MonascaTrainPTG

General

This page contains a summary of Monasca related subjects covered during the Train PTG.

Place

Denver, Colorado

Time

May 3-4, 2019

Etherpad

https://etherpad.openstack.org/p/monasca-ptg-train

Priorisation game

https://docs.google.com/spreadsheets/d/1MWp8sCTeoJPnAysFzRKkxhvd8MR4AaJT04LxoJiE2QM/edit#gid=0

Development process

Actions

 * create a board for tracking Train tasks
 * clean up orphaned reviews and bugs

Threshold engine replacement

https://storyboard.openstack.org/#!/story/2005598

Option 1 - Prometheus remote read

https://docs.google.com/presentation/d/1tvllnWaridOG-t-qj9D2brddeQXsYNyZwoYUfby_3Ns/edit?usp=sharing

Pros

replaces thresh, notification and transform components

relatively low effort

Cons

creates strong dependency on Prometheus

not clear how to distribute configuration to instances (no API available)

not clear about performance and resources usage of remote read

Option 2 - New stream processing application

https://faust.readthedocs.io/

Pros

keeps existing architecture

scalable, HA application implemented in Python

only Kafka dependency (no Storm)

new implementation would integrate the new QL

makes transform (aggregation) engine obsolete (no Spark)

Cons

large effort

Actions

 * evaluate plausibility of Faust based implementation
 * ask decision makers for developer commitments

Reuse Prometheus dashboards

https://storyboard.openstack.org/#!/story/2005617

Pros

leverage community resources

low effort

Cons

additional (but optional) Prometheus dependency

New Query Language

https://storyboard.openstack.org/#!/story/2005618

Pros

Extends querying capabilities

Makes transform (aggregation) engine obsolete

Prometheus dashboards can be easily adapted

Cons

Cassandra support probably expensive

Actions

 * create spec
 * ask decision makers for developer commitments

High availability/scalability for InfluxDB

https://storyboard.openstack.org/#!/story/2005620

• make use of Kafka to replicate and partition the measurements

Alternatives

existing Apache Cassandra

add Gnocchi support

Actions

 * Document HA options on d.o.o

Application Credentials

https://storyboard.openstack.org/#!/story/2005622
https://storyboard.openstack.org/#!/story/2005623

• simplify secure tenant agent deployment
• decouple Grafana datasource from Grafana auth

Actions

 * Add scripts for Grafana configuration to monasca-grafana-datasource

Grafana Loki

Actions

 * Investigate as possible alternative for ELK stack

Vitrage integration

https://storyboard.openstack.org/#!/story/2004550
https://etherpad.openstack.org/p/vitrage-train-ptg

Agreement

* Implement alarm based mapping to Vitrage resources

Actions

 * Update spec
 * Merge initial patch

Middleware upgrades

https://storyboard.openstack.org/#!/story/2005624

• Apache Kafka
• InfluxDB
• Apache Storm
• ELK

Openstack Helm

Actions

 * Aim Monasca lite deployment
 * Create story

Self-healing SIG

https://etherpad.openstack.org/p/DEN-self-healing-SIG

• collaborate on instrumentation of OpenStack services
• collaborate on and consume API health checks

Java monasca persister deprecation

https://storyboard.openstack.org/#!/story/2005628

Actions

 * tag Java implementation as deprecated
 * revisit Cassandra Python implementation

Auto-scaling SIG

https://etherpad.openstack.org/p/DEN-auto-scaling-SIG

• large interest on Monasca in the SIG discussions

Actions

 * document auto-scaling with Heat
 * link to workshop material from d.o.o
 * add more examples into heat-templates
 * work on Senlin integration

Documentation

https://storyboard.openstack.org/#!/story/2005625

Actions

 * The Monasca landing page should be continuously filled with useful information.
 * Relevant content from wiki page should be moved to main documentation page.
 * Update documentation references on monasca.io.
 * Expose support for collecting Prometheus measurements.
 * Add installation guide and link to existing documents, e.g. Kolla.
 * Convert monasca-agent documentation to rst and add CI jobs.
 * Generate documentation from source code: api-ref, python client.