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https://github.com/findopendata/findopendata

A search engine for Open Data
https://github.com/findopendata/findopendata

crawler dataset-search opendata

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A search engine for Open Data

Lists

README 

        
# Find Open Data



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



![Screenshot](screencapture.gif)



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.



![System Overview](system_overview.png)



## 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 ('');

```



### 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.