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 iowa_median_earnings_in_past_12_months_for_the table in this repository, by referencing it like:
"mydata-iowa-gov/iowa-median-earnings-in-past-12-months-for-the-2jg9-y5ct:latest"."iowa_median_earnings_in_past_12_months_for_the"or in a full query, like:
SELECT
":id", -- Socrata column ID
"occupation", -- Occupations are subcategories within occupation groups and include the following: Management occupations, Business and financial operations occupations, Computer and mathematical occupations, Architecture and engineering occupations, Life physical and social science occupations, Community and social service occupations, Legal occupations, Education training and library occupations, Arts design entertainment sports and media occupations, Health diagnosing and treating practitioners and other technical occupations, Health technologists and technicians, Healthcare support occupations, Fire fighting and prevention and other protective service workers including supervisors, Law enforcement workers including supervisors, Food preparation and serving related occupations, Building and grounds cleaning and maintenance occupations, Personal care and service occupations, Sales and related occupations, Office and administrative support occupations, Farming fishing and forestry occupations, Construction and extraction occupations, Installation maintenance and repair occupations, Production occupations, Transportation occupations, and Material moving occupations. Those with NA are records reflecting median earnings for total population, occupation category, or occupation group.
"occupation_group", -- Occupation groups are subcategories within individual occupation categories and include the following: Management business and financial occupations, Computer engineering and science occupations, Education legal community service arts and media occupations, Healthcare practitioners and technical occupations, Healthcare support occupations, Protective service occupations, Food preparation and serving related occupations, Building and grounds cleaning and maintenance occupations, Personal care and service occupations, Sales and related occupations, Office and administrative support occupations, Farming fishing and forestry occupations, Construction and extraction occupations, Installation maintenance and repair occupations, Production occupations, Transportation occupations, and Material moving occupations. Those with NA are records reflecting median earnings for total population or occupation category.
"occupation_category", -- Occupation categories are broad occupational groupings and include the following: Total, Management business science and arts occupations, Service occupations, Sales and office occupations, Natural resources construction and maintenance occupations, and Production transportation and material moving occupations.
"date", -- The date when the five year period of data collection concluded.
"variable", -- Variable name identified by the US Census Bureau.
"name", -- Name of geography associated with the record.
"geography_id", -- Specific geography id used by the U.S. Census for the state, county, place or census tract associated with the record.
"value", -- Median earnings in the past 12 months (in inflation-adjusted dollars) associated with geography, variable and data collection period.
"type", -- Specifies the type of geography associated with the record. Categories include: state, county, place and tract.
"location", -- Primary point for the specific geography.
"sex", -- Sex categories: Male, Female, and Both
"variable_description", -- Describes the characteristics associated with the variable.
"data_collection_period", -- The data collection period reflects the years associated with the 60 month data collection period.
":@computed_region_y683_txed"
FROM
"mydata-iowa-gov/iowa-median-earnings-in-past-12-months-for-the-2jg9-y5ct:latest"."iowa_median_earnings_in_past_12_months_for_the"
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 mydata-iowa-gov/iowa-median-earnings-in-past-12-months-for-the-2jg9-y5ct 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 mydata-iowa-gov/iowa-median-earnings-in-past-12-months-for-the-2jg9-y5ct: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 mydata-iowa-gov/iowa-median-earnings-in-past-12-months-for-the-2jg9-y5ctCheckout 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 mydata-iowa-gov/iowa-median-earnings-in-past-12-months-for-the-2jg9-y5ct:latestThis will download all the objects for the latest tag of mydata-iowa-gov/iowa-median-earnings-in-past-12-months-for-the-2jg9-y5ct 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 mydata-iowa-gov/iowa-median-earnings-in-past-12-months-for-the-2jg9-y5ct: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 mydata-iowa-gov/iowa-median-earnings-in-past-12-months-for-the-2jg9-y5ct: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, mydata-iowa-gov/iowa-median-earnings-in-past-12-months-for-the-2jg9-y5ct is just another Postgres schema.