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 highway_data_element_dictionary table in this repository, by referencing it like:
"datahub-transportation-gov/highway-data-element-dictionary-nhvr-exvq:latest"."highway_data_element_dictionary"or in a full query, like:
SELECT
":id", -- Socrata column ID
"data_element_name", -- Unit of data that is considered in context to be indivisible. A Data Element is considered to be a basic unit of data of interest to an organization. It is a unit of data for which the definition, identification, representation, and permissible values are specified by means of a set of attributes. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Data+Element+Name
"preferred_physical_name", -- This is the preferred name for physical models or data definition language (DDL). https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Preferred+Physical+Name
"maximum_length", -- This field is the maximum number of units of length, where units of length varies depending on the type that is being derived from. The value of maxLength must be a nonNegativeInteger. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Maximum+Length
"value_domain", -- Value Domain is a class each instance of which models a value domain, a collection of permissible values. A value domain provides representation, but has no implication as to what data element concept the values are associated with, nor what the values mean. Permissible values are designations, bindings of signs (values) to their corresponding value meanings. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Value+Domain
"business_owner_name", -- The organization and contact within the organization that is responsible for the definition and other mandatory attributes by which the metadata item is specified. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Business+Owner+Name
"data_asset_name", -- The name of the data asset or data set. A data asset is collection of data elements or datasets that make sense to group together. A given Data Asset may represent an entire database consisting of multiple distinct entity classes, or may represent a single entity class. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Data+Asset+Name
"system_number", -- This is the number of systems the data element is used. The current systems that have been crosswalked are FMIS, HPMS, TMAS, and LTPP.
"data_type_code", -- The format used for the collection of letters, digits, and/or symbols, to depict values of a data element, determined by the operations that may be performed on a data element. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Data+Type+Code
"precision_number", -- The number of decimal places permitted in any associated data element values. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Precision+Number
"subject_category_code", -- This is the primary high-level taxonomy subject category for the data element. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Subject+Category+Code
"description_text", -- The definition text is a statement hich specifies the meaning of the Data Element. It may additionally record a source for the text. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Description+Text
"data_asset_abbreviation", -- The abbreviation for the data asset or data set. A data asset is collection of data elements or datasets that make sense to group together. A given Data Asset may represent an entire database consisting of multiple distinct entity classes, or may represent a single entity class. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Data+Asset+Abbreviation
"pattern_text", -- Pattern is a constraint on the value space of a datatype which is achieved by constraining the lexical space to literals which match a specific pattern. The value of pattern should be a regular expression. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Pattern+Number
"unit_of_measure" -- Magnitude for some quantity, either physical or immaterial. https://data.transportation.gov/resource/3enx-g69f.csv?data_element_name=Unit+of+Measure
FROM
"datahub-transportation-gov/highway-data-element-dictionary-nhvr-exvq:latest"."highway_data_element_dictionary"
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-transportation-gov/highway-data-element-dictionary-nhvr-exvq 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-transportation-gov/highway-data-element-dictionary-nhvr-exvq: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-transportation-gov/highway-data-element-dictionary-nhvr-exvqCheckout 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-transportation-gov/highway-data-element-dictionary-nhvr-exvq:latestThis will download all the objects for the latest tag of datahub-transportation-gov/highway-data-element-dictionary-nhvr-exvq 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-transportation-gov/highway-data-element-dictionary-nhvr-exvq: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-transportation-gov/highway-data-element-dictionary-nhvr-exvq: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-transportation-gov/highway-data-element-dictionary-nhvr-exvq is just another Postgres schema.