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 count_and_rate_of_court_cases_and_drug_court table in this repository, by referencing it like:
"pa-gov/count-and-rate-of-court-cases-and-drug-court-e4tx-vmn6:latest"."count_and_rate_of_court_cases_and_drug_court"or in a full query, like:
SELECT
":id", -- Socrata column ID
"county_code_number", -- Pennsylvania county code provided as a number (1-67 for counties, 0 for Commonwealth).
":@computed_region_nmsq_hqvv",
":@computed_region_d3gw_znnf",
":@computed_region_amqz_jbr4",
":@computed_region_r6rf_p9et",
":@computed_region_rayf_jjgk",
"year", -- Calendar year of Court of Common Pleas disposition date or adult drug court discharge date (January 1–December 31).
"court_of_common_pleas_opioid_1", -- Describes Court of Common Pleas opioid cases.
"gender", -- Gender of defendant.
"opioid_drug_court_cases_1", -- Describes adult drug participants self-reporting opioid use.
"successfully_completed_opioid_1", -- Describes adult drug court participants self-reporting opioid use successfully graduating.
"court_of_common_pleas_cases_1", -- Describes Court of Common Pleas cases.
"geographic_area", -- Region for measure, either total for Commonwealth or individual county.
"geographic_name", -- Name of geographic area.
"drug_court_cases", -- Number of discharged adult drug court participants exiting a problem-solving court.
"successfully_completed_drug_1", -- Describes adult drug court participants successfully graduating.
"county_code_text", -- Pennsylvania county code provided as text (1-67 for counties sorted alphabetically, 0 for Commonwealth).
"time_period", -- Period of Court of Common Pleas disposition dates (annual or quarterly) or adult drug court discharge dates (annual only).
"percent_of_drug_court_cases_1", -- Describes percent of adult drug court participants self-reporting opioid use.
"drug_court_cases_description", -- Describes adult drug court participants.
"percent_of_drug_court_cases_3", -- Describes percent of adult drug court participants successfully graduating.
"longitude", -- Longitude coordinates in degrees for a centroid point for geographic area.
"geocoded_column", -- Georeferenced latitude and longitude point which can be used to create a map.
"age", -- Age group of defendant (18 years and above).
"time_period_dates", -- Start and end dates of time period.
"court_of_common_pleas_opioid", -- Number of Court of Common Pleas cases with a specified opioid drug.
"court_of_common_pleas_cases", -- Total number of Court of Common Pleas criminal cases disposed; used to calculate the rate (total cases for the geographic area and time period) or the percentage (total opioid cases for the geographic area and time period).
"rate_of_court_of_common_pleas", -- Rate of annual Court of Common Pleas cases specifying opioids per 1,000 cases in the state or the county.
"type_of_rate", -- Describes the rate of Court of Common Pleas opioid cases.
"percent_of_court_of_common", -- Percent of Court of Common Pleas cases with opioid use for a given time period and group (sex, age).
"percent_of_court_of_common_1", -- Describes percent of Court of Common Pleas cases with opioid use.
"successfully_completed_drug", -- Adult drug court participants discharged and successfully graduated from program.
"opioid_drug_court_cases", -- Adult drug court participants discharged and self-reporting opioids (heroin or opiates) as their drug of choice.
"successfully_completed_opioid", -- Adult drug court participants self-reporting opioids (heroin or opiates) as their drug of choice and graduated successfully from the program.
"percent_of_drug_court_cases", -- Percent of adult drug court participants discharged that self-reported opioids (heroin or opiates) as their drug of choice.
"percent_of_drug_court_cases_2", -- Percent of adult drug court participants discharged and successfully graduated from the program.
"percent_of_drug_court_cases_4", -- Percent of adult drug court participants self-reporting opioids (heroin or opiates) as their drug of choice and graduated successfully from the program.
"percent_of_drug_court_cases_5", -- Describes percent of adult drug court participants self-reporting opioid use successfully graduating.
"state_fips_code", -- 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.
"county_fips_code", -- Last 3 digits of the 5-digit Federal Information Processing Standard (FIPS) code that designate the county 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.
"latitude" -- Latitude coordinates in degrees for a centroid point for geographic area.
FROM
"pa-gov/count-and-rate-of-court-cases-and-drug-court-e4tx-vmn6:latest"."count_and_rate_of_court_cases_and_drug_court"
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/count-and-rate-of-court-cases-and-drug-court-e4tx-vmn6 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/count-and-rate-of-court-cases-and-drug-court-e4tx-vmn6: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/count-and-rate-of-court-cases-and-drug-court-e4tx-vmn6Checkout 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/count-and-rate-of-court-cases-and-drug-court-e4tx-vmn6:latestThis will download all the objects for the latest tag of pa-gov/count-and-rate-of-court-cases-and-drug-court-e4tx-vmn6 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/count-and-rate-of-court-cases-and-drug-court-e4tx-vmn6: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/count-and-rate-of-court-cases-and-drug-court-e4tx-vmn6: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/count-and-rate-of-court-cases-and-drug-court-e4tx-vmn6 is just another Postgres schema.