usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-wry7-h9ge
Icon for Socrata external plugin

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 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 ethiopia_pastoralist_areas_resilience_improvement table in this repository, by referencing it like:

"usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-wry7-h9ge:latest"."ethiopia_pastoralist_areas_resilience_improvement"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "e503", -- E5.03: How much did you pay (how much did (item) cost) in total?
    "int_group", -- Intervention group
    "pastor_status", -- DERIVED: Pastoralist Status
    "edmax_sec", -- DERIVED: Highest education level in hh: secondary or higher
    "edmax_none_f", -- DERIVED: Highest education level of females in hh: none
    "e501", -- E5.01: Item Code:
    "pm0_16", -- DERIVED: Percent males 0-16 years of age
    "ea", -- Unique enumeration area code
    "asset_ind_p_01", -- DERIVED: Total consumption assets owned, 0-100 scale
    "tlu", -- DERIVED: Tropical Livestock Unit
    "asset_ind_p", -- DERIVED: Total production assets owned
    "kebele", -- Kebele
    "asset_ind_c", -- DERIVED: Total consumption assets owned
    "stratum", -- Survey strata
    "asset_poor", -- DERIVED: Asset poor corresponds to USD 1.25 poverty threshold
    "pm16_30", -- DERIVED: Percent males 16-30 years of age
    "proj_area", -- DERIVED: Project Area (Zone)
    "pf0_16", -- DERIVED: Percent females 0-16 years of age
    "pm30p", -- DERIVED: Percent males 30 years of age
    "item_num", -- Item number
    "item_e5", -- Item
    "kcal_req_l", -- DERIVED: Hh calorie req: light
    "edmax_none", -- DERIVED: Highest education level in hh: none
    "com_hh_merge", -- Code to merge community and hh datasets
    "fem_only", -- DERIVED: Household with all adult females
    "pf16_30", -- DERIVED: Percent females 16-30 years of age
    "samplewt", -- DERIVED: sample weights
    "e502", -- E5.02: Over the past 1 year(12 months), did your household use buy any item ?
    "pf30p", -- DERIVED: Percent females 30 years of age
    "woreda", -- Woreda
    "hhae", -- DERIVED: Number of household adult equivalents
    "asset_ind_c_01", -- DERIVED: Total consumption assets owned, 0-100 scale
    "asset_index", -- DERIVED: Total Asset Ownership Index
    "edmax_prim_f", -- DERIVED: Highest education level of females in hh: primary
    "edmax_sec_f", -- DERIVED: Highest education level of females in hh: secondary or higher
    "edmax_prim", -- DERIVED: Highest education level in hh: primary
    "pbs_id", -- Household id
    "hhsize", -- DERIVED: Number of household members
    "income_poor" -- DERIVED: Total Per Capita Daily Expenditures USD 1.25
FROM
    "usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-wry7-h9ge:latest"."ethiopia_pastoralist_areas_resilience_improvement"
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-wry7-h9ge with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.usaid.gov. When you queryusaid-gov/ethiopia-pastoralist-areas-resilience-improvement-wry7-h9ge:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).

We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.

Query Your Local Engine

Install Splitgraph Locally
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 (like this repository), 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, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.

Mounting Data

This repository is an external repository. It's not hosted by Splitgraph. It is hosted by data.usaid.gov, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.

First, install Splitgraph if you haven't already.

Mount the table with sgr mount

sgr mount socrata \
  "usaid-gov/ethiopia-pastoralist-areas-resilience-improvement-wry7-h9ge" \
  --handler-options '{
    "domain": "data.usaid.gov",
    "tables": {
        "ethiopia_pastoralist_areas_resilience_improvement": "wry7-h9ge"
    }
}'

That's it! Now you can query the data in the mounted table like any other Postgres table.

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-wry7-h9ge is just another Postgres schema.