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 ethiopia_pastoralist_areas_resilience_improvement table in this repository, by referencing it like:


or in a full query, like:

    ":id", -- Socrata column ID
    "pm16_30", -- DERIVED: Percent males 16-30 years of age
    "pm30p", -- DERIVED: Percent males 30 years of age
    "edmax_prim_f", -- DERIVED: Highest education level of females in hh: primary
    "com_hh_merge", -- Code to merge community and hh datasets
    "pbs_id", -- Household id
    "asset_ind_p", -- DERIVED: Total production assets owned
    "asset_ind_p_01", -- DERIVED: Total consumption assets owned, 0-100 scale
    "hhae", -- DERIVED: Number of household adult equivalents
    "edmax_prim", -- DERIVED: Highest education level in hh: primary
    "edmax_none_f", -- DERIVED: Highest education level of females in hh: none
    "asset_ind_c_01", -- DERIVED: Total consumption assets owned, 0-100 scale
    "edmax_sec_f", -- DERIVED: Highest education level of females in hh: secondary or higher
    "asset_ind_c", -- DERIVED: Total consumption assets owned
    "asset_poor", -- DERIVED: Asset poor corresponds to USD 1.25 poverty threshold
    "e201", -- E2.01: Item Code
    "pf30p", -- DERIVED: Percent females 30 years of age
    "ea", -- Unique enumeration area code
    "fem_only", -- DERIVED: Household with all adult females
    "pastor_status", -- DERIVED: Pastoralist Status
    "pm0_16", -- DERIVED: Percent males 0-16 years of age
    "edmax_none", -- DERIVED: Highest education level in hh: none
    "woreda", -- Woreda
    "pf16_30", -- DERIVED: Percent females 16-30 years of age
    "item_e2", -- Item
    "kcal_req_l", -- DERIVED: Hh calorie req: light
    "item_num", -- Item number
    "int_group", -- Intervention group
    "kebele", -- Kebele
    "proj_area", -- DERIVED: Project Area (Zone)
    "edmax_sec", -- DERIVED: Highest education level in hh: secondary or higher
    "pf0_16", -- DERIVED: Percent females 0-16 years of age
    "stratum", -- Survey strata
    "tlu", -- DERIVED: Tropical Livestock Unit
    "asset_index", -- DERIVED: Total Asset Ownership Index
    "hhsize", -- DERIVED: Number of household members
    "income_poor", -- DERIVED: Total Per Capita Daily Expenditures USD 1.25
    "e202", -- E2.02: Over the past one week (7 days), did your household use or buy any (item)
    "e203", -- E2.03: How much did you pay (how much did (item) cost) in total?
    "samplewt" -- DERIVED: sample weights
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 usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-hzj8-ji6n 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 usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-hzj8-ji6n: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 usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-hzj8-ji6n

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 usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-hzj8-ji6n:latest

This will download all the objects for the latest tag of usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-hzj8-ji6n 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 usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-hzj8-ji6n: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 usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-hzj8-ji6n: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, usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-hzj8-ji6n is just another Postgres schema.

Related Documentation: