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 dsny_donatenyc_directory table in this repository, by referencing it like:
"cityofnewyork-us/dsny-donatenyc-directory-gkgs-za6m:latest"."dsny_donatenyc_directory"or in a full query, like:
SELECT
":id", -- Socrata column ID
"borough", -- On of the five borough of NYC: Bronx, Brooklyn, Manhattan, Queens, and Staten Island.
"ntaname", -- Neighborhood Tabulation Area Name. Neighborhood Tabulation Areas are small area boundaries that were initially created by the Department of City Planning for small area population projections. However, NTAs are now being used to present data from the Decennial Census and American Community Survey.
"site", -- Name of site in Donate Directory.
"address", -- Street address associated with site.
"categoriesaccepted", -- Categories of donations that are accepted by the site.
"categoriesavailable", -- Categories of donations that are available from the site.
"additionalmaterialinformation", -- Lists additional information about the site.
"pickupstatus", -- Indicates whether a site listed in the Donate Directory is a pickup site.
"hours", -- Open hours for site listed in the Donate Directory.
"website", -- Website for site listed in Donate Directory, if available.
"phone", -- Contact phone number for site listed in Donate Directory, if available.
"email", -- Contact email address for site listed in Donate Directory, if available.
"partnerstatus", -- Indicates whether a site listed in the Donate Directory is a Donate partner.
"dsny_zone", -- Zone abbreviation as defined by DSNY
"dsny_district", -- District abbreviation as defined by DSNY
"dsny_section", -- Sections are subdivisions of DSNY Districts.
"borocd", -- Borough and Community District which is represented by a single-digit borough number followed by two-digit borough community district number.
"community_district", -- Borough and Community District which is represented by a single-digit borough number followed by two-digit borough community district number.
"councildist", -- NYC Council District Number. There are 51 Council districts throughout the five boroughs and each one is represented by an elected Council Member.
"senate_district", -- New York City area State Senate District name.
"congressional_district", -- New York City area Congressional District name.
"assembly_district", -- New York City area Assembly District name.
"police_precinct", -- Police precinct in which the site is located.
"bbl", -- Ten digit Borough-Block-Lot (BBL) or parcel numbers that identify the location of buildings or properties.
"bin", -- Building Identification Number (BIN). A seven-digit numerical identifier unique to each building in the City of New York.
"census_tract", -- Decennial census tracts of 2020. The naming convention follows NYC's standard; merged string of borough code and 2020 census tract number.
"latitude", -- Latitude of site for mapping purposes.
"longitude", -- Longitude of site for mapping purposes.
"objectid", -- ObjectIDs are sequential, non-null integers serving as primary keys for the database to read. However, these should not be used as primary identifiers by users as the sequential numbers are subject to change during updates.
"point_1", -- Longitude and Latitude formatted for map "pin"
":@computed_region_yeji_bk3q", -- This column was automatically created in order to record in what polygon from the dataset 'Borough Boundaries' (yeji-bk3q) the point in column 'point_1' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_92fq_4b7q", -- This column was automatically created in order to record in what polygon from the dataset 'City Council Districts' (92fq-4b7q) the point in column 'point_1' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_sbqj_enih", -- This column was automatically created in order to record in what polygon from the dataset 'Police Precincts' (sbqj-enih) the point in column 'point_1' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_efsh_h5xi", -- This column was automatically created in order to record in what polygon from the dataset 'Zip Codes' (efsh-h5xi) the point in column 'point_1' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_f5dn_yrer" -- This column was automatically created in order to record in what polygon from the dataset 'Community Districts' (f5dn-yrer) the point in column 'point_1' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
FROM
"cityofnewyork-us/dsny-donatenyc-directory-gkgs-za6m:latest"."dsny_donatenyc_directory"
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/dsny-donatenyc-directory-gkgs-za6m 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/dsny-donatenyc-directory-gkgs-za6m: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/dsny-donatenyc-directory-gkgs-za6mCheckout 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/dsny-donatenyc-directory-gkgs-za6m:latestThis will download all the objects for the latest tag of cityofnewyork-us/dsny-donatenyc-directory-gkgs-za6m 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/dsny-donatenyc-directory-gkgs-za6m: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/dsny-donatenyc-directory-gkgs-za6m: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/dsny-donatenyc-directory-gkgs-za6m is just another Postgres schema.