Query the Data Delivery Network
Query the DDNThe 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 covid19_case_surveillance_public_use_data table in this repository, by referencing it like:
"cdc-gov/covid19-case-surveillance-public-use-data-vbim-akqf:latest"."covid19_case_surveillance_public_use_data"or in a full query, like:
SELECT
":id", -- Socrata column ID
"race_ethnicity_combined", -- Race and ethnicity (combined): American Indian/Alaska Native, Non-Hispanic; Asian, Non-Hispanic; Black, Non-Hispanic; Multiple/Other, Non-Hispanic; Native Hawaiian/Other Pacific Islander, Non-Hispanic; White, Non-Hispanic; Hispanic/Latino; Unknown; Missing; NA. If more than race was reported, race was categorized into multiple/other races.
"sex", -- Sex (Case Report Form): Male; Female; Unknown; Other; Missing; NA
"current_status", -- Case Status (Case Report Form: What is the current status of this person?) -- Values: Laboratory-confirmed case; Probable case; Please see latest CSTE case definition for more information.
"onset_dt", -- Symptom onset date, if symptomatic (Case Report Form)
"death_yn", -- Death status (Case Report Form: Did the patient die as a result of this illness?) -- Values: Yes; No; Unknown; Missing;
"pos_spec_dt", -- Date of first positive specimen collection (Case Report Form)
"medcond_yn", -- Presence of underlying comorbidity or disease (Case Report Form: Pre-existing medical conditions?) -- Values: Yes; No; Unknown; Missing;
"icu_yn", -- ICU admission status (Case Report Form: Was the patient admitted to an intensive care unit (ICU)?) -- Values: Yes; No; Unknown; Missing;
"hosp_yn", -- Hospitalization status (Case Report Form: Was the patient hospitalized?) -- Values: Yes; No; Unknown; Missing;
"age_group", -- Age Group: 0 - 9 Years; 10 - 19 Years; 20 - 39 Years; 40 - 49 Years; 50 - 59 Years; 60 - 69 Years; 70 - 79 Years; 80 + Years; Missing; NA; The age group categorizations were populated using the age value that was reported on the case report form. Date of birth was used to fill in missing/unknown age values using the difference in time between date of birth and onset date.
"cdc_report_dt", -- Date case was first reported to the CDC. Calculated date-- Depreciated; CDC recommends researchers use cdc_case_earliest_dt in time series and other analyses. This date was populated using the date at which a case record was first submitted to the database. If missing, then the report date entered on the case report form was used. If missing, then the date at which the case first appeared in the database was used. If none available, then left blank.
"cdc_case_earliest_dt" -- The earlier of the Clinical Date (date related to the illness or specimen collection) or the Date Received by CDC. Calculated date-- Cdc_case_earliest_dt uses the best available date from the set of dates related to illness/specimen collection and the set of dates related to when a case is reported. It is an option to end-users who need a date variable with optimized completeness. The logic of cdc_case_earliest_dt is to use the non-null date of one variable when the other is null and to use the earliest valid date when both dates are available. If no date available, then left blank.
FROM
"cdc-gov/covid19-case-surveillance-public-use-data-vbim-akqf:latest"."covid19_case_surveillance_public_use_data"
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/covid19-case-surveillance-public-use-data-vbim-akqf 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/covid19-case-surveillance-public-use-data-vbim-akqf: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
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/covid19-case-surveillance-public-use-data-vbim-akqf" \
--handler-options '{
"domain": "data.cdc.gov",
"tables": {
"covid19_case_surveillance_public_use_data": "vbim-akqf"
}
}'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/covid19-case-surveillance-public-use-data-vbim-akqf is just another Postgres schema.