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 cambridge_municipal_greenhouse_gas_inventory_2008 table in this repository, by referencing it like:
"cambridgema-gov/cambridge-municipal-greenhouse-gas-inventory-2008-m5zs-2fuf:latest"."cambridge_municipal_greenhouse_gas_inventory_2008"or in a full query, like:
SELECT
":id", -- Socrata column ID
"ch4_metric_tons", -- Metric tons of methane (CH4) emissions. One metric ton is equivalent to 1,000 kg. Calculated values based on kg of CH4, converted to metric tons using a standard conversion factor
"scope", -- Scope 1, or direct emissions are GHG emissions from fuels combusted within the city boundary. Scope 2, or indirect emissions, are GHG emissions occurring outside the City boundary, but as a consequence of the purchase and use of grid-supplied electricity, heat, steam and/or cooling within the city boundary
"total_co2e_metric_tons", -- Metric tons of carbon dioxide equivalent (CO2e). Calculated value based on energy use data tracked by the City in MassEnergyInsight, and emission factors and Global Warming Potential (GWP) factors approved by the The Climate Registry. The GWP was developed to allow comparisons of the global warming impacts of different gases. Specifically, it is a measure of how much energy the emissions of one ton of a gas will absorb over a given period of time, relative to the emissions of one ton of carbon dioxide.
"source", -- The process that generated greenhouse gas emissions.
"use_mmbtu", -- One MMBtu is equivalent to one million Btu. Calculated value based on energy use in native units, converted to MMBtu using standard conversion factors.
"co2e_from_n2o_metric_tons", -- Calculated value based on NO2 emissions in metric tons and Global Warming Potential (GWP) factor for NO2 . GWP was developed to allow comparisons of the global warming impacts of different gases. Specifically, it is a measure of how much energy the emissions of one ton of a gas will absorb over a given period of time, relative to the emissions of one ton of carbon dioxide.
"use_gallons", -- Energy use data in native units based on periodic fuel delivery bills
"n2o_metric_tons", -- Metric tons of nitrous oxide (N20) emissions. One metric ton is equivalent to 1,000 kg. Calculated values based on kg of N2O, converted to metric tons using a standard conversion factor
"use_therms", -- A unit of heat equivalent to 100,000 British Thermal Units (Btu) or 1.055 × 108 joules. Energy use data in native units based on monthly natural gas bills.
"type", -- The fuel that generated greenhouse gas emissions.
"use_kwh", -- Kilowatt hours. Energy use data in native units based on monthly electricity bills
"ch4_kg", -- Kilograms of methane (CH4) emissions. Calculated value based on energy use data and fuel specific CH4 emissions factors
"co2_metric_tons", -- Metric tons of carbon dioxide (CO2). One metric ton is equivalent to 1,000 kg. Calculated values based on kg of CO2, converted to metric tons using a standard conversion factor
"co2_kg", -- Kilograms of carbon dioxide (CO2). Calculated value based on energy use data and fuel specific CO2 emissions factors.
"uniqueid", -- Row ID provided for ease of analysis. A concatenation of scope, year, fuel, and source.
"n2o_kg", -- Kilograms of nitrous oxide (N20) emissions. Calculate value based on energy use data and fuel specific N20 emissions factors.
"co2e_from_ch4_metric_tons", -- Calculated value based on CH4 emissions in metric tons and global warming potential (GWP) factor for CH4. The Global Warming Potential (GWP) was developed to allow comparisons of the global warming impacts of different gases. Specifically, it is a measure of how much energy the emissions of one ton of a gas will absorb over a given period of time, relative to the emissions of one ton of carbon dioxide.
"year" -- The year during which emissions took place.
FROM
"cambridgema-gov/cambridge-municipal-greenhouse-gas-inventory-2008-m5zs-2fuf:latest"."cambridge_municipal_greenhouse_gas_inventory_2008"
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 cambridgema-gov/cambridge-municipal-greenhouse-gas-inventory-2008-m5zs-2fuf 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 cambridgema-gov/cambridge-municipal-greenhouse-gas-inventory-2008-m5zs-2fuf: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 cambridgema-gov/cambridge-municipal-greenhouse-gas-inventory-2008-m5zs-2fufCheckout 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 cambridgema-gov/cambridge-municipal-greenhouse-gas-inventory-2008-m5zs-2fuf:latestThis will download all the objects for the latest tag of cambridgema-gov/cambridge-municipal-greenhouse-gas-inventory-2008-m5zs-2fuf 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 cambridgema-gov/cambridge-municipal-greenhouse-gas-inventory-2008-m5zs-2fuf: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 cambridgema-gov/cambridge-municipal-greenhouse-gas-inventory-2008-m5zs-2fuf: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, cambridgema-gov/cambridge-municipal-greenhouse-gas-inventory-2008-m5zs-2fuf is just another Postgres schema.