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  • Prerequisites
  • Data Import
  • Tuning the Helm chart
  • Resources
  • Installing the chart
  • Next steps

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  1. Reference
  2. Deployment

Kubernetes

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Last updated 10 months ago

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Metarank can be deployed in a distributed fashion inside a Kubernetes cluster.

Prerequisites

For a distributed K8S deployment, metarank requires the following external services and tools to be already available:

  1. Helm: used to install the Metarank chart.

  2. Redis: as an almost-persistent data store for inference. Can be also installed either inside k8s with helm, or as a managed service like AWS ElastiCache Redis.

  3. Distributed event bus for event ingestion: Kafka, Pulsar, Kinesis and internal RESTful API are supported.

Data Import

Metarank supports multiple ways of ingesting training data into the system:

  • event file can be HTTT POSTed to the /feedback endpoint using the REST API. Metarank does not do any in-memory buffering, so if your dataset is below 1GiB in size, this may be the simplest way to ingest.

  • event can be imported from a Kafka/Pulsar/Kinesis topic or read from files locally. Note that distributed import is not yet supported.

We suggest to start with a HTTP-based event import, and switch to offline local import if you have any issues with it.

Tuning the Helm chart

With Helm installed according to its official installation guide, you need to add a Metarank Helm repo:

$> helm repo add metarank https://metarank.github.io/helm-charts
"metarank" has been added to your repositories

$> helm repo update
Hang tight while we grab the latest from your chart repositories...
...Successfully got an update from the "metarank" chart repository
Update Complete. Happy Helming!

$> helm pull metarank/metarank --untar
$> cd metarank

In the chart directory there are metarank.conf and values.yaml files you'll need to update before the deployment:

total 24
drwxr-xr-x 2 user user 4096 Oct  4 14:25 charts
-rw-r--r-- 1 user user  124 Oct  4 17:23 Chart.yaml
-rw-r--r-- 1 user user  376 Oct  4 17:23 metarank.conf
-rw-r--r-- 1 user user  989 Oct  4 17:23 README.md
drwxr-xr-x 3 user user 4096 Oct  4 17:23 templates
-rw-r--r-- 1 user user 1889 Oct  4 17:23 values.yaml

The metarank.conf file is a regular metarank configuration file, so you can check the configuration guide to set things up manually, or use an automatic data-based config generator.

The metarank.conf file requires you to define a Redis endpoint for state store. A good-looking config file is shown below:

api:
  host: 0.0.0.0

state:
  type: redis
  host: add-redis-hostname-here
  port: 6379

models:
  xgboost:
    type: lambdamart
    backend:
      type: xgboost
      iterations: 50
    weights:
      click: 1
    features:
      - popularity

features:
  - name: popularity
    type: number
    scope: item
    source: metadata.popularity

The values.yaml is a generic helm deployment configuration file. You can tune it, but default one usually requires no extra changes.

Resources

The default helm chart sets no specific memory requests & limits, but it can be configured with values.yaml.

The Metarank docker container accepts a JAVA_OPTS environment variable to control the JVM memory usage. It defaults to JAVA_OPTS="-Xmx1g -verbose:gc" which means:

  • Use 1Gb for JVM heap. The actual RSS memory usage should be a bit higher due to JVM extra overhead.

  • Enable verbose GC logging. You may notice the following lines in the log, they are normal:

[282.621s][info][gc] GC(26) Pause Young (Allocation Failure) 55M->36M(67M) 2.718ms

Installing the chart

The chart itself is agnostic to the Metarank version, and has separate versioning. For the latest Metarank 0.7.9 release, use the following command to install the chart:

helm install metarank . --set-file config=metarank.conf --set image.tag=0.7.9

NAME: metarank
LAST DEPLOYED: Tue Oct  4 15:32:47 2022
NAMESPACE: default
STATUS: deployed
REVISION: 1

After that, a single metarank pod will be running:

$> kubectl get pods
NAME                        READY   STATUS    RESTARTS   AGE
metarank-6c577f46f6-9c9mz   1/1     Running   0          136m
redis-master-0              1/1     Running   0          171m

Next steps

After successful deployment you may want to do the following:

  • Enable ingress in values.yaml so Metarank can be accessible from outside.

  • HTTP POST the training data to the /feedback REST API.

  • Send your first reranking request according to the quickstart guide

  • Configure Kafka/Pulsar/Kinesis as a real-time data source.

installation overview