Query the Data Delivery Network
Query the DDNThe 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 highway_traffic_counts_in_colorado_2022 table in this repository, by referencing it like:
"colorado-gov/highway-traffic-counts-in-colorado-2022-u2hn-4r65:latest"."highway_traffic_counts_in_colorado_2022"or in a full query, like:
SELECT
":id", -- Socrata column ID
"yr20factor", -- A traffic forecasting statistic that when multiplied by the current AADT yields an estimate of AADT for 20 years in the future
"vcratio20", -- The estimate of the hourly V/C ratio for 20 years from the base year
"vcratio", -- The hourly traffic volume divided by the capacity of the highway segment.
"updateyr", -- The calendar year in which the database record was updated.
"shape_length", -- Length of feature in internal units.
"seasonalgroupid", -- Numeric Designation to adjust short term data for seasonal variation
"runlength_to", -- Ending measure in actual measured mileage
"runlength_from", -- Beginning measure in actual measured mileage
"routecapac", -- Route Capacity - Hourly maximum service flow at a level of service E per 1985 Highway Capacity Manual
"route", -- The number designating the State Route, including a section (log) identifier designated by a letter for the State database.
"roadterrain", -- The predominant type of terrain through which the road passes
"refpt", -- Beginning reference point for the highway segment, measured in miles.
"pctoffpksu", -- Percent Average Daily Single Unit Trucks - Percentage of AADT for Single Unit Trucks During Off-Peak Periods
"pctoffpkcomb", -- Percent Average Daily Combination Unit Trucks - Percentage of AADT for Combination Trucks During Off-Peak Periods
"pctdhsu", -- Percent Design Hour Single Unit Trucks
"pctdhcomb", -- Percent Design Hour Combination Unit Trucks
"offpktrk", -- The Off-Peak percent of AADT that is composed of trucks of all types, traveling along the highway segment. Measured in percentage.
"objectid", -- Esri Internal feature number, Sequential unique whole numbers that are automatically generated.
"length_", -- Segment length in miles.
"isramp", -- Indicates if count on a ramp
"endrefpt", -- Ending reference point for the highway segment, measured in miles.
"dhv", -- Design Hour Volume - The 30th highest annual hourly traffic volume of AADT count
"dhtrk", -- Design Hour Percent of AADT that is composed of trucks of all types, traveling along the highway segment.
"dd", -- Directional Distribution - The percentage of the design hour value flowing in the peak direction
"countyear", -- Year of traffic count
"countstationid", -- Numeric Designation for the location where data are collected
"calyr", -- The calendar year (YYYY) in which the average daily traffic count was taken for the highway segment
"aadtyr", -- The calendar year (YYYY) in which the average daily traffic count was taken for the highway segment
"aadtsingle", -- The Average Daily Traffic count that consists of trucks on a single chassis, excluding pickups and vans. Measured as a count.
"aadtderiv", -- Method of how the AADT was determined.
"aadtcomb", -- The Average Daily Traffic count that consists of trucks on multiple chassis (combined units), excluding pickups and vans. Measured as a count
"aadt", -- The annual average daily traffic count for the highway segment, in both directions, representing an average 24-hour day in a year. (Total of all vehicles counted divided by 365.)
"aadttrucks", -- Annual Average Daily Traffic for Trucks
"edla" -- Equivalent Daily Load Axle
FROM
"colorado-gov/highway-traffic-counts-in-colorado-2022-u2hn-4r65:latest"."highway_traffic_counts_in_colorado_2022"
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 colorado-gov/highway-traffic-counts-in-colorado-2022-u2hn-4r65 with SQL in under 60 seconds.
Query Your Local Engine
bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"Read the installation docs.
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, 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 (like this repository), where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Cloning Data
Because colorado-gov/highway-traffic-counts-in-colorado-2022-u2hn-4r65:latest is a Splitgraph Image, you can clone the data from Spltgraph Cloud to your local engine, where you can query it like any other Postgres database, using any of your existing tools.
First, install Splitgraph if you haven't already.
Clone the metadata with sgr clone
This will be quick, and does not download the actual data.
sgr clone colorado-gov/highway-traffic-counts-in-colorado-2022-u2hn-4r65Checkout the data
Once you've cloned the data, you need to "checkout" the tag that you want. For example, to checkout the latest tag:
sgr checkout colorado-gov/highway-traffic-counts-in-colorado-2022-u2hn-4r65:latestThis will download all the objects for the latest tag of colorado-gov/highway-traffic-counts-in-colorado-2022-u2hn-4r65 and load them into the Splitgraph Engine. Depending on your connection speed and the size of the data, you will need to wait for the checkout to complete. Once it's complete, you will be able to query the data like you would any other Postgres database.
Alternatively, use "layered checkout" to avoid downloading all the data
The data in colorado-gov/highway-traffic-counts-in-colorado-2022-u2hn-4r65:latest is 0 bytes. If this is too big to download all at once, or perhaps you only need to query a subset of it, you can use a layered checkout.:
sgr checkout --layered colorado-gov/highway-traffic-counts-in-colorado-2022-u2hn-4r65:latestThis will not download all the data, but it will create a schema comprised of foreign tables, that you can query as you would any other data. Splitgraph will lazily download the required objects as you query the data. In some cases, this might be faster or more efficient than a regular checkout.
Read the layered querying documentation to learn about when and why you might want to use layered queries.
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, colorado-gov/highway-traffic-counts-in-colorado-2022-u2hn-4r65 is just another Postgres schema.