be installed and netdata restarted, or can it push data into netdata?

real yeti:

  • get a yeti node
  • debug the snakemake file one step at a time using already-downloaded files (faster step)
  • let the snakemake file run with netdata and friends running

Flags



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