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Implement filesystem scanners and landing zones

Project description

iRODS Automated Ingest Framework

The automated ingest framework gives iRODS an enterprise solution that solves two major use cases: getting existing data under management and ingesting incoming data hitting a landing zone.

Based on the Python iRODS Client and Celery, this framework can scale up to match the demands of data coming off instruments, satellites, or parallel filesystems.

The example diagrams below show a filesystem scanner and a landing zone.

Automated Ingest: Filesystem Scanner Diagram

Automated Ingest: Landing Zone Diagram

Usage options

Redis options

option effect default
redis_host Domain or IP address of Redis host localhost
redis_port Port number for Redis 6379
redis_db Redis DB number to use for ingest 0

S3 options

To scan S3 bucket, minimally requires --s3_keypair and source path of the form /bucket_name/path/to/root/folder.

option effect default
s3_keypair path to S3 keypair file None
s3_endpoint_domain S3 endpoint domain s3.amazonaws.com
s3_region_name S3 region name us-east-1
s3_proxy_url URL to proxy for S3 access None

Logging/Profiling options

option effect default
log_filename Path to output file for logs None
log_level Minimum level of message to log None
log_interval Time interval with which to rollover ingest log file None
log_when Type/units of log_interval (see TimedRotatingFileHandler) None

--profile allows you to use vis to visualize a profile of Celery workers over time of ingest job.

option effect default
profile_filename Specify name of profile filename (JSON output) None
profile_level Minimum level of message to log for profiling None
profile_interval Time interval with which to rollover ingest profile file None
profile_when Type/units of profile_interval (see TimedRotatingFileHandler) None

Ingest start options

These options are used at the "start" of an ingest job.

option effect default
job_name Reference name for ingest job a generated uuid
interval Restart interval (in seconds). If absent, will only sync once. None
file_queue Name for the file queue. file
path_queue Name for the path queue. path
restart_queue Name for the restart queue. restart
event_handler Path to event handler file None (see "event_handler methods" below)
synchronous Block until sync job is completed False
progress Show progress bar and task counts (must have --synchronous flag) False
ignore_cache Ignore last sync time in cache - like starting a new sync False

Optimization options

option effect default
exclude_file_type types of files to exclude: regular, directory, character, block, socket, pipe, link None
exclude_file_name a list of space-separated python regular expressions defining the file names to exclude such as "(\S+)exclude" "(\S+).hidden" None
exclude_directory_name a list of space-separated python regular expressions defining the directory names to exclude such as "(\S+)exclude" "(\S+).hidden" None
files_per_task Number of paths to process in a given task on the queue 50
initial_ingest Use this flag on initial ingest to avoid check for data object paths already in iRODS False
irods_idle_disconnect_seconds Seconds to hold open iRODS connection while idle 60

available --event_handler methods

method effect default
pre_data_obj_create user-defined python none
post_data_obj_create user-defined python none
pre_data_obj_modify user-defined python none
post_data_obj_modify user-defined python none
pre_coll_create user-defined python none
post_coll_create user-defined python none
pre_coll_modify user-defined python none
post_coll_modify user-defined python none
as_user takes action as this iRODS user authenticated user
target_path set mount path on the irods server which can be different from client mount path client mount path
to_resource defines target resource request of operation as provided by client environment
operation defines the mode of operation Operation.REGISTER_SYNC
max_retries defines max number of retries on failure 0
timeout defines seconds until job times out 3600
delay defines seconds between retries 0

Event handlers can use logger to write logs. See structlog for available logging methods and signatures.

operation mode

operation new files updated files
Operation.REGISTER_SYNC (default) registers in catalog updates size in catalog
Operation.REGISTER_AS_REPLICA_SYNC registers first or additional replica updates size in catalog
Operation.PUT copies file to target vault, and registers in catalog no action
Operation.PUT_SYNC copies file to target vault, and registers in catalog copies entire file again, and updates catalog
Operation.PUT_APPEND copies file to target vault, and registers in catalog copies only appended part of file, and updates catalog
Operation.NO_OP no action no action

--event_handler usage examples can be found in the examples directory.

Deployment

Basic: manual redis, Celery, pip

Running the sync job and Celery workers requires a valid iRODS environment file for an authenticated iRODS user on each node.

Starting Redis Server

Install redis-server:

sudo yum install redis-server
sudo apt-get install redis-server

Or, build it yourself: https://redis.io/topics/quickstart

Start redis:

redis-server

Or, dameonized:

sudo service redis-server start
sudo systemctl start redis

The Redis documentation also recommends an additional step:

Make sure to set the Linux kernel overcommit memory setting to 1. Add vm.overcommit_memory = 1 to /etc/sysctl.conf and then reboot or run the command sysctl vm.overcommit_memory=1 for this to take effect immediately.

This allows the Linux kernel to overcommit virtual memory even if this exceeds the physical memory on the host machine. See kernel.org documentation for more information.

Note: If running in a distributed environment, make sure Redis server accepts connections by editing the bind line in /etc/redis/redis.conf or /etc/redis.conf.

Setting up virtual environment

You may need to upgrade pip:

pip install --upgrade pip

Install virtualenv:

pip install virtualenv

Create a virtualenv with python3:

virtualenv -p python3 rodssync

Activate virtual environment:

source rodssync/bin/activate

Install this package

pip install irods_capability_automated_ingest

Set up environment for Celery:

export CELERY_BROKER_URL=redis://<redis host>:<redis port>/<redis db> # e.g. redis://127.0.0.1:6379/0
export PYTHONPATH=`pwd`

Start celery worker(s):

celery -A irods_capability_automated_ingest.sync_task worker -l error -Q restart,path,file -c <num workers> 

Note: Make sure queue names match those of the ingest job (default queue names shown here).

Run tests

Note: The test suite requires Python version >=3.5. Note: The tests should be run without running Celery workers.

python -m irods_capability_automated_ingest.test.test_irods_sync

Start sync job

python -m irods_capability_automated_ingest.irods_sync start <source dir> <destination collection>

List jobs

python -m irods_capability_automated_ingest.irods_sync list

Stop jobs

python -m irods_capability_automated_ingest.irods_sync stop <job name>

Watch jobs (same as using --progress)

python -m irods_capability_automated_ingest.irods_sync watch <job name>

Intermediate: dockerize, manually config (needs to be updated for Celery)

/tmp/event_handler.py

from irods_capability_automated_ingest.core import Core
from irods_capability_automated_ingest.utils import Operation

class event_handler(Core):

    @staticmethod
    def target_path(session, meta, **options):
        return "/tmp/host" + path

icommands.env

IRODS_PORT=1247
IRODS_HOST=172.17.0.1
IRODS_USER_NAME=rods
IRODS_ZONE_NAME=tempZone
IRODS_PASSWORD=rods
docker run --rm --name some-redis -d redis:4.0.8
docker run --rm --link some-redis:redis irods_rq-scheduler:0.1.0 worker -u redis://redis:6379/0 restart path file
docker run --rm --link some-redis:redis -v /tmp/host/event_handler.py:/event_handler.py irods_capability_automated_ingest:0.1.0 start /data /tempZone/home/rods/data --redis_host=redis --event_handler=event_handler
docker run --rm --link some-redis:redis --env-file icommands.env -v /tmp/host/data:/data -v /tmp/host/event_handler.py:/event_handler.py irods_rq:0.1.0 worker -u redis://redis:6379/0 restart path file

Advanced: kubernetes (needs to be updated for Celery)

This does not assume that your iRODS installation is in kubernetes.

kubeadm

setup Glusterfs and Heketi

create storage class

create a persistent volume claim data

install minikube and helm

set memory to at least 8g and cpu to at least 4

minikube start --memory 8192 --cpus 4

enable ingress on minikube

minikube addons enable ingress

mount host dirs

This is where you data and event handler. In this setup, we assume that your event handler is under /tmp/host/event_handler and you data is under /tmp/host/data. We will mount /tmp/host/data into /host/data in minikube which will mount /host/data into /data in containers,

/tmp/host/data -> minikube /host/data -> container /data.

and similarly,

/tmp/host/event_handler -> minikube /host/event_handler -> container /event_handler. Your setup may differ.

mkdir /tmp/host/event_handler
mkdir /tmp/host/data

/tmp/host/event_handler/event_handler.py

from irods_capability_automated_ingest.core import Core
from irods_capability_automated_ingest.utils import Operation

class event_handler(Core):

    @staticmethod
    def target_path(session, meta, **options):
        return path
minikube mount /tmp/host:/host --gid 998 --uid 998 --9p-version=9p2000.L

enable incubator

helm repo add incubator https://kubernetes-charts-incubator.storage.googleapis.com/

build local docker images (optional)

If you want to use local docker images, you can build the docker images into minikube as follows:

fish

eval (minikube docker-env)

bash

eval $(minikube docker-env)
cd <repo>/docker/irods-postgresql
docker build . -t irods-provider-postgresql:4.2.2
cd <repo>/docker/irods-cockroachdb
docker build . -t irods-provider-cockroachdb:4.3.0
cd <repo>/docker
docker build . -t irods_capability_automated_ingest:0.1.0
cd <repo>/docker/rq
docker build . -t irods_rq:0.1.0
cd <repo>/docker/rq-scheduler
docker build . -t irods_rq-scheduler:0.1.0

install irods

postgresql
cd <repo>/kubernetes/irods-provider-postgresql
helm dependency update
cd <repo>/kubernetes
helm install ./irods-provider-postgresql --name irods
cockroachdb
cd <repo>/kubernetes/irods-provider-cockroachdb
helm dependency update
cd <repo>/kubernetes
helm install ./irods-provider-cockroachdb --name irods

when reinstalling, run

kubectl delete --all pv
kubectl delete --all pvc 

update irods configurations

Set configurations in <repo>/kubernetes/chart/values.yaml or --set command line argument.

install chart

cd <repo>/kubernetes/chart

We call our release icai.

cd <repo>/kubernetes
helm install ./chart --name icai

scale rq workers

kubectl scale deployment.apps/icai-rq-deployment --replicas=<n>

access by REST API (recommended)

submit job

submit.yaml

root: /data
target: /tempZone/home/rods/data
interval: <interval>
append_json: <yaml>
timeout: <timeout>
all: <all>
event_handler: <event_handler>
event_handler_data: |
    from irods_capability_automated_ingest.core import Core
    from irods_capability_automated_ingest.utils import Operation

    class event_handler(Core):

        @staticmethod
        def target_path(session, meta, **options):
            return path

fish

curl -XPUT "http://"(minikube ip)"/job/<job name> -H "Content-Type: application/x-yaml" --data-binary=`submit.yaml`

bash

curl -XPUT "http://$(minikube ip)/job/<job name>" -H "Content-Type: application/x-yaml" --data-binary "@submit.yaml"

fish

curl -XPUT "http://"(minikube ip)"/job" -H "Content-Type: application/x-yaml" --data-binary "@submit.yaml"

bash

curl -XPUT "http://$(minikube ip)/job" -H "Content-Type: application/x-yaml" --data-binary "@submit.yaml"
list job

fish

curl -XGET "http://"(minikube ip)"/job"

bash

curl -XGET "http://$(minikube ip)/job"
delete job

fish

curl -XDELETE "http://"(minikube ip)"/job/<job name>"

bash

curl -XDELETE "http://$(minikube ip)/job/<job name>"

access by command line (not recommended)

submit job
kubectl run --rm -i icai --image=irods_capability_automated_ingest:0.1.0 --restart=Never -- start /data /tempZone/home/rods/data -i <interval> --event_handler=event_handler --job_name=<job name> --redis_host icai-redis-master
list job
kubectl run --rm -i icai --image=irods_capability_automated_ingest:0.1.0 --restart=Never -- list --redis_host icai-redis-master
delete job
kubectl run --rm -i icai --image=irods_capability_automated_ingest:0.1.0 --restart=Never -- stop <job name> --redis_host icai-redis-master

install logging tool

Install chart with set log_level to INFO.

helm del --purge icai
cd <repo>/kubernetes
helm install ./chart --set log_level=INFO --name icai

set parameters for elasticsearch

minikube ssh 'echo "sysctl -w vm.max_map_count=262144" | sudo tee -a /var/lib/boot2docker/bootlocal.sh'
minikube stop
minikube start
cd <repo>/kubernetes
helm install ./elk --name icai-elk
Grafana

look for service port

kubectl get svc icai-elk-grafana

forward port

kubectl port-forward svc/icai-elk-grafana 8000:80

If --set grafana.adminPassword="" system generates a random password, lookup admin password

kubectl get secret --namespace default icai-elk-grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echo

open browser url localhost:8000

login with username admin and password admin click on icai dashboard

Kibana

Uncomment kibana sections in the yaml files under the <repo>/kubernetes/elk directory

look for service port

kubectl get svc icai-elk-kibana

forward port

kubectl port-forward svc/icai-elk-kibana 8000:443

open browser url localhost:8000

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