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 nibrs_group_a_offense_crimes table in this repository, by referencing it like:
"datahub-austintexas-gov/nibrs-group-a-offense-crimes-i7fg-wrk5:latest"."nibrs_group_a_offense_crimes"or in a full query, like:
SELECT
":id", -- Socrata column ID
"occurred_day_of_week", -- The date when the incident was reported.
"person_sex_crimes_flag", -- Created for the Collective Sex Crimes Response Model team, a Yes/No flag indicating if an incident had an adult victim of a NIBRS sex offense (11A, 11B, 11C, or 11D).
"census_block_group", -- Shortened Block Group Name based on the Census Bureau's Geo ID. State digits have been removed due to system limitations.
"occ_month", -- The name of the month the incident occurred.
"occ_year", -- The four-digit year the incident occurred.
"nibrs_group", -- The FBI National Incident Based Reporting System category the NIBRS Code and Description fall into.
"nibrs_desc", -- The FBI National Incident Based Reporting System description of the offense.
"nibrs_category", -- The FBI National Incident Based Reporting System crimes against category consisting of Persons, Property, and Society crimes.
"sector", -- The Austin Police Department patrol sector where the incident occurred.
"occurred_date", -- The date the incident occurred for which the case report is written. Not all incidents are reported on the day they occurred, therefore reports may be written after the occurred date.
"council_district", -- The council district where the incident occurred.
"reported_date", -- The date when the incident was reported
"primary_key", -- The case report number for the incident may have many crimes associated with it, as distinguished by Offense ID. The case report number typically is a four-digit year, three-digit day of year, and system generated four-digit occurrence number. Case report numbers that do not follow this pattern may indicate a report intake method outside of computer aided dispatch.
"offenseid", -- A distinct value that represents a crime that was perpetrated against a victim in the case of Crimes Against Person. For Crimes Against Property and Crimes Against Society it is a value for each crime. A single case can have many crimes under UCR NIBRS.
"internal_clearance_status", -- Coded value of the internal operational status of the incident.
"victim_race_ethnicity", -- A person's self-identified association with one or more social groups.
"victim_age_at_offense", -- Victim's age at the date the offense occurred.
"occurred_on_time", -- The time the incident occurred for which the case report is written. Not all incidents are reported on the day they occurred, therefore reports may be written after the occurred date and time.
"victim_gender", -- Coded value for victim gender, can be male, female, gender diverse or unknown.
"location_description", -- The coded value for category of the type of location such as Residence/Home, Storage, etc.
"zip_code" -- The 5-digit zip code of the offense location.
FROM
"datahub-austintexas-gov/nibrs-group-a-offense-crimes-i7fg-wrk5:latest"."nibrs_group_a_offense_crimes"
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 datahub-austintexas-gov/nibrs-group-a-offense-crimes-i7fg-wrk5 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 datahub-austintexas-gov/nibrs-group-a-offense-crimes-i7fg-wrk5: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 datahub-austintexas-gov/nibrs-group-a-offense-crimes-i7fg-wrk5Checkout 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 datahub-austintexas-gov/nibrs-group-a-offense-crimes-i7fg-wrk5:latestThis will download all the objects for the latest tag of datahub-austintexas-gov/nibrs-group-a-offense-crimes-i7fg-wrk5 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 datahub-austintexas-gov/nibrs-group-a-offense-crimes-i7fg-wrk5: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 datahub-austintexas-gov/nibrs-group-a-offense-crimes-i7fg-wrk5: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, datahub-austintexas-gov/nibrs-group-a-offense-crimes-i7fg-wrk5 is just another Postgres schema.