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https://github.com/Ensembl/ensembl-hive

EnsEMBL Hive - a system for creating and running pipelines on a distributed compute resource
https://github.com/Ensembl/ensembl-hive

docker docker-swarm ehive ensembl high-performance-computing htcondor java lsf mysql pbs-pro pbspro perl pipeline postgresql python sge slurm sqlite workflow-management-system

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EnsEMBL Hive - a system for creating and running pipelines on a distributed compute resource

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README 

        
eHive

=====



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eHive is a system for running computation pipelines on distributed computing resources - clusters, farms or grids.



The name comes from the way pipelines are processed by a swarm of autonomous agents.



Available documentation

-----------------------



The main entry point is available online in the [user

manual](https://ensembl-hive.readthedocs.io/en/version-2.6/), from where it can

be downloaded for offline access.



eHive in a nutshell

-------------------



### Blackboard, Jobs and Workers



In the centre of each running pipeline is a database that acts as a blackboard with individual tasks to be run.

These tasks (we call them Jobs) are claimed and processed by "Worker bees" or just Workers - autonomous processes

that are continuously running on the compute farm and connect to the pipeline database to report about the progress of Jobs

or claim some more. When a Worker discovers that its predefined time is up or that there are no more Jobs to do,

it claims no more Jobs and exits the compute farm freeing the resources.



### Beekeeper



A separate Beekeeper process makes sure there are always enough Workers on the farm.

It regularly checks the states of both the blackboard and the farm and submits more Workers when needed.

There is no direct communication between Beekeeper and Workers, which makes the system rather fault-tolerant,

as crashing of any of the agents for whatever reason doesn't stop the rest of the system from running.



### Analyses



Jobs that share same code, common parameters and resource requirements are typically grouped into Analyses,

and generally an Analysis can be viewed as a "base class" for the Jobs that belong to it.

However in some sense an Analysis also acts as a "container" for them.



An analysis is implemented as a Runnable file which is a Perl, Python or

Java module conforming to a special interface. eHive provides some basic

Runnables, especially one that allows running arbitrary commands (programs

and scripts written in other languages).



### PipeConfig file defines Analyses and dependency rules of the pipeline



eHive pipeline databases are molded according to PipeConfig files which are Perl modules conforming to a special interface.

A PipeConfig file defines the stucture of the pipeline, which is a graph whose nodes are Analyses

(with their code, parameters and resource requirements) and edges are various dependency rules:



* Dataflow rules define how data that flows out of an Analysis can be used to trigger creation of Jobs in other Analyses

* Control rules define dependencies between Analyses as Jobs' containers ("Jobs of Analysis Y can only start when all Jobs of Analysis X are done")

* Semaphore rules define dependencies between individual Jobs on a more fine-grained level



There are also other parameters of Analyses that control, for example:



* how many Workers can simultaneously work on a given Analysis,

* how many times a Job should be tried until it is considered failed,

* what should be automatically done with a Job if it needs more memory/time,

  etc.



Grid scheduler and Meadows

--------------------------



eHive has a generic interface named _Meadow_ that describes how to interact with an underlying grid scheduler (submit jobs, query job's status, etc). eHive is compatible with

[IBM Platform LSF](http://www-03.ibm.com/systems/spectrum-computing/products/lsf/),

Sun Grid Engine (now known as Oracle Grid Engine),

[HTCondor](https://research.cs.wisc.edu/htcondor/),

[PBS Pro](http://www.pbspro.org),

[Docker Swarm](https://docs.docker.com/engine/swarm/) and maybe others. Read more about this on [the user manual](http://ensembl-hive.readthedocs.io/en/version-2.6/contrib/alternative_meadows.html).



Docker image

------------



We have a Docker image available on the [Docker

Hub](https://hub.docker.com/r/ensemblorg/ensembl-hive/). It can be used to

showcase eHive scripts (`init_pipeline.pl`, `beekeeper.pl`, `runWorker.pl`) in a

container



### Open a session in a new container (will run bash)



```bash

docker run -it ensemblorg/ensembl-hive

```



### Initialize and run a pipeline



```bash

docker run -it ensemblorg/ensembl-hive init_pipeline.pl Bio::EnsEMBL::Hive::Examples::LongMult::PipeConfig::LongMult_conf -pipeline_url $URL

docker run -it ensemblorg/ensembl-hive beekeeper.pl -url $URL -loop -sleep 0.2

docker run -it ensemblorg/ensembl-hive runWorker.pl -url $URL

```



Docker Swarm

------------



Once packaged into Docker images, a pipeline can actually be run under the

Docker Swarm orchestrator, and thus on any cloud infrastructure that supports

it (e.g. [Amazon Web Services](https://docs.docker.com/docker-cloud/cloud-swarm/create-cloud-swarm-aws/),

[Microsoft Azure](https://docs.docker.com/docker-cloud/cloud-swarm/create-cloud-swarm-azure/)).



Read more about this on [the user manual](http://ensembl-hive.readthedocs.io/en/version-2.6/contrib/docker-swarm.html).



Contact us (mailing list)

-------------------------



eHive was originally conceived and used within EnsEMBL Compara group

for running Comparative Genomics pipelines, but since then it has been separated

into a separate software tool and is used in many projects both in Genome Campus, Cambridge and outside.

There is eHive users' mailing list for questions, suggestions, discussions and announcements.



To subscribe to it please visit