Graphs are the glue that ties transformations together. They are the only data-structure bonobo can execute directly. Graphs must be acyclic, and can contain as many nodes as your system can handle. However, although in theory the number of nodes can be rather high, practical use cases usually do not exceed more than a few hundred nodes and only then in extreme cases.

Within a graph, each node are isolated and can only communicate using their input and output queues. For each input row, a given node will be called with the row passed as arguments. Each return or yield value will be put on the node’s output queue, and the nodes connected in the graph will then be able to process it.

Bonobo is a line-by-line data stream processing solution.

Handling the data-flow this way brings the following properties:

  • First in, first out: unless stated otherwise, each node will receeive the rows from FIFO queues, and so, the order of rows will be preserved. That is true for each single node, but please note that if you define “graph bubbles” (where a graph diverge in different branches then converge again), the convergence node will receive rows FIFO from each input queue, meaning that the order existing at the divergence point wont stay true at the convergence point.
  • Parallelism: each node run in parallel (by default, using independant threads). This is useful as you don’t have to worry about blocking calls. If a thread waits for, let’s say, a database, or a network service, the other nodes will continue handling data, as long as they have input rows available.
  • Independance: the rows are independant from each other, making this way of working with data flows good for line-by-line data processing, but also not ideal for “grouped” computations (where an output depends on more than one line of input data). You can overcome this with rolling windows if the input required are adjacent rows, but if you need to work on the whole dataset at once, you should consider other software.

Graphs are defined using bonobo.Graph instances, as seen in the previous tutorial step.


This document is currently reviewed to check for correctness after the 0.6 release.

What can be a node?

TL;DR: … anything, as long as it’s callable().


def get_item(id):
    return id, items.get(id)

Each node of a graph will be executed in isolation from the other nodes, and the data is passed from one node to the next using FIFO queues, managed by the framework. It’s transparent to the end-user, though, and you’ll only use function arguments (for inputs) and return/yield values (for outputs).

Each input row of a node will cause one call to this node’s callable. Each output is cast internally as a tuple-like data structure (or more precisely, a namedtuple-like data structure), and for one given node, each output row must have the same structure.

If you return/yield something which is not a tuple, bonobo will create a tuple of one element.


Bonobo assists you with defining the data-flow of your data engineering process, and then streams data through your callable graphs.

  • Each node call will process one row of data.
  • Queues that flows the data between node are first-in, first-out (FIFO) standard python queue.Queue.
  • Each node will run in parallel
  • Default execution strategy use threading, and each node will run in a separate thread.

Fault tolerance

Node execution is fault tolerant.

If an exception is raised from a node call, then this node call will be aborted but bonobo will continue the execution with the next row (after outputing the stack trace and incrementing the “err” counter for the node context).

It allows to have ETL jobs that ignore faulty data and try their best to process the valid rows of a dataset.

Some errors are fatal, though.

If you pass a 2 elements tuple to a node that takes 3 args, Bonobo will raise an bonobo.errors.UnrecoverableTypeError, and exit the current graph execution as fast as it can (finishing the other node executions that are in progress first, but not starting new ones if there are remaining input rows).



A directed acyclic graph of transformations, that Bonobo can inspect and execute.


A transformation within a graph. The transformations are stateless, and have no idea whether or not they are included in a graph, multiple graph, or not at all.

Creating a graph

Graphs should be instances of bonobo.Graph. The bonobo.Graph.add_chain() method can take as many positional parameters as you want.

import bonobo

graph = bonobo.Graph()
graph.add_chain(a, b, c)

Resulting graph:

digraph { rankdir = LR; stylesheet = "../_static/graphs.css"; BEGIN [shape="point"]; BEGIN -> "a" -> "b" -> "c"; }

Non-linear graphs

Divergences / forks

To create two or more divergent data streams (“forks”), you should specify the _input kwarg to add_chain.

import bonobo

graph = bonobo.Graph()
graph.add_chain(a, b, c)
graph.add_chain(f, g, _input=b)

Resulting graph:

digraph { rankdir = LR; stylesheet = "../_static/graphs.css"; BEGIN [shape="point"]; BEGIN -> "a" -> "b" -> "c"; "b" -> "f" -> "g"; }


Both branches will receive the same data and at the same time.

Convergence / merges

To merge two data streams, you can use the _output kwarg to add_chain, or use named nodes (see below).

import bonobo

graph = bonobo.Graph()

# Here we set _input to None, so normalize won't start on its own but only after it receives input from the other chains.
graph.add_chain(normalize, store, _input=None)

# Add two different chains
graph.add_chain(a, b, _output=normalize)
graph.add_chain(f, g, _output=normalize)

Resulting graph:

digraph { rankdir = LR; stylesheet = "../_static/graphs.css"; BEGIN [shape="point"]; BEGIN -> "a" -> "b" -> "normalize"; BEGIN2 [shape="point"]; BEGIN2 -> "f" -> "g" -> "normalize"; "normalize" -> "store" }


This is not a “join” or “cartesian product”. Any data that comes from b or g will go through normalize, one at a time. Think of the graph edges as data flow pipes.

Named nodes

Using above code to create convergences often leads to code which is hard to read, because you have to define the “target” stream before the streams that logically goes to the beginning of the transformation graph. To overcome that, one can use “named” nodes:

graph.add_chain(x, y, z, _name=’zed’) graph.add_chain(f, g, h, _input=’zed’)
import bonobo

graph = bonobo.Graph()

# Add two different chains
graph.add_chain(a, b, _output="load")
graph.add_chain(f, g, _output="load")

# Here we mark _input to None, so normalize won't get the "begin" impulsion.
graph.add_chain(normalize, store, _input=None, _name="load")

Resulting graph:

digraph { rankdir = LR; stylesheet = "../_static/graphs.css"; BEGIN [shape="point"]; BEGIN -> "a" -> "b" -> "normalize (load)"; BEGIN2 [shape="point"]; BEGIN2 -> "f" -> "g" -> "normalize (load)"; "normalize (load)" -> "store" }

Inspecting graphs

Bonobo is bundled with an “inspector”, that can use graphviz to let you visualize your graphs.

Read How to inspect and visualize your graph.

Executing graphs

There are two options to execute a graph (which have a similar result, but are targeting different use cases).

  • You can use the bonobo command line interface, which is the highest level interface.
  • You can use the python API, which is lower level but allows to use bonobo from within your own code (for example, a django management command).

Executing a graph with the command line interface

If there is no good reason not to, you should use bonobo run … to run transformation graphs found in your python source code files.

$ bonobo run

You can also run a python module:

$ bonobo run -m my.own.etlmod

In each case, bonobo’s CLI will look for an instance of bonobo.Graph in your file/module, create the plumbing needed to execute it, and run it.

If you’re in an interactive terminal context, it will use bonobo.ext.console.ConsoleOutputPlugin for display.

If you’re in a jupyter notebook context, it will (try to) use bonobo.ext.jupyter.JupyterOutputPlugin.

Executing a graph using the internal API

To integrate bonobo executions in any other python code, you should use It behaves very similar to the CLI, and reading the source you should be able to figure out its usage quite easily.

Where to jump next?

We suggest that you go through the tutorial first.

Then, you can read the guides, either using the order suggested or by picking the chapter that interest you the most at one given moment: