cdc-gov/ah-provisional-cancer-death-counts-by-month-and-2na8-fe6s
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 ah_provisional_cancer_death_counts_by_month_and table in this repository, by referencing it like:

"cdc-gov/ah-provisional-cancer-death-counts-by-month-and-2na8-fe6s:latest"."ah_provisional_cancer_death_counts_by_month_and"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "all_other_and_unspecified", -- All other and unspecified malignant neoplasms (C17,C23-C24,C26-C31,C37-C41,C44-C49,C51-C52,C57-C60,C62-C63,C66,C68-C69,C73-C80,C97)
    "malignant_neoplasms_of_1", -- Malignant neoplasms of lymphoid, hematopoietic and related tissue (C81-C96)
    "malignant_neoplasm_of_breast", -- Malignant neoplasm of breast (C50)
    "malignant_melanoma_of_skin", -- Malignant melanoma of skin (C43)
    "malignant_neoplasms_of_colon", -- Malignant neoplasms of colon, rectum and anus (C18-C21)
    "malignant_neoplasm_of_stomach", -- Malignant neoplasm of stomach (C16)
    "malignant_neoplasm_of", -- Malignant neoplasm of esophagus (C15)
    "malignant_neoplasms_c00_c97", -- Malignant neoplasms (C00-C97)
    "end_date", -- Last date of data period
    "malignant_neoplasms_of", -- Malignant neoplasms of meninges, brain and other parts of central nervous system (C70-C72)
    "malignant_neoplasms_of_kidney", -- Malignant neoplasms of kidney and renal pelvis (C64-C65)
    "malignant_neoplasm_of_ovary", -- Malignant neoplasm of ovary (C56)
    "malignant_neoplasms_of_corpus", -- Malignant neoplasms of corpus uteri and uterus, part unspecified (C54-C55)
    "malignant_neoplasms_of_lip", -- Malignant neoplasms of lip, oral cavity and pharynx (C00-C14)
    "age_group", -- Age group
    "sex", -- Sex
    "start_date", -- First date of data period
    "malignant_neoplasms_of_trachea", -- Malignant neoplasms of trachea, bronchus and lung (C33-C34)
    "malignant_neoplasms_of_liver", -- Malignant neoplasms of liver and intrahepatic bile ducts (C22)
    "malignant_neoplasm_of_bladder", -- Malignant neoplasm of bladder (C67)
    "malignant_neoplasm_of_cervix", -- Malignant neoplasm of cervix uteri (C53)
    "malignant_neoplasm_of_pancreas", -- Malignant neoplasm of pancreas (C25)
    "race_and_hispanic_origin", -- Race and Hispanic Origin Group
    "month", -- Month of reporting
    "year", -- Year of reporting
    "country", -- Jurisdiction of occurrence
    "data_as_of", -- Date of analysis
    "malignant_neoplasm_of_prostate", -- Malignant neoplasm of prostate (C61)
    "malignant_neoplasm_of_larynx" -- Malignant neoplasm of larynx (C32)
FROM
    "cdc-gov/ah-provisional-cancer-death-counts-by-month-and-2na8-fe6s:latest"."ah_provisional_cancer_death_counts_by_month_and"
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 cdc-gov/ah-provisional-cancer-death-counts-by-month-and-2na8-fe6s with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.cdc.gov. When you querycdc-gov/ah-provisional-cancer-death-counts-by-month-and-2na8-fe6s: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.cdc.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 \
  "cdc-gov/ah-provisional-cancer-death-counts-by-month-and-2na8-fe6s" \
  --handler-options '{
    "domain": "data.cdc.gov",
    "tables": {
        "ah_provisional_cancer_death_counts_by_month_and": "2na8-fe6s"
    }
}'

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, cdc-gov/ah-provisional-cancer-death-counts-by-month-and-2na8-fe6s is just another Postgres schema.