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 food_scrap_dropoff_locations_in_nyc table in this repository, by referencing it like:
"cityofnewyork-us/food-scrap-dropoff-locations-in-nyc-if26-z6xq:latest"."food_scrap_dropoff_locations_in_nyc"or in a full query, like:
SELECT
":id", -- Socrata column ID
"location", -- Street address or cross streets associated with food scrap drop-off location
"bbl", -- Ten digit Borough-Block-Lot (BBL) or parcel numbers that identify the location of buildings or properties.
"notes", -- Additional site notes
"senate_district", -- New York City area State Senate District name.
"bin", -- Building Identification Number (BIN). A seven-digit numerical identifier unique to each building in the City of New York.
"dsny_district", -- District abbreviation as defined by DSNY
"_assembly_district", -- New York City area Assembly District name.
"latitude", -- Latitude of food scrap drop-off location for mapping purposes.
"app_android", -- URL to download the application on Android.
"borocd", -- Borough and Community District which is represented by a single-digit borough number followed by two-digit borough community district number.
"dsny_zone", -- Zone abbreviation as defined by DSNY
"object_id", -- An ObjectID is a unique, not null integer field used to uniquely identify rows in tables in a geodatabase.
"precinct", -- Police precinct in which the site is located.
"location_point", -- Longitude and Latitude formatted for map "pin"
"operation_day_hours", -- Days and hours when food scraps can be dropped off.
"borough", -- NYC Borough where vendor is located. New York City’s boroughs are five county-level administrative divisions, with each one also being a state county.
"longitude", -- Longitude of food scrap drop-off location for mapping purposes.
"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.
"_congress_district", -- New York City area Congressional District name.
"_dsny_section", -- Sections are subdivisions of DSNY Districts
"app_ios", -- URL to download the application on iOS
":@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 'location_point' 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 'location_point' 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 'location_point' 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 'location_point' 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 'location_point' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"hosted_by", -- Name of the organization that services the food scraps that are dropped off.
"website", -- Website associated with food scrap drop-off location.
"ct2010", -- Census Tract (CT2010). The 2010 census tract in which the tax lot is located.
"councildist", -- NYC Council District Number. There are 51 Council districts throughout the five boroughs and each one is represented by an elected Council Member.
"open_months", -- Months when food scraps can be dropped off at the location.
"food_scrap_drop_off_site" -- Name of food scrap drop-off location
FROM
"cityofnewyork-us/food-scrap-dropoff-locations-in-nyc-if26-z6xq:latest"."food_scrap_dropoff_locations_in_nyc"
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/food-scrap-dropoff-locations-in-nyc-if26-z6xq 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/food-scrap-dropoff-locations-in-nyc-if26-z6xq: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/food-scrap-dropoff-locations-in-nyc-if26-z6xqCheckout 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/food-scrap-dropoff-locations-in-nyc-if26-z6xq:latestThis will download all the objects for the latest tag of cityofnewyork-us/food-scrap-dropoff-locations-in-nyc-if26-z6xq 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/food-scrap-dropoff-locations-in-nyc-if26-z6xq: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/food-scrap-dropoff-locations-in-nyc-if26-z6xq: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/food-scrap-dropoff-locations-in-nyc-if26-z6xq is just another Postgres schema.