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 local_area_unemployment_statistics_laus_cy_2016 table in this repository, by referencing it like:
"pa-gov/local-area-unemployment-statistics-laus-cy-2016-rqq6-7e5m:latest"."local_area_unemployment_statistics_laus_cy_2016"or in a full query, like:
SELECT
":id", -- Socrata column ID
"geocoded_column", -- Georeferenced Latitude and Longitude column as generic points for each county that can be used for creating visuals such as maps.
"longitude", -- This is a generic longitude point for the county so that a map can be created.
"latitude", -- This is a generic latitude point for the county so that a map can be created.
"unemployment_rate", -- The unemployed divided by the labor force.
"employed", -- Count of persons who (a) did any work as paid employees, self-employed, agricultural workers, or worked 15 hours or more as unpaid family workers, or (b) were not working but who had jobs from which they were temporarily absent. Each employed person is counted only once, even if the person holds more than one job.
"labor_force", -- Count of persons classified as employed or unemployed.
"county_code", -- The code that represents the county. There are 67 counties in Pennsylvania. They are numbered 01 thru 67, and 00 identifies the statewide total.
"benchmark_year", -- On an annual basis, many of the federal Bureau of Labor Statistics cooperative programs’ estimated data are aligned to known, universal data, i.e., benchmarked. The LAUS data are recalculated using the benchmarked source data. The Benchmark Year represents the year of the source data for LAUS calculations.
"county_fips", -- FIPS code. The Federal Information Processing Standard (FIPS) code, used by the United States government to uniquely identify counties, is provided with each entry. FIPS codes are five-digit numbers; for Pennsylvania the codes start with 42 and are completed with the three-digit county code. The County FIPS is the last three digits of the five digit FIPS and the code 000 is for statewide.
"area_name", -- The name of the State or the County name that represents this line of data.
"unemployed", -- Count of persons aged 16 years and older who had no employment, were available for work, and had made specific efforts to find employment. Includes persons who were waiting to be recalled to jobs from which they had been laid off.
"state_fips", -- The Federal Information Processing Standard (FIPS) code, used by the United States government to uniquely identify states and counties. FIPS codes are five-digit numbers; for Pennsylvania the codes start with 42 and are completed with the three-digit county code. The state code is the first two digits of the five digit FIPS code.
":@computed_region_rayf_jjgk",
":@computed_region_r6rf_p9et",
":@computed_region_amqz_jbr4",
":@computed_region_d3gw_znnf",
":@computed_region_nmsq_hqvv",
"calendar_year" -- Represents the period inclusive of January 1st through December 31st.
FROM
"pa-gov/local-area-unemployment-statistics-laus-cy-2016-rqq6-7e5m:latest"."local_area_unemployment_statistics_laus_cy_2016"
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/local-area-unemployment-statistics-laus-cy-2016-rqq6-7e5m 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/local-area-unemployment-statistics-laus-cy-2016-rqq6-7e5m: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/local-area-unemployment-statistics-laus-cy-2016-rqq6-7e5mCheckout 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/local-area-unemployment-statistics-laus-cy-2016-rqq6-7e5m:latestThis will download all the objects for the latest tag of pa-gov/local-area-unemployment-statistics-laus-cy-2016-rqq6-7e5m 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/local-area-unemployment-statistics-laus-cy-2016-rqq6-7e5m: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/local-area-unemployment-statistics-laus-cy-2016-rqq6-7e5m: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/local-area-unemployment-statistics-laus-cy-2016-rqq6-7e5m is just another Postgres schema.