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 board_of_adjustment_cases table in this repository, by referencing it like:
"datahub-austintexas-gov/board-of-adjustment-cases-ykxk-t5y9:latest"."board_of_adjustment_cases"or in a full query, like:
SELECT
":id", -- Socrata column ID
":@computed_region_m2th_e4b7", -- This column was automatically created in order to record in what polygon from the dataset 'Community Registry' (m2th-e4b7) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"unusual_hardship", -- the unusual hardship provided by applicant (note: this information was not entered, applicable, or otherwise or avaible for many records)
"status_date", -- the date for the current status
"work_class", -- whether it is residential or commercial
"case_type", -- the type of case
":@computed_region_rxpj_nzrk", -- This column was automatically created in order to record in what polygon from the dataset 'Zipcodes' (rxpj-nzrk) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_q9nd_rr82", -- This column was automatically created in order to record in what polygon from the dataset 'BOUNDARIES_single_member_districts' (q9nd-rr82) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_jcrc_4uuy", -- This column was automatically created in order to record in what polygon from the dataset 'Boundaries: Zip Code Tabulation Areas, 2017' (jcrc-4uuy) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_e9j2_6w3z", -- This column was automatically created in order to record in what polygon from the dataset 'Neighborhood Planning Areas' (e9j2-6w3z) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_8spj_utxs", -- This column was automatically created in order to record in what polygon from the dataset 'Single Member Council Districts' (8spj-utxs) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"day_issued", -- the issued day of the case
"case_manager", -- the manager of the case
"boa_consideration", -- what the board of adjustment's consideration was (note: this information was not entered, applicable, or otherwise or avaible for many records)
"effect_adj_property", -- the effective adjacent property (note: this information was not entered, applicable, or otherwise or avaible for many records)
"folder_name", -- the folder name
"location_zip",
"appraisal_id", -- the id of the appraisal record
"applicant_full_name", -- the applicant's full name
"structure_type", -- type of structure (note: this information was not entered, applicable, or otherwise or avaible for many records)
"other_phone", -- the other's phone
"latitude", -- Latitude
"location_state",
"variance_reason", -- reason for variance (note: this information was not entered, applicable, or otherwise or avaible for many records)
"web_link", -- the link to the Austin Build and Connect web site for the case
"folder_type", -- the type of AMANDA folder
"variance_type", -- the type of variance given for the case
"council_district", -- the Council District of the case
"unique_hardship", -- the unique hardship provided by applicant (note: this information was not entered, applicable, or otherwise or avaible for many records)
"issued_in_last_30_days", -- whether the issued date of the case was in the last 30 days
"sub_type", -- the type of variance
"applicant_organization_name", -- the applicant's organization name
"historic_review", -- whether case was part of historic review (note: this information was not entered, applicable, or otherwise or avaible for many records)
"folder_description", -- the folder description of the case
"applicant_phone", -- the applicant's phone number
"propy", -- the state plane Y coordinate of the case
"location_city",
"other_address", -- the other's address
"hearing_date", -- the date of the hearing for the case
"parking_crit_safety_haz", -- parking criteria safety hazard (note: this information was not entered, applicable, or otherwise or avaible for many records)
"calendar_year_issued", -- Calendar year of the issued date of the case was in the last 30 days
"legal_description", -- the legal description of the case
"location", -- the latitude and longitude used to locate the case in the Socrata/data.austintexas.gov application
"location_address",
"owner_organization_name", -- the owner's organization name
"fiscal_year_issued", -- Fiscal year of the issued date of the case was in the last 30 days
"permit_number", -- the permit number
"update_date", -- the update of the data.austintexas.gov record
"referencefile", -- the case number
"applicant_address", -- the applicant's address
"other_full_name", -- name of other entitiy associated with the case
"parking_crit_free_flow", -- parking criteria free flow (note: this information was not entered, applicable, or otherwise or avaible for many records)
"owner_full_name", -- the owner's full name
"folderrsn", -- the AMANDA folderrsn tracking number
"other_organization_name", -- name of other entitiy's organization associated with the case
"propx", -- the state plane X coordinate of the case
"longitude", -- Longitude
"owner_phone", -- the owner's phone
"applied_date", -- the date the case was applied for
"project_name", -- the project name
"owner_address", -- the owner's address
"parking_crit_spec_to_use", -- parking criteria specific to use (note: this information was not entered, applicable, or otherwise or avaible for many records)
"parking_crit_traffic_vol", -- parking criteria traffic volume (note: this information was not entered, applicable, or otherwise or avaible for many records)
"zoning_district", -- the zoning district for the case
"status_current", -- the current status of the record
"issued_date", -- the issued date of the case
"expires_date", -- the date the vairance would expire
"propertyrsn" -- the AMANDA and address point GIS layer property case identifier
FROM
"datahub-austintexas-gov/board-of-adjustment-cases-ykxk-t5y9:latest"."board_of_adjustment_cases"
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 datahub-austintexas-gov/board-of-adjustment-cases-ykxk-t5y9 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 datahub-austintexas-gov/board-of-adjustment-cases-ykxk-t5y9: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 datahub-austintexas-gov/board-of-adjustment-cases-ykxk-t5y9Checkout 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 datahub-austintexas-gov/board-of-adjustment-cases-ykxk-t5y9:latestThis will download all the objects for the latest tag of datahub-austintexas-gov/board-of-adjustment-cases-ykxk-t5y9 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 datahub-austintexas-gov/board-of-adjustment-cases-ykxk-t5y9: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 datahub-austintexas-gov/board-of-adjustment-cases-ykxk-t5y9: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, datahub-austintexas-gov/board-of-adjustment-cases-ykxk-t5y9 is just another Postgres schema.