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 connect_chicago_locations_historical table in this repository, by referencing it like:
"cityofchicago/connect-chicago-locations-historical-bmus-hp7e:latest"."connect_chicago_locations_historical"or in a full query, like:
SELECT
":id", -- Socrata column ID
":@computed_region_bdys_3d7i",
":@computed_region_6mkv_f3dw",
":@computed_region_vrxf_vc4k",
":@computed_region_rpca_8um6",
"location_city",
"location_zip",
"location_state",
"location_address",
":@computed_region_awaf_s7ux",
"training_url", -- If training is offered, website for more information.
"organization_type", -- Type of organization, which may be the following: Chicago Housing Authority; Chicago Public Library; Chicago Public School; City Colleges of Chicago; Community Service Center; Community Technology Center; Senior Center; WorkForce Center; WorkNet Chicago;
"time_allowed_per_user", -- Either timed, unlimited or empty.
"pc_use_restrictions", -- Restrictions on the use of computers at this location.
"internet_upload", -- Upload speed in Mbps
"appointment", -- Does this location take appointments? (1=yes, 0=no, 2=unknown)
"training", -- Does this location offer training? (1=yes, 0=no, 2=unknown)
"full_address", -- Combination of all address fields
"pcc_staff_person_email", -- Email address for PCC STAFF PERSON.
"training_headline", -- If training is offered, the name of the training program.
"time_allowed_per_user_detail", -- Time in minutes if there is a limit.
"url", -- Detail page on the Connect Chicago website
"longitude",
"location",
"pcc_staff_person", -- Contact person for the computer lab.
"location_leadership_email", -- Email address for LOCATION LEADERSHIP.
"id", -- Number to uniquely identify each location
"flickr_tag", -- Used for tagging photos in Flickr. Generated based Id and location name
"twitter_handle", -- Twitter account for the location
"agency_staff_person_contact_email", -- Email address for AGENCY LEADERSHIP CONTACT.
"agency_leadership_contact", -- Contact person for the parent organization or department.
"nearest_parking_detail", -- Details and directions for nearest parking lot.
"volunteers_wanted_how", -- If volunteers are needed, for what purpose?
"volunteers_used_how", -- If volunteers are used, for what purpose?
"assistive_technology", -- Software for aiding the blind or disabled.
"internet", -- Does this location have internet access? (1=yes, 0=no, 2=unknown)
"organization_name", -- Name of the location
"hours", -- Location hours of operation.
"org_phone", -- Location phone number, including area code.
"zip_code",
"state",
"address",
"internet_download", -- Download speed in Mbps.
"website", -- If the location has a website or webpage, the URL for it.
"hardware_public", -- Number of public computers available.
"handicap_access_detail", -- Details for handicap accessibility
"location_leadership", -- Contact person who is responsible for running the location.
"latitude",
"public_transportation_detail", -- Details and directions for nearest CTA or Metra stations or bus stops.
"wifi", -- Does this location have wireless internet? (1=yes, 0=no, 2=unknown)
"public_wifi_detail", -- Wifi encryption (if any), other notes about WiFi.
"training_types", -- Types of technology training provided at the location. For more information, see: http://weconnectchicago.org/learn/
"training_description", -- If training is offered, a description of the training program.
"friendly_description",
"room_list", -- Location of computer labs within the address.
"nearest_parking", -- Either free or paid.
"volunteers_used", -- Are volunteers used at this location?
"city",
":@computed_region_43wa_7qmu"
FROM
"cityofchicago/connect-chicago-locations-historical-bmus-hp7e:latest"."connect_chicago_locations_historical"
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 cityofchicago/connect-chicago-locations-historical-bmus-hp7e 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 cityofchicago/connect-chicago-locations-historical-bmus-hp7e: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 cityofchicago/connect-chicago-locations-historical-bmus-hp7eCheckout 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 cityofchicago/connect-chicago-locations-historical-bmus-hp7e:latestThis will download all the objects for the latest tag of cityofchicago/connect-chicago-locations-historical-bmus-hp7e 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 cityofchicago/connect-chicago-locations-historical-bmus-hp7e: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 cityofchicago/connect-chicago-locations-historical-bmus-hp7e: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, cityofchicago/connect-chicago-locations-historical-bmus-hp7e is just another Postgres schema.