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 daily_tasks_park_cleaning_records table in this repository, by referencing it like:
"cityofnewyork-us/daily-tasks-park-cleaning-records-kwte-dppd:latest"."daily_tasks_park_cleaning_records"or in a full query, like:
SELECT
":id", -- Socrata column ID
"njtp", -- A count of the number of staff in the Job Training Participant title on the cleaning crew during the indicated activity.
"sector_desc", -- The numbers of the Park Maintenance Districts that comprise the associated Sector
"fiscal_week", -- The relative week of the fiscal year.
"sector_name", -- The name of the sector in which the property is located.
"sector", -- Unique identification string of the Park Maintenance Sector in which the property is located.
"route_id", -- Unique identification number for the route as part of which the indicated activity was performed
"propid", -- Unique identification number for a property or portion of a property. In some cases – a standalone, smaller park for example – this number will be equivalent to the GIS Property Number. In other cases – a zone, playground or other site within a larger park – an additional designation of letters and/or numbers will be added.
"row_id",
"end_time", -- The date and time at indicated which the activity ended.
"fiscal_day", -- The relative day of the fiscal year.
"off_route", -- An indicator as to whether or not a property is included on the selected route.
"ncsa", -- A count of the number of staff in the City Seasonal Aide title on the cleaning crew during the indicated activity.
"ncpw", -- A count of the number of staff in the City Park Worker title on the cleaning crew during the indicated activity.
"fiscal_qtr", -- The fiscal year followed by the fiscal quarter for a given record.
"overlap_flag", -- An indicator as to whether or not the number of hours (nhours) were adjusted for overlapping times and tasks.
"daily_task_activity_id", -- A unique ID assigned to each Daily Task activity entry.
"vehicle_number", -- The vehicle number used for the given date and route.
"signname", -- The name of the entire site as it appears on the signs located on the exterior of the park.
"gispropnum", -- Unique identification number for each park property, identified by borough/county (B = Brooklyn; M = Manhattan; Q = Queens; R = Richmond (Staten Island); X = Bronx) and followed by a number.
"animal_waste", -- An indicator as to whether or not animal waste was cleaned during a visit.
"date_worked", -- The date on which the indicated activity was performed.
"district", -- The name of the Park Maintenance District in which the property is located.
"nhours", -- A count of the adjusted number of hours a given task was performed.
"activity", -- The type of activity captured for a record. Cleaning is indicated by the "Work" activity, while other activity codes are used for other actions.
"fixed_post", -- An indicator as to whether or not an entry was made using the "Fixed Post" module of the application.
"napsw", -- A count of the number of staff in the Associate Park Service Worker title on the cleaning crew during the indicated activity.
"medical_waste", -- An indicator as to whether or not medical waste was cleaned during a visit.
"broken_glass", -- An indicator as to whether or not broken glass was cleaned during a visit.
"ncrew", -- A count of the total number of workers on the cleaning crew during the indicated activity.
"graffiti", -- An indicator as to whether or not graffiti was cleaned during a visit.
"dumping", -- An indicator as to whether or not dumping was cleaned during a visit.
"obj_gisobjid", -- Unique identifier that links the property being serviced to an asset in AMPS.
"nnpw", -- A count of the number of non-Parks employees on the cleaning crew during the indicated activity.
"start_time", -- The date and time at which the indicated activity started.
"daily_task_id" -- A unique ID assigned to each Daily Task entry.
FROM
"cityofnewyork-us/daily-tasks-park-cleaning-records-kwte-dppd:latest"."daily_tasks_park_cleaning_records"
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/daily-tasks-park-cleaning-records-kwte-dppd 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/daily-tasks-park-cleaning-records-kwte-dppd: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/daily-tasks-park-cleaning-records-kwte-dppdCheckout 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/daily-tasks-park-cleaning-records-kwte-dppd:latestThis will download all the objects for the latest tag of cityofnewyork-us/daily-tasks-park-cleaning-records-kwte-dppd 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/daily-tasks-park-cleaning-records-kwte-dppd: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/daily-tasks-park-cleaning-records-kwte-dppd: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/daily-tasks-park-cleaning-records-kwte-dppd is just another Postgres schema.