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 adult_hospital_inpatient_sepsis_performance table in this repository, by referencing it like:
"health-data-ny-gov/adult-hospital-inpatient-sepsis-performance-hj24-gmxx:latest"."adult_hospital_inpatient_sepsis_performance"or in a full query, like:
SELECT
":id", -- Socrata column ID
"_6_hour_bundle_quintile_of", -- The quintile of hospital performance relative to all reporting hospitals for the 6-Hour Bundle measure. Values range from 1-Lowest quintile of performance to 5-Highest quintile of performance.
"discharge_year", -- The year of the hospital severe sepsis discharge.
"data_dictionary_version", -- Version of the data dictionary that was used for data collection.
"protocol_initiation_quintile", -- The quintile of hospital performance relative to all reporting hospitals for the protocol initiation measure. Values range from 1-Lowest quintile of performance to 5-Highest quintile of performance.
"_3_hour_bundle_denominator", -- The total number of cases eligible for completion of the 3-Hour Management Bundle.
"_6_hour_bundle_denominator", -- The total number of cases eligible for completion of the 6-Hour Management Bundle.
"met_3_hour_bundle", -- The percentage of eligible cases for whom the 3-Hour Management Bundle was completed.
"risk_adujsted_mortality", -- The total number of cases eligible to be included in calculation of facilities’ Risk Adjusted Mortality Rate.
"met_6_hour_bundle", -- The percentage of eligible cases for whom the 6-Hour Management Bundle was completed.
"observed_deaths", -- The total number observed in-hospital deaths as determined by patient discharge status.
"high_low_performer", -- Performance status for risk adjusted mortality rate (RAMR) determined by a significant (α=0.05) difference between the facilities' RAMR and the statewide rate; 'LOW' - facilities performed significantly worse than the statewide rate, 'HIGH' - facilities performed significantly better than the statewide rate, and ‘AVERAGE’ - facilities did not significantly differ from the statewide rate.
"protocol_initiated", -- The percentage of eligible cases for whom the sepsis protocol was initiated.
"_3_hour_bundle_quintile_of", -- The quintile of hospital performance relative to all reporting hospitals for the 3-Hour Bundle measure. Values range from 1-Lowest quintile of performance to 5-Highest quintile of performance.
"pfi", -- The facility’s Permanent Facility Identifier (PFI) as maintained by the NYSDOH Division of Health Facility Planning.
"facility_name", -- The name of the hospital in which the discharge took place.
"risk_adjusted_mortality_rate", -- The number of observed deaths divided by the number of expected deaths (as determined by the risk adjustment model), multiplied by the statewide mortality rate.
"protocol_initiation" -- The total number of cases eligible to have sepsis protocol initiated.
FROM
"health-data-ny-gov/adult-hospital-inpatient-sepsis-performance-hj24-gmxx:latest"."adult_hospital_inpatient_sepsis_performance"
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 health-data-ny-gov/adult-hospital-inpatient-sepsis-performance-hj24-gmxx 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 health-data-ny-gov/adult-hospital-inpatient-sepsis-performance-hj24-gmxx: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 health-data-ny-gov/adult-hospital-inpatient-sepsis-performance-hj24-gmxxCheckout 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 health-data-ny-gov/adult-hospital-inpatient-sepsis-performance-hj24-gmxx:latestThis will download all the objects for the latest tag of health-data-ny-gov/adult-hospital-inpatient-sepsis-performance-hj24-gmxx 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 health-data-ny-gov/adult-hospital-inpatient-sepsis-performance-hj24-gmxx: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 health-data-ny-gov/adult-hospital-inpatient-sepsis-performance-hj24-gmxx: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, health-data-ny-gov/adult-hospital-inpatient-sepsis-performance-hj24-gmxx is just another Postgres schema.