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 statewide_greenhouse_gas_emissions_beginning_1990 table in this repository, by referencing it like:
"ny-gov/statewide-greenhouse-gas-emissions-beginning-1990-5i6e-asw6:latest"."statewide_greenhouse_gas_emissions_beginning_1990"or in a full query, like:
SELECT
":id", -- Socrata column ID
"sub_category_2", -- Groupings of related processes, sources and sinks within each Sub-category 1. “Not Applicable” indicates that no finer granularity of data is available.
"sector", -- Organizes emissions following guidelines from Intenational Panel on Climate Change Taskforce on Governmental GHG Inventories, which are groupings of related processes, sources and sinks. Each sector is comprised of individual categories.
"year", -- Full calendar year in which emissions occurred.
"category", -- Groupings of related processes, sources and sinks within each Sector.
"gross", -- Defines whether the emission values are included in Statewide Gross GHG Emissions total, as per requirements of Climate Leadership and Community Protection Act (CLCPA) and 6 NYCRR Part 496. Gross annual emissions are used to track progress towards reaching the State’s GHG emissions limits of 60% of 1990 emissions by 2030 and 15% of 1990 emissions by 2050
"net", -- Defines whether the emission values are included in Statewide Net GHG Emissions total per CLCPA. Net emissions omit biogenic CO2 and include Net Emission Removals. Net annual emissions are used to track progress towards reaching the State’s goal of net zero emissions.
"conventional_accounting", -- Defines whether emission values would be included in conventional accounting utilized by other governments and organizations. Conventional accounting applies a 100-year GWP, omits biogenic CO2, and does not include emissions outside of New York State. This format was developed for national parties to the United National Framework Convention on Climate Change.
"economic_sector", -- Sectoral organization that reflects economic drivers and policy. Aligns with sectoral definitions of Climate Action Council Draft Scoping Plan. Rows containing “Excluded” are emissions that are reported but NOT used in calculating gross, net, or conventional accounting totals.
"sub_category_1", -- Groupings of related processes, sources and sinks within each Category.
"sub_category_3", -- Groupings of related processes, sources and sinks within each Sub-category 2. “Not Applicable” indicates that no finer granularity of data is available.
"gas", -- Chemical formula of species or grouping of Greenhouse Gas emissions. Common and chemical names provided below: CO2 – Carbon dioxide; CH4 – Methane; N2O – Nitrous oxide; HFCs – Hydrofluorocarbons; PFCs – Perfluorocarbons; SF6 – Sulfur hexafluoride; NF3 – Nitrogen trifluoride
"mt_co2e_ar5_20_yr", -- Quantity of annual emissions in metric tons (MT) carbon dioxide equivalent (CO2e) using a 20-year global warming potential (GWP) provided in the Intergovernmental Panel on Climate Change (IPPC) Fifth Annual Report (AR5). Use of the 20-year GWP is a requirement of the CLCPA and 6 NYCRR Part 496. The IPCC developed the Global Warming Potential (GWP) metric as one method to compare the impact of a pulse emission of a particular greenhouse gas to a pulse emission of an equal amount of CO2, integrated over a time horizon that is chosen based on the specific goals that policy makers are trying to achieve. The GWP is reported in units of carbon dioxide equivalence, or “CO2e”. Table 2 of the Summary Report of the Statewide GHG Emissions Report provides GWP values for each gas. The report is available available at: https://www.dec.ny.gov/energy/99223.html.
"mt_co2e_ar4_100_yr" -- Quantity of annual emissions in metric tons (MT) carbon dioxide equivalent (CO2e) using a 100-year global warming potential (GWP) provided in the Intergovernmental Panel on Climate Change (IPPC) Fourth Annual Report (AR4). Use of the 100-year GWP allows for comparison to reporting by other organizations but is not applicable to the CLCPA. The IPCC developed the Global Warming Potential (GWP) metric as one method to compare the impact of a pulse emission of a particular greenhouse gas to a pulse emission of an equal amount of CO2, integrated over a time horizon that is chosen based on the specific goals that policy makers are trying to achieve. The GWP is reported in units of carbon dioxide equivalence, or “CO2e”. Table 2 of the Summary Report of the Statewide GHG Emissions Report provides GWP values for each gas. The report is available available at: https://www.dec.ny.gov/energy/99223.html.
FROM
"ny-gov/statewide-greenhouse-gas-emissions-beginning-1990-5i6e-asw6:latest"."statewide_greenhouse_gas_emissions_beginning_1990"
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 ny-gov/statewide-greenhouse-gas-emissions-beginning-1990-5i6e-asw6 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 ny-gov/statewide-greenhouse-gas-emissions-beginning-1990-5i6e-asw6: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 ny-gov/statewide-greenhouse-gas-emissions-beginning-1990-5i6e-asw6Checkout 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 ny-gov/statewide-greenhouse-gas-emissions-beginning-1990-5i6e-asw6:latestThis will download all the objects for the latest tag of ny-gov/statewide-greenhouse-gas-emissions-beginning-1990-5i6e-asw6 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 ny-gov/statewide-greenhouse-gas-emissions-beginning-1990-5i6e-asw6: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 ny-gov/statewide-greenhouse-gas-emissions-beginning-1990-5i6e-asw6: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, ny-gov/statewide-greenhouse-gas-emissions-beginning-1990-5i6e-asw6 is just another Postgres schema.