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 new_well_inspections_fy_20152020_environmental table in this repository, by referencing it like:
"pa-gov/new-well-inspections-fy-20152020-environmental-f8fx-8zip:latest"."new_well_inspections_fy_20152020_environmental"or in a full query, like:
SELECT
":id", -- Socrata column ID
"count", -- Column used to count the rows in the file when creating an aggregated view.
"inspection_type", -- The description for the type of inspection.
"spud_date", -- The date reported to DEP by the well Operator that drilling of the well has commenced.
"well_status", -- Active – permit has been issued and well may or may not have been drilled or producing, but has not been plugged. Proposed but Never Materialized – permit was issued, but expired prior to the commencement of drilling. Plugged OG Well – permit issued and well has been plugged by well operator. Operator Reported Not Drilled – permit issued, but operator reported to DEP that they never drilled the well. DEP Abandoned List –an abandoned well that has been inspected by DEP. DEP Orphan List – A well abandoned prior to April 18, 1985, that has not been affected or operated by the present owner or operator and from which the present owner, operator or lessee has received no economic benefit other than as a land. DEP Plugged – a DEP Abandoned or DEP Orphan well that has been plugged by DEP, Regulatory Inactive Status – a well status that is requested by well operator and has been granted by DEP. Well is capable of producing, but is temporarily shut in. Granted for initial 5 years and must be renewed yearly after first 5 years. Abandoned –a well that has not been used to produce, extract or inject any gas, petroleum or other liquid within the preceding 12 months; for which equipment necessary for production, extraction or injection has been removed; or considered dry and not equipped for production.
"configuration_code", -- The system generated code for the well configuration: deviated wellbore, horizontal well bore, well configuration has not been determined, vertical wellbore.
"unconventional_ind", -- Unconventional well indicator Field values Y or N, Y = Unconventional An unconventional gas well is a bore hole drilled or being drilled for the purpose of or to be used for the production of natural gas from an unconventional formation. An unconventional formation is defined as a geologic shale formation below the base of the Elk Sandstone or its geologic equivalent where natural gas generally cannot be produced except by horizontal or vertical well bores stimulated by hydraulic fracturing. A conventional gas well, also known as a traditional well, is a well that produces oil or gas from a conventional formation. Conventional formations are variable in age, occurring both above and below the Elk Sandstone. While a limited number of such gas wells are capable of producing sufficient quantities of gas without stimulation by hydraulic fracturing, most conventional wells require this stimulation technique due to the reservoir characteristics in Pennsylvania. Stimulation of conventional wells, however, generally does not require the volume of fluids typically required for unconventional wells.
"county", -- The County located in Pennsylvania where the facility is located.
"permit", -- The unique system-generated number (otherwise known as American Petroleum Institute (API) Number) assigned to the permit, the primary facility and the well
"farm", -- The operator chosen name for the primary facility or well.
"ogo_num", -- The Oil and Gas Operator (OGO) number that is assigned to the Well operator when they apply for a well permit.
"client", -- The name of the Oil and Gas Operator associated to the well.
"inspection_date", -- The date the inspection was completed at the well. Each date is recorded in the following format: MM/DD/YYYY. For unconventional well sites, there will be four inspection dates: one per quarter, unless an inspection was not performed. If only one date appears, it may be the first day of the reporting year (01/01/YYYY), i.e., default date, or it may be the date the inspection was performed at the well.
"geocoded_column_state",
"geocoded_column_zip",
"geocoded_column", -- Latitude and Longitude together for each well to be used when creating mapping visualizations
":@computed_region_amqz_jbr4", -- This column was automatically created in order to record in what polygon from the dataset 'Municipality Boundary' (amqz-jbr4) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_r6rf_p9et", -- This column was automatically created in order to record in what polygon from the dataset 'Pa House Districts (2017-01)' (r6rf-p9et) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_nmsq_hqvv", -- This column was automatically created in order to record in what polygon from the dataset 'Pennsylvania County Boundaries' (nmsq-hqvv) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_d3gw_znnf", -- This column was automatically created in order to record in what polygon from the dataset 'Pa Senatorial Districts (2017-01)' (d3gw-znnf) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_rayf_jjgk", -- This column was automatically created in order to record in what polygon from the dataset 'Pa School Districts (2017)' (rayf-jjgk) the point in column 'geocoded_column' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"geocoded_column_address",
"geocoded_column_city",
"inspection_result_desc", -- The description of the associated inspection result.
"region", -- The location of the DEP Office in Pennsylvania where the facility is located.
"client_id", -- The system generated number created when the operator first applied to be an Oil and Gas Operator in Pennsylvania and was added to the database.
"municipality", -- The municipality located in Pennsylvania where the facility is located.
"well_code", -- The type of the well chosen from the following list: Coalbed Methane, Dry Hole, Gas, Injection, Multiple Well Bore, Observation, Comb. Oil and Gas, Oil, Storage Well, Test Well, Temp Unspecified, Undetermined, Waste Disposal.
"year_end_date", -- The last day of the Fiscal Year that is included in that year.
"inspection_id", -- UID - The unique, system-generated identification number assigned to the inspection record.
"conservation", -- Conservation indicator Field values Y or N, Y = Conservation A conservation well is defined as any well penetrating the top of the Onondaga Limestone (or equivalent formation when the Onondaga is absent) and is at least 3,800 feet deep. This term is defined by the Oil and Gas Conservation Law. The Pennsylvania Geological Survey considers a “deep” well to be any well that penetrates the Middle Devonian Tully Limestone.
"fiscal_year" -- Fiscal Year of the Data row
FROM
"pa-gov/new-well-inspections-fy-20152020-environmental-f8fx-8zip:latest"."new_well_inspections_fy_20152020_environmental"
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/new-well-inspections-fy-20152020-environmental-f8fx-8zip 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/new-well-inspections-fy-20152020-environmental-f8fx-8zip: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/new-well-inspections-fy-20152020-environmental-f8fx-8zipCheckout 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/new-well-inspections-fy-20152020-environmental-f8fx-8zip:latestThis will download all the objects for the latest tag of pa-gov/new-well-inspections-fy-20152020-environmental-f8fx-8zip 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/new-well-inspections-fy-20152020-environmental-f8fx-8zip: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/new-well-inspections-fy-20152020-environmental-f8fx-8zip: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/new-well-inspections-fy-20152020-environmental-f8fx-8zip is just another Postgres schema.