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 nyc_parks_monuments table in this repository, by referencing it like:
"cityofnewyork-us/nyc-parks-monuments-6rrm-vxj9:latest"."nyc_parks_monuments"or in a full query, like:
SELECT
":id", -- Socrata column ID
"architect", -- Architect of monument (if applicable).
"descrip", -- Physical description only, does not include materials, dimensions, condition, or inscriptions. Example: "group of one female and two male figures (heroic scale) on pedestal".
"inscribed", -- Inscription on monument (if applicable)
"extant", -- Denotes whether monument exists or not (i.e. Was stolen, broken etc.)
"cost", -- Original cost, if more than one cost the original and replacement cost will be listed (ie. if monument was refabricated).
"name3", -- Tertiary or colloquial name for monument if applicable
"donor", -- Donor of monument (if applicable). In most cases this entry denotes the group or organization that sponsored the monument at the time of its inception.
"categories", -- Concatenation of all subject categories monument fits in. Used to sort monuments on Parks website.
"dedicated", -- Date or approximate date monument was dedicated, if known.
"y", -- The Y coordinate of the monument for mapping purposes.
"borough", -- Full name of the borough monument is in
"location", -- Describes the location of the monument
"contractor", -- Donors who contributed to the maintenance fund
"parkname", -- Official park name in which monument is situated, if it is in a park
"dimen", -- Dimensions of monument
"cast", -- Date or approximate date monument was cast, if known.
"sculptor", -- Sculptor of monument (if applicable)
"fabricator", -- Fabricator of monument (if applicable)
"sponsor", -- Fiscal sponsor who holds endowment fund (if monument was sponsored and the sponsor is known)
"maintainedbyparks", -- Indicates whether or not the monument is maintained by parks. Y - Yes, N - No
"name2", -- Secondary or colloquial name for monument if applicable
"foundry", -- Foundry where monument was cast (if it was cast in a foundry and that foundry is known)
"commboard", -- Community board monument is in
"x", -- The X coordinate of the monument for mapping purposes.
"council", -- Council district monument is in
"installation", -- Date monument was installed
"name", -- Official name of the monument
"parknumber", -- Unique ID number of park in which monument is situated, if it is in a park
"materials", -- Material composition of monument
"fileorder", -- This is used for sorting monuments in Parks' nondigital archives. Generally, in the case of monuments named after people, the last name will come first.
"maintain", -- Yes/No field indicating a maintenance endowment has been established for this monument
"parkprop", -- Denotes whether monument is on parkland. Y - Yes, N - No, O - Other (i.e. The Bronx Zoo, which is located on parkland, but operated by a non-Parks entity)
"number", -- Parks’ Monument database unique ID number
"rededication" -- When a monument is moved or undergoes significant restoration often times it is rededicated. This field denotes the full calendar date of rededication.
FROM
"cityofnewyork-us/nyc-parks-monuments-6rrm-vxj9:latest"."nyc_parks_monuments"
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 cityofnewyork-us/nyc-parks-monuments-6rrm-vxj9 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 cityofnewyork-us/nyc-parks-monuments-6rrm-vxj9: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 cityofnewyork-us/nyc-parks-monuments-6rrm-vxj9Checkout 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 cityofnewyork-us/nyc-parks-monuments-6rrm-vxj9:latestThis will download all the objects for the latest tag of cityofnewyork-us/nyc-parks-monuments-6rrm-vxj9 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 cityofnewyork-us/nyc-parks-monuments-6rrm-vxj9: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 cityofnewyork-us/nyc-parks-monuments-6rrm-vxj9: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, cityofnewyork-us/nyc-parks-monuments-6rrm-vxj9 is just another Postgres schema.