Exporter for Storage Area Network (SAN)

Overview

SAN Exporter

license CI Docker Pulls Code size

Prometheus exporter for Storage Area Network (SAN).

We all know that each SAN Storage vendor has their own glossary of terms, health/performance metrics and monitoring tool.

But from operator view,

  • We normally focus on some main metrics which are similar on different storage platform.
  • We are not only monitoring SAN storage but also other devices and services at multi-layer (application, virtual Machine, hypervisor, operating system and physical).

That's why we build this to have an unified monitoring/alerting solution with Prometheus and Alermanager.

Architecture overview

SAN exporter architecture

Features

There are some main features you might want to know, for others, please see example configuration.

  • Enable/disable optinal metrics for each backend
  • Enable/disable backend
  • Backend will automatically stop collecting data from SAN system after timeout seconds from last request of client. With this feature, we can deploy two instances as Active/Passive mode for high availability.

Note: Backend may not respond metrics in the first interval while collecting, calculating and caching metrics.

Quick start

  • Start a dummy driver with Docker
$ git clone [email protected]:vCloud-DFTBA/san_exporter.git
$ cd san_exporter/
$ cp examples/dummy_config.yml config.yml
# docker run --rm -p 8888:8888 -v $(pwd)/config.yml:/san-exporter/config.yml --name san-exporter daikk115/san-exporter:0.1.0

See the result at http://localhost:8888/dummy_backend

  • Start a dummy driver manually
$ git clone [email protected]:vCloud-DFTBA/san_exporter.git
$ cd san_exporter/
$ cp examples/dummy_config.yml config.yml
$ sudo apt-get install libxml2-dev libxslt1-dev python3.7-dev
$ pip3 install -r requirements.txt
$ python3.7 manage.py

See the result at http://localhost:8888/dummy_backend

Deployment

Create configuration file

# mkdir /root/san-exporter
# cp /path/to/san_exporter/examples/config.yml.sample /root/san-exporter/config.yml

Update /root/san-exporter/config.yml for corresponding to SAN storage

Run new container

# docker volume create san-exporter
# docker run -d -p 8888:8888 -v san-exporter:/var/log/ -v /root/san-exporter/config.yml:/san-exporter/config.yml --name san-exporter daikk115/san-exporter:latest

Supported Drivers

  • Matrix of driver's generic metrics
Capacity all Capacity pool IOPS/Throuhgput pool Latency pool IOPS/Throughput node Latency node CPU node RAM node IOPS/Throughput LUN Latency LUN IOPS/Throughput disk Latency disk IOPS/Throughput port Latency port Alert
HPMSA X X X X X X X X
DellUnity X X X X X X X X X X
HitachiG700 X X X
HPE3Par X X X X X X X X
NetApp X X X X X X
SC8000 X X X X X X X X X X X
V7k X X X X X X
  • Connection port requirements
    • For some SAN system, we collect metrics over SP API but some others, we collect metrics dirrectly from controller API.
    • In some special cases, we collect alerts over SSH.
SAN System Service Processor Connection Port
HPMSA NO 443
Dell Unity NO 443
Hitachi G700 YES 23451
IBM V7000 NO #TODO
IBM V5000 NO #TODO
HPE 3PAR YES #TODO
NetApp ONTAP NO 443
SC8000 NO 3033

Metrics

All metrics are prefixed with "san_" and has at least 2 labels: backend_name and san_ip

Info metrics:

Metrics name Type Help
san_storage_info gauge Basic information: serial, version, ...

Controller metrics:

Metrics name Type Help
san_totalNodes gauge Total nodes
san_masterNodes gauge Master nodes
san_onlineNodes gauge Online nodes
san_compress_support gauge Compress support, 1 = Yes, 0 = No
san_thin_provision_support gauge Thin provision support, 1 = Yes, 0 = No
san_system_reporter_support gauge System reporter support, 1 = Yes, 0 = No
san_qos_support gauge QoS support, 1 = Yes, 0 = No
san_totalCapacityMiB gauge Total system capacity in MiB
san_allocatedCapacityMiB gauge Total allocated capacity in MiB
san_freeCapacityMiB gauge Total free capacity in MiB
san_cpu_system_utilization gauge The average percentage of time that the processors on nodes are busy doing system I/O tasks
san_cpu_compression_utilization gauge The approximate percentage of time that the processor core was busy with data compression tasks
san_cpu_total gauge The cpus spent in each mode

Pool metrics:

Metrics name Type Help
san_pool_totalLUNs gauge Total LUNs (or Volumes)
san_pool_total_capacity_mib gauge Total capacity of pool in MiB
san_pool_free_capacity_mib gauge Free of pool in MiB
san_pool_provisioned_capacity_mib gauge Provisioned of pool in MiB
san_pool_number_read_io gauge Read I/O Rate - ops/s
san_pool_number_write_io gauge Write I/O Rate - ops/s
san_pool_read_cache_hit gauge Read Cache Hits - %
san_pool_write_cache_hit gauge Write Cache Hits - %
san_pool_read_kb gauge gauge Read Data Rate - KiB/s
san_pool_write_kb gauge Write Data Rate - KiB/s
san_pool_read_service_time_ms gauge Read Response Time - ms/op
san_pool_write_service_time_ms gauge Write Response Time - ms/op
san_pool_read_IOSize_kb gauge Read Transfer Size - KiB/op
san_pool_write_IOSize_kb gauge Write Transfer Size - KiB/op
san_pool_queue_length gauge Queue length of pool

Port metrics:

Metrics name Type Help
san_port_number_read_io gauge Port Read I/O Rate - ops/s
san_port_number_write_io gauge Port Write I/O Rate - ops/s
san_port_write_kb gauge Port Write Data Rate - KiB/s
san_port_read_kb gauge Port Read Data Rate - KiB/s
san_port_write_IOSize_kb gauge Port Write Transfer Size - KiB/op
san_port_read_IOSize_kb gauge Port Read Transfer Size - KiB/op
san_port_queue_length gauge Queue length of port

For more information about specific metrics of SANs, see Specific SAN Metrics

Integrate with Prometheus, Alertmanager and Grafana

Some grafana images:

SAN exporter dashboard overview

SAN exporter dashboard pool

SAN exporter dashboard port

You might also like...
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Neurolab is a simple and powerful Neural Network Library for Python

Neurolab Neurolab is a simple and powerful Neural Network Library for Python. Contains based neural networks, train algorithms and flexible framework

A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

Visualizer for neural network, deep learning, and machine learning models
Visualizer for neural network, deep learning, and machine learning models

Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Tens

Graph neural network message passing reframed as a Transformer with local attention

Adjacent Attention Network An implementation of a simple transformer that is equivalent to graph neural network where the message passing is done with

data/code repository of "C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer"

C2F-FWN data/code repository of "C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer" (https://arxiv.org/abs/

Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network)
Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network)

Deep Daze mist over green hills shattered plates on the grass cosmic love and attention a time traveler in the crowd life during the plague meditative

End-to-End Object Detection with Fully Convolutional Network
End-to-End Object Detection with Fully Convolutional Network

This project provides an implementation for "End-to-End Object Detection with Fully Convolutional Network" on PyTorch.

TensorFlow-based neural network library
TensorFlow-based neural network library

Sonnet Documentation | Examples Sonnet is a library built on top of TensorFlow 2 designed to provide simple, composable abstractions for machine learn

Comments
  • Support purestorage please!

    Support purestorage please!

    Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

    Describe the solution you'd like A clear and concise description of what you want to happen.

    Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

    Additional context Add any other context or screenshots about the feature request here. Can you support purestorage?

    opened by wanbeepeto 0
Releases(v0.8.0)
  • v0.8.0(Aug 17, 2021)

    • Release notes:
      • Add Dell Unnity driver
      • Add Hitachi G700 driver
      • Add HPE 3PAR driver
      • Add HPMSA driver
      • Add NetApp ONTAP driver
      • Add Dell SC800 driver
      • Add IBM V7000 driver
    • Docker image: daikk115/san-exporter:0.8.0
    Source code(tar.gz)
    Source code(zip)
  • v0.1.0(Aug 15, 2021)

Owner
vCloud
Not Only vCloud - Don’t Forget To Be Awesome
vCloud
Spatial Contrastive Learning for Few-Shot Classification (SCL)

This repo contains the official implementation of Spatial Contrastive Learning for Few-Shot Classification (SCL), which presents of a novel contrastive learning method applied to few-shot image class

Yassine 34 Dec 25, 2022
22 Oct 14, 2022
This is a repo of basic Machine Learning!

Basic Machine Learning This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resource

Ekram Asif 53 Dec 31, 2022
Multi-Objective Loss Balancing for Physics-Informed Deep Learning

Multi-Objective Loss Balancing for Physics-Informed Deep Learning Code for ReLoBRaLo. Abstract Physics Informed Neural Networks (PINN) are algorithms

Rafael Bischof 16 Dec 12, 2022
MIRACLE (Missing data Imputation Refinement And Causal LEarning)

MIRACLE (Missing data Imputation Refinement And Causal LEarning) Code Author: Trent Kyono This repository contains the code used for the "MIRACLE: Cau

van_der_Schaar \LAB 15 Dec 29, 2022
The Wearables Development Toolkit - a development environment for activity recognition applications with sensor signals

Wearables Development Toolkit (WDK) The Wearables Development Toolkit (WDK) is a framework and set of tools to facilitate the iterative development of

Juan Haladjian 114 Nov 27, 2022
Official code repository for Continual Learning In Environments With Polynomial Mixing Times

Official code for Continual Learning In Environments With Polynomial Mixing Times Continual Learning in Environments with Polynomial Mixing Times This

Sharath Raparthy 1 Dec 19, 2021
codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification

DLCF-DCA codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification. submitted t

15 Aug 30, 2022
📖 Deep Attentional Guided Image Filtering

📖 Deep Attentional Guided Image Filtering [Paper] Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao ,Xiangyang Ji Harbin Institute of Technology,

9 Dec 23, 2022
Learning Synthetic Environments and Reward Networks for Reinforcement Learning

Learning Synthetic Environments and Reward Networks for Reinforcement Learning We explore meta-learning agent-agnostic neural Synthetic Environments (

AutoML-Freiburg-Hannover 16 Sep 02, 2022
Encoding Causal Macrovariables

Encoding Causal Macrovariables Data Natural climate data ('El Nino') Self-generated data ('Simulated') Experiments Detecting macrovariables through th

Benedikt Höltgen 3 Jul 31, 2022
DeepFaceLab fork which provides IPython Notebook to use DFL with Google Colab

DFL-Colab — DeepFaceLab fork for Google Colab This project provides you IPython Notebook to use DeepFaceLab with Google Colaboratory. You can create y

779 Jan 05, 2023
Jupyter notebooks for using & learning Keras

deep-learning-with-keras-notebooks 這個github的repository主要是個人在學習Keras的一些記錄及練習。希望在學習過程中發現到一些好的資訊與範例也可以對想要學習使用 Keras來解決問題的同好,或是對深度學習有興趣的在學學生可以有一些方便理解與上手範例

ErhWen Kuo 2.1k Dec 27, 2022
Little tool in python to watch anime from the terminal (the better way to watch anime)

ani-cli Script working again :), thanks to the fork by Dink4n for the alternative approach to by pass the captcha on gogoanime A cli to browse and wat

Harshith 4.5k Dec 31, 2022
Accelerating BERT Inference for Sequence Labeling via Early-Exit

Sequence-Labeling-Early-Exit Code for ACL 2021 paper: Accelerating BERT Inference for Sequence Labeling via Early-Exit Requirement: Please refer to re

李孝男 23 Oct 14, 2022
TraSw for FairMOT - A Single-Target Attack example (Attack ID: 19; Screener ID: 24):

TraSw for FairMOT A Single-Target Attack example (Attack ID: 19; Screener ID: 24): Fig.1 Original Fig.2 Attacked By perturbing only two frames in this

Derry Lin 21 Dec 21, 2022
Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts

t5-japanese Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts. The following is a list of models that

Kimio Kuramitsu 1 Dec 13, 2021
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening Introduction This is an implementation of the model used for breast

757 Dec 30, 2022
Meta-learning for NLP

Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks Code for training the meta-learning models and fine-tuning on downstr

IESL 43 Nov 08, 2022
A dual benchmarking study of visual forgery and visual forensics techniques

A dual benchmarking study of facial forgery and facial forensics In recent years, visual forgery has reached a level of sophistication that humans can

8 Jul 06, 2022