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 sentencing table in this repository, by referencing it like:
"datacatalog-cookcountyil-gov/sentencing-tg8v-tm6u:latest"."sentencing"or in a full query, like:
SELECT
":id", -- Socrata column ID
"felony_review_date", -- Date Felony Review result was reached
"updated_offense_category", -- Offense category for the case updated based upon the primary charge
"felony_review_result", -- Result of the Felony Review process
"disposition_charged_aoic", -- Administrative Office of the Illinois Courts ID for law of the charge at disposition
"commitment_unit", -- Unit of sentence length
"length_of_case_in_days", -- Number of days between a charge being arraigned and a charge being sentenced
"age_at_incident", -- Age of defendant at date of incident, as recorded by law enforcement or self-reported by defendant
"incident_city", -- The city where the offense took place
"current_sentence", -- Binary flag representing current sentence
"gender", -- Gender of defendant reported by law enforcement or self-reported
"case_id", -- Internal unique identifier for each case
"received_date", -- Date when Felony Review Unit received the case
"offense_category", -- Broad offense category before specific charges are filed on a case
"primary_charge", -- A binary flag indicating whether this row records the most severe charge against the accused
"charge_id", -- Internal unique identifier for each charge filed
"charge_version_id", -- Internal unique identifier for each version of a charge associated with charges filed
"charge_count", -- Number of charges associated with one defendant in one case
"disposition_date", -- Date charge disposed
"disposition_charged_chapter", -- Legal Chapter for the charge at disposition
"disposition_charged_act", -- Legal Act for the charge at disposition
"disposition_charged_section", -- Legal Section for the charge at disposition
"disposition_charged_class", -- Legal Class for the charge at disposition
"charge_disposition", -- Result of the charge
"charge_disposition_reason", -- Additional information about the result of the charge
"sentence_judge", -- Judge who oversaw the sentencing
"court_facility", -- Courthouse in which the sentence was determined
"sentence_date", -- Date of when the charge was sentenced
"sentence_type", -- Broad type of sentence issued
"commitment_type", -- A more specific type of sentence issued
"commitment_term", -- The number associated with the sentence
"race", -- Race of defendant reported by law enforcement or self-reported
"incident_begin_date", -- Date offense occurred/began
"disposition_charged_offense_title", -- Specific title of the charged offense at disposition
"case_participant_id", -- Internal unique identifier for each defendant associated with a case
"sentence_phase", -- When this version of the sentence was created
"court_name", -- Circuit Court District in which the sentence was determined
"incident_end_date", -- Date offense ended
"law_enforcement_agency", -- Law Enforcement agency associated with the arrest
"unit", -- Law Enforcement Unit within Chicago Police Department associated with the arrest
"arrest_date", -- Date and time of arrest
"arraignment_date" -- Date of the arraignment
FROM
"datacatalog-cookcountyil-gov/sentencing-tg8v-tm6u:latest"."sentencing"
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 datacatalog-cookcountyil-gov/sentencing-tg8v-tm6u 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 datacatalog-cookcountyil-gov/sentencing-tg8v-tm6u: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 datacatalog-cookcountyil-gov/sentencing-tg8v-tm6uCheckout 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 datacatalog-cookcountyil-gov/sentencing-tg8v-tm6u:latestThis will download all the objects for the latest tag of datacatalog-cookcountyil-gov/sentencing-tg8v-tm6u 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 datacatalog-cookcountyil-gov/sentencing-tg8v-tm6u: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 datacatalog-cookcountyil-gov/sentencing-tg8v-tm6u: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, datacatalog-cookcountyil-gov/sentencing-tg8v-tm6u is just another Postgres schema.