Snowplow

Metarank can be integrated into existing Snowplow Analytics setup.

We provide a set of Iglu Schemes that you will use to track metadata and interaction events that can later on be read from Snowplow's enriched event stream by Metarank.

Snowplow Trackers are used to track Metarank-specific events.
Metarank will use Snowplow's enriched event stream as a source of events.

Typical Snowplow architecture

Typical Snowplow Analytics setup consists of the following parts:

Using Snowplow Trackers, your application emits a clickstream telemetry to the Stream Collector
Stream Collector writes all incoming events into the raw stream
Enrichment validates these events according to the predefined schemas from the Schema Registry
Validated and enriched events are written to the enriched stream
Enriched events are delivered to the Analytics DB

Metarank exposes a set of Snowplow-compatible event schemas, and can read events directly from the enriched stream, as shown on the diagram below:

Schema registry

All incoming raw events have a strict JSON schema, that consists of the following parts:

predefined fields according to the Snowplow Tracker Protocol
unstructured payload with user-defined schema
multiple context payloads with user-defined schemas

These user-defined schemas are pulled from the Iglu Registry, and these schemas are standard JSON Schema definitions, describing the payload structure.

There are four different Metarank event types with the corresponding schemas:

ai.metarank/item/1-0-0: item metadata event
ai.metarank/user/1-0-0: user metadata event
ai.metarank/ranking/1-0-0: ranking event
ai.metarank/interaction/1-0-0: interaction event

These schemas are describing native Metarank event types without any modifications.

Check out github.com/metarank/metarank-snowplow for more details about Metarank schemas.

Stream transport types

Snowplow supports multiple streaming platforms for event delivery:

AWS Kinesis: supported by Metarank
Kafka: supported by Metarank
GCP Pubsub: support is planned in the future
NSQ: not supported
Amazon SQS: not supported
[stdout]: not supported

Setting up event tracking

Metarank needs to receive 4 types of events, describing items, users and how users interact with items:

item metadata: like titles, inventory, tags
user metadata: country, age, location
ranking: what items and in what order were displayed to a visitor
interaction: how visitor interacted with the ranking

These events can be generated both on the frontend side, and on the backend side, depending on your setup and data availability on the front and back ends.

Frontend tracking

Using Snowplow JS Tracker SDK, you can track self-describing events, which are JSONs with attached schema references.

An example of tracking a ranking event:

import { trackSelfDescribingEvent } from '@snowplow/browser-tracker';

trackSelfDescribingEvent({
  event: {
    schema: 'iglu:ai.metarank/ranking/jsonschema/1-0-0',
    data: {
        event: 'ranking',
        id: '81f46c34-a4bb-469c-8708-f8127cd67d27',
        timestamp: '1599391467000',
        user: 'user1',
        session: 'session1',
        fields: [
            { name: 'query', value: 'cat' },
            { name: 'source', value: 'search' }
        ],
        items: [
            { id: "item1" },
            { id: "item2" }
        ]
    }
  }
});

Check out the JSON-Schema definitions for events and event format articles for details on fields, event types and their meaning.

Metarank schemas are language-agnostic and you can instrument your app using any supported Snowplow Tracker SDK for your favourite language/framework of choice.

Backend tracking

It Often happens that the frontend doesn't have all the required information to generate events. A good example is item metadata event (usually tags, titles and price are altered in some back-office system and are not directly exposed to the frontend).

In this case you can generate such events on the backend side.

For a sample Java backend application, you can track an item update event with the following code, using the Snowplow Java Tracker SDK:

import com.snowplowanalytics.snowplow.tracker.*;
import com.snowplowanalytics.snowplow.tracker.emitter.*;
import com.snowplowanalytics.snowplow.tracker.events.Unstructured;
import com.snowplowanalytics.snowplow.tracker.payload.SelfDescribingJson;

import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class JavaTrackerExample {
    public static void main(String[] args) {
        BatchEmitter emitter = BatchEmitter.builder()
                .url("http://collectorEndpoint")
                .build();

        Tracker tracker = new Tracker
                .TrackerBuilder(emitter, "trackerNamespace", "appId")
                .build();

        Map<String, Object> payload = new HashMap<>();
        payload.put("event", "item");
        payload.put("id", "81f46c34-a4bb-469c-8708-f8127cd67d27");
        payload.put("timestamp", String.valueOf(System.currentTimeMillis()));
        payload.put("item", "item1");

        Map<String, Object> fields = new HashMap<>();
        fields.put("title", "your cat");
        fields.put("color", List.of("white", "black"));
        payload.put("fields", fields);

        Unstructured unstructured = Unstructured.builder()
                .eventData(new SelfDescribingJson("iglu:ai.metarank/item/1-0-0", payload))
                .build();

        tracker.track(unstructured);
    }
}

Installing ai.metarank schemas

Metarank schemas are available on a public Iglu server on https://iglu.metarank.ai. To use it, add the following snippet to the resolver.json snowplow-enrich config file:

{
  "schema": "iglu:com.snowplowanalytics.iglu/resolver-config/jsonschema/1-0-1",
  "data": {
    "cacheSize": 500,
    "repositories": [
      {
        "name": "Metarank",
        "priority": 0,
        "vendorPrefixes": [ "ai.metarank" ],
        "connection": {
          "http": {
            "uri": "https://iglu.metarank.ai"
          }
        }
      }
    ]
  }
}

Both http and https schemas are supported, but https is recommended.

Connecting Metarank with Snowplow

Snowplow enrich emits processed records in TSV format into the downstream Kinesis/Pubsub/etc. topic. This topic is usually later monitored by "Loaders", like a Snowflake loader, or S3 Loader.

An example loader integration diagram is shown below:

Snowplow is flexible enough to use different data loading destinations (Redshift, Postgres, Snowflake, S3, etc.), but to access both live and historical enriched event data, Metarank needs an access to:

Enriched event stream
Historical enriched stream dumps done with S3 Loader

At the moment Metarank supports loading historical events only from S3 Loader.

Realtime events from AWS Kinesis

Snowplow enrich is usually configured with three destination streams output/pii/bad, with the same HOCON definition:

  "output": {
    # Enriched events output
    "good": {
      "type": "Kinesis"

      # Name of the Kinesis stream to write to
      "streamName": "enriched"

      # Optional. Maximum amount of time an enriched event may spend being buffered before it gets sent
      "maxBufferedTime": 100 millis
    }
  }

To make metarank connect to this stream, configure the kinesis source in the following way:

inference:
  port: 8080
  host: "0.0.0.0"
  source:
    type: kinesis
    region: us-east-1
    topic: enriched
    offset: latest
    format: snowplow:tsv

All the supported Metarank sources have an optional format field, which defines the underlying format of the payload in this stream. Valid options are:

json: default value, Metarank native format
snowplow, snowplow:tsv: Snowplow default TSV stream format
snowplow:json: Snowplow optional JSON stream format

With the format: snowplow:tsv, Metarank will read TSV events and transform them into native format automatically.

Historical events from AWS S3

Snowplow S3 Loader offloads realtime enriched events to gzip/lzo compressed files on S3. Given the following sample S3 Loader config snippet:

{
  # Optional, but recommended
  "region": "us-east-1",

  # Options are: RAW, ENRICHED_EVENTS, JSON
  "purpose": "ENRICHED_EVENTS",

  # Input Stream config
  "input": {
    # Kinesis Client Lib app name (corresponds to DynamoDB table name)
    "appName": "acme-s3-loader",
    # Kinesis stream name
    "streamName": "enriched",
    # Options are: LATEST, TRIM_HORIZON, AT_TIMESTAMP
    "position": "LATEST",
    # Max batch size to pull from Kinesis
    "maxRecords": 10
  },

  "output": {
    "s3": {
      # Full path to output data
      "path": "s3://acme-snowplow-output/enriched/",

      # Partitioning format; Optional
      # Valid substitutions are {vendor}, {schema}, {format}, {model} for self-describing jsons
      # and {yy}, {mm}, {dd}, {hh} for year, month, day, hour
      partitionFormat: "{vendor}.{schema}/model={model}/date={yy}-{mm}-{dd}"

      # Output format; Options: GZIP, LZO
      "compression": "GZIP"
    }
  }
}

You can instrument Metarank to load these GZIP-compressed event dumps for the bootstrapping process with the file source in the following way:

bootstrap:
  source:
    type: file
    path: "s3://acme-snowplow-output/enriched/"
    offset: earliest
    format: snowplow:tsv

Validating the setup

With Metarank configured to pick live events from the enriched stream, and historical events from the offloaded files in S3, it should be straightforward to do the usual routine of setting up and running it Metarank.

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Last updated 1 year ago