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 public_and_private_education_institutions_2017 table in this repository, by referencing it like:
"pa-gov/public-and-private-education-institutions-2017-a5nq-sy2w:latest"."public_and_private_education_institutions_2017"or in a full query, like:
SELECT
":id", -- Socrata column ID
"georeferenced_latitude_longitude", -- A georeferenced latitude and longitude based on the mailing address that can be used to create maps.
"longitude", -- The angular distance of a place east or west of the meridian at Greenwich, England, or west of the standard meridian of a celestial object, usually expressed in degrees and minutes.
"latitude", -- The angular distance of a place north or south of the earth's equator, or of a celestial object north or south of the celestial equator, usually expressed in degrees and minutes.
"location_2", -- The actual location address of an entity, if different than the mailing address.
"location_1", -- The actual location address of an entity, if different than the mailing address.
"lea_school_key", -- Key for each school
"zip_code_extension", -- The 4-digit zip extension associated with the mailing address.
"zip_code", -- The zip code associated with the mailing address.
"state", -- The state associated with the mailing address.
"city", -- City associated with the mailing address.
"address_line_1", -- The address used to send information via mail to an entity. Each entity is required to have a mailing address stored in EdNA.
"county", -- County associated based on the location address.
"school", -- Name of the School.
"school_number", -- The 4-digit school ID assigned to the school by the Department of Education.
"lea_type", -- Grouping that denotes the type of organization an entity is, such as “School District,” “Library,” or “State University.”
":@computed_region_75dh_jrw3", -- This column was automatically created in order to record in what polygon from the dataset 'PA State Senate Districts 2016 Health' (75dh-jrw3) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_gbji_5m4q", -- This column was automatically created in order to record in what polygon from the dataset 'US House Districts for PA 2019 Health' (gbji-5m4q) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_4fjn_fq7k", -- This column was automatically created in order to record in what polygon from the dataset 'PA County Boundaries Spatial Data Current Transportation' (4fjn-fq7k) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_3x3q_vpda", -- This column was automatically created in order to record in what polygon from the dataset 'US House Districts for PA 2019' (3x3q-vpda) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_its3_bt6a", -- This column was automatically created in order to record in what polygon from the dataset 'PA State House Districts 2016 Health' (its3-bt6a) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_amqz_jbr4", -- This column was automatically created in order to record in what polygon from the dataset 'Municipality Boundary 2' (amqz-jbr4) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_rayf_jjgk", -- This column was automatically created in order to record in what polygon from the dataset 'Pa School Districts (2017) 2' (rayf-jjgk) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_r6rf_p9et", -- This column was automatically created in order to record in what polygon from the dataset 'Pa House Districts (2017-01) 2' (r6rf-p9et) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_d3gw_znnf", -- This column was automatically created in order to record in what polygon from the dataset 'Pa Senatorial Districts (2017-01) 2' (d3gw-znnf) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_nmsq_hqvv", -- This column was automatically created in order to record in what polygon from the dataset 'Pennsylvania County Boundaries 2' (nmsq-hqvv) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"school_year_2017_2018", -- This is the school year for which the data is based. An 'X' in the column means the institution was open for the indicated year.
"address_line_2", -- The address used to send information via mail to an entity. Each entity is required to have a mailing address stored in EdNA.
"local_education_agency_lea", -- The name of the institution.
"administrative_unit_number" -- A unique, 9-digit Administrative Unit Number (AUN) assigned by the Pennsylvania Department of Education.
FROM
"pa-gov/public-and-private-education-institutions-2017-a5nq-sy2w:latest"."public_and_private_education_institutions_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 pa-gov/public-and-private-education-institutions-2017-a5nq-sy2w 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 pa-gov/public-and-private-education-institutions-2017-a5nq-sy2w: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 pa-gov/public-and-private-education-institutions-2017-a5nq-sy2wCheckout 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 pa-gov/public-and-private-education-institutions-2017-a5nq-sy2w:latestThis will download all the objects for the latest tag of pa-gov/public-and-private-education-institutions-2017-a5nq-sy2w 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 pa-gov/public-and-private-education-institutions-2017-a5nq-sy2w: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 pa-gov/public-and-private-education-institutions-2017-a5nq-sy2w: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, pa-gov/public-and-private-education-institutions-2017-a5nq-sy2w is just another Postgres schema.