internal-agtransport-usda-gov/export-sales-wnn7-29tu
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 export_sales table in this repository, by referencing it like:

"internal-agtransport-usda-gov/export-sales-wnn7-29tu:latest"."export_sales"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "unit", -- Metric tons.
    "outstanding_sales_total", -- Sum of CMY and NMY outstanding sales.
    "myear", -- Calculated based on date, where corn and soybean marketing years start in September and wheat starts in June.
    "totcommcmy", -- The grand total of outstanding sales plus accumulated exports by country and/or commodity at any given time during the marketing year. 
    "netsalesnmy", -- For the next marketing year, the sum total resulting from new export sales, increases resulting from changes in destination, decreases resulting from changes in destination, decreases resulting from purchases from foreign sellers, and cancellations resulting from contract adjustments, buybacks, loading tolerances, changes in marketing year, or change in commodity.
    "marketingmonth", -- Calculated for each commodity by offsetting the calendar year month to match the marketing year.
    "accexportscmy", -- Accumulated shipments of reportable commodities from the beginning of the marketing year (for each commodity) to the current week ending date. * Note:Accumulated exports are revised periodically due to adjustments made by reporting exporters. 
    "country", -- Export destination.
    "year", -- Year number calculated from the reported week ending date (Date field). Week ending dates falling on or after January 4 correspond to the calendar year of the reported week. Week ending dates falling on or before January 3 correspond to the previous calendar year. For example, a week ending date falling on January 2, 2018 would be “2017.” Values range from 2000 to the present.
    "week", -- Week number calculated from the reported week ending date (Date field). Week 1 is the first week of the year with a week ending date falling on or after January 4. In other words, it is the first reported week of data which includes four days in the new year. Values range from 1 to 52 or 53, depending on the year.
    "date", -- Calendar week ending date.
    "net_sales_total", -- Sum of CMY and NMY net sales.
    "month", -- Month number calculated from the reported week ending date (Date field). Week ending dates falling before the fourth day of the month are assigned to the previous month. For example, a week ending date falling on February 1st, 2nd, or 3rd would correspond to “1” (January), since there are less than four days in the reported week that fall in February. Values range from 1 through 12, where 1 refers to January, 2 to February, etc.
    "wkexportscmy", -- Shipments of reportable commodities exported against sales for a reporting week Friday through Thursday. 
    "my", -- When the calendar year spans the marketing year end, this row designates the values attributable to the previous and next marketing years.
    "commodity", -- Wheat, Corn, or Soybeans.
    "outsalescmy", -- The total outstanding export sales contracts for the current marketing year that have not yet been shipped.
    "grosalescmy", -- Includes increases resulting from new export sales, contract adjustments, loading tolerances, changes in marketing year, change in commodity or sales made against exports for the exporter's own account. Note: Gross new sales will include sales that were unshipped (carryover sales) at the end of the marketing year. 
    "netsalescmy", -- For the current marketing year, the sum total resulting from new export sales, increases resulting from changes in destination, decreases resulting from changes in destination, decreases resulting from purchases from foreign sellers, and cancellations resulting from contract adjustments, buybacks, loading tolerances, changes in marketing year, or change in commodity.
    "outsalesnmy", -- The total outstanding export sales contracts for the next marketing year.
    "type" -- Subcategory of commodity, if it exists.
FROM
    "internal-agtransport-usda-gov/export-sales-wnn7-29tu:latest"."export_sales"
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 internal-agtransport-usda-gov/export-sales-wnn7-29tu with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at internal.agtransport.usda.gov. When you queryinternal-agtransport-usda-gov/export-sales-wnn7-29tu: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 internal.agtransport.usda.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 \
  "internal-agtransport-usda-gov/export-sales-wnn7-29tu" \
  --handler-options '{
    "domain": "internal.agtransport.usda.gov",
    "tables": {
        "export_sales": "wnn7-29tu"
    }
}'

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, internal-agtransport-usda-gov/export-sales-wnn7-29tu is just another Postgres schema.