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 police_calls_for_service_december_2020_to_year_to table in this repository, by referencing it like:
"littlerock-gov/police-calls-for-service-december-2020-to-year-to-piyt-g5xb:latest"."police_calls_for_service_december_2020_to_year_to"or in a full query, like:
SELECT
":id", -- Socrata column ID
"incidentnumber", -- Unique Identifier for each Police Incident
"incidenttypecode", -- Code for call type
"incidenttypedescription", -- Description of call type
"address", -- Generalized address of call
"insertedtimestamp", -- Date and Time the Call For Service was inserted into CAD
"incidentstarteddatetime", -- Data and Time the Incident began
"callreceivedtime",
"firstunitdispatchedtime", -- Time the first police unit was notified of the call for service
"firstunitenroutetime", -- Time the first police unit indicated they were en route to the incident
"firstunitarrivedtime", -- Time the first police unit indicated they had arrived at the scene
"firstdispatchwitharrivaltime", -- The time that the first police unit dispatched to the incident indicated they had arrived at the incident location
"firstenroutewitharrivaltime", -- The time that the first police unit that indicated they were en route to the call arrived at the incident location.
":@computed_region_3ti5_7qwp", -- This column was automatically created in order to record in what polygon from the dataset 'Neighborhood Association Boundaries 2019' (3ti5-7qwp) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"firstunitassignmentwitha", -- Time the first unit assigned to the call arrived at the scene of the incident
"latitude", -- Approximate latitude of incident
"district", -- Police district where incident occurred
"location", -- Approximate location of incident
"zipcode", -- Zip code of call
"areaname", -- Police division where incident occurred
"firstunitassignmenttime", -- Time the first unit was assigned to the call
"longitude", -- Approximate longitude of incident
":@computed_region_kp8k_tt9x", -- This column was automatically created in order to record in what polygon from the dataset 'Neighborhood Associations' (kp8k-tt9x) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_2m93_nqiv", -- This column was automatically created in order to record in what polygon from the dataset 'Neighborhood Associations 2021' (2m93-nqiv) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_t2qk_n4g3", -- This column was automatically created in order to record in what polygon from the dataset 'Wards' (t2qk-n4g3) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_efe8_9vpd",
":@computed_region_bvk6_adqe", -- This column was automatically created in order to record in what polygon from the dataset 'LRPD Patrol Districts 2021' (bvk6-adqe) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_r36e_4rwh",
"incidentdate" -- Date of Call For Service
FROM
"littlerock-gov/police-calls-for-service-december-2020-to-year-to-piyt-g5xb:latest"."police_calls_for_service_december_2020_to_year_to"
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 littlerock-gov/police-calls-for-service-december-2020-to-year-to-piyt-g5xb 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 littlerock-gov/police-calls-for-service-december-2020-to-year-to-piyt-g5xb: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 littlerock-gov/police-calls-for-service-december-2020-to-year-to-piyt-g5xbCheckout 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 littlerock-gov/police-calls-for-service-december-2020-to-year-to-piyt-g5xb:latestThis will download all the objects for the latest tag of littlerock-gov/police-calls-for-service-december-2020-to-year-to-piyt-g5xb 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 littlerock-gov/police-calls-for-service-december-2020-to-year-to-piyt-g5xb: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 littlerock-gov/police-calls-for-service-december-2020-to-year-to-piyt-g5xb: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, littlerock-gov/police-calls-for-service-december-2020-to-year-to-piyt-g5xb is just another Postgres schema.