semvec/pypi-packages huggingface dataset:
sqlalchemy-orm-tree2 0.5.1
An implementation for SQLAlchemy-based applications of the nested-sets/modified-pre-order-tree-traversal technique for storing hierarchical data in a relational database.

.. image:: https://travis-ci.org/monetizeio/sqlalchemy-orm-tree.png?branch=master
    :target: https://travis-ci.org/monetizeio/sqlalchemy-orm-tree

.. image:: https://badge.fury.io/py/SQLAlchemy-ORM-tree.png
    :target: http://badge.fury.io/py/sqlalchemy-orm-tree

.. image:: https://coveralls.io/repos/monetizeio/sqlalchemy-orm-tree/badge.png?branch=master
    :target: https://coveralls.io/r/monetizeio/sqlalchemy-orm-tree?branch=master

SQLAlchemy-ORM-tree
-------------------

An implementation for SQLAlchemy-based applications of the nested-sets /
modified-pre-order-tree-traversal technique for storing hierarchical data
in a relational database.

==============  ==========================================================
Python support  Python 2.6+, 3.3+
SQLAlchemy      SQLAlchemy >=0.7.5, >=0.8, >=0.9
Source          https://github.com/monetizeio/sqlalchemy-orm-tree
Issues          https://github.com/monetizeio/sqlalchemy-orm-tree/issues
Docs            https://sqlalchemy-orm-tree.readthedocs.org/
API             https://sqlalchemy-orm-tree.readthedocs.org/api.html
Travis          http://travis-ci.org/monetizeio/sqlalchemy-orm-tree
Test coverage   https://coveralls.io/r/monetizeio/sqlalchemy-orm-tree
pypi            https://pypi.python.org/pypi/sqlalchemy-orm-tree
ohloh           http://www.ohloh.net/p/sqlalchemy-orm-tree
License         `BSD`_.
git repo        .. code-block:: bash

                    $ git clone https://github.com/monetizeio/sqlalchemy-orm-tree.git
install         .. code-block:: bash

                    $ pip install sqlalchemy-orm-tree

install dev     .. code-block:: bash

                    $ git clone https://github.com/monetizeio/sqlalchemy-orm-tree.git sqlalchemy-orm-tree
                    $ cd ./sqlalchemy-orm-tree
                    $ virtualenv .env
                    $ source .env/bin/activate
                    $ pip install -e .
tests           .. code-block:: bash

                    $ python setup.py test
==============  ==========================================================

.. _BSD: http://opensource.org/licenses/BSD-3-Clause


Simple Example
==============

::

    import sqlalchemy_tree
    Model = declarative_base(metaclass=sqlalchemy_tree.DeclarativeMeta)

    class Page(Model):

        # This activates sqlalchemy-orm-tree.
        __tree_manager__ = 'tree'


Page.tree.register()
quintoandar-kafka 2.0.0
Checks messages to avoid reprocessing events.

[![Build Status](https://travis-ci.org/quintoandar/python-kafka.svg?branch=master)](https://travis-ci.org/quintoandar/python-kafka)

# QuintoAndar Kafka Python Library

QuintoAndar's kafka-python lib wrapper with additional functionalities.

## KafkaIdempotentConsumer

A simple wrapper for kafka-python lib that uses redis to check duplicate events.

### Configuration

|        Name       |                 Description                  |
| ----------------- | -------------------------------------------- |
| group_id          | The consumer group id                        |
| bootstrap_servers | The bootstrap servers                        |
| redis_host        | The topic to consume from                    |
| redis_port        | The function that processes the event        |
| idempotent_key    | Function which extract an unique identifier from the event |


See [examples](/examples)

## Development

### Environment

At the bare minimum you'll need the following for your development
environment:

1. [Python 3.6.8](http://www.python.org/)

It is strongly recommended to also install and use [pyenv](https://github.com/pyenv/pyenv):

 - [pyenv-installer](https://github.com/pyenv/pyenv-installer)

This tool eases the burden of dealing with virtualenvs and having to activate and
deactivate'em by hand. Once you run `pyenv local my-project-venv` the directory you're
in will be bound to the `my-project-venv` virtual environment and then you will have
never to bother again activating the correct venv.

### Getting Started

#### 1. Clone the project:

```bash
git clone git@github.com:quintoandar/kafka-python.git
cd kafka-python
```

#### 2. Setup the python environment for the project:

```bash
make environment
```

#### 3. Install dependencies

You can just the make recipe:

```bash
make install
```
pears-orchard 0.0.16
A decentralized search engine which can be run in local machine without letting anyone know the details about your search
Requires Python: >=3.5

# Orchard PeARS

## What and why

PeARS (version Orchard) is a search engine that you can install and run locally from your browser. It allows you to 'index' pages (i.e. to produce a computer-readable representation of the pages' content, essential to the search process), and to search pages that you or your friends have indexed. Search happens entirely on your machine, meaning that no one knows what you are searching and when.

One feature of PeARS Orchard is the ability to convert a small index into a greyish, unassuming picture, called a 'snow pod'. Snow pods are the mini-weapons of the indexing revolution. They can easily be shared with others by email, on social media, or through any other means, so you can be your very own search engine, for yourself and for your friends.


### How does this fit with peer-to-peer PeARS?

The fully-fledged PeARS system (Peer-to-peer Agent for Reciprocated Search) is supposed to be completely distributed. You can imagine it as an 'automated' version of Phase 1, where you don't have to go and hunt for pods yourself. Your PeARS install will automatically find them on other users' systems and connect to them. We are still working on this phase of the project.


## Usage

NB: we have some instructions for Windows users [on the wiki](https://github.com/PeARSearch/PeARS-orchard/wiki/Windows-installation).

#### 1. Clone this repo on your machine:

    git clone https://github.com/PeARSearch/PeARS-orchard.git


#### 2. **Optional step** Setup a virtualenv in your directory. 

If you haven't yet set up virtualenv on your machine, please install it via pip:

    sudo apt-get update

    sudo apt-get install python3-setuptools

    sudo apt-get install python3-pip

    sudo pip3 install virtualenv

Then run:

    virtualenv -p python3 env && source env/bin/activate

Then change into the PeARS-orchard directory:

    cd PeARS-orchard

#### 3. Install the build dependencies:

    pip3 install -r requirements.txt

#### 4. Unpack the semantic space

Head over to the app/static/spaces directory and unzip english.dm.zip.

    cd app/static/spaces

    unzip english.dm.zip

#### 5. Run it!

In the root of the repo, run:

    python3 run.py

Now, go to your browser at localhost:8080. You should see the search page for PeARS. You don't have any pages indexed yet, so go to the F.A.Q. page (link at the top of the page) and follow the short instructions to get you going!