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 opioid_seizures_and_arrests_cy_2013_current table in this repository, by referencing it like:
"pa-gov/opioid-seizures-and-arrests-cy-2013-current-wmgc-6qvd:latest"."opioid_seizures_and_arrests_cy_2013_current"or in a full query, like:
SELECT
":id", -- Socrata column ID
"incident_count", -- The number of PSP seizure incidents associated with the reported drug. Incidents may include undercover buys, search warrants, traffic stops and other investigative encounters. An incident is a Pennsylvania State Police (PSP) recorded violation of the Controlled Substance Act and an entry into the PSP Statistical Narcotics System. By regulation, entry is made by the PSP as stated in PSP Administrative Regulation 9-6: When violations of The Controlled Substance, Drug, Device and Cosmetic Act are reported, the required statistical information concerning the incident shall be entered into the Statistical Narcotic Reporting System (SNRS). Incidents may include undercover buys, search warrants, traffic stops and other investigative encounters So, an “incident” is not based on any arrest, but on a reported violation, though it often can include arrests. The incidents that are selected and forwarded to the portal are those that include a record of one or more seizures of the opioid drugs. In turn, a subset of those selected incidents also contains a record of one or more arrests. This is PSP data only, it would not include any Federal case/incident data.
"drug_quantity", -- The amount of the Opioid drug seized in a decimal numeric format.
"arrests", -- The number of arrests, both felony and misdemeanor, in connection with the seizure of the reported drug.
"lat_long", -- Latitude random point within the county boundary to help with creating a boundary map within the county lines.If the location was out of state, this field is left blank.
"longitude", -- Longitude random point within the county boundary to help with creating a boundary map within the county lines.If the location was out of state, this field is left blank.
"unit_used", -- The unit used to report the quantity, measured in Kilograms.
":@computed_region_amqz_jbr4",
":@computed_region_rayf_jjgk",
":@computed_region_r6rf_p9et",
"county_code_text", -- Code of 01 - 67 for the 67 counties in Pennsylvania. Location in Pennsylvania where the seizure took place. If the location was out of state, this field is left blank. This is a text column so that leading zeroes show on the low numbers. This column maybe used to connect to other datasets.
"qtr_start_date", -- The first day of the reporting quarter as a date field.
"fips_county_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. If the location was out of state, this field is left blank.
"county_code", -- Code of 1 - 67 for the 67 counties in Pennsylvania. Location in Pennsylvania where the seizure took place. If the location was out of state, this field is left blank. This column may be used to connect to other datasets.
"qtr", -- The quarter of reporting. Possible values include: 1 represents January 1 - March 31 2 represents April 1 - June 30 3 represents July 1 - September 30 4 represents October 1 - December 31
"geocoded_column", -- Latitude and Longitude. One random point within the county boundary to help with creating a boundary map within the county lines.If the location was out of state, this field is left blank.
"year", -- Calendar Year January 1 – December 31
":@computed_region_nmsq_hqvv",
":@computed_region_d3gw_znnf",
"county_name", -- The name of the county. This says out of state if the location was not within Pennsylvania.
"drug", -- The name of the Opioid drug being reported. Possible values include: Fentanyl – category used to enter all synthetic opioids typically a white powder however sometimes seized in patch form. (Schedule II controlled substance) Heroin - category used to enter a white or brownish colored crystalline powder derived from morphine, usually seized packaged in glassine packets or as bulk powder. (Schedule I controlled substance) Opium - category used to enter opioids or opiate pills such as Hydrocodone, Codeine and OxyContin pills. (Most exhibits submitted to the crime laboratories in this category are Schedule II, however, III, IV and V controlled substances may also be included.) *Note: Entries regarding the drugs seized are made by investigators using presumptive tests then verified by lab analysis. Lab analysis can take up to three months and therefore drug type values could change after lab analysis. Quarterly quantity values could change as a result of updated entries following lab analysis.
"fips_state_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/geo/reference/codes/cou.htmlIf the location was out of state, this field is left blank. https://www.census.gov/library/reference/code-lists/ansi.html
FROM
"pa-gov/opioid-seizures-and-arrests-cy-2013-current-wmgc-6qvd:latest"."opioid_seizures_and_arrests_cy_2013_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/opioid-seizures-and-arrests-cy-2013-current-wmgc-6qvd 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/opioid-seizures-and-arrests-cy-2013-current-wmgc-6qvd: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/opioid-seizures-and-arrests-cy-2013-current-wmgc-6qvdCheckout 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/opioid-seizures-and-arrests-cy-2013-current-wmgc-6qvd:latestThis will download all the objects for the latest tag of pa-gov/opioid-seizures-and-arrests-cy-2013-current-wmgc-6qvd 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/opioid-seizures-and-arrests-cy-2013-current-wmgc-6qvd: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/opioid-seizures-and-arrests-cy-2013-current-wmgc-6qvd: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/opioid-seizures-and-arrests-cy-2013-current-wmgc-6qvd is just another Postgres schema.