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 dob_now_safety_boiler table in this repository, by referencing it like:
"cityofnewyork-us/dob-now-safety-boiler-52dp-yji6:latest"."dob_now_safety_boiler"or in a full query, like:
SELECT
":id", -- Socrata column ID
"defects_exist", -- This indicates if any defects were found during the inspection. Expected values: • true = Defects were found • false = Defects were not found
"owner_first_name", -- The first name of the building owner.
"tracking_number", -- The tracking number for the boiler compliance filing.
"filing_fee",
"inspection_type", -- Low pressure boilers only require an external inspection. High pressure boilers require both an external and an internal inspection. Expected values: • Internal = inspection by DOB staff • External = inspection performed by an inspector hired by the building owner.
"bin_number",
"applicant_last_name", -- Last name of the person who filed the application.
"applicantfirst_name", -- First name of the person who filed the application
"inspection_date", -- Date that the boiler was inspected. Date in ISO 8601 format
"boiler_make", -- The manufacturer of the boiler.
"boiler_id", -- The identification number for the Boiler. It's a combination of the Borough code, the DOB device number, the multiple dwelling flag, and the serial number.
"total_amount_paid", -- The total amount paid for the boiler compliance filing.
"lff_45_days", -- Inspection reports submitted to DOB after the forty-five (45) days of the annual inspection date but within twelve (12) months from the inspection date will be considered “late filings” and will be subject to the appropriate civil penalties.
"lff_180_days", -- Inspection reports submitted as Unsatisfactory (with defects) must be corrected within one hundred eighty days (180) from the initial date of inspection else the owner will be subject to the appropriate civil penalties.
"owner_last_name", -- The last name of the building owner.
"applicant_license_number", -- The applicant's license number on file with the Department.
"applicant_license_type", -- The license type that the applicant used to file the application. Expected values: • BB = Insurance Agency Inspector Certified by NYS • E = High Pressure Boiler Operator • O = Oil Burner Installer • P = Master Plumber
"boiler_model", -- The model of the boiler.
"report_type", -- The type of compliance filing. Expected values: • Initial = the initial boiler compliance filing for this device in this inspection cycle • Subsequent = a subsequent filing is submitted to correct defects on a previous filing.
"pressure_type", -- The type of boiler: either low or high pressure.
"report_status" -- Work Flow Status for the life cycle of the filing. Expected Values: Pre-Filing, QA Review, Rejected, Accepted
FROM
"cityofnewyork-us/dob-now-safety-boiler-52dp-yji6:latest"."dob_now_safety_boiler"
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/dob-now-safety-boiler-52dp-yji6 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/dob-now-safety-boiler-52dp-yji6: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/dob-now-safety-boiler-52dp-yji6Checkout 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/dob-now-safety-boiler-52dp-yji6:latestThis will download all the objects for the latest tag of cityofnewyork-us/dob-now-safety-boiler-52dp-yji6 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/dob-now-safety-boiler-52dp-yji6: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/dob-now-safety-boiler-52dp-yji6: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/dob-now-safety-boiler-52dp-yji6 is just another Postgres schema.