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 covid19_cases_tests_and_deaths_by_zip_code table in this repository, by referencing it like:
"cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v:latest"."covid19_cases_tests_and_deaths_by_zip_code"or in a full query, like:
SELECT
":id", -- Socrata column ID
":@computed_region_bdys_3d7i",
"death_rate_weekly", -- Death rate per 100,000 population in the week.
"death_rate_cumulative", -- Death rate per 100,000 population through the week.
"population", -- ZIP Code population.
"test_rate_weekly", -- Test rate per 100,000 population in the week. Because, as described in the dataset description, not all tests are reported, this rate is a low-end estimate. The true rate may be higher.
"row_id",
"zip_code", -- Home ZIP Code of the cases and people tested.
"week_start", -- The first date of the week.
"week_end", -- The last date of the week.
"cases_weekly", -- Number of cases in the week. Values are removed for privacy until the cumulative total for the ZIP Code has reached 5 cases. A blank indicates a suppressed number from 0 to 4.
"cases_cumulative", -- Total number of cases through the week. Values less than 5 are removed for privacy. A blank indicates a suppressed number from 0 to 4.
"case_rate_weekly", -- Case rate per 100,000 population in the week.
"case_rate_cumulative", -- Total case rate per 100,000 population through the week.
"tests_weekly", -- Number of tests in the week. Please note data limitations in the dataset description.
"tests_cumulative", -- Total number of tests through the week. Please note data limitations in the dataset description.
"test_rate_cumulative", -- Total test rate per 100,000 population through the week. Because, as described in the dataset description, not all tests are reported, this rate is a low-end estimate. The true rate may be higher.
"percent_tested_positive_weekly", -- Percentage of tests returning positive results in the week based on specimen collection date. Because, as described in the dataset description, some not-positive tests may not be received, this rate is a high-end estimate. The true rate may be lower. Values are removed for privacy until the cumulative total for the ZIP Code has reached 5 cases.
"percent_tested_positive_cumulative", -- Percentage of tests returning positive results through the week based on specimen collection date. Because, as described in the dataset description, some not-positive tests may not be received, this rate is a high-end estimate. The true rate may be lower. Values are removed for privacy until the cumulative total for the ZIP Code has reached 5 cases.
":@computed_region_rpca_8um6",
":@computed_region_vrxf_vc4k",
":@computed_region_43wa_7qmu",
":@computed_region_6mkv_f3dw",
"zip_code_location", -- A point within the ZIP Code to allow for geographic analysis. The precise point shown has no other meaning.
"week_number", -- A sequential count of weeks, starting at the beginning of 2020. These numbers are aligned to CDC MMWR weeks.
"deaths_weekly", -- Number of deaths in the week.
"deaths_cumulative" -- Number of deaths through the week.
FROM
"cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v:latest"."covid19_cases_tests_and_deaths_by_zip_code"
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 cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v with SQL in under 60 seconds.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.cityofchicago.org. When you querycityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).
We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.
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 (like this repository), 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, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Mounting Data
This repository is an external repository. It's not hosted by Splitgraph. It is hosted by data.cityofchicago.org, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.
First, install Splitgraph if you haven't already.
Mount the table with sgr mount
sgr mount socrata \
"cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v" \
--handler-options '{
"domain": "data.cityofchicago.org",
"tables": {
"covid19_cases_tests_and_deaths_by_zip_code": "yhhz-zm2v"
}
}'That's it! Now you can query the data in the mounted table like any other Postgres table.
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, cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v is just another Postgres schema.