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 advanced_driver_assistance_system_adasequipped table in this repository, by referencing it like:
"datahub-transportation-gov/advanced-driver-assistance-system-adasequipped-iie8-uenj:latest"."advanced_driver_assistance_system_adasequipped"or in a full query, like:
SELECT
":id", -- Socrata column ID
"pos_x_sv_f", -- x position of subject vehicle 1 (SV1) in Frenet frame in meters
"heading_sv", -- heading angle of SV1 in degrees
"pos_y_sv_m", -- y position of SV1 in map frame in meters
"pos_x_sv_m", -- x position of SV1 in map frame in meters
"lanelet_id_sv", -- Lanelet ID of the SV1's center point
"pos_y_sv_f", -- y position of SV1 in Frenet frame in meters
"total_lanes", -- Total lanes at the current position
"acc_av", -- acceleration of adjacent vehicle in meters per second squared
"speed_av", -- speed of adjacent vehicle in meters per second
"dim_z_av", -- height of adjacent vehicle in meters
"dim_y_av", -- width of adjacent vehicle in meters
"dim_x_av", -- length of adjacent vehicle in meters
"date", -- Date of data collection
"time_of_day", -- Time stamp of data collection
"following_distance", -- Following distance setting for SV1
"type_of_vehicle", -- Mode of operation: RI/DI/Baseline
"road_condition", -- Condition of surface of road: wet/dry
"roadway_type", -- Type of roadway: Limited access/Divided/Non-divided arterial
"lane_id_av", -- Lane ID of the adjacent vehicle's center point
"annual_traffic_density", -- AADT for the route
"lane_id_sv", -- Lane ID of the SV1's center point
"run_number", -- number from the set of processed runs
"heading_av_m", -- heading angle of adjacent vehicle (orientation of vehicle) in degrees
"pos_y_av_m", -- y position of adjacent vehicle in map frame in meters
"road_origin_y_m", -- y position of map origin in meters
"pos_x_av_m", -- x position of adjacent vehicle in map frame in meters
"pos_y_av_f", -- y position of adjacent vehicle in Frenet frame in meters
"pos_x_av_f", -- x position of adjacent vehicle in Frenet frame in meters
"route_starting_point_rs", -- Google map start point
"route_ending_point_re", -- Google map end point
"distance_av_headway", -- Distance between the center of the adjacent vehicle and the SV in meters
"distance", -- Distance of the route in miles
"road_origin_x_m", -- x position of map origin in meters
"time", -- Timestamp (ascending by start time) of the corresponding row in CSV. Time is the ROS time converted into more user- friendly format in seconds
"speed_limits", -- Speed limits along the route in MPH
"special_notes", -- Any interesting observations
"acc_sv", -- acceleration of SV1 in meters per second squared
"speed_sv", -- speed of SV1 in meters per second
"dim_z_sv", -- height of SV1 in meters
"dim_x_sv", -- length of SV1 in meters
"maplink", -- Google maps link of the route
"id", -- dentification number of the adjacent vehicle (ascending by time of entry into the sensor range of the SV)
"aggressiveness", -- Aggressiveness setting for SV1
"sub_run_number", -- Number of sub run for respective run number from processed data set
"sub_run_start_time", -- Start time from the original run from where data was processed
"closest_distance_longitudinal", -- The closest distance between adjacent vehicle and SV1 in the longitudinal direction in meters
"closest_distance_lateral", -- The closest distance between adjacent vehicle and SV1 in the lateral direction in meters
"map_origin_x", -- longitude of map origin in degrees
"map_origin_y", -- latitude of map origin in degrees
"map_origin_z", -- altitude of map origin in degrees
"road_origin_x_ecef", -- longitude of road origin in degrees
"road_origin_y_ecef", -- latitude of road origin in degrees
"lanelet_id_av", -- Lanelet ID of the adjacent vehicle's center point
"dim_y_sv" -- width of SV1 in meters
FROM
"datahub-transportation-gov/advanced-driver-assistance-system-adasequipped-iie8-uenj:latest"."advanced_driver_assistance_system_adasequipped"
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-transportation-gov/advanced-driver-assistance-system-adasequipped-iie8-uenj 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-transportation-gov/advanced-driver-assistance-system-adasequipped-iie8-uenj: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-transportation-gov/advanced-driver-assistance-system-adasequipped-iie8-uenjCheckout 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-transportation-gov/advanced-driver-assistance-system-adasequipped-iie8-uenj:latestThis will download all the objects for the latest tag of datahub-transportation-gov/advanced-driver-assistance-system-adasequipped-iie8-uenj 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-transportation-gov/advanced-driver-assistance-system-adasequipped-iie8-uenj: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-transportation-gov/advanced-driver-assistance-system-adasequipped-iie8-uenj: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-transportation-gov/advanced-driver-assistance-system-adasequipped-iie8-uenj is just another Postgres schema.