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 alternative_fuel_stations_in_us table in this repository, by referencing it like:
"sharefulton-fultoncountyga-gov/alternative-fuel-stations-in-us-3m27-geqj:latest"."alternative_fuel_stations_in_us"or in a full query, like:
SELECT
":id", -- Socrata column ID
"ownertypecode", -- The type of organization that owns the fueling infrastructure. Owner types are given as code values as described below: FG=Federal Government Owned; J=Jointly Owned; LG=Local/Municipal Government Owned; P=Privately Owned; SG=State/Provincial Government Owned; T=Utility Owned
"expecteddate", -- For planned stations, the date the station is expected to open or start carrying alternative fuel. For temporarily unavailable stations, the date the station is expected to reopen. This date is estimated.
"updatedat", -- The time the station's details were last updated
"opendate", -- The date that the station began offering the fuel. Note that most propane (LPG) stations do not have open dates. Some open dates are approximate. For electric vehicle charging stations added to the Station Locator through automated data feeds from charging networks, this is either the date provided by the charging network or the date it appeared in the Station Locator if the charging network does not provide an open date.
"facilitytype", -- The type of facility at which the station is located.
"evpricing", -- For electric stations, information about whether and how much users must pay to use the EVSE port.
"y", -- The latitude of geocoded lcoation.
"geoid", -- The state and county FIPS code.
"county", -- The name of the county of lcocation.
"population", -- The population of th county of location.
"location", -- The point geometry.
"zip", -- The ZIP code (postal code) of the station's location.
"state", -- The two character code for the U.S. state or Canadian province/territory of the station's location.
"city", -- The city of the station's location.
"streetaddress", -- The name of the station.
"stationname",
"fueltypecode", -- The type of alternative fuel the station provides. Fuel types are given as code values as described below:BD=Biodiesel (B20 and above); CNG=Compressed Natural Gas (CNG); ELEC=Electric; E85=Ethanol (E85); HY=Hydrogen; LNG=Liquefied Natural Gas (LNG); LPG=Propane (LPG)
"id",
"x", -- The longitude of the geocoded location.
"geocodestatus", -- A rating indicating the approximate accuracy of the latitude and longitude for the station's address, given as code values as described below: GPS=GPS; 200-9=Point; 200-8=Address; 200-7=Intersection; 200-6=Street; 200-5=Neighborhood; 200-5=Postal Code - Extended; 200-5=Postal Code; 200-4=City/Town; 200-3=County; 200-2=State/Province; 200-1=Country; 200-0=Unknown
":@computed_region_qwgy_fhiq",
":@computed_region_e962_7nez", -- This column was automatically created in order to record in what polygon from the dataset 'Fulton County Boundary' (e962-7nez) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_xni8_taep", -- This column was automatically created in order to record in what polygon from the dataset 'Zip Codes' (xni8-taep) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_25hj_jisj", -- This column was automatically created in order to record in what polygon from the dataset 'City Limits' (25hj-jisj) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_ieh4_ywrm", -- This column was automatically created in order to record in what polygon from the dataset 'Commission Districts' (ieh4-ywrm) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"accesscode", -- A description of who is allowed to access the station, given as code values as described below: public=Public; private=Private
"groupswithaccesscode", -- A description of who is allowed to access the station and other station access information.
"statuscode" -- The current status of the station, given as code values as described below: E=Available; P=Planned; T=Temporarily Unavailable
FROM
"sharefulton-fultoncountyga-gov/alternative-fuel-stations-in-us-3m27-geqj:latest"."alternative_fuel_stations_in_us"
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 sharefulton-fultoncountyga-gov/alternative-fuel-stations-in-us-3m27-geqj 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 sharefulton-fultoncountyga-gov/alternative-fuel-stations-in-us-3m27-geqj: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 sharefulton-fultoncountyga-gov/alternative-fuel-stations-in-us-3m27-geqjCheckout 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 sharefulton-fultoncountyga-gov/alternative-fuel-stations-in-us-3m27-geqj:latestThis will download all the objects for the latest tag of sharefulton-fultoncountyga-gov/alternative-fuel-stations-in-us-3m27-geqj 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 sharefulton-fultoncountyga-gov/alternative-fuel-stations-in-us-3m27-geqj: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 sharefulton-fultoncountyga-gov/alternative-fuel-stations-in-us-3m27-geqj: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, sharefulton-fultoncountyga-gov/alternative-fuel-stations-in-us-3m27-geqj is just another Postgres schema.