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 2009_yellow_taxi_trip_data table in this repository, by referencing it like:

"cityofnewyork-us/2009-yellow-taxi-trip-data-f9tw-8p66:latest"."2009_yellow_taxi_trip_data"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "ratecodeid", -- The final rate code in effect at the end of the trip. 1= Standard rate 2=JFK 3=Newark 4=Nassau or Westchester 5=Negotiated fare 6=Group ride
    "pickup_latitude", -- Latitude where the meter was engaged.
    "fare_amount", -- The time-and-distance fare calculated by the meter.
    "extra", -- Miscellaneous extras and surcharges. Currently, this only includes the $0.50 and $1 rush hour and overnight charges.
    "tpep_dropoff_datetime", -- The date and time when the meter was disengaged.
    "trip_distance", -- The elapsed trip distance in miles reported by the taximeter.
    "tolls_amount", -- Total amount of all tolls paid in trip.
    "pickup_location",
    "pickup_longitude", -- Longitude where the meter was engaged.
    "dropoff_location",
    "dropoff_longitude", -- Longitude where the meter was disengaged.
    "passenger_count", -- The number of passengers in the vehicle. This is a driver-entered value.
    "tpep_pickup_datetime", -- The date and time when the meter was engaged.
    "total_amount", -- The total amount charged to passengers. Does not include cash tips.
    "mta_tax", -- $0.50 MTA tax that is automatically triggered based on the metered rate in use.
    "tip_amount", -- Tip amount – This field is automatically populated for credit card tips. Cash tips are not included.
    "dropoff_latitude", -- Latitude where the meter was disengaged.
    "payment_type",
    "store_and_fwd_flag", -- This flag indicates whether the trip record was held in vehicle memory before sending to the vendor, aka “store and forward,” because the vehicle did not have a connection to the server. Y= store and forward trip N= not a store and forward trip
    "vendorid" -- A designation for the technology vendor that provided the record. CMT=Creative Mobile Technologies VTS= VeriFone, Inc. DDS=Digital Dispatch Systems
FROM
    "cityofnewyork-us/2009-yellow-taxi-trip-data-f9tw-8p66:latest"."2009_yellow_taxi_trip_data"
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 cityofnewyork-us/2009-yellow-taxi-trip-data-f9tw-8p66 with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.cityofnewyork.us. When you querycityofnewyork-us/2009-yellow-taxi-trip-data-f9tw-8p66: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.cityofnewyork.us, 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 \
  "cityofnewyork-us/2009-yellow-taxi-trip-data-f9tw-8p66" \
  --handler-options '{
    "domain": "data.cityofnewyork.us",
    "tables": {
        "2009_yellow_taxi_trip_data": "f9tw-8p66"
    }
}'

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, cityofnewyork-us/2009-yellow-taxi-trip-data-f9tw-8p66 is just another Postgres schema.