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 campaign_finance_disclosure_contributions_data table in this repository, by referencing it like:
"pa-gov/campaign-finance-disclosure-contributions-data-wb79-wsa4:latest"."campaign_finance_disclosure_contributions_data"or in a full query, like:
SELECT
":id", -- Socrata column ID
"employer_location_2_zip",
"employer_location_2_city",
"employer_location_1_zip",
"employer_location_1_address",
"contributor_location_2_address",
"contributor_location_2_city",
"contributor_location_1_state",
"contributor_location_1_city",
"contributor_location_2_state",
"contributor_location_1_address",
"employer_location_2_address",
"employer_location_1_state",
"contributor_location_1_zip",
"contributor_location_2_zip",
"employer_location_1_city",
"employer_location_2_state",
"employer_location_2", -- Employer Location based on Employer Address 2. If Employer Address 2 is blank but the Employer City, State, and Zip are not blank, this is likely a generalized location.
"employer_location_1", -- Employer Location based on Employer Address 1.
"contributor_location_2", -- Contributor Location based on Contributor Address 2. If Contributor Address 2 is blank, this location will likely be a general location for the address. This column includes the latitude and longitude geocoded during the data import.
"contributor_location_1", -- Contributor Location based on Contributor Address 1. This column includes the Latitude and Longitude geocoded during the data import.
"contribution_description", -- A field for the candidate or committee to explain the recorded contribution.
"contribution_date", -- This field represents the date of the contribution being recorded by the candidate or committee. (Formatted as YYYYMMDD)
"employer_zip_code", -- This field represents the zip code of the employer of the contributor if their aggregate contribution(s) surpass $250 in a reporting period.
"employer_city", -- This field denotes the employer city of the contributor if their aggregate contribution(s) surpass $250 in a reporting period.
"employer_state", -- This field denotes the state of the employer of the contributor if their aggregate contribution(s) surpass $250 in a reporting period.
"employer_address_2", -- This field is a secondary address field for the employer of the contributor if their aggregate contribution(s) are equal to or greater than $250 in a reporting period.
"employer_address_1", -- This field denotes the employer address of the contributor if their aggregate contribution(s) are equal to or greater than $250 in a reporting period.
"employer_name", -- This field denotes the employer name of the contributor if their aggregate contribution(s) are equal to or greater than $250 in a reporting period.
"occupation", -- This field denotes the occupation of the contributor if their aggregate contribution(s) are equal to or greater than $250 in a reporting period.
"contributor_zip_code", -- The zip code of the contributor at the time of their contribution.
"contributor_state", -- The state of the contributor at the time of their contribution.
"contributor_city", -- The city of the contributor at the time of their contribution.
"contributor_address_2", -- This is a secondary address field for the individual or entity contributing to the candidate or committee if they meet the reporting requirements.
"contributor_address_1", -- The address of the individual or entity contributing to the candidate or committee if they meet the reporting requirements.
"contributor", -- The name of the individual or entity contributing to the candidate or committee if they meet the reporting requirements.
"cycle", -- This specifies the reporting cycle or timeframe the contributions were accepted by the candidate or committee.
"election_year", -- This denotes the election cycle or year the candidate or committee is filing
"filer_identification_number", -- This is the Filer Identification Number which is unique to each entity reporting campaign finance contributions (i.e. candidates and committees)
"section", -- This denotes the location of the line item on the campaign finance report form.
"contribution_amount" -- The amount of the contribution recorded by the candidate or committee.
FROM
"pa-gov/campaign-finance-disclosure-contributions-data-wb79-wsa4:latest"."campaign_finance_disclosure_contributions_data"
LIMIT 100;Connecting to the DDN is easy. All you need is an existing SQL client that can connect to Postgres. As long as you have a SQL client ready, you'll be able to query pa-gov/campaign-finance-disclosure-contributions-data-wb79-wsa4 with SQL in under 60 seconds.
Query Your Local Engine
bash -c "$(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 pa-gov/campaign-finance-disclosure-contributions-data-wb79-wsa4: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 pa-gov/campaign-finance-disclosure-contributions-data-wb79-wsa4Checkout 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 pa-gov/campaign-finance-disclosure-contributions-data-wb79-wsa4:latestThis will download all the objects for the latest tag of pa-gov/campaign-finance-disclosure-contributions-data-wb79-wsa4 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 pa-gov/campaign-finance-disclosure-contributions-data-wb79-wsa4: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 pa-gov/campaign-finance-disclosure-contributions-data-wb79-wsa4: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, pa-gov/campaign-finance-disclosure-contributions-data-wb79-wsa4 is just another Postgres schema.