example schema registry

Overview

Before you can send your own event and context types into Snowplow (using the track unstructured events or track self-describing events and custom contexts features of Snowplow), you need to:

1. Define a JSON schema for each of the events and context types
2. Upload those schemas to your Iglu schema registry
3. Define a corresponding jsonpath file, and make sure this is uploaded your jsonpaths directory in Amazon S3
4. Create a corresponding Redshfit table definition, and create this table in your Redshift cluster

Once you have completed the above, you can send in data that conforms to the schemas as custom unstructured events or custom contexts.

Prerequisites

We recommend setting up the following two tools before staring:

1. Git so you can easily clone the repo and make updates to it.
2. Igluctl igluctl. This is a command line tool for validating schemas, auto-generating associated SQL table definition and jsonpath files and publishing them to snowplow-mini or S3

1. Creating the schemas

In order to start sending a new event or context type into Snowplow, you first need to define a new schema for that event.

1. Create a file in the repo for the new schema e.g. /schemas/com.mycompany/new_event_or_context_name/jsonschema/1-0-0
2. Create the schema in that file. Follow the /schemas/com.example_company/example_event/jsonschema/1-0-0 example
3. Save the schema file

Note that if you have JSON data already and you want to create a corresponding schema, you can do so using Schema Guru, both the web UI and the CLI.

Once you have your schema, make sure to validate it using igluctl:

$ /path/to/igluctl lint /path/to/schemas/com.mycompany/my_new_event_or_context

For Windows:

> java -jar /path/to/igluctl lint /path/to/schemas/com.mycompany/my_new_event_or_context

Igluctl has two severity levels that it can use when validating schemas. By default it uses level (1), which checks that the schemas are simply valid. We recommend validating schemas against a higher level (2). This will fail schemas that:

1. Define a string field without a maxLength property. That ensures that when e.g. the corresponding Redshift table DDL is generated, the correct associated column length can be unambiguously set
2. Define a numeric field without a minimum and maximum properties. That ensures that the when e.g. the corresponding Redshift table DDL is generated, the right numeric field type is set.

To lint the schemas using the higher severity level (2) run:

$ /path/to/igluctl lint /path/to/schemas/com.mycompany/my_new_event_or_context --severityLevel 2

2. Uploading the schemas to Iglu

Once you have created or updated your schemas, you need to push them to Iglu so that they're accessible to the Snowplow data pipeline.

This is a two part process:

1. Upload the schemas to snowplow-mini (for testing)
2. Upload the schemas to Iglu for the full pipeline

2.1 Upload the schemas to snowplow-mini

In order to upload schemas you need to have Snowplow Mini IP and Iglu Registry Master key. Here we are refer to them as environment variables (those are just examples, you'll have unique IP and key):

SNOWPLOW_MINI_IP=127.0.0.1
IGLU_REGISTRY_MASTER_KEY=fd08697f-435c-4916-9c85-d0e50bbb8913

Or for Windows:

SET SNOWPLOW_MINI_IP=127.0.0.1
SET IGLU_REGISTRY_MASTER_KEY=fd08697f-435c-4916-9c85-d0e50bbb8913

Run the following command to publish all schemas to the Iglu server bundled with Snowplow-mini:

$ /path/to/igluctl static push ./schemas $SNOWPLOW_MINI_IP:8081 $IGLU_REGISTRY_MASTER_KEY --public

Note that you can specify individual schemas if you prefer e.g.

$ /path/to/igluctl static push ./schemas/com.mycompany/my_new_event_schema $SNOWPLOW_MINI_IP:8081 $IGLU_REGISTRY_MASTER_KEY --public

Also note that if you're editing existing schemas, the server will need to be rebooted to clear the schema cache. This can be done directly in the EC2 console, or ping support@snowplowanalytics.com to ask a member of the Snowplow team to do so.

2.2 Upload the schemas to Iglu for the full pipeline

Once you've created your schemas, you need to upload them to Iglu. In practice, this means copying them into S3.

This can also be done via Igluctl. In the project root, first commit the schema to Git:

git add .
git commit -m "Committed finalized schema"
git push

Then push it to S3 bucket:

$ /path/to/igluctl static s3cp ./schemas snowplow-com-mycompany-iglu-schemas-bucket --accessKeyId ABCDEF --secretAccessKey GHIJKILM/12345XYZ --region us-east-1

Note that you also can pass credentials via configuration file or environment variables, as with any [AWS tool] aws-credentials.

Useful resources

• Iglu schema repository 0.1.0 release blog post
• Iglu central - centralized registry for all the schemas hosted by the Snowplow team
• Iglu - respository with both Iglu server and client libraries

3. Creating the JSON Path files and SQL table definitions

Once you've defined the jsonschema for your new event or context type you need to create a correpsonding jsonpath file and sql table definition. This can be done programmatically using Igluctl. From the root of the repo:

/path/to/igluctl static generate --with-json-paths /path/to/schemas/com.mycompany/new_event_or_context_name

A corresponding jsonpath file and sql table definition file will be generated in the appropriate folder in the repo.

Note that you can create SQL table definition and jsonpath files for all the events / contexts schema'd as follows:

/path/to/igluctl static generate --with-json-paths /path/to/schemas/com.mycompany

4. Uploading the jsonpath files to Iglu

Once you've finalized the new jsonpath file, commit it to Git. From the project root:

git add .
git commit -m "Committed finalized jsonpath"
git push

Then push to Iglu:

$ /path/to/igluctl static s3cp ./jsonpaths snowplow-com-mycompany-iglu-jsonpaths-bucket --accessKeyId ABCDEF --secretAccessKey GHIJKILM/12345XYZ --region us-east-1

5. Creating or updating the table definition in Redshift

Once you've committed your updated table definition into Github, you need to either create or modify the table in Redshift, either by executing the CREATE TABLE... statement directly, or ALTER TABLE (if you're e.g. adding a column to an existing table).

Note that it is essential that any new tables you create are owned by the storageloader user. This is the user that we use to load and model data in Redshift. Once you've created your new table:

CREATE TABLE my_new_table...

Please run the following statement to assign ownership of it to storageloader

ALTER TABLE my_new_table OWNER TO storageloader;

6. Sending data into Snowplow using the schema reference as custom unstructured events or contexts

Once you have gone through the above process, you can start sending data that conforms to the schema(s) you've created into Snowplow as unstructured events and custom contexts.

In both cases (custom unstructured events and contexts), the data is sent in as a JSON with two fields, a schema field with a reference to the location of the schema in Iglu, and a data field, with the actual data being sent, e.g.

{
    "schema": "iglu: com.acme_company/viewed_product/jsonschema/2-0-0",
    "data": {
        "productId": "ASO01043",
        "category": "Dresses",
        "brand": "ACME",
        "price": 49.95,
        "sizes": [
            "xs",
            "s",
            "l",
            "xl",
            "xxl"
        ],
        "availableSince": "2013-03-07"
    }
}

For more detail, please see the technical documentation for the specific tracker you're implementing.

Note: we recommend testing that the data you're sending into Snowplow conforms to the schemas you've defined and uploaded into Iglu, before pushing updates into production. This online JSON schema validator is a very useful resource for doing so.

We also recommend testing that the events are sent successfully using Snowplow-Mini. You do this by configuring the collector in the tracker to $SNOWPLOW_MINI_IP:8080 and then logging onto http://$SNOWPLOW_MINI_IP to review the results e.g. in Kibana. (Follow the links on the page.) Note that you need to have your IP whitelisted before you can view data on Snowplow-mini.

7. Managing schema migrations

When you use Snowplow, the schema for each event and context lives with the data. That means you have the flexibility to evolve your schema definition over time.

If you want to change your schema over time, you will need to:

1. Create a new jsonschema file. Depending on how different this is to your current version, you will need to give it the appropriate version number. The SchemaVer specification we use when versioning data schemas can be found here
2. Rerun igluctl static generate --with-json-paths to update the jsonpath file, sql table definition and create a SQL migration script. Note that you'll need to add the --force flag to ensure that the updated jsonpath and sql table definition files overwrite the existing files.
3. Start sending data into Snowplow using the new schema version (i.e. update the Iglu reference to point at the new version e.g. 2-0-0 or 1-0-1 rather than 1-0-0). Note that you will continue to be able to send in data that conforms to the old schema at the same time. In the event that you have an event with two different major schema definitions, each event version will be loaded into a different Redshift table

Additional resources

Documentation on jsonschemas:

• Other example jsonschemas can be found in Iglu Central. Note how schemas are namespaced in different folders
• [Schema Guru] schema-guru-online is an [online] schema-guru-online and [command line tool] schema-guru-github for programmatically generating schemas from existing JSON data
• Snowplow 0.9.5 release blog post, which gives an overview of the way that Snowplow uses jsonschemas to process, validate and shred unstructured event and custom context JSONs
• It can be useful to test jsonschemas using online validators e.g. this one
• json-schema.org contains links to the actual jsonschema specification, examples and guide for schema authors
• The original specification for self-describing JSONs, produced by the Snowplow team, can be found here

Documentation on jsonpaths:

• Example jsonpath files can be found on the Snowplow repo. Note that the corresponding jsonschema definitions are stored in Iglu central
• Amazon documentation on jsonpath files can be found here

Documentaiton on creating tables in Redshift:

• Example Redshift table definitions can be found on the Snowplow repo. Note that corresponding jsonschema definitions are stored in Iglu central
• Amazon documentation on Redshift create table statements can be found here. A list of Redshift data types can be found here