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 road_traffic_counts_in_colorado_2019 table in this repository, by referencing it like:
"colorado-gov/road-traffic-counts-in-colorado-2019-4j38-u7sj:latest"."road_traffic_counts_in_colorado_2019"or in a full query, like:
SELECT
":id", -- Socrata column ID
"urban", -- The appropriate urbanized area code, depending on which urbanized area the section mileage falls within. City FIPS code is used for small urban cities (5,000-50,000 population).
"to_descr", -- To Description
"thrulnwd", -- Through lane width in feet
"thrulnqty", -- Through Lane Quantity
"surfwd", -- Surface width
"specialsys", -- A code that indicates whether the roadway segment is on the Strategic Highway Network (STRAHNET) or is a STRAHNET connector. STRAHNET is used by the Department of Defense (DOD) to identify strategic deployment routes.
"segmid", -- Road segment ID
"segmdir", -- Compass direction
"seg_length", -- Segment length in miles
"runlength_", -- Beginning measure in actual measured mileage
"runlength1", -- Ending measure in actual measured mileage
"routename", -- Name of street
"route", -- Route identifier
"population", -- A numeric code, established by the U.S. Bureau of census, which categorizes a geographic area by the population count.
"nhsdesig", -- The road segment has been designated as being part of the National Highway System
"lrsroute", -- CDOT local route identifier
"jursplit", -- Denotes a Road Segment with a shared Maintenance Agreement.
"iri", -- International Roughness Index for pavement
"gisid", -- Identifier used internal to GRDMS (roadway) database
"fundid", -- Funding Identifier
"funcclassi", -- Functional class identifier
"from_descr", -- From Description
"forestrout", -- A numbering system establlished by the U.S. Forest Service to identify national Forest access roads.
"fipscounty", -- Federal Information Processing Standard (FIPS) code for county
"fips", -- Federal Information Processing Standard (FIPS) code for city, or for county if not in within a city
"countstati", -- Numeric Designation for the location where data are collected
"builtyr", -- Date new roads began receiving Highway User Tax Funds (HUTF)
"adminclass", -- Administrative Class
"aadtderiv", -- Method of how the AADT was determined.
"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.)
"surf_type", -- Roadway surface type
"aadtyr", -- The calendar year (YYYY) in which the average daily traffic count was taken for the highway segment
"govlevel", -- A designation of the level of government responsible for the naming of the segment of the road and establish traffic controls on the segment as defined by FHWA.
"operation", -- One-way or two-way operation
"psi", -- Present Serviceability Index for pavement
"surfname", -- Surface name
"hpmsid", -- Highway Performance Monitoring System ID
"the_geom"
FROM
"colorado-gov/road-traffic-counts-in-colorado-2019-4j38-u7sj:latest"."road_traffic_counts_in_colorado_2019"
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/road-traffic-counts-in-colorado-2019-4j38-u7sj 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/road-traffic-counts-in-colorado-2019-4j38-u7sj: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/road-traffic-counts-in-colorado-2019-4j38-u7sjCheckout 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/road-traffic-counts-in-colorado-2019-4j38-u7sj:latestThis will download all the objects for the latest tag of colorado-gov/road-traffic-counts-in-colorado-2019-4j38-u7sj 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/road-traffic-counts-in-colorado-2019-4j38-u7sj: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/road-traffic-counts-in-colorado-2019-4j38-u7sj: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/road-traffic-counts-in-colorado-2019-4j38-u7sj is just another Postgres schema.