Skip to content

Latest commit

 

History

History
82 lines (68 loc) · 3.91 KB

README.md

File metadata and controls

82 lines (68 loc) · 3.91 KB
Raw

Python CI actions Coverage Status

Parser Pipeline Flowchart

Setting Up a Development Environment

Installing dependencies and hooks

This project uses pyproject.toml to install necessary dependencies and otherwise set up a working development environment. To set up a local working environment, simply run the following:

virtualenv .venv
source .venv/bin/activate
pip install .[dev]
pip install .
pre-commit install
pre-commit install --hook-type commit-msg

To install gRPC, the following variables may need to be set in order for the build to succeed:

export GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1
export GRPC_PYTHON_BUILD_SYSTEM_ZLIB=1

Testing with pytest

Tests can be run from the main directory using pytest:

pytest

The pytest command line arguments are already specified in pyproject.toml.

Testing Against Kafka

The Kafka Environment

In order to set up a full development environment, a kafka instance must be created that contains at least:

  • kafka broker
  • kafka zookeeper
  • kafka schema registry
  • postgres
  • redis
  • minIO (or AWS S3 bucket)

The following can also be helpful:

  • kafka-ui
  • pgadmin

Next, we need to copy config.py to local_config.py and update the environment variables to point to reflect the values of the local environment. For postgres, we will need a database to store data. We will also need an S3 bucket created either on AWS or locally on a minIO instance. We will also need to create Pipeline input and output topics which can be done either through python or by using the kafka-ui. The relevant AVRO schemas from SciXParser/AVRO_schemas/ must also be added to the schema registry using either python or the UI.

Launching The Pipeline

All SciX Pipelines require librdkafka be installed. The source can be found here. Installation on most `nix systems can be done by running the following:

git clone https://github.com/edenhill/librdkafka
cd librdkafka && ./configure && make && make install && ldconfig

To launch the pipeline, the following commands need to be run within the SciXParser/ directory

#Start gRPC API
python3 run.py PARSER_API
#Start Parser pipeline consumer and producer
python3 run.py PARSER_APP

Sending commands to the gRPC API

Currently, there are three methods that have been defined in the API for interacting with the Parser Pipeline.

#This command tells the server to initialize a job by adding a message to the Parser Topic
python3 API/parser_client.py REPARSE --uuid "<string of space separated uuids>"
#This command tells the server to initialize a job by adding a message to the Parser Topic using uuids read from a line separated file.
python3 API/parser_client.py REPARSE --uuid-file "$PATH_TO_UUID_FILE"
#This command asks the server to check on the current status of a record with id <uuid>
python3 API/parser_client.py MONITOR --uuid '<single uuid>'
#This command returns the current parsed record for a given <uuid>
python3 API/parser_client.py VIEW --uuid '<single uuid>'

REPARSE takes optional arguments:

  • --force : forces reparsed data to be sent to the Kafka topic regardless of whether the data has changed.
  • --resend-only : Only resends the current parsed data stored in the database without doing any parsing.

Additionally, calling REPARSE or MONITOR command with --persistence will open a persistent connection that streams updates for the specificed uuid if only one is provided.

Maintainers

Taylor Jacovich, Kelly Lockhart, Sergi Blanco-Cuaresma, The ADS team