Pipes
Build features over your data
Pipes are a sequence of one or more SQL queries (Nodes) that are executed in order, and they result in either a Published or Materialized pipe.
This page dives deeper into the structure and usage of individual pipes. For an introduction to how pipes are composed into pipelines, see pipelines.
UI
Creating Draft pipe
Navigate to “Pipes” on the left menu column, and click on ”+”: Enter a name for this query, then press “Create”.
Here, you have a node where you can enter a single SQL SELECT
statement to query your tables directly.
These SQL statements can interact with your tables in the same way as standard SQL tables, with additional AI columns you can leverage for deeper insights.
For example, to get the top referrers as in our quickstart example we can run the following command.
We can run this by clicking on “Run”:
This is your Draft pipe, you can run SQL queries against your database for testing sql’s you can use the draft pipe.
Creating Published pipe
Click on “Save” to save this node, and “Publish” to publish it as an endpoint:
That’s it, your draft pipe just became a publihsed pipe, you can go to view endpoints and use any of the Javascript, Python or curl command to ping the endpoint.
Creating Incremental Materialized pipe
Let’s say we want to get the signup, purchases and page_views metrics along with total_events from the web events data. we could run the query below to get the metrics per day.
This runs fine for smaller dataset, but as no. of rows increases, the above query will take large amount of time to query all the rows and calculate the metrics. We can use an incremental materialized pipe here, and store the aggregated data on a new source as the data is being inserted and have the API query the new source. This significantly decreases the number of rows queries and hence the time taken for the query to run.
Note the materialized pipe example uses countIfState()
instead of countIf()
.
Airfold uses ClickHouse under the hood which requires appending State
to aggregate functions when used in a materialized pipe.
Fortunately, Airfold does it automatically when materializing a draft pipe using af materialize <pipe_name>
.
Learn more about Materialization in ClickHouse, about the *State
combinator.
Creating Refreshable Materialized pipe
Let’s consider example, if we want to get the top_pages visited by unique visitors per page_url, we can do it by the following command.
This will eventually take a long time to load when the data increases, this computation should be performed on the whole dataset and hence we will use refreshable materialized pipe.
CLI
Creating Draft pipe
These are the starting point for all pipes, similar to database views in functionality.
They cannot be referenced by other pipes using the FROM
clause.
Draft pipes are temporary and primarily used for development. Once finalized, a draft pipe can be transitioned into either a published or a materialized pipe, but not both.
A brief description of the pipe’s purpose or functionality. Optional.
The sequence of nodes within the pipe. Each node represents a stage in data processing.
Specifies the target source for appending the incremental results. When set, it converts the pipe into a materialized pipe. This option cannot be used with publish
and requires the final node’s schema to match the provided source schema. Optional.
The endpoint name where the results are published, turning the pipe into a published pipe. This option cannot be used with to
. Optional.
A list of parameters for parameterizing node SQL queries through Jinja2 templating e.g. {{ param }}
. These parameters are passed via the API and can be used to dynamically alter the pipe’s behavior. Optional.
A refresh schedule definition. Used in refreshable materialization where the pipe is executed on schedule. Optional.
Creating Published pipes
This creates an API endpoint that serves the result of your pipe, the result is accessible in JSON, NDJSON, CSV, and Parquet formats.
To publish a draft pipe, assign an endpoint name to the publish
field.
Published pipes can parameterize their SQL using Jinja2 via the params
field.
Unlike draft pipes, published pipes can be accessed (via FROM
) by other pipes.
Creating Incremental Materialized pipe
Differing from the batch nature of draft and published pipes, materialized pipes stand out for their ability to incrementally transform data as it is ingested and append their results to a designated source.
To materialize a draft pipe, set the to
field to the name of the target source.
Although materialized pipes themselves cannot be accessed (via FROM
) by other pipes, the source they write to is accessible like any other source.
Note the materialized pipe example uses countState()
instead of count()
.
Airfold uses ClickHouse under the hood which requires appending State
to aggregate functions when used in a materialized pipe.
Fortunately, Airfold does it automatically when materializing a draft pipe using af materialize <pipe_name>
.
Learn more about Materialization in ClickHouse, about the *State
combinator.
Best Practices
Published pipes operate in batch mode and compute their results on read; therefore, they are not suited for intensive data transformations.
Instead, a common pattern is to place a published pipe in front of a materialized pipe. The materialized pipe incrementally transforms data as it is ingested and writes it to the source, enabling the published pipe to instantly access the pre-transformed data during reads.
Creating Refreshable Materialized pipe
Some sources are batch in nature, like prices
table or products
table.
And may be refreshed from a connector or any other external process.
To work with these sources efficiently and create transformations/queries use a "refreshable"
materialization.
Push
Push pipes to your workspace using the CLI command af push
.
For example, to push draft_pipe.yaml
: