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_quality_in_colorado_2017 table in this repository, by referencing it like:
"colorado-gov/highway-quality-in-colorado-2017-7amf-p4uj:latest"."highway_quality_in_colorado_2017"or in a full query, like:
SELECT
":id", -- Socrata column ID
"fatg", -- The amount of fatigue normalized on a scale of 0-100 (where 0 is the worst pavement in existence and 100 is a perfect pavement)
"iri", -- International roughness index (IRI) correlates somewhat with human exposure to whole-body vibration in vehicles and thus to perceived ride quality reading for the surface condition in the Primary Direction of Travel. Scaled to a score of 1-100.
"crbk", -- The amount of corner breaks normalized on a scale of 0-100 (where 0 is the worst pavement in existence and 100 is a perfect pavement)
"tran", -- The amount of transverse cracking normalized on a scale of 0-100 (where 0 is the worst pavement in existence and 100 is a perfect pavement)
"rut", -- A value between 0 and 100 that is used to calculate Remaining Service Life for rutting. A value of 100 indicates .15 inch or less rutting. A value of 50 is the threshold that indicates no more remaining service life. This occurs at an average rut depth of .55 inches.
"depth", -- Thickness Classifications: 1 - Asphalt <4" thick or Concrete <8" thick, 2 - Asphalt <6" thick or Concrete >=8" thick, 3 - Asphalt >= 6" thick
"hwy", -- A Unique (3 Number - 1 Letter) Highway Identification Code or Designating for a State Highway, Business Route, U.S. Route or Tolled Facility. Odd numbers typically run North and South and Even numbers typically Run East and West
"ptyp", -- Pavement Type Classifications: 1 - Asphalt, 2 - Asphalt over concrete, 3 - Concrete, 4 - Concrete over asphalt
"mtcd", -- For CDOT internal use
"long", -- The amount of logitudinal cracking normalized on a scale of 0-100 (where 0 is the worst pavement in existence and 100 is a perfect pavement)
"dlidx", -- For CDOT internal use
"curve", -- For CDOT internal use
"nhs", -- A Domained Value Element (NHSDesig: 0-9) used to identify whether the Road Segment is designated as being part of the National Highway System. Also contains the text value (added at the end)
"cond", -- Condition of the road: LOW, MODERATE, HIGH
"mtcy", -- For CDOT internal use
"bmp", -- Beginning milepoint
"emp", -- Ending milepoint
"dl", -- The drivability life, which is the number of years a pavement has left until it is no longer an acceptable driving surface
"trafz", -- Traffic Classifications: 1 - Low (<0.3 million design equivalent single access load [ESAL]), 2 - Medium (0.3 - 3 million ESAL), 3 - High (3 - 10 million ESAL), 4 - Very High (10 - 30 million ESAL), 5 - Very Very High (> 30 million ESAL)
"length", -- Length of highway segment (in miles)
"year", -- Year finished
"county", -- Name of the county the road segment lies in
"numlanes", -- Number of lanes in the road segment
"envz", -- Climate Classifications: 1 - Very cool (< 81 degrees), 2 - Cool (81 - 88), 3 - Moderate (88 - 97), 4 - Hot (>97)
"funcl", -- Indicates the functional category and usage limitations of the segment of road, as defined by FHWA, and is broken down between rural and urban areas.
"region", -- A Domained Value Element (Region: 1-6) used to identify the Engineering Region number in which the Road Segment is located
"dir", -- Traffic flow direction
"pgrp" -- Pavement group, which is a combination of the pavement, traffic, and climate classifications. 1323 means Asphalt, High Traffic, Cool Environment, >= 6" Thick
FROM
"colorado-gov/highway-quality-in-colorado-2017-7amf-p4uj:latest"."highway_quality_in_colorado_2017"
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-quality-in-colorado-2017-7amf-p4uj 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-quality-in-colorado-2017-7amf-p4uj: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-quality-in-colorado-2017-7amf-p4ujCheckout 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-quality-in-colorado-2017-7amf-p4uj:latestThis will download all the objects for the latest tag of colorado-gov/highway-quality-in-colorado-2017-7amf-p4uj 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-quality-in-colorado-2017-7amf-p4uj: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-quality-in-colorado-2017-7amf-p4uj: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-quality-in-colorado-2017-7amf-p4uj is just another Postgres schema.