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 educator_average_salary table in this repository, by referencing it like:
"delaware-gov/educator-average-salary-rv4m-vy79:latest"."educator_average_salary"or in a full query, like:
SELECT
":id", -- Socrata column ID
"staff_category", -- Represents a respective staff category that certain educators fall under, such as Classroom Teacher, Instructional Support, or Official/Administrative.
"job_classification", -- Job classification category, such as Assistant Principal, Teacher, Special Secondary, or Librarian.
"average_state_salary", -- Average state salary for the particular set of educators.
"grade", -- Represents the grade level of the unique group of students within a school/district.
"average_years_of_experience", -- Average years of experience for the particular set of educators.
"gender", -- Represents the gender of the unique group of students within a school/district.
"educators_fte", -- The total number of full-time equivalent positions (FTEs) in the specified Organization.
"specialdemo", -- Represents the special population status of the unique group of students within a school/district.
"average_local_salary", -- Average local salary for the particular set of educators.
"schoolyear", -- School year for which record is applicable. For example, 2019 = school year which ended in June 2019.
"district", -- Full name of the School District. Statewide data rows have "State of Delaware" in this column.
"organization", -- Full name of the Organization which is the School if School Code is given in the row. Districtwide data rows give full name of School District. Statewide rows give "State of Delaware" in this column.
"average_years_of_age", -- Average years of age for the particular set of educators.
"experience", -- Represents experience category in which a specific educator falls under.
"average_total_salary", -- Average total salary for the particular set of educators.
"geography", -- Represents the geography of the unique group of educators within a school/district/state.
"subgroup", -- Names the unique group of educators within a school/district/state described by the combination of Race, Gender, Grade, SpecialDemo, and Geography.
"staff_type", -- Column identifying staff type.
"average_federal_salary", -- Average federal salary for the particular set of educators.
"districtcode", -- Number representing each School District. Statewide data rows will have “0” in this column.
"schoolcode", -- Number representing each School within the school district. Statewide or Districtwide data rows will have “0” in this column.
"race" -- Represents the race/ethnicity of the unique group of students within a school/district.
FROM
"delaware-gov/educator-average-salary-rv4m-vy79:latest"."educator_average_salary"
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 delaware-gov/educator-average-salary-rv4m-vy79 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 delaware-gov/educator-average-salary-rv4m-vy79: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 delaware-gov/educator-average-salary-rv4m-vy79Checkout 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 delaware-gov/educator-average-salary-rv4m-vy79:latestThis will download all the objects for the latest tag of delaware-gov/educator-average-salary-rv4m-vy79 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 delaware-gov/educator-average-salary-rv4m-vy79: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 delaware-gov/educator-average-salary-rv4m-vy79: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, delaware-gov/educator-average-salary-rv4m-vy79 is just another Postgres schema.