2017 FOSS4G Boston Maintaining Spatial Data Infrastructures (SDIs) using distributed task queues Paolo Corti and Ben Lewis Harvard Center for Geographic Analysis
Background Harvard Center for Geographic Analysis WorldMap http://worldmap.harvard.edu Biggest GeoNode instance on the planet https://github.com/cga-harvard/cga-worldmap HHypermap http://hh.worldmap.harvard.edu Map service registry https://github.com/cga-harvard/hhypermap
Note Billion Object Platform (BOP) https://github.com/cga-harvard/hhypermap-bop
Demo of WorldMap / HHypermap
The need for an asynchronous processor In WorldMap and HHypermap there are operations run by users which are time consuming and cannot be handled in the context of a web request Harvest the metadata of a service and its layers Synchronize the metadata of a new or updated layer to the search engine Feed a gazetteer when a new layer is uploaded or updated Upload a spatial datasets to the server Create a new layer using a table join
HTTP request/response cycle must be fast In web applications the HTTP request/response cycle can be synchronous as long as there are very quick interactions between the client and the server unfortunately there are cases when the cycle become slower In these situations the best practice for a web application is to process asynchronously these tasks using a task queue
Task Queues Asynchronous processing in a web application can be delegated to a task queue, which is a system for parallel execution of tasks in a non-blocking fashion
Asynchronous processing model
Asynchronous processing model The asynchronous processing model is composed by services that produce processing tasks (producers) and by services which consume and process these tasks (consumers) accordingly A message queue is a broker which facilitates message passing by providing a protocol or interface which other services can access. Work can be distributed across threads or machines In the context of a web application the producer is the client application that creates messages based on the user interaction. The consumer is a daemon process that can consume the messages and run the needed process
Glossary Task Queue: a system for parallel execution of tasks in a non-blocking fashion Broker or Message Queue: provides a protocol or interface for messages exchanging between different services and applications Producer: the code that places the tasks to be executed later in the broker Consumer or Worker: takes tasks from the broker and process them Exchange: takes a message from a producer and route it to zero or more queues (messages routing) Tasks must be consumed faster than being produced. If not, add more workers
Use cases for task queues in web applications some process is taking too much time and must be processed asynchronously heterogeneous applications/services in a given system architecture need an easy way to reliably communicate between each other periodic operations (vs crontab) a way of parallelizing tasks in multi processors monitor processes and analyze failing tasks (and execute them again)
Typical use cases for a task queue in a web application Thumbnails generation Sending bulk email Fetching large amounts of data from APIs Performing time-intensive calculations Expensive queries Search engine index synchronization Interaction with another application/service Replacing cron jobs (backups, maintenance, etc )
Typical use cases for a task queue in a GIS Portal/SDI Upload a shapefile to the server (GeoNode) Thumbnails generation for layers and maps (GeoNode) OGC services harvesting (Harvard Hypermap) Geoprocessing operations Geospatial data maintenance
Producer, broker and consumer architecture Producer Broker Consumer Producer Consumer Broker Producer Broker Consumer Producer Consumer Producer
Message brokers implementations Most of them are open source! RabbitMQ (AMQP, STOMP, JMS) Apache ActiveMQ (STOMP, JMS) Amazon Simple Queue Service (JMS) Apache Kafka Several standard protocols: AMQP, STOMP, JMS, MSMQ (Microsoft.NET)
Tasks (Jobs) queues implementations Celery (RabbitMQ, Redis, Amazon SQS, Zookeeper) Redis Queue (Redis) Resque (Redis) Kue (Redis) And many others!
Celery asynchronous task queue based on distributed message passing focused on real-time operation, but supports scheduling as well the execution units, called tasks, are executed concurrently on a single or more worker servers it supports many message brokers (RabbitMQ, Redis, MongoDB, CouchDB,...) written in Python but it can operate with other languages great integration with Django! great monitoring tools (Flower, django-celery-results)
RabbitMQ RabbitMQ is a message broker: it accepts and forwards messages most widely deployed open source broker (35k+ deployments) support many message protocols supported by many operating systems and languages Written in Erlang
Architecture of Celery/RabbitMQ https://tests4geeks.com/python-celery-rabbitmq-tutorial/
A real use case: Harvard Hypermap HHypermap (Harvard Hypermap) Registry is a platform that manages OWS, Esri REST, and other types of map service harvesting, and orchestration and maintains uptime statistics for services and layers. Where possible, layers are cached by MapProxy. HHypermap provides thousands of remote layers to WorldMap users
Harvard Hypermap WorldMap Architecture
HHypermap interface
Need for a task queue SLOW!!!
Producer Is the code that places the tasks to be executed later in the broker
Celery messages
Consumer Takes tasks from the broker and process them in a worker
Replacing cron jobs
Replacing cron jobs
Workers and threads with htop
Monitoring
Monitoring a task
Thanks! Question and Answer