Query the Data Delivery Network

The easiest way to query any data on Splitgraph is via the "Data Delivery Network" (DDN). The DDN is a single endpoint that speaks the PostgreSQL wire protocol. Any Splitgraph user can connect to it at data.splitgraph.com:5432 and query any version of over 40,000 datasets that are hosted or proxied by Splitgraph.

For example, you can query the 2019_kansas_city_energy_and_water_consumption table in this repository, by referencing it like:

"kcmo/2019-kansas-city-energy-and-water-consumption-p8a5-sdg4:latest"."2019_kansas_city_energy_and_water_consumption"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "water_use_all_water_sources", -- Water metric from indoor, outdoor, and mixed meters
    "year_ending", -- The last date in the full year period in which metrics are taken
    "state_province", -- State the building is located in: Missouri
    "property_gfa_calculated_2", -- Total Parking SQFT (does not include building SQFT)
    "property_name", -- Building name assigned by point of contact for property
    "street_address", -- Street Address
    "city", -- City that the building is located in: Kansas City Missouri.
    "postal_code", -- The zip code the building is located in.
    "kansas_city_building_reporting", -- Unique (with a few exceptions) ID that identifies the building being submitted.
    "primary_property_type_self", -- A self-selected main property use type
    "primary_property_type", -- A calculated property use type based of the use that has the most SQFT assigned to it.
    "property_gfa_calculated", -- Total SQFT between the building and parking
    "property_gfa_calculated_1", -- Total building SQFT (does not include parking)
    "weather_normalized_source", -- Energy use per SQFT that has been normalized to account for both weather and source of energy
    "weather_normalized_site_eui", -- Energy use per SQFT that has been normalized to account for weather. compared to Site, this is more reflective of what you'd find on a utility billing statement.
    "direct_ghg_emissions_metric", -- Direct GHG (low amounts tend to round to 0)
    "indirect_ghg_emissions_metric", -- Indirect GHG (low amounts tend to round to 0)
    "energy_star_score" -- A score generated by the national Energy Star Program used to compare the building to other similar building types. The score is out of 1-100 where 50 is the median and 75 is at the upper quartile. Not all buildings are eligible for a score.
FROM
    "kcmo/2019-kansas-city-energy-and-water-consumption-p8a5-sdg4:latest"."2019_kansas_city_energy_and_water_consumption"
LIMIT 100;

Connecting to the DDN is easy. All you need is an existing SQL client that can connect to Postgres. As long as you have a SQL client ready, you'll be able to query kcmo/2019-kansas-city-energy-and-water-consumption-p8a5-sdg4 with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.kcmo.org. When you querykcmo/2019-kansas-city-energy-and-water-consumption-p8a5-sdg4:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).

We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.

Query Your Local Engine

bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"

Splitgraph Cloud is built around Splitgraph Core (GitHub), which includes a local Splitgraph Engine packaged as a Docker image. Splitgraph Cloud is basically a scaled-up version of that local Engine. When you query the Data Delivery Network or the REST API, we mount the relevant datasets in an Engine on our servers and execute your query on it.

It's possible to run this engine locally. You'll need a Mac, Windows or Linux system to install sgr, and a Docker installation to run the engine. You don't need to know how to actually use Docker; sgrcan manage the image, container and volume for you.

There are a few ways to ingest data into the local engine.

For external repositories (like this repository), the Splitgraph Engine can "mount" upstream data sources by using sgr mount. This feature is built around Postgres Foreign Data Wrappers (FDW). You can write custom "mount handlers" for any upstream data source. For an example, we blogged about making a custom mount handler for HackerNews stories.

For hosted datasets, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.

Mounting Data

This repository is an external repository. It's not hosted by Splitgraph. It is hosted by data.kcmo.org, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.

First, install Splitgraph if you haven't already.

Mount the table with sgr mount

sgr mount socrata \
  "kcmo/2019-kansas-city-energy-and-water-consumption-p8a5-sdg4" \
  --handler-options '{
    "domain": "data.kcmo.org",
    "tables": {
        "2019_kansas_city_energy_and_water_consumption": "p8a5-sdg4"
    }
}'

That's it! Now you can query the data in the mounted table like any other Postgres table.

Query the data with your existing tools

Once you've loaded the data into your local Splitgraph engine, you can query it with any of your existing tools. As far as they're concerned, kcmo/2019-kansas-city-energy-and-water-consumption-p8a5-sdg4 is just another Postgres schema.