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 rodent_inspection table in this repository, by referencing it like:
"cityofnewyork-us/rodent-inspection-p937-wjvj:latest"."rodent_inspection"or in a full query, like:
SELECT
":id", -- Socrata column ID
"census_tract",
"location_state",
"bbl", -- Borough, Block, and Lot code, which is a unique identifier of NYC Taxlots. Every NYC property has its own BBL code, and inspections are conducted at the taxlot level. This dataset can be mapped by joining these inspectional results to a taxlot geography file such as the Department of City Planning’s PLUTO dataset.
"borough", -- The name of the NYC borough
"latitude", -- Latitude in decimal degrees of the inspected taxlot (WGS 1984)
"nta",
"council_district",
":@computed_region_yeji_bk3q",
":@computed_region_efsh_h5xi",
":@computed_region_sbqj_enih",
"block", -- The block number for the inspected taxlot; block numbers repeat in different boroughs.
"job_id", -- Internal DOHMH administrative number to identify a job
"location_zip",
"street_name", -- The street name portion of the address of the taxlot that was inspected
"zip_code", -- The postal zipcode of the taxlot that was inspected
"community_board",
"longitude", -- Longitude in decimal degrees of the inspected taxlot (WGS 1984)
"job_progress", -- An indicator of the progress made in the job. Jobs may involve multiple inspections or other actions, and the progress indicator shows the order in which they took place.
"location_city",
":@computed_region_92fq_4b7q",
"result", -- Result of the inspection, including: Active Rat Signs (ARS) - ARS include any of six different signs: 1) fresh tracks, 2) fresh droppings, 3) active burrows, 4) active runways and rub marks, 5) fresh gnawing marks, and 6) live rats. Problem Conditions - Problem conditions include garbage (poor containerization of food waste resulting in the feeding of rats), harborage (clutter and dense vegetation promoting the nesting of rats), and mice.
"approved_date", -- Date inspection is approved by supervisor at DOHMH.
"location",
":@computed_region_f5dn_yrer",
"inspection_type", -- "In conjunction with the JOBTICKETORWORKORDERID, uniquely identifies records in the dataset. Initial Inspection - Inspection conducted in response to a 311 complaint, or a proactive inspection conducted through our neighborhood indexing program. Compliance Inspection - If a property fails its initial inspection, the Health Department will conduct a follow up (Compliance) inspection. Baiting - Application of rodenticide, or monitoring visit by a Health Department Pest Control Professional. Clean Up - The removal of garbage and clutter from a property by the Health Department. Initial Inspection - Inspection conducted in response to a 311 complaint, or a proactive inspection conducted through our neighborhood indexing program. Compliance Inspection - If a property fails its initial inspection, the Health Department will conduct a follow up (Compliance) inspection. Baiting - Application of rodenticide, or monitoring visit by a Health Department Pest Control Professional. Clean Up - The removal of garbage and clutter from a property by the Health Department."
"job_ticket_or_work_order_id", -- In conjunction with the INSPECTION_TYPE, uniquely identifies records in the dataset.
"bin",
"y_coord", -- Y coordinate of the inspected taxlot in NY State Plane Long Island Coordinate system
"x_coord", -- X coordinate of the inspected taxlot in NY State Plane Long Island Coordinate system
"boro_code", -- The code assigned to the NYC borough, this code can also be found as the first digit in the BBL taxlot code above.
"lot", -- The lot number for the inspected taxlot; lot numbers can repeat in different blocks.
"house_number", -- The address number of a building on the taxlot that was inspected
"location_address",
"inspection_date" -- Date of the inspection.
FROM
"cityofnewyork-us/rodent-inspection-p937-wjvj:latest"."rodent_inspection"
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/rodent-inspection-p937-wjvj 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/rodent-inspection-p937-wjvj: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/rodent-inspection-p937-wjvjCheckout 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/rodent-inspection-p937-wjvj:latestThis will download all the objects for the latest tag of cityofnewyork-us/rodent-inspection-p937-wjvj 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/rodent-inspection-p937-wjvj: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/rodent-inspection-p937-wjvj: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/rodent-inspection-p937-wjvj is just another Postgres schema.