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Import any kind of server logs in Matomo for powerful log analytics. Universal log file parsing and reporting.

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This application is part of a new SciELO Usage Countage Solution.

Requirements

  • Python 2.7
  • Matomo 3.14.1
  • Docker

Setting up environmental variables

Install

  1. /app/usage-logs (a directory that contains usage-logs files)
  2. /app/data (a directory where all the resulting files will be stored)

You have to set up all the environmental variables (in the .env file), such as:

COLLECTION=collection_acronym
DIRS_USAGE_LOGS=/app/usage-logs/
LOG_FILE_DATABASE_STRING=mysql://user:pass@localhost:3306/database
LOGGING_LEVEL=DEBUG

Run

Update control_log_file table

Execute the file update_available_logs using a docker run proccess.

docker run --rm --env-file .env -v {HOST_DIR_LOGS}:/app/usage-logs -v {HOST_DIR_DATA}:/app/data scielo-matomo-manager update_available_logs

Load available logs

Execute the file load_logs using a docker run proccess.

docker run --rm --env-file .env -v {HOST_DIR_LOGS}:/app/usage-logs -v {HOST_DIR_DATA}:/app/data scielo-matomo-manager load_logs

Data Structure

There are three tables in a schema database called control. These tables are responsible for controlling the data flow during the importing of logs, as follows: (a) control_log_file, (b) control_log_file_summary, and (c) control_date_status.

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Import any kind of server logs in Matomo for powerful log analytics. Universal log file parsing and reporting.

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