[CKAN Service Provider]: https://github.com/ckan/ckan-service-provider
[Messytables]: https://github.com/okfn/messytables
[qsv]: https://github.com/jqnatividad/qsv#qsv-ultra-fast-csv-data-wrangling-toolkit
# DataPusher+
DataPusher+ is a fork of [Datapusher](https://github.com/ckan/datapusher) that combines the speed and robustness of
[ckanext-xloader](https://github.com/ckan/ckanext-xloader) with the data type guessing of Datapusher.
Datapusher+ is built using [CKAN Service Provider][], with [Messytables] replaced by [qsv].
[TNRIS](https://tnris.org)/[TWDB](https://www.twdb.texas.gov/) provided the use cases that informed and supported the development
of Datapusher+, specifically, to support a [Resource-first upload workflow](docs/RESOURCE_FIRST_WORKFLOW.md#Resource-first-Upload-Workflow).
For a more detailed overview, see the [CKAN Monthly Live Jan 2023 presentation](https://docs.google.com/presentation/d/e/2PACX-1vT0BfmrrtaEINRGg4UI_m7B02_X6HlFr4yN_DXmgX9goVtgu2DNmZjl-SowL9ZA2ibQhDjScRRJh95q/pub?start=false&loop=false&delayms=3000).
It features:
* **"Bullet-proof", ultra-fast data type inferencing with qsv**
Unlike [Messytables][] which scans only the the first few rows to guess the type of
a column, [qsv][] scans the entire table so its data type inferences are guaranteed[^1].
Despite this, qsv is still exponentially faster even if it scans the whole file, not
only inferring data types, it also calculates [summary statistics](https://github.com/jqnatividad/qsv/blob/b0fbd0e575e2e80f57f94ce916438edf9dc32859/src/cmd/stats.rs#L2-L18) as well. For example,
[scanning a 2.7 million row, 124MB CSV file for types and stats took 0.16 seconds](https://github.com/jqnatividad/qsv/blob/master/docs/whirlwind_tour.md#a-whirlwind-tour)[^2].
It is very fast as qsv is written in [Rust](https://www.rust-lang.org/), is multithreaded,
and uses all kinds of [performance techniques](https://github.com/jqnatividad/qsv/blob/master/docs/PERFORMANCE.md#performance-tuning)
especially designed for data-wrangling.
* **Exponentially faster loading speed**
Similar to xloader, we use PostgreSQL COPY to directly pipe the data into the datastore,
short-circuiting the additional processing/transformation/API calls used by Datapusher.
But unlike xloader, we load everything using the proper data types and not as text, so there's
no need to reload the data again after adjusting the Data Dictionary, as you would with xloader.
* **Far more Storage Efficient AND Performant Datastore with easier to compose SQL queries**
As we create the Datastore tables using the most efficient PostgreSQL data type for each column
using qsv's guaranteed type inferences - the Datastore is not only more storage efficient, it is
also far more more performant for loading AND querying.
With its "smartint" data type (with qsv inferring the most efficient integer data type for the range of
values in the column); comprehensive date format inferencing (supporting [19 date formats](https://github.com/jqnatividad/belt/tree/main/dateparser#accepted-date-formats), with each
format having several variants & with configurable DMY/MDY preference parsing) & auto-formatting dates to
RFC3339 format so they are stored as Postgres timestamps; cardinality-aware, configurable auto-indexing;
automatic sanitization of column names to valid PostgreSQL column identifiers; auto PostgreSQL vacuuming &
analysis of resources after loading; and more - DP+ enables the Datastore to tap into PostgreSQL's full power.
Configurable auto-aliasing of resources also makes it easier to compose SQL queries, as you can
use more intuitive resource aliases instead of cryptic resource IDs.
* **Production-ready Robustness**
In production, the number one source of support issues is Datapusher - primarily, because of
data quality issues and Datapusher's inability to correctly infer data types, gracefully handle
errors[^3], and provide the Data Publisher actionable information to correct the data.
Datapusher+'s design directly addresses all these issues.
* **More informative datastore loading messages**
Datapusher+ messages are designed to be more verbose and actionable, so the data publisher's
user experience is far better and makes it possible to have a resource-first upload workflow.
* **Extended preprocessing with qsv**
qsv is leveraged by Datapusher+ to:
* create "Smarter" Data Dictionaries, with:
* guaranteed data type inferences
* optional ability to automatically choose the best integer PostgreSQL data type ("smartint") based on the range of the numeric column ([PostgreSQL's int, bigint and numeric types](https://www.postgresql.org/docs/12/datatype-numeric.html)) for optimal storage/indexing efficiency and SQL query performance.
* sanitized column names (guaranteeing valid PostgreSQL column identifiers) while preserving the original column name as a label, which is used to label columns in DataTables_view.
* an optional "summary stats" resource as an extension of the Data Dictionary, with comprehensive summary statistics for each column - sum, min/max/range, min/max length, mean, stddev, variance, nullcount, sparsity, quartiles, IQR, lower/upper fences, skewness, median, mode/s, antimode/s & cardinality.
* convert Excel & OpenOffice/LibreOffice Calc (ODS) files to CSV, with the ability to choose which sheet to use by default (e.g. 0 is the first sheet, -1 is the last sheet, -2 the second to last sheet, etc.)
* convert various date formats ([19 date formats are recognized](https://github.com/jqnatividad/belt/tree/main/dateparser#accepted-date-formats) with each format having several variants; ~80 date format permutations in total) to a standard [RFC 3339](https://www.rfc-editor.org/rfc/rfc3339) format
* enable random access of a CSV by creating a CSV index - which also enables parallel processing of different parts of a CSV simultaneously (a major reason type inferencing and stats calculation is so fast)
* instantaneously count the number of rows with a CSV index
* validate if an uploaded CSV conforms to the [RFC-4180](https://datatracker.ietf.org/doc/html/rfc4180) standard
* normalizes and transcodes CSV/TSV dialects into a standard UTF-8 encoded RFC-4180 CSV format
* optionally create a preview subset, with the ability to only download the first `n` preview rows of a file, and not the entire file (e.g. only download first 1,000 rows of 3 gb CSV file - especially good for harvesting/cataloging external sites where you only want to harvest the metadata and a small sample of each file).
* optionally create a preview subset from the end of a file (e.g. last 1,000 rows, good for time-series/sensor data)
* auto-index columns based on its cardinality/format (unique indices created for columns with all unique values, auto-index columns whose cardinality is below a given threshold; auto-index date columns)
* check for duplicates, and optionally deduplicate rows
* optionally screen for Personally Identifiable Information (PII), with an option to "quarantine" the PII-candidate rows in a separate resource, while still creating the screened resource.
* optional ability to specify a custom PII screening regex set, instead of the [default PII screening regex set](https://github.com/dathere/datapusher-plus/blob/master/default-pii-regexes.txt).
Even with all these pre-processing tasks, qsv typically takes less than 5 seconds to finish all its analysis tasks, even for a 100mb CSV file.
Future versions of Datapusher+ will further leverage qsv's 80+ commands to do additional
preprocessing, data-wrangling and validation. The Roadmap is available [here](https://github.com/dathere/datapusher-plus/issues/5).
Ideas, suggestions and your feedback are most welcome!
[^1]: [Why use qsv instead of a "proper" python data analysis library like pandas?](https://github.com/dathere/datapusher-plus/discussions/15)
[^2]: It takes 0.16 seconds with an index to run `qsv stats` against the [qsv whirlwind tour sample file](https://raw.githubusercontent.com/wiki/jqnatividad/qsv/files/wcp.zip) on a Ryzen 4800H (8 physical/16 logical cores) with 32 gb memory and a 1 TB SSD.
Without an index, it takes 1.3 seconds.
[^3]: Imagine you have a 1M row CSV, and the last row has an invalid value for a numeric column (e.g. "N/A" instead of a number).
After spending hours pushing the data very slowly, legacy datapusher will abort on the last row and the ENTIRE job is invalid.
Ok, that's bad, but what makes it worse is that the old table has been deleted already, and Datapusher doesn't tell you what
caused the job to fail! YIKES!!!!
## Development Installation
Datapusher+ is a drop-in replacement for Datapusher, so it's installed the same way.
1. Install the required packages.
```bash
sudo apt install python3-virtualenv python3-dev python3-pip python3-wheel build-essential libxslt1-dev libxml2-dev zlib1g-dev git libffi-dev libpq-dev file
```
2. Create a virtual environment for Datapusher+ using at least python 3.8.
```bash
cd /usr/lib/ckan
sudo python3.8 -m venv dpplus_venv
sudo chown -R $(whoami) dpplus_venv
. dpplus_venv/bin/activate
cd dpplus_venv
```
> ℹ️ **NOTE:** DP+ requires at least python 3.8 as it makes extensive use of new capabilities introduced in 3.7/3.8
> to the [subprocess module](https://docs.python.org/3.8/library/subprocess.html).
> If you're using Ubuntu 18.04 or earlier, follow the procedure below to install python 3.8:
>
> ```bash
> sudo add-apt-repository ppa:deadsnakes/ppa
> # we use 3.8 here, but you can get a higher version by changing the version suffix of the packages below
> sudo apt install python3.8 python3.8-venv python3.8-dev
> # install additional dependencies
> sudo apt install build-essential libxslt1-dev libxml2-dev zlib1g-dev git libffi-dev
> ```
>
> Note that DP+ still works with CKAN<=2.8, which uses older versions of python.
3. Get the code.
```bash
mkdir src
cd src
git clone --branch 0.11.0 https://github.com/datHere/datapusher-plus
cd datapusher-plus
```
4. Install the dependencies.
```bash
pip install wheel
pip install -r requirements-dev.txt
pip install -e .
```
5. Install [qsv](https://github.com/jqnatividad/qsv).
[Download the appropriate precompiled binaries](https://github.com/jqnatividad/qsv/releases/latest) for your platform and copy
it to the appropriate directory, e.g. for Linux:
```bash
wget https://github.com/jqnatividad/qsv/releases/download/0.108.0/qsv-0.108.0-x86_64-unknown-linux-gnu.zip
unzip qsv-0.108.0-x86_64-unknown-linux-gnu.zip
rm qsv-0.108.0-x86_64-unknown-linux-gnu.zip
sudo mv qsv* /usr/local/bin
```
Alternatively, if you want to install qsv from source, follow
the instructions [here](https://github.com/jqnatividad/qsv#installation). Note that when compiling from source,
you may want to look into the [Performance Tuning](https://github.com/jqnatividad/qsv#performance-tuning)
section to squeeze even more performance from qsv.
Also, if you get glibc errors when starting qsv, your Linux distro may not have the required version of the GNU C Library
(This will be the case when running Ubuntu 18.04 or older).
If so, use the `qsvdp_glibc-2.31` binary as its linked to an older version of glibc. If that still fails, the use the
`unknown-linux-musl.zip` archive as it is statically linked with the MUSL C Library.
If you already have qsv, update it to the latest release by using the --update option.
`qsvdp --update`
> ℹ️ **NOTE:** qsv is a general purpose CSV data-wrangling toolkit that gets regular updates. To update to the latest version, just run
qsv with the `--update` option and it will check for the latest version and update as required.
6. Configure the Datapusher+ database.
Make sure to create the `datapusher` PostgreSQL user and the `datapusher_jobs` database
(see [DataPusher+ Database Setup](#datapusher-database-setup)).
7. Copy the `datapusher/dot-env.template` to `datapusher/.env` and [modify your configuration](#datapusher-configuration).
```bash
cd /usr/lib/ckan/dpplus_env/src/datapusher-plus/datapusher
cp dot-env.template .env
# configure your installation as required
nano .env
```
8. Run Datapusher+ in the `dpplus_venv` virtual environment.
```bash
python main.py config.py
```
By default, DP+ should be running at the following port:
http://localhost:8800/
## Production Deployment
There are two ways to deploy Datapusher+:
1. Manual Deployment
These instructions set up the DataPusher web service on
[uWSGI](https://uwsgi-docs.readthedocs.io/en/latest/) running on port 8800, but
can be easily adapted to other WSGI servers like Gunicorn. You'll probably need
to set up Nginx as a reverse proxy in front of it and something like Supervisor
to keep the process up.
```bash
# Install requirements for DataPusher+. Be sure to have at least Python 3.8
sudo apt install python3-virtualenv python3-dev python3-pip python3-wheel build-essential libxslt1-dev libxml2-dev zlib1g-dev git libffi-dev libpq-dev file
# Install qsv, if required
wget https://github.com/jqnatividad/qsv/releases/download/0.108.0/qsv-0.108.0-x86_64-unknown-linux-gnu.zip -P /tmp
unzip /tmp/qsv-0.108.0-x86_64-unknown-linux-gnu.zip -d /tmp
rm /tmp/qsv-0.108.0-x86_64-unknown-linux-gnu.zip
sudo mv /tmp/qsv* /usr/local/bin
# if qsv is already installed, be sure to update it to the latest release
sudo qsvdp --update
# if you get a glibc error when running `qsvdp --update`
# you're on an old distro (e.g. Ubuntu 18.04) without the required version of the glibc libraries.
# If so, try running the qsvdp_glibc-2.31 binary instead. If it runs, you can use it instead of the default qsvdp binary.
# If that still doesnt work, use the statically linked MUSL version instead
# https://github.com/jqnatividad/qsv/releases/download/0.108.0/qsv-0.108.0-x86_64-unknown-linux-musl.zip
# find out the locale settings
locale
# ONLY IF LANG is not "en_US.UTF-8", set locale
export LC_ALL="en_US.UTF-8"
export LC_CTYPE="en_US.UTF-8"
sudo dpkg-reconfigure locales
# Create a virtualenv for DataPusher+. DP+ requires at least python 3.8.
sudo python3.8 -m venv /usr/lib/ckan/dpplus_venv
sudo chown -R $(whoami) dpplus_venv
# install datapusher-plus in the virtual environment
. /usr/lib/ckan/dpplus_venv/bin/activate
pip install wheel
pip install datapusher-plus
# create an .env file and tune DP+ settings. Tune the uwsgi.ini file as well
sudo mkdir -p /etc/ckan/datapusher-plus
sudo curl https://raw.githubusercontent.com/dathere/datapusher-plus/master/datapusher/dot-env.template -o /etc/ckan/datapusher-plus/.env
sudo curl https://raw.githubusercontent.com/dathere/datapusher-plus/master/deployment/datapusher-uwsgi.ini -o /etc/ckan/datapusher-plus/uwsgi.ini
# Be sure to initialize the database if required. (See Database Setup section below)
# Be sure to edit the .env file and set the right database connect strings!
# Create a user to run the web service (if necessary)
sudo addgroup www-data
sudo adduser -G www-data www-data
```
At this point you can run DataPusher+ with the following command:
```bash
/usr/lib/ckan/dpplus_venv/bin/uwsgi --enable-threads -i /etc/ckan/datapusher-plus/uwsgi.ini
```
You might need to change the `uid` and `guid` in the `uwsgi.ini` file when using a different user.
To deploy it using supervisor:
```bash
sudo curl https://raw.githubusercontent.com/dathere/datapusher-plus/master/deployment/datapusher-uwsgi.conf -o /etc/supervisor/conf.d/datapusher-uwsgi.conf
sudo service supervisor restart
```
2. Dockerized Deployment
As Datapusher+ is quite involved as evinced by the above procedure, a containerized installation
will make it far easier not only to deploy DP+ to production, but also to experiment with.
Instructions to set up the DP+ Docker instance can be found [here](https://github.com/dathere/datapusher-plus-docker).
The DP+ Docker will also expose additional features and administrative interface to manage
not only Datapusher+ jobs, but also to manage the CKAN Datastore.
## Configuring
### CKAN Configuration
Add `datapusher` to the plugins in your CKAN configuration file
(generally located at `/etc/ckan/default/ckan.ini`):
```ini
ckan.plugins = <other plugins> datapusher
```
In order to tell CKAN where this webservice is located, the following must be
added to the `[app:main]` section of your CKAN configuration file :
```ini
ckan.datapusher.url = http://127.0.0.1:8800/
```
There are other CKAN configuration options that allow to customize the CKAN - DataPusher
integration. Please refer to the [DataPusher Settings](https://docs.ckan.org/en/latest/maintaining/configuration.html#datapusher-settings) section in the CKAN documentation for more details.
> ℹ️ **NOTE:** DP+ recognizes some additional TSV and spreadsheet subformats - `xlsm` and `xlsb` for Excel Spreadsheets,
> and `tab` for TSV files. To process these subformats, set `ckan.datapusher.formats` as follows in your CKAN.INI file:
>
>```ini
> ckan.datapusher.formats = csv xls xlsx xlsm xlsb tsv tab application/csv application/vnd.ms-excel application/vnd.openxmlformats-officedocument.spreadsheetml.sheet ods application/vnd.oasis.opendocument.spreadsheet
>```
>
>and add this entry to your CKAN's `resource_formats.json` file.
>
>```json
> ["TAB", "Tab Separated Values File", "text/tab-separated-values", []],
>```
### DataPusher+ Configuration
The DataPusher+ instance is configured in the `.env` file located in the working directory of DP+
(`/etc/ckan/datapusher-plus` when running a production deployment. The `datapusher-plus/datapusher`
source directory when running a development installation.)
See [dot-env.template](datapusher/dot-env.template) for a summary of configuration options available.
### DataPusher+ Database Setup
DP+ requires a dedicated PostgreSQL account named `datapusher` to connect to the CKAN Datastore.
To create the `datapusher` user and give it the required privileges to the `datastore_default` database:
```bash
su - postgres
psql -d datastore_default
CREATE ROLE datapusher LOGIN PASSWORD 'YOURPASSWORD';
GRANT CREATE, CONNECT, TEMPORARY, SUPERUSER ON DATABASE datastore_default TO datapusher;
GRANT SELECT, INSERT, UPDATE, DELETE, TRUNCATE ON ALL TABLES IN SCHEMA public TO datapusher;
\q
```
DP+ also requires its own job_store database to keep track of all the DP+ jobs. In the original Datapusher,
this was a sqlite database by default. Though DP+ can still use a sqlite database, we are discouraging its use.
To setup the `datapusher_jobs` database and its user:
```bash
sudo -u postgres createuser -S -D -R -P datapusher_jobs
sudo -u postgres createdb -O datapusher_jobs datapusher_jobs -E utf-8
```
## Usage
Any file that has one of the supported formats (defined in [`ckan.datapusher.formats`](https://docs.ckan.org/en/latest/maintaining/configuration.html#ckan-datapusher-formats)) will be attempted to be loaded
into the DataStore.
You can also manually trigger resources to be resubmitted. When editing a resource in CKAN (clicking the "Manage" button on a resource page), a new tab named "DataStore" will appear. This will contain a log of the last attempted upload and a button to retry the upload. Once a resource has been "pushed" into the Datastore, a "Data Dictionary" tab will also be available where the data pusblisher can fine-tune the inferred data dictionary.
### Command line
Run the following command to submit all resources to datapusher, although it will skip files whose hash of the data file has not changed:
ckan -c /etc/ckan/default/ckan.ini datapusher resubmit
On CKAN<=2.8:
paster --plugin=ckan datapusher resubmit -c /etc/ckan/default/ckan.ini
To Resubmit a specific resource, whether or not the hash of the data file has changed::
ckan -c /etc/ckan/default/ckan.ini datapusher submit {dataset_id}
On CKAN<=2.8:
paster --plugin=ckan datapusher submit <pkgname> -c /etc/ckan/default/ckan.ini
### Testing
To test Datapusher-plus, you can use the following test script available on GitHub: [test script](https://github.com/dathere/testing-datapusher-plus).
### Uninstalling Datapusher+
Should you need to remove Datapusher+, and you followed either the Development or Production Installation procedures above:
```bash
# if you're running inside the dpplus_venv virtual environment, deactivate it first
deactivate
# remove the DP+ python virtual environment
sudo rm -rf /usr/lib/ckan/dpplus_venv
# remove the supervisor DP+ configuration
sudo rm -rf /etc/supervisor/conf.d/datapusher-uwsgi.conf
# remove the DP+ production deployment directory
sudo rm -rf /etc/ckan/datapusher-plus
# remove qsv binary variants
sudo rm /usr/local/bin/qsv /usr/local/bin/qsvdp /usr/local/bin/qsvlite /usr/local/bin/qsv_nightly /usr/local/bin/qsvdp_nightly /usr/local/bin/qsvlite_nightly
# restart the supervisor, without the Datapusher+ service
sudo service supervisor reload
# ========= DATABASE objects ============
# OPTIONAL: backup the datapusher_jobs database first if
# you want to retain the DP+ job history
sudo -u postgres pg_dump --format=custom -d datapusher_jobs > datapusher_jobs.dump
# to remove the Datapusher+ job database and the datapusher_jobs user/role
sudo -u postgres dropdb datapusher_jobs
sudo -u postgres dropuser datapusher_jobs
# to drop the datapusher user which DP+ uses to write to the CKAN Datastore
sudo -u postgres dropuser datapusher
```
To ensure the Datapusher+ service is not automatically invoked when tabular resources are uploaded, remove `datapusher` from `ckan.plugins` in your `ckan.ini` file.
Also remove/comment out the following `ckan.datapusher` entries in your `ckan.ini`:
* `ckan.datapusher.formats`
* `ckan.datapusher.url`
* `ckan.datapusher.callback_url_base`
* `ckan.datapusher.assume_task_stale_after`
Note that resources which has been pushed previously will still be available on the CKAN Datastore.
You will have to delete these resources separately using the UI or the CKAN [resource_delete](https://docs.ckan.org/en/2.9/api/index.html#ckan.logic.action.delete.resource_delete) API.
If you're no longer using the CKAN Datastore:
* Edit your `ckan.ini` and remove/comment `datastore` from `ckan.plugins`.
* Remove/comment out the `ckan.datastore.write_url` and `ckan.datastore.read_url` entries.
To confirm the uninstallation is successful, upload a new tabular resource and check if:
* tabular Resource Views (e.g. datatables_view, recline_view, etc.) are no longer available
* the **Datastore** and **Data Dictionary** tabs are no longer available
* the **Download** button on the resource page will no longer offer alternate download formats (CSV, TSV, JSON, XML)
* the **Datastore API** button will no longer display on tabular resources
## License
This material is copyright (c) 2020 Open Knowledge Foundation and other contributors
It is open and licensed under the GNU Affero General Public License (AGPL) v3.0
whose full text may be found at:
http://www.fsf.org/licensing/licenses/agpl-3.0.html
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"description": "[CKAN Service Provider]: https://github.com/ckan/ckan-service-provider\n[Messytables]: https://github.com/okfn/messytables\n[qsv]: https://github.com/jqnatividad/qsv#qsv-ultra-fast-csv-data-wrangling-toolkit\n\n# DataPusher+\n\nDataPusher+ is a fork of [Datapusher](https://github.com/ckan/datapusher) that combines the speed and robustness of\n[ckanext-xloader](https://github.com/ckan/ckanext-xloader) with the data type guessing of Datapusher.\n\nDatapusher+ is built using [CKAN Service Provider][], with [Messytables] replaced by [qsv].\n\n[TNRIS](https://tnris.org)/[TWDB](https://www.twdb.texas.gov/) provided the use cases that informed and supported the development\nof Datapusher+, specifically, to support a [Resource-first upload workflow](docs/RESOURCE_FIRST_WORKFLOW.md#Resource-first-Upload-Workflow).\n\nFor a more detailed overview, see the [CKAN Monthly Live Jan 2023 presentation](https://docs.google.com/presentation/d/e/2PACX-1vT0BfmrrtaEINRGg4UI_m7B02_X6HlFr4yN_DXmgX9goVtgu2DNmZjl-SowL9ZA2ibQhDjScRRJh95q/pub?start=false&loop=false&delayms=3000).\n\nIt features:\n\n* **\"Bullet-proof\", ultra-fast data type inferencing with qsv**\n\n Unlike [Messytables][] which scans only the the first few rows to guess the type of\n a column, [qsv][] scans the entire table so its data type inferences are guaranteed[^1].\n\n Despite this, qsv is still exponentially faster even if it scans the whole file, not\n only inferring data types, it also calculates [summary statistics](https://github.com/jqnatividad/qsv/blob/b0fbd0e575e2e80f57f94ce916438edf9dc32859/src/cmd/stats.rs#L2-L18) as well. For example,\n [scanning a 2.7 million row, 124MB CSV file for types and stats took 0.16 seconds](https://github.com/jqnatividad/qsv/blob/master/docs/whirlwind_tour.md#a-whirlwind-tour)[^2].\n\n It is very fast as qsv is written in [Rust](https://www.rust-lang.org/), is multithreaded,\n and uses all kinds of [performance techniques](https://github.com/jqnatividad/qsv/blob/master/docs/PERFORMANCE.md#performance-tuning)\n especially designed for data-wrangling.\n\n* **Exponentially faster loading speed**\n\n Similar to xloader, we use PostgreSQL COPY to directly pipe the data into the datastore,\n short-circuiting the additional processing/transformation/API calls used by Datapusher.\n\n But unlike xloader, we load everything using the proper data types and not as text, so there's\n no need to reload the data again after adjusting the Data Dictionary, as you would with xloader.\n\n* **Far more Storage Efficient AND Performant Datastore with easier to compose SQL queries**\n\n As we create the Datastore tables using the most efficient PostgreSQL data type for each column\n using qsv's guaranteed type inferences - the Datastore is not only more storage efficient, it is\n also far more more performant for loading AND querying.\n \n With its \"smartint\" data type (with qsv inferring the most efficient integer data type for the range of\n values in the column); comprehensive date format inferencing (supporting [19 date formats](https://github.com/jqnatividad/belt/tree/main/dateparser#accepted-date-formats), with each\n format having several variants & with configurable DMY/MDY preference parsing) & auto-formatting dates to\n RFC3339 format so they are stored as Postgres timestamps; cardinality-aware, configurable auto-indexing;\n automatic sanitization of column names to valid PostgreSQL column identifiers; auto PostgreSQL vacuuming &\n analysis of resources after loading; and more - DP+ enables the Datastore to tap into PostgreSQL's full power.\n\n Configurable auto-aliasing of resources also makes it easier to compose SQL queries, as you can\n use more intuitive resource aliases instead of cryptic resource IDs.\n\n* **Production-ready Robustness**\n\n In production, the number one source of support issues is Datapusher - primarily, because of\n data quality issues and Datapusher's inability to correctly infer data types, gracefully handle\n errors[^3], and provide the Data Publisher actionable information to correct the data.\n\n Datapusher+'s design directly addresses all these issues.\n\n* **More informative datastore loading messages**\n\n Datapusher+ messages are designed to be more verbose and actionable, so the data publisher's\n user experience is far better and makes it possible to have a resource-first upload workflow.\n\n* **Extended preprocessing with qsv**\n\n qsv is leveraged by Datapusher+ to:\n\n * create \"Smarter\" Data Dictionaries, with:\n * guaranteed data type inferences\n * optional ability to automatically choose the best integer PostgreSQL data type (\"smartint\") based on the range of the numeric column ([PostgreSQL's int, bigint and numeric types](https://www.postgresql.org/docs/12/datatype-numeric.html)) for optimal storage/indexing efficiency and SQL query performance.\n * sanitized column names (guaranteeing valid PostgreSQL column identifiers) while preserving the original column name as a label, which is used to label columns in DataTables_view.\n * an optional \"summary stats\" resource as an extension of the Data Dictionary, with comprehensive summary statistics for each column - sum, min/max/range, min/max length, mean, stddev, variance, nullcount, sparsity, quartiles, IQR, lower/upper fences, skewness, median, mode/s, antimode/s & cardinality.\n * convert Excel & OpenOffice/LibreOffice Calc (ODS) files to CSV, with the ability to choose which sheet to use by default (e.g. 0 is the first sheet, -1 is the last sheet, -2 the second to last sheet, etc.)\n * convert various date formats ([19 date formats are recognized](https://github.com/jqnatividad/belt/tree/main/dateparser#accepted-date-formats) with each format having several variants; ~80 date format permutations in total) to a standard [RFC 3339](https://www.rfc-editor.org/rfc/rfc3339) format\n * enable random access of a CSV by creating a CSV index - which also enables parallel processing of different parts of a CSV simultaneously (a major reason type inferencing and stats calculation is so fast)\n * instantaneously count the number of rows with a CSV index\n * validate if an uploaded CSV conforms to the [RFC-4180](https://datatracker.ietf.org/doc/html/rfc4180) standard\n * normalizes and transcodes CSV/TSV dialects into a standard UTF-8 encoded RFC-4180 CSV format\n * optionally create a preview subset, with the ability to only download the first `n` preview rows of a file, and not the entire file (e.g. only download first 1,000 rows of 3 gb CSV file - especially good for harvesting/cataloging external sites where you only want to harvest the metadata and a small sample of each file).\n * optionally create a preview subset from the end of a file (e.g. last 1,000 rows, good for time-series/sensor data)\n * auto-index columns based on its cardinality/format (unique indices created for columns with all unique values, auto-index columns whose cardinality is below a given threshold; auto-index date columns)\n * check for duplicates, and optionally deduplicate rows\n * optionally screen for Personally Identifiable Information (PII), with an option to \"quarantine\" the PII-candidate rows in a separate resource, while still creating the screened resource.\n * optional ability to specify a custom PII screening regex set, instead of the [default PII screening regex set](https://github.com/dathere/datapusher-plus/blob/master/default-pii-regexes.txt).\n\n Even with all these pre-processing tasks, qsv typically takes less than 5 seconds to finish all its analysis tasks, even for a 100mb CSV file.\n\n Future versions of Datapusher+ will further leverage qsv's 80+ commands to do additional\n preprocessing, data-wrangling and validation. The Roadmap is available [here](https://github.com/dathere/datapusher-plus/issues/5).\n Ideas, suggestions and your feedback are most welcome!\n\n[^1]: [Why use qsv instead of a \"proper\" python data analysis library like pandas?](https://github.com/dathere/datapusher-plus/discussions/15)\n[^2]: It takes 0.16 seconds with an index to run `qsv stats` against the [qsv whirlwind tour sample file](https://raw.githubusercontent.com/wiki/jqnatividad/qsv/files/wcp.zip) on a Ryzen 4800H (8 physical/16 logical cores) with 32 gb memory and a 1 TB SSD.\nWithout an index, it takes 1.3 seconds.\n[^3]: Imagine you have a 1M row CSV, and the last row has an invalid value for a numeric column (e.g. \"N/A\" instead of a number).\n After spending hours pushing the data very slowly, legacy datapusher will abort on the last row and the ENTIRE job is invalid.\n Ok, that's bad, but what makes it worse is that the old table has been deleted already, and Datapusher doesn't tell you what\n caused the job to fail! YIKES!!!!\n\n## Development Installation\n\nDatapusher+ is a drop-in replacement for Datapusher, so it's installed the same way.\n\n1. Install the required packages.\n\n ```bash\n sudo apt install python3-virtualenv python3-dev python3-pip python3-wheel build-essential libxslt1-dev libxml2-dev zlib1g-dev git libffi-dev libpq-dev file\n ```\n\n2. Create a virtual environment for Datapusher+ using at least python 3.8.\n\n ```bash\n cd /usr/lib/ckan\n sudo python3.8 -m venv dpplus_venv\n sudo chown -R $(whoami) dpplus_venv\n . dpplus_venv/bin/activate\n cd dpplus_venv\n ```\n\n > \u2139\ufe0f **NOTE:** DP+ requires at least python 3.8 as it makes extensive use of new capabilities introduced in 3.7/3.8\n > to the [subprocess module](https://docs.python.org/3.8/library/subprocess.html).\n > If you're using Ubuntu 18.04 or earlier, follow the procedure below to install python 3.8:\n >\n > ```bash\n > sudo add-apt-repository ppa:deadsnakes/ppa\n > # we use 3.8 here, but you can get a higher version by changing the version suffix of the packages below\n > sudo apt install python3.8 python3.8-venv python3.8-dev\n > # install additional dependencies\n > sudo apt install build-essential libxslt1-dev libxml2-dev zlib1g-dev git libffi-dev\n > ```\n >\n > Note that DP+ still works with CKAN<=2.8, which uses older versions of python.\n\n3. Get the code.\n\n ```bash\n mkdir src\n cd src\n git clone --branch 0.11.0 https://github.com/datHere/datapusher-plus\n cd datapusher-plus\n ```\n\n4. Install the dependencies.\n\n ```bash\n pip install wheel\n pip install -r requirements-dev.txt\n pip install -e .\n ```\n\n5. Install [qsv](https://github.com/jqnatividad/qsv).\n\n [Download the appropriate precompiled binaries](https://github.com/jqnatividad/qsv/releases/latest) for your platform and copy\n it to the appropriate directory, e.g. for Linux:\n\n ```bash\n wget https://github.com/jqnatividad/qsv/releases/download/0.108.0/qsv-0.108.0-x86_64-unknown-linux-gnu.zip\n unzip qsv-0.108.0-x86_64-unknown-linux-gnu.zip\n rm qsv-0.108.0-x86_64-unknown-linux-gnu.zip\n sudo mv qsv* /usr/local/bin\n ```\n\n Alternatively, if you want to install qsv from source, follow\n the instructions [here](https://github.com/jqnatividad/qsv#installation). Note that when compiling from source,\n you may want to look into the [Performance Tuning](https://github.com/jqnatividad/qsv#performance-tuning)\n section to squeeze even more performance from qsv.\n\n Also, if you get glibc errors when starting qsv, your Linux distro may not have the required version of the GNU C Library\n (This will be the case when running Ubuntu 18.04 or older).\n If so, use the `qsvdp_glibc-2.31` binary as its linked to an older version of glibc. If that still fails, the use the\n `unknown-linux-musl.zip` archive as it is statically linked with the MUSL C Library.\n\n If you already have qsv, update it to the latest release by using the --update option.\n\n `qsvdp --update`\n\n > \u2139\ufe0f **NOTE:** qsv is a general purpose CSV data-wrangling toolkit that gets regular updates. To update to the latest version, just run\n qsv with the `--update` option and it will check for the latest version and update as required.\n\n6. Configure the Datapusher+ database.\n\n Make sure to create the `datapusher` PostgreSQL user and the `datapusher_jobs` database\n (see [DataPusher+ Database Setup](#datapusher-database-setup)).\n\n7. Copy the `datapusher/dot-env.template` to `datapusher/.env` and [modify your configuration](#datapusher-configuration).\n\n ```bash\n cd /usr/lib/ckan/dpplus_env/src/datapusher-plus/datapusher\n cp dot-env.template .env\n # configure your installation as required\n nano .env\n ```\n\n8. Run Datapusher+ in the `dpplus_venv` virtual environment.\n\n ```bash\n python main.py config.py\n ```\n\n By default, DP+ should be running at the following port:\n\n http://localhost:8800/\n\n## Production Deployment\n\nThere are two ways to deploy Datapusher+:\n\n1. Manual Deployment\n\n These instructions set up the DataPusher web service on\n [uWSGI](https://uwsgi-docs.readthedocs.io/en/latest/) running on port 8800, but\n can be easily adapted to other WSGI servers like Gunicorn. You'll probably need\n to set up Nginx as a reverse proxy in front of it and something like Supervisor\n to keep the process up.\n\n ```bash\n # Install requirements for DataPusher+. Be sure to have at least Python 3.8\n sudo apt install python3-virtualenv python3-dev python3-pip python3-wheel build-essential libxslt1-dev libxml2-dev zlib1g-dev git libffi-dev libpq-dev file\n\n # Install qsv, if required\n wget https://github.com/jqnatividad/qsv/releases/download/0.108.0/qsv-0.108.0-x86_64-unknown-linux-gnu.zip -P /tmp\n unzip /tmp/qsv-0.108.0-x86_64-unknown-linux-gnu.zip -d /tmp\n rm /tmp/qsv-0.108.0-x86_64-unknown-linux-gnu.zip\n sudo mv /tmp/qsv* /usr/local/bin\n\n # if qsv is already installed, be sure to update it to the latest release\n sudo qsvdp --update\n\n # if you get a glibc error when running `qsvdp --update`\n # you're on an old distro (e.g. Ubuntu 18.04) without the required version of the glibc libraries.\n # If so, try running the qsvdp_glibc-2.31 binary instead. If it runs, you can use it instead of the default qsvdp binary.\n # If that still doesnt work, use the statically linked MUSL version instead\n # https://github.com/jqnatividad/qsv/releases/download/0.108.0/qsv-0.108.0-x86_64-unknown-linux-musl.zip\n\n # find out the locale settings\n locale\n\n # ONLY IF LANG is not \"en_US.UTF-8\", set locale\n export LC_ALL=\"en_US.UTF-8\"\n export LC_CTYPE=\"en_US.UTF-8\"\n sudo dpkg-reconfigure locales\n\n # Create a virtualenv for DataPusher+. DP+ requires at least python 3.8.\n sudo python3.8 -m venv /usr/lib/ckan/dpplus_venv\n sudo chown -R $(whoami) dpplus_venv\n\n # install datapusher-plus in the virtual environment\n . /usr/lib/ckan/dpplus_venv/bin/activate\n pip install wheel\n pip install datapusher-plus\n\n # create an .env file and tune DP+ settings. Tune the uwsgi.ini file as well\n sudo mkdir -p /etc/ckan/datapusher-plus\n sudo curl https://raw.githubusercontent.com/dathere/datapusher-plus/master/datapusher/dot-env.template -o /etc/ckan/datapusher-plus/.env\n sudo curl https://raw.githubusercontent.com/dathere/datapusher-plus/master/deployment/datapusher-uwsgi.ini -o /etc/ckan/datapusher-plus/uwsgi.ini\n\n # Be sure to initialize the database if required. (See Database Setup section below)\n # Be sure to edit the .env file and set the right database connect strings!\n\n # Create a user to run the web service (if necessary)\n sudo addgroup www-data\n sudo adduser -G www-data www-data\n ```\n\n At this point you can run DataPusher+ with the following command:\n\n ```bash\n /usr/lib/ckan/dpplus_venv/bin/uwsgi --enable-threads -i /etc/ckan/datapusher-plus/uwsgi.ini\n ```\n\n You might need to change the `uid` and `guid` in the `uwsgi.ini` file when using a different user.\n\n To deploy it using supervisor:\n\n ```bash\n sudo curl https://raw.githubusercontent.com/dathere/datapusher-plus/master/deployment/datapusher-uwsgi.conf -o /etc/supervisor/conf.d/datapusher-uwsgi.conf\n sudo service supervisor restart\n ```\n\n2. Dockerized Deployment\n\n As Datapusher+ is quite involved as evinced by the above procedure, a containerized installation\n will make it far easier not only to deploy DP+ to production, but also to experiment with.\n\n Instructions to set up the DP+ Docker instance can be found [here](https://github.com/dathere/datapusher-plus-docker).\n\n The DP+ Docker will also expose additional features and administrative interface to manage\n not only Datapusher+ jobs, but also to manage the CKAN Datastore.\n\n## Configuring\n\n\n### CKAN Configuration\n\nAdd `datapusher` to the plugins in your CKAN configuration file\n(generally located at `/etc/ckan/default/ckan.ini`):\n\n```ini\nckan.plugins = <other plugins> datapusher\n```\n\nIn order to tell CKAN where this webservice is located, the following must be\nadded to the `[app:main]` section of your CKAN configuration file :\n\n```ini\nckan.datapusher.url = http://127.0.0.1:8800/\n```\n\nThere are other CKAN configuration options that allow to customize the CKAN - DataPusher\nintegration. Please refer to the [DataPusher Settings](https://docs.ckan.org/en/latest/maintaining/configuration.html#datapusher-settings) section in the CKAN documentation for more details.\n\n> \u2139\ufe0f **NOTE:** DP+ recognizes some additional TSV and spreadsheet subformats - `xlsm` and `xlsb` for Excel Spreadsheets,\n> and `tab` for TSV files. To process these subformats, set `ckan.datapusher.formats` as follows in your CKAN.INI file:\n>\n>```ini\n> ckan.datapusher.formats = csv xls xlsx xlsm xlsb tsv tab application/csv application/vnd.ms-excel application/vnd.openxmlformats-officedocument.spreadsheetml.sheet ods application/vnd.oasis.opendocument.spreadsheet\n>```\n>\n>and add this entry to your CKAN's `resource_formats.json` file.\n>\n>```json\n> [\"TAB\", \"Tab Separated Values File\", \"text/tab-separated-values\", []],\n>```\n\n\n### DataPusher+ Configuration\n\nThe DataPusher+ instance is configured in the `.env` file located in the working directory of DP+\n(`/etc/ckan/datapusher-plus` when running a production deployment. The `datapusher-plus/datapusher`\nsource directory when running a development installation.)\n\nSee [dot-env.template](datapusher/dot-env.template) for a summary of configuration options available.\n\n\n### DataPusher+ Database Setup\n\nDP+ requires a dedicated PostgreSQL account named `datapusher` to connect to the CKAN Datastore.\n\nTo create the `datapusher` user and give it the required privileges to the `datastore_default` database:\n\n```bash\nsu - postgres\npsql -d datastore_default\nCREATE ROLE datapusher LOGIN PASSWORD 'YOURPASSWORD';\nGRANT CREATE, CONNECT, TEMPORARY, SUPERUSER ON DATABASE datastore_default TO datapusher;\nGRANT SELECT, INSERT, UPDATE, DELETE, TRUNCATE ON ALL TABLES IN SCHEMA public TO datapusher;\n\\q\n```\n\nDP+ also requires its own job_store database to keep track of all the DP+ jobs. In the original Datapusher,\nthis was a sqlite database by default. Though DP+ can still use a sqlite database, we are discouraging its use.\n\nTo setup the `datapusher_jobs` database and its user:\n\n```bash\nsudo -u postgres createuser -S -D -R -P datapusher_jobs\nsudo -u postgres createdb -O datapusher_jobs datapusher_jobs -E utf-8\n```\n\n## Usage\n\nAny file that has one of the supported formats (defined in [`ckan.datapusher.formats`](https://docs.ckan.org/en/latest/maintaining/configuration.html#ckan-datapusher-formats)) will be attempted to be loaded\ninto the DataStore.\n\nYou can also manually trigger resources to be resubmitted. When editing a resource in CKAN (clicking the \"Manage\" button on a resource page), a new tab named \"DataStore\" will appear. This will contain a log of the last attempted upload and a button to retry the upload. Once a resource has been \"pushed\" into the Datastore, a \"Data Dictionary\" tab will also be available where the data pusblisher can fine-tune the inferred data dictionary.\n\n\n\n\n### Command line\n\nRun the following command to submit all resources to datapusher, although it will skip files whose hash of the data file has not changed:\n\n ckan -c /etc/ckan/default/ckan.ini datapusher resubmit\n\nOn CKAN<=2.8:\n\n paster --plugin=ckan datapusher resubmit -c /etc/ckan/default/ckan.ini\n\nTo Resubmit a specific resource, whether or not the hash of the data file has changed::\n\n ckan -c /etc/ckan/default/ckan.ini datapusher submit {dataset_id}\n\nOn CKAN<=2.8:\n\n paster --plugin=ckan datapusher submit <pkgname> -c /etc/ckan/default/ckan.ini\n\n### Testing\nTo test Datapusher-plus, you can use the following test script available on GitHub: [test script](https://github.com/dathere/testing-datapusher-plus).\n\n\n\n### Uninstalling Datapusher+\n\nShould you need to remove Datapusher+, and you followed either the Development or Production Installation procedures above:\n\n```bash\n# if you're running inside the dpplus_venv virtual environment, deactivate it first\ndeactivate\n\n# remove the DP+ python virtual environment\nsudo rm -rf /usr/lib/ckan/dpplus_venv\n\n# remove the supervisor DP+ configuration\nsudo rm -rf /etc/supervisor/conf.d/datapusher-uwsgi.conf\n\n# remove the DP+ production deployment directory\nsudo rm -rf /etc/ckan/datapusher-plus\n\n# remove qsv binary variants\nsudo rm /usr/local/bin/qsv /usr/local/bin/qsvdp /usr/local/bin/qsvlite /usr/local/bin/qsv_nightly /usr/local/bin/qsvdp_nightly /usr/local/bin/qsvlite_nightly\n\n# restart the supervisor, without the Datapusher+ service\nsudo service supervisor reload\n\n# ========= DATABASE objects ============\n# OPTIONAL: backup the datapusher_jobs database first if \n# you want to retain the DP+ job history\nsudo -u postgres pg_dump --format=custom -d datapusher_jobs > datapusher_jobs.dump\n\n# to remove the Datapusher+ job database and the datapusher_jobs user/role\nsudo -u postgres dropdb datapusher_jobs\nsudo -u postgres dropuser datapusher_jobs\n\n# to drop the datapusher user which DP+ uses to write to the CKAN Datastore\nsudo -u postgres dropuser datapusher\n```\n\nTo ensure the Datapusher+ service is not automatically invoked when tabular resources are uploaded, remove `datapusher` from `ckan.plugins` in your `ckan.ini` file.\n\nAlso remove/comment out the following `ckan.datapusher` entries in your `ckan.ini`:\n\n* `ckan.datapusher.formats`\n* `ckan.datapusher.url`\n* `ckan.datapusher.callback_url_base`\n* `ckan.datapusher.assume_task_stale_after`\n\nNote that resources which has been pushed previously will still be available on the CKAN Datastore.\nYou will have to delete these resources separately using the UI or the CKAN [resource_delete](https://docs.ckan.org/en/2.9/api/index.html#ckan.logic.action.delete.resource_delete) API.\n\nIf you're no longer using the CKAN Datastore:\n\n* Edit your `ckan.ini` and remove/comment `datastore` from `ckan.plugins`.\n* Remove/comment out the `ckan.datastore.write_url` and `ckan.datastore.read_url` entries.\n\nTo confirm the uninstallation is successful, upload a new tabular resource and check if:\n\n* tabular Resource Views (e.g. datatables_view, recline_view, etc.) are no longer available\n* the **Datastore** and **Data Dictionary** tabs are no longer available\n* the **Download** button on the resource page will no longer offer alternate download formats (CSV, TSV, JSON, XML)\n* the **Datastore API** button will no longer display on tabular resources\n\n## License\n\nThis material is copyright (c) 2020 Open Knowledge Foundation and other contributors\n\nIt is open and licensed under the GNU Affero General Public License (AGPL) v3.0\nwhose full text may be found at:\n\nhttp://www.fsf.org/licensing/licenses/agpl-3.0.html\n",
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