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_with table in this repository, by referencing it like:
"cdc-gov/covid19-case-surveillance-public-use-data-with-n8mc-b4w4:latest"."covid19_case_surveillance_public_use_data_with"or in a full query, like:
SELECT
":id", -- Socrata column ID
"current_status", -- What is the current status of this person? [Laboratory-confirmed case, Probable case]
"case_onset_interval", -- Weeks between earliest date and date of symptom onset.
"case_positive_specimen", -- Weeks between earliest date and date of first positive specimen collection
"race", -- Race [American Indian/Alaska Native; Asian; Black; Multiple/Other; Native Hawaiian/Other Pacific Islander; White; Unknown; Missing; NA, if value suppressed for privacy protection.]
"age_group", -- Age group [0 - 17 years; 18 - 49 years; 50 - 64 years; 65 + years; Unknown; Missing; NA, if value suppressed for privacy protection.]
"res_county", -- County of residence
"case_month", -- The earlier of month the Clinical Date (date related to the illness or specimen collection) or the Date Received by CDC
"symptom_status", -- What is the symptom status of this person? [Asymptomatic, Symptomatic, Unknown, Missing]
"sex", -- Sex [Female; Male; Other; Unknown; Missing; NA, if value suppressed for privacy protection.]
"county_fips_code", -- County FIPS code
"icu_yn", -- Was the patient admitted to an intensive care unit (ICU)? [Yes, No, Unknown, Missing]
"death_yn", -- Did the patient die as a result of this illness? [Yes; No; Unknown; Missing; NA, if value suppressed for privacy protection.]
"hosp_yn", -- Was the patient hospitalized? [Yes, No, Unknown, Missing]
"process", -- Under what process was the case first identified? [Clinical evaluation; Routine surveillance; Contact tracing of case patient; Multiple; Other; Unknown; Missing]
"ethnicity", -- Ethnicity [Hispanic; Non-Hispanic; Unknown; Missing; NA, if value suppressed for privacy protection.]
"state_fips_code", -- State FIPS code
"res_state", -- State of residence
"exposure_yn", -- In the 14 days prior to illness onset, did the patient have any of the following known exposures: domestic travel, international travel, cruise ship or vessel travel as a passenger or crew member, workplace, airport/airplane, adult congregate living facility (nursing, assisted living, or long-term care facility), school/university/childcare center, correctional facility, community event/mass gathering, animal with confirmed or suspected COVID-19, other exposure, contact with a known COVID-19 case? [Yes, Unknown, Missing]
"underlying_conditions_yn" -- Did the patient have one or more of the underlying medical conditions and risk behaviors: diabetes mellitus, hypertension, severe obesity (BMI>40), cardiovascular disease, chronic renal disease, chronic liver disease, chronic lung disease, other chronic diseases, immunosuppressive condition, autoimmune condition, current smoker, former smoker, substance abuse or misuse, disability, psychological/psychiatric, pregnancy, other. [Yes, No, blank]
FROM
"cdc-gov/covid19-case-surveillance-public-use-data-with-n8mc-b4w4:latest"."covid19_case_surveillance_public_use_data_with"
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-with-n8mc-b4w4 with SQL in under 60 seconds.
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, 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 (like this repository), where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Cloning Data
Because cdc-gov/covid19-case-surveillance-public-use-data-with-n8mc-b4w4:latest is a Splitgraph Image, you can clone the data from Spltgraph Cloud to your local engine, where you can query it like any other Postgres database, using any of your existing tools.
First, install Splitgraph if you haven't already.
Clone the metadata with sgr clone
This will be quick, and does not download the actual data.
sgr clone cdc-gov/covid19-case-surveillance-public-use-data-with-n8mc-b4w4Checkout the data
Once you've cloned the data, you need to "checkout" the tag that you want. For example, to checkout the latest tag:
sgr checkout cdc-gov/covid19-case-surveillance-public-use-data-with-n8mc-b4w4:latestThis will download all the objects for the latest tag of cdc-gov/covid19-case-surveillance-public-use-data-with-n8mc-b4w4 and load them into the Splitgraph Engine. Depending on your connection speed and the size of the data, you will need to wait for the checkout to complete. Once it's complete, you will be able to query the data like you would any other Postgres database.
Alternatively, use "layered checkout" to avoid downloading all the data
The data in cdc-gov/covid19-case-surveillance-public-use-data-with-n8mc-b4w4:latest is 0 bytes. If this is too big to download all at once, or perhaps you only need to query a subset of it, you can use a layered checkout.:
sgr checkout --layered cdc-gov/covid19-case-surveillance-public-use-data-with-n8mc-b4w4:latestThis will not download all the data, but it will create a schema comprised of foreign tables, that you can query as you would any other data. Splitgraph will lazily download the required objects as you query the data. In some cases, this might be faster or more efficient than a regular checkout.
Read the layered querying documentation to learn about when and why you might want to use layered queries.
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-with-n8mc-b4w4 is just another Postgres schema.