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Schema registry

Ihor Tomilenko edited this page Apr 27, 2017 · 4 revisions

HOME » EVENTS AND CONTEXTS » Event Dictionary » Schema Registry


Before you can send your own event and context types into Snowplow using the track self-describing events (also called unstructured 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 repository
  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 self-describing events or custom contexts.


We recommend setting up the following 3 tools before staring:

  1. Git so you can easily clone the repo and make updates to it
  2. Schema Guru. This will auto-generate your jsonpath and sql table definitions
  3. The AWS CLI. This will make it easy to push updates to Iglu at the command line.

Building schema registry

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
  3. Save the file schema

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.

2. Uploading the schemas to Iglu

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

This can be done directly via the AWS CLI. In the project root, first commit the schema to Git:

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

Then push it to Iglu. Note that as a trial user you will have to ask the Snowplow team to do this for you. As a Managed Services customer you would be able to do it yourself as follows:

aws s3 cp schemas s3://snowplow-company-name-iglu-schemas/schemas --include "*" --recursive

Note you'll need to update the above command to replace the bucket s3://snowplow-company-name-iglu-schemas with your S3 backed Iglu static server repo.

Useful resources

3. Creating the jsonpath files and SQL table definitions

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

/path/to/schema-guru-0.5.0 ddl --with-json-paths 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 schemas as follows:

/path/to/schema-guru-0.5.0 ddl --with-json-paths schemas/com.mycompany

4. Uploading the jsonpath files to Iglu

One 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. Again, you can only do this yourself as a Managed Services customers. As a trial user you will need to ask a member of the Snowplow Analytics team to do this for you.

aws s3 cp jsonpaths s3://snowplow-company-name-iglu-jsonpaths/jsonpaths --include "*" --recursive

Note you'll need to update the s3 location s3://snowplow-company-name-iglu-jsonpaths/jsonpaths with the s3 location you've configured Snowplow to fetch jsonpath files from.

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

6. Sending data into Snowplow using the schema reference as self-describing 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 self-describing events (also called unstructured events) and custom contexts.

In both cases (self-describing events and custom 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": [
        "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.

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. Update the corresponding jsonpath files. If you've created a new major schema version, you'll need to create a new jsonpath file e.g. exmaple_event_2.json, that exists alongside your existing example_event_1.json.
  3. For minor schema updates, you should be able to update your existing Redshift table definition e.g. to add add additional columns. For major schema updates, you'll need to create a new Redshift table definition e.g. com_mycompany_exmaple_event_2.sql.
  4. 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.

Further reading

To find out more about the concepts mentioned above and ultimately how to set up custom events and contexts and send them to Snowplow pipeline, follow the links below.

Documentation on jsonschemas:

  • Other example jsonschemas can be found in Iglu Central. Note how schemas are namespaced in different folders.
  • Schema Guru is an online and command line tool 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.
  • contains links to the actual jsonschema specification, examples and guide for schema authors.
  • The original specification for 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.

Documentation 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.
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