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 hcd_property_investments_fy10q1_24 table in this repository, by referencing it like:
"memphistn-gov/hcd-property-investments-fy10q1-24-62rn-kka3:latest"."hcd_property_investments_fy10q1_24"or in a full query, like:
SELECT
":id", -- Socrata column ID
"status", -- Activity status (Complete, Open, etc.)
"city",
"state",
"property_type", -- Type of property receiving services (single family, multifamily, commercial, etc.)
"tenure_type", -- Type of occupant for which the project was intended (owner or renter)
"activity_type_simple", -- Simplified activity/development type category, indicating whether project was Acquisition, New Construction, Rehab, Demolition, etc.
":@computed_region_stch_rtry",
":@computed_region_me95_vbw4",
":@computed_region_qnac_u4vd",
":@computed_region_xf2b_u4zq",
":@computed_region_gxae_hf64",
":@computed_region_fuzm_ngj2", -- This column was automatically created in order to record in what polygon from the dataset 'Council District Boundaries Dec 2022' (fuzm-ngj2) 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_scpn_qt9a", -- This column was automatically created in order to record in what polygon from the dataset 'Council Super District Boundaries Dec 2022' (scpn-qt9a) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"project_name", -- Project name from IDIS or manager spreadsheets
"lat",
"accelerate_memphis", -- Designates programs funded through the Accelerate Memphis program
"address_rounded", -- Rounded address to the nearest 100 block. (E.g., 340 Main St becomes 300 Main, 370 Main St becomes 400 Main). This is to protect privacy while allowing for detailed spatial analysis.
"completion_date", -- Approximate completion date for activities, based on completion date in the relevant reporting system (e.g. IDIS completion date). In some cases activity may have been "completed" in the system some time after real-world project completion, though typically closeout occurs in the same fiscal year.
"units_hhs", -- Number of housing units completed for each row
"organization", -- Organization receiving funding to carry out this project
"total_funds_committed", -- Total project funds administered from/via HCD
"long",
"award_number",
"zip_code",
"project_type", -- Specific project type and/or program
"funding_type", -- Source of funding
"geocoded_column" -- Geo coordinates
FROM
"memphistn-gov/hcd-property-investments-fy10q1-24-62rn-kka3:latest"."hcd_property_investments_fy10q1_24"
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 memphistn-gov/hcd-property-investments-fy10q1-24-62rn-kka3 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 memphistn-gov/hcd-property-investments-fy10q1-24-62rn-kka3: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 memphistn-gov/hcd-property-investments-fy10q1-24-62rn-kka3Checkout 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 memphistn-gov/hcd-property-investments-fy10q1-24-62rn-kka3:latestThis will download all the objects for the latest tag of memphistn-gov/hcd-property-investments-fy10q1-24-62rn-kka3 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 memphistn-gov/hcd-property-investments-fy10q1-24-62rn-kka3: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 memphistn-gov/hcd-property-investments-fy10q1-24-62rn-kka3: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, memphistn-gov/hcd-property-investments-fy10q1-24-62rn-kka3 is just another Postgres schema.