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findopendata-1.0.5
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مشاهده بیشترتوضیحات
A search engine for Open Data.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | findopendata-1.0.5 |
| نام | findopendata |
| نسخه کتابخانه | 1.0.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Eric Zhu |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/findopendata/findopendata |
| آدرس اینترنتی | https://pypi.org/project/findopendata/ |
| مجوز | - |
# Find Open Data
[](https://travis-ci.org/findopendata/findopendata)
Table of Content:
1. [Introduction](#introduction)
2. [System Overview](#system-overview)
3. [Development Guide](#development-guide)
4. [Cloud Storage Systems](#cloud-storage-systems)
5. [Crawler Guide](#crawler-guide)
## Introduction
This is the source code repository for [findopendata.com](https://findopendata.com).
The project goal is to make a search engine for Open Data with rich
features beyond simple keyword search. The current search methods are:
* Keyword search based on metadata
* Similar dataset search based on metadata similarity
* Joinable table search based on content (i.e., data values) similarity using LSH index
Next steps:
* Unionable/similar table search based on content similarity
* Time and location-based serach based on extracted timestamps and Geo tags
* Dataset versioning
* API for external data science tools (e.g., Jupyter Notebook, Plot.ly)
**This is a work in progress.**
## System Overview
The Find Open Data system has the following components:
1. **Frontend**: a React app, located in `frontend`.
2. **API Server**: a Flask web server, located in `apiserver`.
3. **LSH Server**: a Go web server, located in `lshserver`.
4. **Crawler**: a set of [Celery](https://docs.celeryproject.org/en/latest/userguide/tasks.html) tasks, located in `findopendata`.
The Frontend, the API Server, and the LSH Server can be
deployed to
[Google App Engine](https://cloud.google.com/appengine/docs/).
We also use two external storage systems for persistence:
1. A PostgreSQL database for storing dataset registry, metadata, and sketches for content-based search.
2. A cloud-based storage system for storing dataset files, currently supporting Google Cloud Storage and Azure Blob Storage. A local storage using file system is also available.
## Development Guide
To develop locally, you need the following:
* PostgreSQL 9.6 or above
* RabbitMQ
#### 1. Install PostgreSQL
[PostgreSQL](https://www.postgresql.org/download/)
(version 9.6 or above) is used by the crawler to register and save the
summaries of crawled datasets. It is also used by the API Server as the
database backend.
If you are using Cloud SQL Postgres, you need to download
[Cloud SQL Proxy](https://cloud.google.com/sql/docs/postgres/connect-admin-proxy#install)
and make it executable.
Once the PostgreSQL database is running, create a database, and
use the SQL scripts in `sql` to create tables:
```
psql -f sql/create_crawler_tables.sql
psql -f sql/create_metadata_tables.sql
psql -f sql/create_sketch_tables.sql
```
#### 2. Install RabbitMQ
[RabbitMQ](https://www.rabbitmq.com/download.html)
is required to manage and queue crawl tasks.
On Mac OS X you can [install it using Homebrew](https://www.rabbitmq.com/install-homebrew.html).
Run the RabbitMQ server after finishing install.
#### 3. Python Environment
It is recommended to use virtualenv for Python development and dependencies:
```
virtualenv -p python3 .venv
source .venv/bin/activate # .\venv\bin\activate on Windows
```
`python-snappy` requires `libsnappy`. On Ubuntu you can
simply install it by `sudo apt-get install libsnappy-dev`.
On Mac OS X use `brew install snappy`.
On Windows, instead of the `python-snappy` binary on Pypi, use the
unofficial binary maintained by UC Irvine
([download here](https://www.lfd.uci.edu/~gohlke/pythonlibs/)),
and install directly, for example (Python 3.7, amd64):
```
pip install python_snappy‑0.5.4‑cp37‑cp37m‑win_amd64.whl
```
Finally, install this package and other dependencies:
```
pip install -e .
```
#### 4. Configuration File
Create a `configs.yaml` by copying `configs-example.yaml`, complete fields
related to PostgreSQL and storage.
If you plan to store all datasets on your local file system,
you can skip the `gcp` and `azure` sections and only complete
the `local` section, and make sure the `storage.provider` is
set to `local`.
For cloud-based storage systems, see
[Cloud Storage Systems](#cloud-storage-systems).
## Cloud Storage Systems
Currently we support using
[Google Cloud Storage](https://cloud.google.com/storage/) and
[Azure Blob Storage](https://azure.microsoft.com/en-us/services/storage/blobs/)
as the dataset storage system.
To use Google Cloud Storage, you need:
* A Google Cloud project with Cloud Storage enabled, and a bucket created.
* A Google Cloud service account key file (JSON formatted) with read and write access to the Cloud Storage bucket.
* Set `storage.provider` to `gcp` in `configs.yaml`.
To use Azure Blob Storage, you need:
* An Azure storage account enabled, and a blob storage container created.
* A connection string to access the storage account.
* Set `storage.provider` to `azure` in `configs.yaml`.
## Crawler Guide
The crawler has a set of [Celery](http://www.celeryproject.org/) tasks that
runs in parallel.
It uses the RabbitMQ server to manage and queue the tasks.
### Setup Crawler
#### Data Sources (CKAN and Socrata APIs)
The crawler uses PostgreSQL to maintain all data sources.
CKAN sources are maintained in the table `findopendata.ckan_apis`.
Socrata Discovery APIs are maintained in the table
`findopendata.socrata_discovery_apis`.
The SQL script `sql/create_crawler_tables.sql` has already created some
initial sources for you.
To show the CKAN APIs currently available to the crawler and whether they
are enabled:
```sql
SELECT * FROM findopendata.ckan_apis;
```
To add a new CKAN API and enable it:
```sql
INSERT INTO findopendata.ckan_apis (endpoint, name, region, enabled) VALUES
('catalog.data.gov', 'US Open Data', 'United States', true);
```
#### Socrata App Tokens
Add your [Socrata app tokens](https://dev.socrata.com/docs/app-tokens.html)
to the table `findopendata.socrata_app_tokens`.
The app tokens are required for harvesting datasets from Socrata APIs.
For example:
```sql
INSERT INTO findopendata.socrata_app_tokens (token) VALUES ('<your app token>');
```
### Run Crawler
[Celery workers](https://docs.celeryproject.org/en/latest/userguide/workers.html)
are processes that fetch crawler tasks from RabbitMQ and execute them.
The worker processes must be started before starting any tasks.
For example:
```
celery -A findopendata worker -l info -Ofair
```
On Windows there are some issues with using prefork process pool.
Use `gevent` instead:
```
celery -A findopendata worker -l info -Ofair -P gevent
```
#### Harvest Datasets
Run `harvest_datasets.py` to start data harvesting tasks that download
datasets from various data sources. Downloaded datasets will be stored on
a Google Cloud Storage bucket (set in `configs.yaml`), and registed in
Postgres tables
`findopendata.ckan_packages` and `findopendata.socrata_resources`.
#### Generate Metadata
Run `generate_metadata.py` to start metadata generation tasks for
downloaded and registed datasets in
`findopendata.ckan_packages` and `findopendata.socrata_resources`
tables.
It generates metadata by extracting titles, description etc. and
annotates them with entities for enrichment.
The metadata is stored in table `findopendata.packages`, which is
also used by the API server to serve the frontend.
#### Sketch Dataset Content
Run `sketch_dataset_content.py` to start tasks for creating
sketches (e.g.,
[MinHash](https://github.com/ekzhu/datasketch),
samples, data types, etc.) of dataset
content (i.e., data values, columns, and records).
The sketches will be used for content-based search such as
finding joinable tables.
نیازمندی
| مقدار | نام |
|---|---|
| >=2.22.0 | requests |
| >=1.17.0 | google-cloud-storage |
| >=12.14.1 | azure-storage-blob |
| >=1.6.3 | google-auth |
| >=0.3.0 | gcsfs |
| >=4.3.0 | celery |
| >=2.7.5 | psycopg2-binary |
| >=2.2.3 | Django |
| >=0.0.6 | rfc6266-content-disposition |
| >=3.16.0 | simplejson |
| >=1.1.0 | genson |
| >=0.22.3 | fastavro |
| >=0.5.4 | python-snappy |
| >=2.8.0 | python-dateutil |
| >=1.4.10 | datasketch |
| >=0.2.2 | pyfarmhash |
| >=2.1.4 | cchardet |
| >=2.1.8 | spacy |
| >=4.8.0 | beautifulsoup4 |
| >=5.1.2 | pyyaml |
| >=1.4.0 | gevent |
| >=0.9.1 | nose2 |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl findopendata-1.0.5:
pip install findopendata-1.0.5.whl
نصب پکیج tar.gz findopendata-1.0.5:
pip install findopendata-1.0.5.tar.gz