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 estimated_prevalence_and_new_diagnoses_of_hiv_and table in this repository, by referencing it like:
"pa-gov/estimated-prevalence-and-new-diagnoses-of-hiv-and-buk2-94cb:latest"."estimated_prevalence_and_new_diagnoses_of_hiv_and"or in a full query, like:
SELECT
":id", -- Socrata column ID
"county_name", -- Last known county of residence 2016. Address data is obtained from Pennsylvania National Electronic Disease Surveillance System and the enhanced HIV/AIDS Reporting System.
"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
"year", -- The Year of the count for this particular row. This data set is by Calendar year.
"location_1", -- Latitude and Longitude. One random point within the county boundary to help with creating a boundary map within the county lines.
"county_fips_code", -- The FIPS county code is a five-digit Federal Information Processing Standard (FIPS) code (FIPS 6-4) which uniquely identifies counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. This is the 3-digit part of the 5-digit county FIPS code specifically standing for the county.
"county_code_number", -- The number that correlates to the County code. 1-67 with 0 representing the state of Pennsylvania.
"county_code", -- The text format of the county code number. This gives a 2-digit format to match to other files.
"location_1_city",
"prevalence_hiv_disease_among_idu_description", -- A description for the Prevalence HIV Disease Among Injection Drug Use (IDU) count column. Providing information about the null fields and a description to use with visualizations. Prevalence means the person had been diagnosed sometime before the end of that calendar year (anytime in any prior year or current year before the end of the year) and still alive as far as we know at the end of the year. So a person diagnosed in 2005 and still alive and living in PA (e.g., in Allegheny County) at the end of 2012 would be counted as a prevalent case for Allegheny County for that year.
"prevalence_hiv_disease_among_idu_count", -- Persons living with HIV Disease with Injection Drug Use (IDU) as an identified risk factor. Risk data is obtained from provider reports (e.g., medical records) and or patient interviews by Partner Counseling and Referral Services provided by PADOH and other disease investigators. Injection Drug Use risk is also obtained by matching HIV surveillance data to other data sets that indicate Injection Drug Use risk (e.g., Ryan White CareWare data). IDU: use of non-prescribed injection drugs (e.g., heroin, fentanyl, cocaine, etc.) HIV Disease: Confirmed infection with the Human Immunodeficiency Virus (HIV). Acquired Immunodeficiency Syndrome (AIDS) is a stage of HIV Disease marked by a low CD4 count and/or certain co-morbid conditions. Prevalence means the person had been diagnosed sometime before the end of that calendar year (anytime in any prior year or current year before the end of the year) and still alive as far as we know at the end of the year. So a person diagnosed in 2005 and still alive and living in PA (e.g., in Allegheny County) at the end of 2012 would be counted as a prevalent case for Allegheny County for that year.
"new_diagnoses_hiv_disease_among_idu_description", -- A description for the New Diagnoses HIV Disease Among Injection Drug Use (IDU) count column. Providing information about the null fields and a description to use with visualizations. Newly diagnosed is a proxy for “incidence.” We shy away from the term “incidence” because it implies newly infected (meaning somebody was newly exposed and infected with HIV) whereas we mean by “Newly Diagnosed” is somebody who was newly found to be infected, even though they may have been “infected” many years ago but did not know it or was not tested to know about it. So newly diagnosed means newly identified cases in a given year. So people who were diagnosed with HIV disease in a given year in a given place (e.g., Allegheny County) would be a newly diagnosed case. If that person is still alive at the end of the year (and still living in Allegheny County) they will also be counted as a prevalent case for Allegheny County. If they died (before the end of the year) or move out of Allegheny County (before the end of that year) then they would no longer be counted as a “Prevalent” case.
"new_diagnoses_hiv_disease_among_idu_count", -- Persons newly diagnosed with Human Immunodeficiency Virus (HIV) Disease with Injection Drug Use (IDU) as an identified risk factor. Risk data is obtained from provider reports (e.g., medical records) and or patient interviews by Partner Counseling and Referral Services provided by PA Department of Health and other disease investigators. Injection Drug Use risk is also obtained by matching HIV surveillance data to other data sets that indicate Injection Drug Use risk (e.g., Ryan White CareWare data). IDU: use of non-prescribed injection drugs (e.g., heroin, fentanyl, cocaine, etc.) HIV Disease: Confirmed infection with the Human Immunodeficiency Virus (HIV). Acquired Immunodeficiency Syndrome (AIDS) is a stage of HIV Disease marked by a low CD4 count and/or certain co-morbid conditions. Newly diagnosed is a proxy for “incidence.” We shy away from the term “incidence” because it implies newly infected (meaning somebody was newly exposed and infected with HIV) whereas we mean by “Newly Diagnosed” is somebody who was newly found to be infected, even though they may have been “infected” many years ago but did not know it or was not tested to know about it. So newly diagnosed means newly identified cases in a given year. So people who were diagnosed with HIV disease in a given year in a given place (e.g., Allegheny County) would be a newly diagnosed case. If that person is still alive at the end of the year (and still living in Allegheny County) they will also be counted as a prevalent case for Allegheny County. If they died (before the end of the year) or move out of Allegheny County (before the end of that year) then they would no longer be counted as a “Prevalent” case.
"prevalence_hiv_disease_description", -- A description for the Prevalence HIV Disease count column. Providing information about the null fields and a description to use with visualizations. Prevalence means the person had been diagnosed sometime before the end of that calendar year (anytime in any prior year or current year before the end of the year) and still alive as far as we know at the end of the year. So a person diagnosed in 2005 and still alive and living in PA (e.g., in Allegheny County) at the end of 2012 would be counted as a prevalent case for Allegheny County for that year.
"prevalence_hiv_disease_count", -- Count of Persons living with HIV Disease. Human Immunodeficiency Virus (HIV) Disease includes all persons with confirmed HIV infection. This includes cases of AIDS and HIV(non-AIDS). Confirmed HIV disease is defined as (1) having a positive HIV confirmatory test, (2) a detectable HIV viral load result and/or (3) a physician confirmed diagnosis of HIV disease. All PLWH (People Living With HIV/AIDS) have had a confirmed HIV diagnosis and were presumed to be alive as of last day of each year. HIV Disease: Confirmed infection with the Human Immunodeficiency Virus (HIV). Acquired Immunodeficiency Syndrome (AIDS) is a stage of HIV Disease marked by a low CD4 count and/or certain co-morbid conditions. Prevalence means the person had been diagnosed sometime before the end of that calendar year (anytime in any prior year or current year before the end of the year) and still alive as far as we know at the end of the year. So a person diagnosed in 2005 and still alive and living in PA (e.g., in Allegheny County) at the end of 2012 would be counted as a prevalent case for Allegheny County for that year.
"new_diagnoses_hiv_disease_description", -- A description for the New Diagnoses HIV Disease count column. Providing information about the null fields and a description to use with visualizations. Newly diagnosed is a proxy for “incidence.” We shy away from the term “incidence” because it implies newly infected (meaning somebody was newly exposed and infected with HIV) whereas we mean by “Newly Diagnosed” is somebody who was newly found to be infected, even though they may have been “infected” many years ago but did not know it or was not tested to know about it. So newly diagnosed means newly identified cases in a given year. So people who were diagnosed with HIV disease in a given year in a given place (e.g., Allegheny County) would be a newly diagnosed case. If that person is still alive at the end of the year (and still living in Allegheny County) they will also be counted as a prevalent case for Allegheny County. If they died (before the end of the year) or move out of Allegheny County (before the end of that year) then they would no longer be counted as a “Prevalent” case.
"new_diagnoses_hiv_disease_count", -- Persons newly diagnosed with Human Immunodeficiency Disease (HIV). HIV Disease includes all persons with confirmed HIV infection. This includes cases of AIDS and HIV(non-AIDS). Confirmed HIV disease is defined as (1) having a positive HIV confirmatory test, (2) a detectable HIV viral load result and/or (3) a physician confirmed diagnosis of HIV disease. HIV Disease: Confirmed infection with the Human Immunodeficiency Virus (HIV). Acquired Immunodeficiency Syndrome (AIDS) is a stage of HIV Disease marked by a low CD4 count and/or certain co-morbid conditions. Newly diagnosed is a proxy for “incidence.” We shy away from the term “incidence” because it implies newly infected (meaning somebody was newly exposed and infected with HIV) whereas we mean by “Newly Diagnosed” is somebody who was newly found to be infected, even though they may have been “infected” many years ago but did not know it or was not tested to know about it. So newly diagnosed means newly identified cases in a given year. So people who were diagnosed with HIV disease in a given year in a given place (e.g., Allegheny County) would be a newly diagnosed case. If that person is still alive at the end of the year (and still living in Allegheny County) they will also be counted as a prevalent case for Allegheny County. If they died (before the end of the year) or move out of Allegheny County (before the end of that year) then they would no longer be counted as a “Prevalent” case.
"location_1_state",
":@computed_region_nmsq_hqvv",
":@computed_region_d3gw_znnf",
":@computed_region_r6rf_p9et",
":@computed_region_rayf_jjgk",
":@computed_region_amqz_jbr4",
"location_1_zip",
"location_1_address"
FROM
"pa-gov/estimated-prevalence-and-new-diagnoses-of-hiv-and-buk2-94cb:latest"."estimated_prevalence_and_new_diagnoses_of_hiv_and"
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/estimated-prevalence-and-new-diagnoses-of-hiv-and-buk2-94cb 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/estimated-prevalence-and-new-diagnoses-of-hiv-and-buk2-94cb: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/estimated-prevalence-and-new-diagnoses-of-hiv-and-buk2-94cbCheckout 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/estimated-prevalence-and-new-diagnoses-of-hiv-and-buk2-94cb:latestThis will download all the objects for the latest tag of pa-gov/estimated-prevalence-and-new-diagnoses-of-hiv-and-buk2-94cb 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/estimated-prevalence-and-new-diagnoses-of-hiv-and-buk2-94cb: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/estimated-prevalence-and-new-diagnoses-of-hiv-and-buk2-94cb: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/estimated-prevalence-and-new-diagnoses-of-hiv-and-buk2-94cb is just another Postgres schema.