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 naloxone_first_responder_program_2017_current table in this repository, by referencing it like:
"pa-gov/naloxone-first-responder-program-2017-current-xqrx-inrr:latest"."naloxone_first_responder_program_2017_current"or in a full query, like:
SELECT
":id", -- Socrata column ID
"county_fips_code", -- These are the last 3 digits of the 5-digit Federal Information Processing Standard (FIPS) code that designate the State association. Each State has its own 2-digit number and each County within the state has its own 3-digit number which are combined into a 5-digit number to uniquely identify every US county. For more technical details : Federal Information Processing Standards Publications (FIPS PUBS) are issued by the National Institute of Standards and Technology (NIST) after approval by the Secretary of Commerce pursuant to Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235. Federal Information Processing Standard (FIPS) 6-4, Counties and Equivalent Entities of the U.S., Its Possessions, and Associated Areas -- 90 Aug 31 , provides the names and codes that represent the counties and other entities treated as equivalent legal and/or statistical subdivisions of the 50 States, the District of Columbia, and the possessions and freely associated areas of the United States. Counties are considered to be the "first-order subdivisions" of each State and statistically equivalent entity, regardless of their local designations (county, parish, borough, etc.). Information gathered from census data - https://www.census.gov/library/reference/code-lists/ansi.html
"state_fips_code", -- These are the first 2 digits of the 5-digit Federal Information Processing Standard (FIPS) code that designate the State association. Each State has its own 2-digit number and each County within the state has its own 3-digit number which are combined into a 5-digit number to uniquely identify every US county. For more technical details : Federal Information Processing Standards Publications (FIPS PUBS) are issued by the National Institute of Standards and Technology (NIST) after approval by the Secretary of Commerce pursuant to Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235. Federal Information Processing Standard (FIPS) 6-4, Counties and Equivalent Entities of the U.S., Its Possessions, and Associated Areas -- 90 Aug 31 , provides the names and codes that represent the counties and other entities treated as equivalent legal and/or statistical subdivisions of the 50 States, the District of Columbia, and the possessions and freely associated areas of the United States. Counties are considered to be the "first-order subdivisions" of each State and statistically equivalent entity, regardless of their local designations (county, parish, borough, etc.). Information gathered from census data - https://www.census.gov/library/reference/code-lists/ansi.html
"reversals_other_eligible", -- Overdose Reversals reported County- Other Eligible First Responders. • Adult Probation and Parole Officers • Juvenile Probation Officers • County Correctional Facilities • Constables • Afterschool Program Personnel • School Personnel • Public Transit Drivers • Domestic Violence Shelter Personnel • Homeless Shelter Personnel • Public Defenders Offices • Library Personnel • Drug Treatment Providers • Children and Youth Workers • Any other community group legally organized and trained to respond to overdose emergencies and administer Intranasal naloxone.
"kits_provided_law_enforcement", -- Cumulative number of kits of Naloxone received by each first responder type- Law Enforcement. Municipal Police, Pennsylvania State Police, Campus Police, Sheriffs, Transit Police and District Attorney's Offices.
"kits_provided_fire_department", -- Cumulative number of kits of Naloxone received by each first responder type- Fire Department. Fire Fighting Companies to include both volunteer and professional, as well as the Philadelphia Fire Department which also includes Emergency Medical Services.
"kits_provided_ems", -- Cumulative number of kits of Naloxone received by each first responder type- EMS. Emergency Medical Services to include Advanced Life Support, Basic Life Support and Emergency Medical Technicians.
"cumulative_doses_left_behind", -- Cumulative number of Kits of naloxone left behind by EMS or redistributed per the current statewide standing order signed by Pennsylvania’s Physician General for Naloxone.
"cumulative_individuals", -- Cumulative number of individuals on whom naloxone was administered by county. A dose, is one 4 mg nasal spray.
"cumulative_doses_used", -- Cumulative number of doses of naloxone reported used by county in an overdose event. A dose, is one 4 mg nasal spray.
"cumulative_kits_provided", -- Cumulative number of Kits of intranasal naloxone provided to each County by PCCD. A Kit contains two doses. A dose, is one 4 mg nasal spray.
"county_longitude_point", -- This is a longitude generic point within the county.
"county_name", -- Pennsylvania county name.
"reversals_fire_department", -- Overdose Reversals reported County- Fire Department. Fire Fighting Companies to include both volunteer and professional, as well as the Philadelphia Fire Department which also includes Emergency Medical Services.
"reversals_ems", -- Overdose Reversals reported County- EMS. Emergency Medical Services to include Advanced Life Support, Basic Life Support and Emergency Medical Technicians.
"kits_provided_other_eligible", -- Cumulative number of kits of Naloxone received by each first responder type- Other Eligible First Responders. • Adult Probation and Parole Officers • Juvenile Probation Officers • County Correctional Facilities • Constables • Afterschool Program Personnel • School Personnel • Public Transit Drivers • Domestic Violence Shelter Personnel • Homeless Shelter Personnel • Public Defenders Offices • Library Personnel • Drug Treatment Providers • Children and Youth Workers • Any other community group legally organized and trained to respond to overdose emergencies and administer Intranasal naloxone.
"cumulative_overdose_reversals", -- Overdose reversals by county.
"county_latitude_point", -- This is a latitude generic point within the county.
"county_code", -- Pennsylvania County number in alphabetical order. Pennsylvania has 67 counties.
"georeferenced_location", -- This is a georeferenced latitude and longitude generic point within the county to help create map visualizations by county.
"reporting_period_begin", -- The beginning date of the report.
"reporting_period", -- The end date of the report.
"reversals_law_enfocement", -- Overdose Reversals reported County- Law Enforcement. Municipal Police, Pennsylvania State Police, Campus Police, Sheriffs, Transit Police and District Attorney's Offices.
":@computed_region_rayf_jjgk", -- This column was automatically created in order to record in what polygon from the dataset 'Pa School Districts (2019-06)' (rayf-jjgk) the point in column 'georeferenced_location' 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 (2020-01)' (r6rf-p9et) the point in column 'georeferenced_location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@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 'georeferenced_location' 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 (2020-01)' (d3gw-znnf) the point in column 'georeferenced_location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@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 'georeferenced_location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
FROM
"pa-gov/naloxone-first-responder-program-2017-current-xqrx-inrr:latest"."naloxone_first_responder_program_2017_current"
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/naloxone-first-responder-program-2017-current-xqrx-inrr 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 pa-gov/naloxone-first-responder-program-2017-current-xqrx-inrr: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 pa-gov/naloxone-first-responder-program-2017-current-xqrx-inrrCheckout 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 pa-gov/naloxone-first-responder-program-2017-current-xqrx-inrr:latestThis will download all the objects for the latest tag of pa-gov/naloxone-first-responder-program-2017-current-xqrx-inrr 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 pa-gov/naloxone-first-responder-program-2017-current-xqrx-inrr: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 pa-gov/naloxone-first-responder-program-2017-current-xqrx-inrr: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, pa-gov/naloxone-first-responder-program-2017-current-xqrx-inrr is just another Postgres schema.