Frontier Backend is where the crawling logic/policies lies, essentially a brain of your crawler. Queue, Metadata and States are classes where all low level code is meant to be placed, and Backend opposite, operates on a higher levels. Frontera is bundled with database and in-memory implementations of Queue, Metadata and States which can be combined in your custom backends or used standalone by directly instantiating FrontierManager and Backend.

Backend methods are called by the FrontierManager after Middleware, using hooks for Request and Response processing according to frontier data flow.

Unlike Middleware, that can have many different instances activated, only one Backend can be used per frontier.

Activating a backend

To activate the frontier backend component, set it through the BACKEND setting.

Here’s an example:

BACKEND = 'frontera.contrib.backends.memory.FIFO'

Keep in mind that some backends may need to be additionally configured through a particular setting. See backends documentation for more info.

Writing your own backend

Each backend component is a single Python class inherited from Backend or DistributedBackend and using one or all of Queue, Metadata and States.

FrontierManager will communicate with active backend through the methods described below.

Inherits all methods of Backend, and has two more class methods, which are called during strategy and db worker instantiation.

Backend should communicate with low-level storage by means of these classes:


Known implementations are: MemoryMetadata and sqlalchemy.components.Metadata.


Known implementations are: MemoryQueue and sqlalchemy.components.Queue.


Known implementations are: MemoryStates and sqlalchemy.components.States.

Built-in backend reference

This article describes all backend components that come bundled with Frontera.

To know the default activated Backend check the BACKEND setting.

Basic algorithms

Some of the built-in Backend objects implement basic algorithms as as FIFO/LIFO or DFS/BFS for page visit ordering.

Differences between them will be on storage engine used. For instance, memory.FIFO and sqlalchemy.FIFO will use the same logic but with different storage engines.

All these backend variations are using the same CommonBackend class implementing one-time visit crawling policy with priority queue.

Memory backends

This set of Backend objects will use an heapq module as queue and native dictionaries as storage for basic algorithms.

class frontera.contrib.backends.memory.BASE

Base class for in-memory Backend objects.

class frontera.contrib.backends.memory.FIFO

In-memory Backend implementation of FIFO algorithm.

class frontera.contrib.backends.memory.LIFO

In-memory Backend implementation of LIFO algorithm.

class frontera.contrib.backends.memory.BFS

In-memory Backend implementation of BFS algorithm.

class frontera.contrib.backends.memory.DFS

In-memory Backend implementation of DFS algorithm.

class frontera.contrib.backends.memory.RANDOM

In-memory Backend implementation of a random selection algorithm.

SQLAlchemy backends

This set of Backend objects will use SQLAlchemy as storage for basic algorithms.

By default it uses an in-memory SQLite database as a storage engine, but any databases supported by SQLAlchemy can be used.

If you need to use your own declarative sqlalchemy models, you can do it by using the SQLALCHEMYBACKEND_MODELS setting.

This setting uses a dictionary where key represents the name of the model to define and value the model to use.

For a complete list of all settings used for SQLAlchemy backends check the settings section.

class frontera.contrib.backends.sqlalchemy.BASE

Base class for SQLAlchemy Backend objects.

class frontera.contrib.backends.sqlalchemy.FIFO

SQLAlchemy Backend implementation of FIFO algorithm.

class frontera.contrib.backends.sqlalchemy.LIFO

SQLAlchemy Backend implementation of LIFO algorithm.

class frontera.contrib.backends.sqlalchemy.BFS

SQLAlchemy Backend implementation of BFS algorithm.

class frontera.contrib.backends.sqlalchemy.DFS

SQLAlchemy Backend implementation of DFS algorithm.

class frontera.contrib.backends.sqlalchemy.RANDOM

SQLAlchemy Backend implementation of a random selection algorithm.

Revisiting backend

Based on custom SQLAlchemy backend, and queue. Crawling starts with seeds. After seeds are crawled, every new document will be scheduled for immediate crawling. On fetching every new document will be scheduled for recrawling after fixed interval set by SQLALCHEMYBACKEND_REVISIT_INTERVAL.

Current implementation of revisiting backend has no prioritization. During long term runs spider could go idle, because there are no documents available for crawling, but there are documents waiting for their scheduled revisit time.

HBase backend

Is more suitable for large scale web crawlers. Settings reference can be found here HBase backend. Consider tunning a block cache to fit states within one block for average size website. To achieve this it’s recommended to use hostname_local_fingerprint

to achieve documents closeness within the same host. This function can be selected with URL_FINGERPRINT_FUNCTION setting.