Storage finders

Graphite-Render searches and fetches metrics from time series databases using an interface called finders. The default finder provided with Graphite-Render is the one that integrates with Whisper databases.

Customizing finders can be done in the finders section of the Graphite-Render configuration file:

finders:
  - graphite_render.finders.whisper.WhisperFinder

Several values are allowed, to let you store different kinds of metrics at different places or smoothly handle transitions from one time series database to another.

The default finder reads data from a Whisper database.

Custom finders

finders being a list of arbitrary python paths, it is relatively easy to write a custom finder if you want to read data from other places than Whisper. A finder is a python class with a find_nodes() method:

class CustomFinder(object):
    def find_nodes(self, query):
        # ...

query is a FindQuery object. find_nodes() is the entry point when browsing the metrics tree. It must yield leaf or branch nodes matching the query:

from graphite_render.node import LeafNode, BranchNode

class CustomFinder(object):
    def find_nodes(self, query):
        # find some paths matching the query, then yield them
        # is_branch or is_leaf are predicates you need to implement
        for path in matches:
            if is_branch(path):
                yield BranchNode(path)
            if is_leaf(path):
                yield LeafNode(path, CustomReader(path))

LeafNode is created with a reader, which is the class responsible for fetching the datapoints for the given path. It is a simple class with 2 methods: fetch() and get_intervals():

from graphite_render.intervals import IntervalSet, Interval

class CustomReader(object):
    __slots__ = ('path',)  # __slots__ is recommended to save memory on readers

    def __init__(self, path):
        self.path = path

    def fetch(self, start_time, end_time):
        # fetch data
        time_info = _from_, _to_, _step_
        return time_info, series

    def get_intervals(self):
        return IntervalSet([Interval(start, end)])

fetch() must return a list of 2 elements: the time info for the data and the datapoints themselves. The time info is a list of 3 items: the start time of the datapoints (in unix time), the end time and the time step (in seconds) between the datapoints.

The datapoints is a list of points found in the database for the required interval. There must be (end - start) / step points in the dataset even if the database has gaps: gaps can be filled with None values.

get_intervals() is a method that hints graphite-web about the time range available for this given metric in the database. It must return an IntervalSet of one or more Interval objects.

Fetching multiple paths at once

If your storage backend allows it, fetching multiple paths at once is useful to avoid sequential fetches and save time and resources. This can be achieved in three steps:

  • Subclass LeafNode and add a __fetch_multi__ class attribute to your subclass:

    class CustomLeafNode(LeafNode):
    __fetch_multi__ = 'custom'

    The string 'custom' is used to identify backends and needs to be unique per-backend.

  • Add the __fetch_multi__ attribute to your finder class:

    class CustomFinder(objects):
    __fetch_multi__ = 'custom'

  • Implement a fetch_multi() method on your finder:

    class CustomFinder(objects):
    def fetch_multi(self, nodes, start_time, end_time):
        paths = [node.path for node in nodes]
        # fetch paths
        return time_info, series

    time_info is the same structure as the one returned by fetch(). series is a dictionnary with paths as keys and datapoints as values.

Installing custom finders

In order for your custom finder to be importable, you need to package it under a namespace of your choice. Python packaging won’t be covered here but you can look at third-party finders to get some inspiration:

Configuration

Graphite-Render instantiates finders and passes it its whole parsed configuration file, as a Python data structure. External finders can require extra sections in the configuration file to setup access to the time series database they communicate with. For instance, let’s say your CustomFinder needs two configuration parameters, a host and a user:

class CustomFinder(object):
    def __init__(self, config):
        config.setdefault('custom', {})
        self.user = config['custom'].get('user', 'default')
        self.host = config['custom'].get('host', 'localhost')

The configuration file would look like:

finders:
  - custom.CustomFinder
custom:
  user: myuser
  host: example.com

When possible, try to use sane defaults that would “just work” for most common setups. Here if the custom section isn’t provided, the finder uses default as user and localhost as host.