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 roof_building_permit table in this repository, by referencing it like:
"cambridgema-gov/roof-building-permit-79ih-g44d:latest"."roof_building_permit"or in a full query, like:
SELECT
":id", -- Socrata column ID
"layers_of_roof_covering", -- How many existing layers of roof covering will remain underneath the new roof covering?
"geocoded_column_address",
"geocoded_column_city",
"above_deck_insulation", -- Replacement of above deck insulation is required. Provide the R-value of new insulation to be installed
"dumpster_license_number", -- Cambridge Dumpster License Number
"status", -- Status of roof construction
"photovoltaic_products", -- Will building-integrated photovoltaic products be used as the roof covering?
":@computed_region_rffn_qbt6", -- This column was automatically created in order to record in what polygon from the dataset 'cambridge_neighborhoods' (rffn-qbt6) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_v7jj_366k", -- This column was automatically created in order to record in what polygon from the dataset 'Police Response Districts' (v7jj-366k) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_guic_hr4a", -- This column was automatically created in order to record in what polygon from the dataset 'Police Neighborhood Regions' (guic-hr4a) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"building_use", -- Type of building (home, commercial, etc.)
"temporary_dumpster_permit", -- Would you like to apply for a temporary dumpster permit as part of this building permit application?
"flashing_installation", -- Will new flashing or fascia be required and installed because of this permit?
"owner_occupied", -- Is the property owner-occupied?
"type_of_demolition",
"geocoded_column_state",
"condo_association", -- Is the property part of a condo association?
"roof_covering_classification", -- Provide the minimum roof covering classification that will be provided
"method_of_removal", -- Method of Removal (Demolition)
"roof_structure_slope", -- Is the roof structure sloped less than 3:12 OR is there rigid insulation that is tapered?
"roof_deck_insulation_removal", -- Does the work include removal of existing insulation entirely above a roof deck?
":@computed_region_e4yd_rwk4", -- This column was automatically created in order to record in what polygon from the dataset 'Census Blocks 2010' (e4yd-rwk4) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_swkg_bavi", -- This column was automatically created in order to record in what polygon from the dataset 'cambridge_cdd_zoning' (swkg-bavi) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"geocoded_column_zip",
"description_other", -- Description: Other / Mixed
"license_expiration_date", -- License Expiration Date
"deck_insulation_tapered", -- Is the above deck insulation tapered?
"debris_removal_location", -- Construction debris will be disposed at/by
"debris_removal_date", -- Construction debris removal will be completed by
"licensed_class", -- Licensed: Class
"demolition_description",
"firm_name", -- Name of firm working on roof construction
"dwelling_units", -- Number of Dwelling Units in the building
"roofing_material", -- Select the roofing material that will be installed
"issue_date", -- Date when permit was issued
"latitude",
"address", -- Address where roof work is being done
"building_cost", -- Building cost of construction
"submit_date", -- Applicant Submit Date
"longitude",
"construction_type",
"temporary_dumpster_count", -- How many temporary dumpsters would you like to apply for?
"license_number", -- License Number
"proposed_work_description", -- Select the option that best describes the proposed work
"roof_work_description", -- Detailed Description of Work, including attachment methods, use of underlayment, and description of fasteners to be use
"id", -- Permit ID
"geocoded_column" -- Use this column to create maps with the open data portal's mapping tools.
FROM
"cambridgema-gov/roof-building-permit-79ih-g44d:latest"."roof_building_permit"
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 cambridgema-gov/roof-building-permit-79ih-g44d 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 cambridgema-gov/roof-building-permit-79ih-g44d: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 cambridgema-gov/roof-building-permit-79ih-g44dCheckout 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 cambridgema-gov/roof-building-permit-79ih-g44d:latestThis will download all the objects for the latest tag of cambridgema-gov/roof-building-permit-79ih-g44d 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 cambridgema-gov/roof-building-permit-79ih-g44d: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 cambridgema-gov/roof-building-permit-79ih-g44d: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, cambridgema-gov/roof-building-permit-79ih-g44d is just another Postgres schema.