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Define HA domains with CRUSH maps

Project description

Proof of concept of using CRUSH maps for HA node placement

Based off of crush maps from ceph and research paper behind it by (linked here)

A Gentle Introduction

The CRUSH Algorithm as it is used in ceph is used to determine on which nodes data of interest may be stored without a central controller. This is done with ‘buckets’ that define the relationship between nodes (or in the case of ceph OSDs) and their failure/allocation domains and a set of rules which specify the placement of data on these nodes (and therefore their high availability characteristics) and is implemented as a simple VM that operates on the buckets.

To put it more simply, the buckets define the ‘topology’ of your setup (how things are linked and how they should fail) and rules define which nodes you select for data and operate on these buckets. By manipulating the rules in relationship to the failure domains in the buckets you can define arbitrary HA polices that prevent against any type of data loss in a generic fashion.

Why you may want to use it

This library or its concepts may be of sue for the following problems

  • cache server selection

  • locating services in a cluster without a central controller

  • making placement decisions for data/jobs

  • reference for building basic byte code interpreters

Available Commands

As this implementation uses an interpreter, there are several instructions that you can use. these map almost 1:1 with those from the original paper with some slight modification.


The use command is used to select a bucket other than the default one provided to the interpreter, eg when you have to select a specific rack or geographical region. As the buckets form a tree this updates the root to point to the specified bucket. Note that you can still go back to the original root with another use command.


Select looks for nodes of the specified type and amount and places them on the stack. This is typically part of a ‘wide’ operation or ‘left to right’ operation where the intent is to cover as many possibilities before drilling down.


Take is the counter part to select, unlike select this is a ‘deep’ or ‘top to bottom’ operation. For each item on the stack take will select count number of nodes


Emit takes the currently accumulated node choices and sends them back to the caller while resetting the node list and stack list. This is useful as a barrier if you have 2 types of criteria that need to be satisfied eg 2 nodes locally, then a node remotely. Selection of the first 2 nodes would be done with the above rules then ‘emited’, resetting the decision tree. The next set of instructions would then satisfy the remote node selection and not be constrained by earlier selections.


The following command can be used to run a demo program:

$ python -m crushha.interpreter

While the output is not that interesting the src code backing it may be of use if you intend to use this module. Setting up and enabling logging will also give a highly detailed and annotated look into the decisions behind each nodes selection for auditing purposes.

Current State

This module is currently in a very alpha state and subject to change however the api defined by the implementation is about 80% of the way there. This will be designed as a coroutine due to the requirement to be able to generate an arbitrary long list of nodes from a large pool and the iteration model it provides has proved useful to work with

The current design makes use of ‘yield from’ from python 3.3, python versions before this will not be considered as a porting target due to the complexity of the required trampolines to make it work correctly. The code is simple and is intended to stay so.

The Future

  • Unit tests

  • Docs

  • Better defined op codes

  • slight optimization for shorter rules (eliminating buckets by name in select)

  • Finalization of the API

  • More examples

Repo: Docs: # comming soon Email: code at

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