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 nyc_city_council_committee_membership table in this repository, by referencing it like:
"cityofnewyork-us/nyc-city-council-committee-membership-aabe-yfm9:latest"."nyc_city_council_committee_membership"or in a full query, like:
SELECT
":id", -- Socrata column ID
"id", -- ID for City Council Member Committee Membership for Single Committee
"guid", -- Unique ID
"memberid", -- Unique ID for Council Member
"firstname", -- First Name of Council Member
"lastname", -- Last Name of Council Member
"fullname", -- Name of Council Member
"committeeid", -- ID for Committee
"committeename", -- Name of City Council Committee
"committee_active", -- The Committee is currently active; "TRUE" = Committee is currently active "FALSE" = Committee is currently not active; Occasionally, an active Committee will cease to exist. In that scenario, "CommitteeActive" will read as "FALSE". The Committee may have existed at the beginning of the term and then ceased to exist by the end of the term.
"membertypeid", -- Committee Role for Council Member; 1= "Chairperson", 3 = "Committee member" or "Council Member" ie, not the Committee Chair; Each Committee has a "Chairperson" who heads the Committee. All other Council Members on the Committee are "Committee Member"s or occasionally, "Council Member". If a Council Person is a "Chairperson" for a Committee, they are not a "Committee Member" or "Council Member".
"title", -- Committee Role for Council Member; "Chairperson" or "Committee member"
"memberactive", -- The Council Member currently has membership on the Committee. "TRUE" = Council Member currently has membership on the Committee "FALSE" = Council Member currently does not have membership on the Committee. Council Members move to different Committees, become Chair of Committees, or Committees cease to exists. In all those scenarios, if a Council Member was on a Committee at the beginning of the term, and their membership changes, then "MemberActive" would be "FALSE"
"startdate", -- Date the Council Member started membership on Committee
"enddate", -- Date the Council Member ended or is projected to end membership on Committee; If the end date is in the future, the date is set to the end of the City Council session.
"version", -- Version ID for Record
"lastupdatedutc" -- Date and time record was last updated
FROM
"cityofnewyork-us/nyc-city-council-committee-membership-aabe-yfm9:latest"."nyc_city_council_committee_membership"
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/nyc-city-council-committee-membership-aabe-yfm9 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/nyc-city-council-committee-membership-aabe-yfm9: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/nyc-city-council-committee-membership-aabe-yfm9Checkout 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/nyc-city-council-committee-membership-aabe-yfm9:latestThis will download all the objects for the latest tag of cityofnewyork-us/nyc-city-council-committee-membership-aabe-yfm9 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/nyc-city-council-committee-membership-aabe-yfm9: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/nyc-city-council-committee-membership-aabe-yfm9: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/nyc-city-council-committee-membership-aabe-yfm9 is just another Postgres schema.