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 building_elevation_and_subgrade_bes table in this repository, by referencing it like:
"cityofnewyork-us/building-elevation-and-subgrade-bes-bsin-59hv:latest"."building_elevation_and_subgrade_bes"or in a full query, like:
SELECT
":id", -- Socrata column ID
"borough", -- The borough code for the borough where the building is located.
":@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 'the_geom' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"pluto_bbl", -- The borough, block, lot from the MapPLUTO 22v2 release. An identifier for the tax lot that the building centroid is located on
"latitude", -- Latitude in WGS84 / SRID:4326
"longitude", -- Longitude in WGS84 / SRID:4326
"council", -- NYC City Council District code. This field contains a two-digit city council district number, which is preceded with a zero when the district number is one digit. There are currently 51 city council districts in the City, which serve as political districts for the legislative branch of city government.
"cdtaname", -- 2020 Community District Tabulation Area Name
"nta2020", -- 2020 Neighborhood Tabulation Area Code
"borocd", -- NYC Community District code. 3 digit code, first digit is boro code, followed by two digit community district or joint interest area number. The city is divided into 59 community districts and 12 joint interest areas, which are large parks or airports that are not considered part of any community district.
"subgrade", -- Whether or not a subgrade space was observed.
"ctlabel", -- The census tract identifier for the polygon. Each census tract number is unique to its borough.
":@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 'the_geom' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"x", -- The X coordinate of the building centroid in the NAD 1983 coordinate system
"notes2", -- Indicates any present ground floor attributes of the building.
"bbl", -- The borough, block, lot identifier based on the 22v1 DOB building footprint dataset. An identifier for the tax lot that the building centroid is located on.
"notes3", -- Indicates how flood water may enter a subgrade space.
":@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 'the_geom' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"notes1", -- Indicates if there were any obstacles to obtaining the z_grade or z_floor measurement.
"z_floor", -- The elevation of what is estimated to be the lowest actively used floor (feet)
":@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 'the_geom' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"z_grade", -- The elevation of the building at it's lowest adjacent grade - the lowest point where the building touches the ground (feet).
"cdta2020", -- 2020 Community District Tabulation Area Code
"address", -- The street address of the building.
"ntaname", -- 2020 Neighborhood Tabulation Area Name
"lot", -- The lot where the building is located.
"bin", -- The building identification number - a unique identifier for every building in New York City, assigned by the Department of Buildings. First digit is the boro code, followed by six digit unique ID.
"boroct2020", -- Merged string of borough code and 2020 census tract number.
"the_geom", -- Geometry type
"y", -- The Y coordinate of the building centroid in the NAD 1983 coordinate system
"block" -- The block where the building is located.
FROM
"cityofnewyork-us/building-elevation-and-subgrade-bes-bsin-59hv:latest"."building_elevation_and_subgrade_bes"
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/building-elevation-and-subgrade-bes-bsin-59hv with SQL in under 60 seconds.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.cityofnewyork.us. When you querycityofnewyork-us/building-elevation-and-subgrade-bes-bsin-59hv:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).
We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.
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 (like this repository), 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, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Mounting Data
This repository is an external repository. It's not hosted by Splitgraph. It is hosted by data.cityofnewyork.us, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.
First, install Splitgraph if you haven't already.
Mount the table with sgr mount
sgr mount socrata \
"cityofnewyork-us/building-elevation-and-subgrade-bes-bsin-59hv" \
--handler-options '{
"domain": "data.cityofnewyork.us",
"tables": {
"building_elevation_and_subgrade_bes": "bsin-59hv"
}
}'That's it! Now you can query the data in the mounted table like any other Postgres table.
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/building-elevation-and-subgrade-bes-bsin-59hv is just another Postgres schema.