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 rate_of_hospitalizations_for_opioid_overdose_per table in this repository, by referencing it like:
"pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-yitu-pit9:latest"."rate_of_hospitalizations_for_opioid_overdose_per"or in a full query, like:
SELECT
":id", -- Socrata column ID
":@computed_region_nmsq_hqvv", -- This column was automatically created in order to record in what polygon from the dataset 'Pennsylvania County Boundaries' (nmsq-hqvv) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_d3gw_znnf", -- This column was automatically created in order to record in what polygon from the dataset 'Pa Senatorial Districts (2017-01)' (d3gw-znnf) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"fips_county_code", -- The FIPS county code is a five-digit Federal Information Processing Standard (FIPS) code which uniquely identifies counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. Values: 42000=Pennsylvania commonwealth total, 42001=Adams, 42003=Allegheny, …, 42133=York
"type_of_rate", -- Describes the Type of Rate displayed. Values: “Rate of hospitalizations for heroin overdose per 100000 population” indicates the number of hospitalizations for heroin overdose for Pennsylvania residents age 15 and above divided by the total population age 15 and above for that county; that percentage is then multiplied by 100,000 and rounded to the nearest tenth. “Rate of hospitalizations for pain medication overdose per 100000 population” indicates the number of hospitalizations for opioid pain medication overdose for Pennsylvania residents age 15 and above divided by the total population age 15 and above for that county; that percentage is then multiplied by 100,000 and rounded to the nearest tenth. “Not Reported due to low volume” – indicates that the rate of hospitalizations for opioid overdose has not been displayed (is blank) due to low volume of hospitalizations for opioid overdose.
"geocoded_column_city",
"geocoded_column_state",
"geocoded_column_address",
"county_fips_code", -- The County FIPS Code is the equivalent of the last three digits in the FIPS County Code. It uniquely identifies the counties throughout Pennsylvania. Values: 000=Pennsylvania commonwealth total, 001=Adams, 003=Allegheny, …, 133=York
":@computed_region_amqz_jbr4", -- This column was automatically created in order to record in what polygon from the dataset 'Municipality Boundary' (amqz-jbr4) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_r6rf_p9et", -- This column was automatically created in order to record in what polygon from the dataset 'Pa House Districts (2017-01)' (r6rf-p9et) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_rayf_jjgk", -- This column was automatically created in order to record in what polygon from the dataset 'Pa School Districts (2017)' (rayf-jjgk) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"geocoded_column_zip",
"state_fips_code", -- The State FIPS Code is the equivalent of the first two digits in the FIPS County Code. It uniquely identifies the state of Pennsylvania. Value: 42= Pennsylvania.
"category", -- Specifies whether the measurement is for hospitalizations for heroin overdose or for opioid pain medication overdose. Values: 1) “Heroin” = Hospitalizations with a principal diagnosis of heroin overdose, and 2) “Pain Medication” = Hospitalizations with a principal diagnosis of opioid pain medication overdose.
"time_period_date_start", -- Beginning of the reporting period that covers the hospitalization's discharge date.
"county_code_number", -- Two-digit code which uniquely identifies each county. Values: 00=Pennsylvania commonwealth total, 01=Adams, 02=Allegheny, …, 67=York
"time_period_date_end", -- End of the reporting period that covers the hospitalization's discharge date.
"time_period", -- Reporting period that the measurement is based upon; "CY-" prefix is shorthand for "calendar year", which begins on January 1st of the stated year and ends on December 31st of the stated year.
"county_name", -- Geographic region in Pennsylvania representing the county where the patient resides. Special values: COMMONWEALTH = Pennsylvania commonwealth total (across all counties, including the totals of the suppressed county lines)
"rate_of_hospitalizations", -- Calculated rate of hospitalizations for heroin overdose or for opioid pain medication overdose per 100,000 residents age 15 and above for the county of residence during the specified time period. Type of Rate column provides additional details.
"geocoded_column" -- Latitude and Longitude georeferenced column generic point for each county so the data can placed in a mapping visual.
FROM
"pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-yitu-pit9:latest"."rate_of_hospitalizations_for_opioid_overdose_per"
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 pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-yitu-pit9 with SQL in under 60 seconds.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.pa.gov. When you querypa-gov/rate-of-hospitalizations-for-opioid-overdose-per-yitu-pit9: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.pa.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 \
"pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-yitu-pit9" \
--handler-options '{
"domain": "data.pa.gov",
"tables": {
"rate_of_hospitalizations_for_opioid_overdose_per": "yitu-pit9"
}
}'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, pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-yitu-pit9 is just another Postgres schema.