Query the Data Delivery Network

Query the DDN

The 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 and query any version of over 40,000 datasets that are hosted or proxied by Splitgraph.

For example, you can query the commercial_permits table in this repository, by referencing it like:


or in a full query, like:

    ":id", -- Socrata column ID
    "usecode", -- Type of structure work will be performed on
    "finaleddate", -- The final date indicates the date that construction has been completed and approved by DPS.  The final inspection was conducted that date/time by the DPS inspector and was acceptable
    "issueddate", -- The date that the permit is issued.  When the permit is issued, construction is allowed to commence.  DPS has reviewed the construction plans according to the applicable building and life safety codes and approved the plans
    "addeddate", -- The date that the applicant applied for the permit and the information entered into the database
    "zip", -- Zip Code of work location
    "permitno", -- Number assigned to uniquely identify permit
    "stname", -- Street name of work location
    "buildingarea", -- The number of square feet for the proposed construction.  For a new home, it is the entire building area.  For an addition or alteration, it represents only the area of the affected space, not the entire structure
    "status", -- Status of permit
    "state", -- State of work location
    "worktype", -- Type of work to be performed
    "declaredvaluation", -- The value or cost of the proposed construction or work as declared by the applicant
    "applicationtype", -- Type of permit application
    "city", -- City of work location
    "suffix", -- Street suffix, such as ST (Street), PL (Place), RD (Road)
    "predir", -- Pre-direction, if the street name has a direction as a prefix. For example, E Jefferson Street
    "stno", -- Street number of work location
    "description", -- Description of planned work
    "postdir", --  Post-direction, if the street name has a direction after the name. For example, University Blvd W
    ":@computed_region_6vgr_duib", -- This column was automatically created in order to record in what polygon from the dataset 'Council Districts 7' (6vgr-duib) the point in column 'location' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
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 montgomerycountymd-gov/commercial-permits-i26v-w6bd with SQL in under 60 seconds.

Query Your Local Engine

Install Splitgraph Locally
bash -c "$(curl -sL"

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 montgomerycountymd-gov/commercial-permits-i26v-w6bd: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 montgomerycountymd-gov/commercial-permits-i26v-w6bd

Checkout 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 montgomerycountymd-gov/commercial-permits-i26v-w6bd:latest

This will download all the objects for the latest tag of montgomerycountymd-gov/commercial-permits-i26v-w6bd 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 montgomerycountymd-gov/commercial-permits-i26v-w6bd: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 montgomerycountymd-gov/commercial-permits-i26v-w6bd:latest

This 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, montgomerycountymd-gov/commercial-permits-i26v-w6bd is just another Postgres schema.

Related Documentation: