Installation

The Brazil Data Cube Collection Builder (bdc-collection-builder) depends essentially on:

Flask
SQLAlchemy
Redis
Celery
RabbitMQ
Brazil Data Cube Catalog Module
Brazil Data Cube Collectors
Sensor Harmonization (Optional)

Compatibility

Before deploy/install BDC-Collection-Builder, please, take a look into compatibility table:

BDC-Collection-Builder	BDC-Catalog	BDC-Collectors
1.0.x	1.0.1	0.9.0
0.8.4	0.8.2	0.6.0
0.6.x	0.8.2	0.2.1
0.4.x	0.2.x	`NaN`

Development Installation

Clone the software repository:

$ git clone https://github.com/brazil-data-cube/bdc-collection-builder.git

Go to the source code folder:

$ cd bdc-collection-builder

Install in development mode:

$ pip3 install -e .[docs,tests,amqp]

Warning

The setuptools v67+ has breaking changes related Pip versions requirements. For now, you should install setuptools<67 for compatibility. The packages in BDC-Collection-Builder will be upgraded to support latest version.

Note

If you have problems during the librabbitmq install with autoreconf, please, install the autoconf build system. In Debian based systems (Ubuntu), you can install autoconf with:

$ sudo apt install autoconf

For more information, please, see [1].

Note

If you would like to publish Hierarchical Data Format (HDF) datasets, you may install the extra gdal. Optionally, you can install all dependencies as following:

$ pip3 install -e .[all]

Make sure you have GDAL installed and available in PATH.

Generate the documentation:

$ python setup.py build_sphinx

The above command will generate the documentation in HTML and it will place it under:

docs/sphinx/_build/html/

Optionally, you can serve these files temporally on http://localhost:8000 using the following command:

cd docs/sphinx/_build/html/
python3 -m http.server

Running in Development Mode

Launch Redis, RabbitMQ and PostgreSQL Containers

The docker-compose command can be used to launch the Redis and RabbitMQ containers:

$ docker-compose up -d redis mq postgres

Let's take a look at each parameter in the above command:

up: tells docker-compose to launch the containers.

-d: tells docker-compose that containers will run in detach mode (as a daemon).

redis: the name of a service in the docker-compose.yml file with all information to prepare a Redis container.

mq: the name of a service in the docker-compose.yml file with all information to prepare a RabbitMQ container.

postgres: the name of a service in the docker-compose.yml file with all information to prepare a PostgreSQL container.

Note

Since docker-compose will map the services to the default system ports on localhost, make sure you are not running Redis, RabbitMQ or PostgreSQL on those ports in your system, otherwise you will have a port conflict during the attempt to launch the new containers.

Note

If you have a PostgreSQL DBMS you can omit the postgres service in the above command.

Note

After launching the containers, check if they are up and running:

$ docker container ls

CONTAINER ID        IMAGE                   COMMAND                  CREATED             STATUS              PORTS                                                                                        NAMES
8c94877e7017        rabbitmq:3-management   "docker-entrypoint.s…"   34 seconds ago      Up 26 seconds       4369/tcp, 5671/tcp, 0.0.0.0:5672->5672/tcp, 15671/tcp, 25672/tcp, 0.0.0.0:15672->15672/tcp   bdc-collection-builder-rabbitmq
acc51ff02295        mdillon/postgis         "docker-entrypoint.s…"   34 seconds ago      Up 24 seconds       0.0.0.0:5432->5432/tcp                                                                       bdc-collection-builder-pg
84bae6370fbb        redis                   "docker-entrypoint.s…"   34 seconds ago      Up 27 seconds       0.0.0.0:6379->6379/tcp                                                                       bdc-collection-builder-redis

Prepare the Database System

You will need an instance of a PostgreSQL DBMS with a database prepared with the Collection Builder schema.

The following steps will show how to prepare the data model:

1. Create a PostgreSQL database and enable the PostGIS extension:

SQLALCHEMY_DATABASE_URI=postgresql://postgres:postgres@localhost:5432/bdc \
bdc-db db init

2. Create extension PostGIS:

SQLALCHEMY_DATABASE_URI=postgresql://postgres:postgres@localhost:5432/bdc \
bdc-db db create-extension-postgis

3. Create table namespaces:

SQLALCHEMY_DATABASE_URI=postgresql://postgres:postgres@localhost:5432/bdc \
bdc-db db create-namespaces

4. After that, run Flask-Migrate command to prepare the Collection Builder data model:

SQLALCHEMY_DATABASE_URI=postgresql://postgres:postgres@localhost:5432/bdc \
bdc-collection-builder alembic upgrade

5. Load BDC-Catalog triggers with command:

SQLALCHEMY_DATABASE_URI=postgresql://postgres:postgres@localhost:5432/bdc \
bdc-db db create-triggers

Note

For a initial data of collections, the BDC-Catalog has a command line utility to load a JSON like structure into database as Collection. We have prepared a minimal JSON files in examples/data. You load them with the following command:

SQLALCHEMY_DATABASE_URI=postgresql://postgres:postgres@localhost:5432/bdc \
bdc-catalog load-data --from-dir examples/data # or individual as --ifile examples/data/sentinel-2-l1.json

The BDC-Collection-Builder requires a list of providers registered in database to collect data. Please, take a look into folder examples/data/providers and set the right credentials for this step. Once credentials is set, you can load them with command:

SQLALCHEMY_DATABASE_URI=postgresql://postgres:postgres@localhost:5432/bdc \
bdc-collection-builder load-providers --from-dir examples/data/providers

If you would like to link a collection with a default provider (S2_L1C-1 with ESA) use the command:

SQLALCHEMY_DATABASE_URI=postgresql://postgres:postgres@localhost:5432/bdc \
bdc-collection-builder set-provider --collection S2_L1C-1 --provider ESA

Always related a Collection with Provider Name. Do not use driver_name.

You can check collection overview with command:

SQLALCHEMY_DATABASE_URI=postgresql://postgres:postgres@localhost:5432/bdc \
bdc-collection-builder overview --collection S2_L1C-1

The following output will be:

Collection S2_L1C-1
-> title: Sentinel-2 - MSI - Level-1C
-> name: S2_L1C
-> version: 1
-> description: Level-1C product provides orthorectified Top-Of-Atmosphere (TOA) reflectance.
-> collection_type: collection
-> Providers:
  - ESA, driver=SciHub, priority=1, active=True

Note

Please refer to :doc:`config` the section Setting up the Credentials for EO Data Providers to set valid access credentials for data providers.

Prepare the containers Sen2Cor and LaSRC 1.3.0

Before launching Sen2Cor and LaSRC processors, please, read the :doc:`config` documentation and make sure you have the right layout of auxiliary data in your filesystem.

If you have all the auxiliary data, edit docker-compose.yml the section atm-correction and fill the following configuration based in the directory where auxiliaries are stored:

# LaSRC / LEDAPS
- "LASRC_AUX_DIR=/path/to/landsat/auxiliaries/L8"
- "LEDAPS_AUX_DIR=/path/to/landsat/ledaps_auxiliaries"
# Sen2Cor
- "SEN2COR_AUX_DIR=/path/to/sen2cor/CCI4SEN2COR"
- "SEN2COR_CONFIG_DIR=/path/to/sen2cor/config/2.8"

Note

Remember that these variables are relative inside container. You may change the mount volume in the section volumes.

The 'SEN2COR_CONFIG_DIR` is base configuration of Sen2Cor instance with folder cfg and file L2A_GIPP.xml.

Launching Collection Builder Workers

1. In order to launch the worker responsible for downloading data, run the following Celery command:

$ DATA_DIR="/home/user/data/bdc-collection-builder" \
  SQLALCHEMY_DATABASE_URI="postgresql://postgres:postgres@localhost:5432/bdc" \
  REDIS_URL="redis://localhost:6379" \
  RABBIT_MQ_URL="pyamqp://guest@localhost" \
  celery -A bdc_collection_builder.celery.worker:celery worker -l INFO --concurrency 2 -Q download

As soon as the worker is launched, it will present a message like:

 -------------- celery@enghaw-dell-note v4.4.2 (cliffs)
--- ***** -----
-- ******* ---- Linux-5.3.0-46-generic-x86_64-with-Ubuntu-18.04-bionic 2020-04-30 08:51:18
- *** --- * ---
- ** ---------- [config]
- ** ---------- .> app:         bdc_collection_builder:0x7fa166e9a490
- ** ---------- .> transport:   amqp://guest:**@localhost:5672//
- ** ---------- .> results:     postgresql://postgres:**@localhost:5432/bdc
- *** --- * --- .> concurrency: 4 (prefork)
-- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker)
--- ***** -----
 -------------- [queues]
                .> download         exchange=download(direct) key=download


[tasks]
  . bdc_collection_builder.celery.tasks.correction
  . bdc_collection_builder.celery.tasks.download
  . bdc_collection_builder.celery.tasks.harmonization
  . bdc_collection_builder.celery.tasks.post
  . bdc_collection_builder.celery.tasks.publish

[2020-04-30 08:51:18,737: INFO/MainProcess] Connected to amqp://guest:**@127.0.0.1:5672//
[2020-04-30 08:51:18,746: INFO/MainProcess] mingle: searching for neighbors
[2020-04-30 08:51:20,040: INFO/MainProcess] mingle: all alone
[2020-04-30 08:51:20,075: INFO/MainProcess] celery@enghaw-dell-note ready.

2. To launch the worker responsible for surface reflection generation (L2A processor based on Sen2Cor or LaSRC for Landsat 8), use the following Celery command:

$ DATA_DIR="/home/user/data/bdc-collection-builder" \
  SQLALCHEMY_DATABASE_URI="postgresql://postgres:postgres@localhost:5432/bdc" \
  REDIS_URL="redis://localhost:6379" \
  RABBIT_MQ_URL="pyamqp://guest@localhost" \
  LASRC_AUX_DIR=/path/to/auxiliaries/L8 \
  LEDAPS_AUX_DIR=/path/to/auxiliaries/ledaps \
  celery -A bdc_collection_builder.celery.worker:celery worker -l INFO --concurrency 4 -Q correction

As soon as the worker is launched, it will present a message like:

 -------------- celery@enghaw-dell-note v4.4.2 (cliffs)
--- ***** -----
-- ******* ---- Linux-5.3.0-46-generic-x86_64-with-Ubuntu-18.04-bionic 2020-04-30 08:53:57
- *** --- * ---
- ** ---------- [config]
- ** ---------- .> app:         bdc_collection_builder:0x7ff25bff5390
- ** ---------- .> transport:   amqp://guest:**@localhost:5672//
- ** ---------- .> results:     postgresql://postgres:**@localhost:5432/bdc
- *** --- * --- .> concurrency: 4 (prefork)
-- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker)
--- ***** -----
 -------------- [queues]
                .> atm-correction   exchange=atm-correction(direct) key=atm-correction


[tasks]
  . bdc_collection_builder.celery.tasks.correction
  . bdc_collection_builder.celery.tasks.download
  . bdc_collection_builder.celery.tasks.harmonization
  . bdc_collection_builder.celery.tasks.post
  . bdc_collection_builder.celery.tasks.publish

[2020-04-30 08:53:57,977: INFO/MainProcess] Connected to amqp://guest:**@127.0.0.1:5672//
[2020-04-30 08:53:58,055: INFO/MainProcess] mingle: searching for neighbors
[2020-04-30 08:53:59,389: INFO/MainProcess] mingle: all alone
[2020-04-30 08:53:59,457: INFO/MainProcess] celery@enghaw-dell-note ready.

Note

This configuration is only for LaSRC/LEDAPS with Fmask4. If you would like to run with Sen2Cor, check CONFIG.

3. To launch the worker responsible for publishing the generated surface reflection data products, use the following Celery command:

$ DATA_DIR="/home/user/data/bdc-collection-builder" \
  SQLALCHEMY_DATABASE_URI="postgresql://postgres:postgres@localhost:5432/bdc" \
  REDIS_URL="redis://localhost:6379" \
  RABBIT_MQ_URL="pyamqp://guest@localhost" \
  celery -A bdc_collection_builder.celery.worker:celery worker -l INFO --concurrency 4 -Q publish

As soon as the worker is launched, it will present a message like:

 -------------- celery@enghaw-dell-note v4.4.2 (cliffs)
--- ***** -----
-- ******* ---- Linux-5.3.0-46-generic-x86_64-with-Ubuntu-18.04-bionic 2020-04-30 08:54:19
- *** --- * ---
- ** ---------- [config]
- ** ---------- .> app:         bdc_collection_builder:0x7f52d876e3d0
- ** ---------- .> transport:   amqp://guest:**@localhost:5672//
- ** ---------- .> results:     postgresql://postgres:**@localhost:5432/bdc
- *** --- * --- .> concurrency: 4 (prefork)
-- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker)
--- ***** -----
 -------------- [queues]
                .> publish          exchange=publish(direct) key=publish


[tasks]
  . bdc_collection_builder.celery.tasks.correction
  . bdc_collection_builder.celery.tasks.download
  . bdc_collection_builder.celery.tasks.harmonization
  . bdc_collection_builder.celery.tasks.post
  . bdc_collection_builder.celery.tasks.publish

[2020-04-30 08:54:19,361: INFO/MainProcess] Connected to amqp://guest:**@127.0.0.1:5672//
[2020-04-30 08:54:19,400: INFO/MainProcess] mingle: searching for neighbors
[2020-04-30 08:54:20,504: INFO/MainProcess] mingle: all alone
[2020-04-30 08:54:20,602: INFO/MainProcess] celery@enghaw-dell-note ready.

Note

In these examples, we have launched individual workers download, atm-correction, publish listening in different queues. For convenience, you may set the parameter -Q download,atm-correction,publish to make the worker listen all these queues in runtime. Just make sure that the worker has the required variables for each kind of processing.

Launching Collection Builder

To launch the Flask application responsible for orchestrating the collection builder components, use the following command:

$ DATA_DIR="/home/user/data/bdc-collection-builder" \
  SQLALCHEMY_DATABASE_URI="postgresql://postgres:postgres@localhost:5432/bdc" \
  REDIS_URL="redis://localhost:6379" \
  RABBIT_MQ_URL="pyamqp://guest@localhost" \
  bdc-collection-builder run

As soon as the Flask application is up and running, it will present a message like:

* Environment: production
  WARNING: This is a development server. Do not use it in a production deployment.
  Use a production WSGI server instead.
* Debug mode: off
* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

Usage

Please, refer to the document :doc:`usage` for information on how to use the collection builder to download and generate surface reflectance data products.

Footnotes

[1]

During librabbitmq installation, if you have a build message such as the one showed below:

...
Running setup.py install for SQLAlchemy-Utils ... done
Running setup.py install for bdc-db ... done
Running setup.py install for librabbitmq ... error
ERROR: Command errored out with exit status 1:
 command: /home/user/bdc-collection-builder/venv/bin/python3.7 -u -c
    'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-1i7mp5js/librabbitmq/setup.py'"'"'...
     cwd: /tmp/pip-install-1i7mp5js/librabbitmq/
Complete output (107 lines):
/tmp/pip-install-1i7mp5js/librabbitmq/setup.py:167: DeprecationWarning: 'U' mode is deprecated
  long_description = open(os.path.join(BASE_PATH, 'README.rst'), 'U').read()
running build
- pull submodule rabbitmq-c...
Cloning into 'rabbitmq-c'...
Note: checking out 'caad0ef1533783729c7644a226c989c79b4c497b'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by performing another checkout.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -b with the checkout command again. Example:

  git checkout -b <new-branch-name>

- autoreconf
sh: 1: autoreconf: not found
- configure rabbitmq-c...
/bin/sh: 0: Can't open configure

You will need to install autoconf:

$ sudo apt install autoconf

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INSTALL.rst

INSTALL.rst

Installation

Compatibility

Development Installation

Running in Development Mode

Launch Redis, RabbitMQ and PostgreSQL Containers

Prepare the Database System

Prepare the containers Sen2Cor and LaSRC 1.3.0

Launching Collection Builder Workers

Launching Collection Builder

Usage

Files

INSTALL.rst

Latest commit

History

INSTALL.rst

File metadata and controls

Installation

Compatibility

Development Installation

Running in Development Mode

Launch Redis, RabbitMQ and PostgreSQL Containers

Prepare the Database System

Prepare the containers Sen2Cor and LaSRC 1.3.0

Launching Collection Builder Workers

Launching Collection Builder

Usage