usaid-gov/feed-the-future-nepal-baseline-household-survey-mpy6-pm9d

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 procotol. 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 feed_the_future_nepal_baseline_household_survey table in this repository, by referencing it like:

"usaid-gov/feed-the-future-nepal-baseline-household-survey-mpy6-pm9d:latest"."feed_the_future_nepal_baseline_household_survey"

or in a full query, like:

SELECT 
    ":id", -- Socrata column ID
    "zid_roof_tp_oth_sp",
    "a21", -- A21 Final outcome of interview enter code
    "a06", -- Type of household
    "f06", -- How often did this happen in the past 4 weeks 30 days
    "zid_drink_water_src_other_1",
    "zid_drink_water_src_other", -- Please specify drinking water source
    "zid_toilet_tp_2", -- What kind of toilet facility does the household have
    "d03", -- Exterior Walls
    "modd_missing", -- Missing all elements from module D
    "zid_water_src_seasonal", -- D06 bDo you use the main drinking water source allyear or only part of the year
    "d07", -- D07 Does this household have electricity
    "hh_size",
    "a09", -- Type of household
    "f02", -- How often did this happen in the past 4 weeks 30 days
    "pbs_id", -- Unique household ID
    "zid_floor_tp_oth_sp",
    "d05", -- D05 a What is the main type of toilets your household uses
    "zid_feces_present", -- Is there human feces in the house compound or rightoutside the compound
    "zid_ext_wall_tp_oth_sp",
    "zid_toilet_tp_obsrv", -- D05 b ENUMERATOR OBSERVE DO NOT ASK Is there a toilet
    "d01", -- Roof top material outer covering
    "d02", -- Floor material
    "zid_fuel_src_2",
    "zi_start_dt",
    "modf_missing", -- Missing all elements from module F
    "zia_visit2_yn", -- Was a second visit necessary to complete the interview
    "zia_supervisor_cd", -- A22 Name code of supervisor
    "f01", -- In the past 4 weeks 30 days was there ever no food to eat of any kind in your house because of lack of resources to get food
    "zia_location_cd", -- A03 Ward
    "zia_visit2_dt", -- A19 Date of second visit dd mm yyyy
    "a05", -- G1 03 Sex of respondent
    "zid_fuel_src_oth_sp",
    "d06", -- D06 aWhat is the main source of drinking water formembers of your household
    "f04", -- How often did this happen in the past 4 weeks 30 days
    "f05", -- In the past 4 weeks 30 days did you or any household member go a whole day and night without eating anything at all because there was not enough food
    "zid_toilet_tp_oth_sp",
    "f03", -- In the past 4 weeks 30 days did you or any household member go to sleep at night hungry because there was not enough food
    "hhwght", -- Derived variable household weight
    "hhhunger",
    "a02", -- A02 Cluster number
    "hungerscale",
    "d08", -- D08 What is the main source of cooking fuel for your household
    "a17", -- A17 Team and interviewer s code number
    "d04" -- D04 How many rooms are there in this dwelling
FROM
    "usaid-gov/feed-the-future-nepal-baseline-household-survey-mpy6-pm9d:latest"."feed_the_future_nepal_baseline_household_survey"
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/feed-the-future-nepal-baseline-household-survey-mpy6-pm9d 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/feed-the-future-nepal-baseline-household-survey-mpy6-pm9d: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)"
 

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/feed-the-future-nepal-baseline-household-survey-mpy6-pm9d" \
  --handler-options '{
    "domain": "data.usaid.gov",
    "tables": {
        "feed_the_future_nepal_baseline_household_survey": "mpy6-pm9d"
    }
}'

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/feed-the-future-nepal-baseline-household-survey-mpy6-pm9d is just another Postgres schema.

Related Documentation: