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 community_health_indicator_reports_chirs_trend table in this repository, by referencing it like:
"health-data-ny-gov/community-health-indicator-reports-chirs-trend-jb5s-mei3:latest"."community_health_indicator_reports_chirs_trend"or in a full query, like:
SELECT
":id", -- Socrata column ID
"county_code", -- County Code. New York State = 999.
"three_year_average_county_value", -- Indicator value of the county three–year average, where available. The cells are blank when data are not available, or suppressed due to insufficient number of observations.
"trend_data_nys_exc_nyc_value", -- Indicator value. Values are for New York State, or are for New York State excluding New York City where the corresponding county is outside of New York City, and blank otherwise
"trend_data_county_value", -- Indicator value. Values are included for counties, and New York State. The cells are blank when data are not available, or suppressed.
"trend_data_nyc_value", -- Indicator value. Values are for New York State, or are for New York City where the corresponding county is one of the NYC counties (Bronx, Kings, New York, Queens, Richmond), and blank otherwise.
"county_name", -- Full county name
"health_topic_number", -- Health Topic ID number
"date_years", -- Year for the indicator rate or percent.
"indicator_name", -- Full text description of the indicator
"comment_for_three_year_average_county_value", -- If the cell in the comment column is blank, the value in the three year average county column is the three year average for the county: the average of: the year before, the current year and the year after. For county data, if the year is the first or last year in the trend, the three year average is not calculated, and the cell in the comment column is “Not applicable” Region values are not available for selected socio-economic indicators, including population, poverty and income, unemployment and health insurance status. Therefore, the cell in the comments column is “Not applicable”. The three year average are also not available when the county is New York State.
"data_source", -- Data sources for the raw data used in indicator computation.
"indicator_id", -- Unique indicator identifier
"comment_for_trend_data_county_value", -- Indicates whether the values in the county values column are for county or for New York State, and whether the data are suppressed or not available. If the cell in the comments column is “Not applicable”, the data are not available. If the cell in the comments column is “Data suppressed” there are insufficient observations and the value in the county value column is suppressed. If the cell in the comment column is blank, the value in the county value column is for county. If the cell in the comment column is “New York State” the value in the county value column is for New York State total.
"health_topic" -- Full title for the specific Health Topic area.
FROM
"health-data-ny-gov/community-health-indicator-reports-chirs-trend-jb5s-mei3:latest"."community_health_indicator_reports_chirs_trend"
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 health-data-ny-gov/community-health-indicator-reports-chirs-trend-jb5s-mei3 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 health-data-ny-gov/community-health-indicator-reports-chirs-trend-jb5s-mei3: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 health-data-ny-gov/community-health-indicator-reports-chirs-trend-jb5s-mei3Checkout 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 health-data-ny-gov/community-health-indicator-reports-chirs-trend-jb5s-mei3:latestThis will download all the objects for the latest tag of health-data-ny-gov/community-health-indicator-reports-chirs-trend-jb5s-mei3 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 health-data-ny-gov/community-health-indicator-reports-chirs-trend-jb5s-mei3: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 health-data-ny-gov/community-health-indicator-reports-chirs-trend-jb5s-mei3: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, health-data-ny-gov/community-health-indicator-reports-chirs-trend-jb5s-mei3 is just another Postgres schema.