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 annual_average_daily_traffic_aadt_beginning_1977 table in this repository, by referencing it like:

"ny-gov/annual-average-daily-traffic-aadt-beginning-1977-6amx-2pbv:latest"."annual_average_daily_traffic_aadt_beginning_1977"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "count", -- Annual Average Daily Traffic (AADT) volume value: On any given day, the average numbers of vehicles that pass the segment of roadway as described by the Begin/End Descriptions. These figures are based on an actual count taken for that year or statistically validated estimated and forecasted values.
    "ending_description", -- The verbal description of the end of the segment of roadway for which the AADT applies
    "municipality", -- Name of the municipality in which the AADT was calculated
    "length", -- The length of the road segment to which the count station applies.
    "fc", -- The functional class of the roadway segment to which the count station applies. For more information on functional class, consult the FHWA link below. https://www.dot.ny.gov/gisapps/functional-class-maps
    "bridge", -- Signifies by either a “Y” for yes or blank for no if a bridge is included in the roadway segment.
    "oneway", -- Signifies by either a “Y” for yes or blank for no if the roadway segment is a one-way direction.
    "beginning_description", -- The verbal description of the start of the segment of roadway for which the AADT applies
    "rr_xing", -- Signifies by either a “Y” for yes or blank for no if a railroad crossing is included in the roadway segment.
    "aadt_year", -- Year for which the annual average daily traffic (AADT) volume value is recorded.
    "station_id", -- Unique identifier for each traffic count station concatenated from the region county code and the station number.
    "ramp", -- Signifies by either a “Y” for yes or blank for no if a ramp is included in the roadway segment.
    "county", -- The county where the count station exists.
    "signing", -- Designates what kind of a route or roadway the segment of roadway is. • Interstate: Interstate designation commonly known as the Interstate Highway System, a network of controlled-access highways that forms a part of the National Highway System. • US: US designation of a set of roads typically called U.S. Routes or U.S. Highways which forms an integrated network of roads and highways numbered within a nationwide grid in the United States. • NY: New York State Highway designation assigned at the State level. A blank field designates a ‘Local’ town, municipality or city roadway.
    "state_route", -- A road designated with an Interstate or NY route number.
    "county_road", -- A road designated with a county-level road number.
    "road_name" -- The name of the road.
FROM
    "ny-gov/annual-average-daily-traffic-aadt-beginning-1977-6amx-2pbv:latest"."annual_average_daily_traffic_aadt_beginning_1977"
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 ny-gov/annual-average-daily-traffic-aadt-beginning-1977-6amx-2pbv with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.ny.gov. When you queryny-gov/annual-average-daily-traffic-aadt-beginning-1977-6amx-2pbv: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.ny.gov, 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 \
  "ny-gov/annual-average-daily-traffic-aadt-beginning-1977-6amx-2pbv" \
  --handler-options '{
    "domain": "data.ny.gov",
    "tables": {
        "annual_average_daily_traffic_aadt_beginning_1977": "6amx-2pbv"
    }
}'

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, ny-gov/annual-average-daily-traffic-aadt-beginning-1977-6amx-2pbv is just another Postgres schema.