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_filer_data_current table in this repository, by referencing it like:
"pa-gov/campaign-finance-disclosure-filer-data-current-53wp-ib3s:latest"."campaign_finance_disclosure_filer_data_current"or in a full query, like:
SELECT
":id", -- Socrata column ID
"office", -- If applicable, and the filer is a candidate, the office at which the individual is seeking at the time of the submitted report
"amended_report_indicator", -- A ‘Y’es or ‘N’o designation to specify if the candidate or committee filed an amended report for the reporting period
"party", -- If applicable, the party affiliation of the candidate or committee
"filer_type", -- This designates a committee, candidate or lobbyist
"filer_location_2_zip",
"filer_location_2_state",
"filer_location_2_address",
"filer_location_1_zip",
"filer_location_1_city",
"filer_location_2_city",
"filer_location_1_state",
"filer_location_1_address",
"filer_location_1", -- Filer Location field is based on the Filer Address 1 field. This location field includes the latitude and longitude created during the data import.
"in_kind", -- The amount of in-kind contributions received by the candidate or committee during the report cycle
"monetary", -- The available cash on hand reported during the reporting cycle
"beginning_balance", -- The starting balance of the filing candidate or committee at the beginning of the reporting cycle
"phone_number", -- The phone number of the filing candidate or committee
"county", -- The county of the filing candidate or committee
"filer_zip_code", -- The zip code of the filing candidate or committee
"filer_state", -- The state of the filing candidate or committee
"filer_city", -- The city of the filing candidate or committee
"filer_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.
"filer_address_1", -- The address of the individual or entity contributing to the candidate or committee if they meet the reporting requirements.
"district", -- If applicable, and the filer is a candidate, the district of the office at which the individual is seeking at the time of the submitted report
"filer_name", -- The name of the reporting candidate or committee at the time of the submitted report
"termination_indicator", -- A ‘Y’es or ‘N’o designation to specify if the candidate or committee had terminated during the reporting period
"election_cycle", -- This specifies the reporting cycle or timeframe the filer is reporting activity
"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 filer reporting campaign activity (i.e. candidates and committees)
"filer_location_2" -- Filer Location based on Filer Address 2. This may be an approximate location if Filer Address 2 is blank or includes a non-physical address like a post office box or apartment number. This location field includes the latitude and longitude created during the data import.
FROM
"pa-gov/campaign-finance-disclosure-filer-data-current-53wp-ib3s:latest"."campaign_finance_disclosure_filer_data_current"
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-filer-data-current-53wp-ib3s 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-filer-data-current-53wp-ib3s: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-filer-data-current-53wp-ib3sCheckout 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-filer-data-current-53wp-ib3s:latestThis will download all the objects for the latest tag of pa-gov/campaign-finance-disclosure-filer-data-current-53wp-ib3s 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-filer-data-current-53wp-ib3s: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-filer-data-current-53wp-ib3s: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-filer-data-current-53wp-ib3s is just another Postgres schema.