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-vhz2-exyi:latest"."advanced_driver_assistance_system_adasequipped"or in a full query, like:
SELECT
":id", -- Socrata column ID
"lane_id_sv2", -- Lane ID of SV2's center point
"closest_distance_lateral", -- The closest distance between adjacent vehicle and SV1 in the lateral direction in Frenet frame in meters
"roadway_type", -- Type of roadway: Limited access/Divided/Non-divided arterial
"dim_x_sv1", -- length of SV1 in meters
"type_of_vehicle", -- Mode of operation: RI/DI/Baseline
"dim_y_sv2", -- width of SV2 in meters
"annual_traffic_density", -- AADT for the route
"dim_x_sv2", -- length of SV2 in meters
"date", -- Date of data collection
"speed_limits", -- Speed limits along the route
"maplink", -- Google maps link of the route
"dim_z_sv1", -- height of SV1 in meters
"road_origin_x_m", -- x position of map origin in meters
"dim_z_sv2", -- height of SV2 in meters
"sub_run_number", -- Number of sub run for respective run number from processed data set
"special_notes", -- Any interesting observations
"gap_level", -- Intended gap between SV1 and SV2 1: (30–60 m) or 2:(60–80 m)
"id", -- Identification number of the adjacent vehicles (ascending by time of entry into the sensor range of the subject vehicle)
"time", -- Timestamp (ascending by start time) of the corresponding row in CSV in seconds
"distance_av_headway", -- Distance between the center of the adjacent vehicle and SV
"pos_x_av_f", -- x position of the adjacent vehicle in Frenet frame in meters
"aggressiveness", -- Aggressiveness setting for SV1
"pos_y_av_f", -- y position of the adjacent vehicle in Frenet frame in meters
"pos_x_av_m", -- x position of the adjacent vehicle in map frame in meters
"pos_y_av_m", -- y position of the adjacent vehicle in map frame in meters
"heading_av_m", -- heading angle of adjacent vehicle in degrees
"dim_x_av", -- length of adjacent vehicle in meters
"road_condition", -- Condition of surface of road: wet/dry
"dim_y_av", -- width of adjacent vehicle in meters
"distance", -- Distance of the route
"dim_z_av", -- height of adjacent vehicle in meters
"route_ending_point_re", -- Google map end point
"route_starting_point_rs", -- Google map start point
"sub_run_start_time", -- Start time from the original run from where data was processed
"speed_av", -- speed of adjacent vehicle in meters per second
"time_of_day", -- Time stamp of data collection
"acc_av", -- acceleration of adjacent vehicle in meters per second squared
"run_number", -- number from the set of processed runs
"pos_x_sv1_f", -- x position of the subject vehicle 1 (SV1) in Frenet frame in meters
"pos_y_sv1_f", -- y position of the subject vehicle 1 (SV1) in Frenet frame in meters
"total_lanes", -- Total lanes at the current position
"pos_x_sv1_m", -- x position of the subject vehicle 1 (SV1) in map frame in meters
"lanelet_id_sv2", -- Lanelet ID of SV2's center point
"pos_y_sv1_m", -- y position of the subject vehicle 1 (SV1) in map frame in meters
"lane_id_sv1", -- Lane ID of SV1's center point
"lanelet_id_sv1", -- Lanelet ID of SV1's center point
"lane_id_av", -- Lane ID of the adjacent vehicle's center point
"heading_sv1", -- heading angle of SV1 in degrees
"lanelet_id_av", -- Lanelet ID of the adjacent vehicle's center point
"following_distance", -- Following distance setting for SV1
"dim_y_sv1", -- width of SV1 in meters
"road_origin_y_ecef", -- latitude of road origin in degrees
"pos_y_sv2_m", -- y position of the subject vehicle 2 (SV2) in map frame in meters
"speed_sv1", -- speed of SV1 in meters per second
"road_origin_x_ecef", -- longitude of road origin in degrees
"acc_sv1", -- acceleration of SV1 in meters per second squared
"road_origin_y_m", -- y position of map origin in meters
"pos_x_sv2_f", -- x position of the subject vehicle 2 (SV2) in Frenet frame in meters
"map_origin_z", -- altitude of map origin in degrees
"pos_y_sv2_f", -- y position of the subject vehicle 2 (SV2) in Frenet frame in meters
"pos_x_sv2_m", -- x position of the subject vehicle 2 (SV2) in map frame in meters
"heading_sv2", -- heading angle of SV2 in degrees
"speed_sv2", -- speed of SV2 in meters per second
"acc_sv2", -- acceleration of SV2 in meters per second squared
"map_origin_y", -- latitude of map origin in degrees
"closest_distance_longitudinal", -- The closest distance between adjacent vehicle and SV1 in the longitudinal direction in Frenet frame in meters
"map_origin_x" -- longitude of map origin in degrees
FROM
"datahub-transportation-gov/advanced-driver-assistance-system-adasequipped-vhz2-exyi: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-vhz2-exyi 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-vhz2-exyi: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-vhz2-exyiCheckout 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-vhz2-exyi:latestThis will download all the objects for the latest tag of datahub-transportation-gov/advanced-driver-assistance-system-adasequipped-vhz2-exyi 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-vhz2-exyi: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-vhz2-exyi: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-vhz2-exyi is just another Postgres schema.