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 baton_rouge_traffic_crash_incidents table in this repository, by referencing it like:
"brla-gov/baton-rouge-traffic-crash-incidents-7wah-qncc:latest"."baton_rouge_traffic_crash_incidents"or in a full query, like:
SELECT
":id", -- Socrata column ID
"district", -- Primary level in the patrol area hierarchy where the crash occurred
"secondary_factor", -- Secondary causative factor for the crash
"manner_of_collision", -- Description of how the which vehicles initially came into contact
"roadway_conditions", -- Description of any abnormalities in the environmental or physical condition of roadway
"access_control", -- Description of the degree of control for public access to the roadway
"relation_to_roadway", -- Location of the crash in relation to the highway
"first_road_surface", -- Wet or dry roadway surface
"longitude", -- Geographic longitude coordinate (east-west) where the crash occurred
"postal_code", -- Postal ZIP code where the crash occurred
"street_name", -- Nearest address of where the crash occurred
"incident_number", -- Unique identifier for the crash report
"is_hit_and_run", -- True or False was the crash a hit and run
"type_of_roadway", -- Description of the engineering aspects of roadway
"latitude", -- Geographic latitude coordinate (north-south) where the crash occurred
"first_at_intersection", -- True or False was the crash in an intersection
"is_train_involved", -- True of False was a train involved in the crash
":@computed_region_uvg4_nwq8", -- This column was automatically created in order to record in what polygon from the dataset 'Neighborhoods_from_qfmj_2fwi' (uvg4-nwq8) the point in column 'the_geom' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_jrqt_zu77", -- This column was automatically created in order to record in what polygon from the dataset 'ZIP Codes_from_tqy7_429i' (jrqt-zu77) the point in column 'the_geom' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_ntzg_c2w3", -- This column was automatically created in order to record in what polygon from the dataset 'Council Districts_2021_from_d8sa-f3ec' (ntzg-c2w3) the point in column 'the_geom' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_4pgc_bhg2", -- This column was automatically created in order to record in what polygon from the dataset 'Census 2010 Tracts' (4pgc-bhg2) the point in column 'the_geom' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"state", -- State where the crash occurred
"the_geom", -- Geocoded location for displaying in a map view
"lighting", -- Description of the lighting condition at the time of the crash
"has_fatality", -- True or False was there a fatality in the crash
"crash_date", -- Date and time of the crash incident
"city", -- Postal city where the crash occurred
"first_street_highway", -- Name of the nearest intersecting street from where the crash occured
"zone", -- Secondary level in the patrol area hierarchy where the crash occurred
"sub_zone", -- Tertiary level in the patrol area hierarchy where the crash occurred
"weather", -- Prevailing weather condition at time of crash
"has_injury", -- True or False was there an injury in the crash
"total_vehicles", -- Total number of vehicles involved in the crash
"primary_factor", -- Primary causative factor for the crash
"crash_occurred_on", -- Type of roadway where the crash occurred
"alignment", -- Orientation and slope of roadway at crash location
"kind_of_location", -- Description of the land use in the area of the crash
"is_pedestrian_involved" -- True or False was there a pedestrian involved in the crash
FROM
"brla-gov/baton-rouge-traffic-crash-incidents-7wah-qncc:latest"."baton_rouge_traffic_crash_incidents"
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 brla-gov/baton-rouge-traffic-crash-incidents-7wah-qncc 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 brla-gov/baton-rouge-traffic-crash-incidents-7wah-qncc: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 brla-gov/baton-rouge-traffic-crash-incidents-7wah-qnccCheckout 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 brla-gov/baton-rouge-traffic-crash-incidents-7wah-qncc:latestThis will download all the objects for the latest tag of brla-gov/baton-rouge-traffic-crash-incidents-7wah-qncc 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 brla-gov/baton-rouge-traffic-crash-incidents-7wah-qncc: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 brla-gov/baton-rouge-traffic-crash-incidents-7wah-qncc: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, brla-gov/baton-rouge-traffic-crash-incidents-7wah-qncc is just another Postgres schema.