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 911_endtoend_data table in this repository, by referencing it like:
"cityofnewyork-us/911-endtoend-data-t7p9-n9dy:latest"."911_endtoend_data"or in a full query, like:
SELECT
":id", -- Socrata column ID
"median_fdny_pickup", -- Median call to FDNY Pickup in the middle of an ordered set of all calls to FDNY pickups
"median_fdny_job_creation", -- Median call to FDNY Job Creation in the middle of an ordered data set containing all calls to FDNY job creations
"average_fd_pickup", -- Average Time from when the PCT connects to the FD phone system until the ARD (in FD) picks up
"of_incidents_calculated", -- Number of Incidents Calculated
"median_dispatch", -- Median call to Agency Dispatch in the middle of an ordered data set containing all calls to agency dispatch
"average_travel", -- Average Time from when a unit is assigned until the unit arrives on-scene
"call_to_agency_arrival", -- Call duration time to call to Agency Arrival (in sec)
"agency", -- The incident agency
"median_travel", -- Median call to agency arrival in the middle of an ordered data set containing all calls to agency arrival
"median_ems_pickup", -- Median call to EMS Pickup in the middle of an ordered data set containing all calls to EMS Pickups
"call_to_first_pickup", -- Call duration time to First Pickup (in sec)
"call_to_ems_pickup", -- Call duration time to call to EMS Pickup (in sec)
"average_ems_processing", -- Average Time from when the ARD (in EMS) answered a call until the ARD (in EMS) electronically creates the job
"average_calltaker_processing", -- Average Time from when the PCT answers a call until the PCT connects with the next agency or creates the job
"date", -- The week start date of the incident category
"call_to_fdny_job_creation", -- Call duration time to call to FDNY Job Creation (in sec)
"median_ems_job_creation", -- Median call to EMS Job Creation in the middle of a data set containing all calls to Agency Job Creation
"call_to_pd_calltaker_handoff", -- Call duration time to PD Calltaker Handoff (in sec)
"final_incident_type", -- The final incident type category
"call_to_fdny_pickup", -- Call duration time to call to FDNY Pickup (in sec)
"call_to_agency_dispatch", -- Call duration time to call to Agency Dispatch (in sec)
"call_to_first_arrival_multi", -- Call duration time to call to First Arrival (Multi-Agency Incidents) (in sec)
"median_cumulative_first", -- Median call to first arrival in the middle of an ordered data set containing all calls to first arrival
"average_dispatch", -- Average Time from when the job was created until a unit is assigned
"average_ems_pickup", -- Average Time from when the PCT connects to the EMS phone system until the ARD (in EMS) picks up
"average_pickup", -- Average Time from when the call enters the phone system until the PCT picks up
"call_to_agency_job_creation", -- Call duration time to call to Agency Job Creation (in sec)
"average_fd_processing", -- Average Time from when the ARD (in FD) picks up a call until the ARD (in FD) electronically creates the job
"median_calltaker_handoff", -- Median call to PD calltaker handoff in the middle of an ordered data set containing all call to calltaker handoffs
"median_pickup" -- Median pickup time in the middle of an ordered data set containing all pickup times
FROM
"cityofnewyork-us/911-endtoend-data-t7p9-n9dy:latest"."911_endtoend_data"
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 cityofnewyork-us/911-endtoend-data-t7p9-n9dy 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 cityofnewyork-us/911-endtoend-data-t7p9-n9dy: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 cityofnewyork-us/911-endtoend-data-t7p9-n9dyCheckout 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 cityofnewyork-us/911-endtoend-data-t7p9-n9dy:latestThis will download all the objects for the latest tag of cityofnewyork-us/911-endtoend-data-t7p9-n9dy 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 cityofnewyork-us/911-endtoend-data-t7p9-n9dy: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 cityofnewyork-us/911-endtoend-data-t7p9-n9dy: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, cityofnewyork-us/911-endtoend-data-t7p9-n9dy is just another Postgres schema.