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 report_card_assessment_data_201819_school_year table in this repository, by referencing it like:
"wa-gov/report-card-assessment-data-201819-school-year-5y3z-mgxd:latest"."report_card_assessment_data_201819_school_year"or in a full query, like:
SELECT
":id", -- Socrata column ID
"count_of_students_expected_to_test_including_previously_passed", -- Of the students that were expected to test, this is the count of students that met standard. Includes students that previously passed the assessment
"schoolname",
"countmetstandard", -- Count of students expected to test (including students the previously passed the assessment)
"percentmetstandard", -- A percent of students that met standard (CountMetStandard divided by Count of Students expected to test including previously passed)
"gradelevel",
"percent_no_score", -- Percent of students that were not given a score
"schoolyear", -- The school year the test was administered
"percentlevel1", -- Percent of students that scored level 1: not proficient
"studentgroup", -- A subgroup of StudentGroupType. designates specific gender, race/ethnicity, or programs and characteristics associated with the data.
"currentschooltype",
"percentlevel3", -- Percent of students that scored level 3: proficient
"percentlevel2", -- Percent of students that scored level 2: not proficient
"test_administration_group", -- General, Alternate, or Other. This field designates the type of assessment General refers to the standard SBAC (ELA & Math) and WCAS (Science) assessments, Alternate designates the AIM (ELA, math, and science assessments), Other designates the English Learner Progress Assessment (ELPA)
"testsubject", -- Designates the tested subject: Science, English Language Arts (ELA), Science, ELPA, and WIDA
"esdorganizationid", -- Educational Servce DIstrict ID
"suppression", -- This field designates if suppression was applied to the row
"testadministration", -- This field designates the assessment: Access to Instruction and Measurement (AIM), Smarter Ballance Assessment Consortium (SBAC), Washington Comprehensive Assessment of Science (WCAS), and English Learner Progress Assessment (ELPA)
"percentmettestedonly", -- Percent of Students that met Standard (using only students that tested as denominator)
"districtcode", -- Unique district code
"districtname",
"esdname", -- Educational Servce DIstrict Name
"count_of_students_expected", -- Count of students expected to test (excluding students the previously passed the assessment)
"percentlevel4", -- Percent of students that scored level 3: proficient
"districtorganizationid", -- unique district ID
"percentparticipation", -- Percent of students that received a score
"dataasof", -- Date data was extracted
"county",
"schoolorganizationid", -- Unique school ID
"schoolcode", -- Unique school code
"studentgrouptype", -- Gender, Race/Ethnicity, Student Programs and Characteristics
"organizationlevel" -- This field designates if the data is for a school, district, or the state.
FROM
"wa-gov/report-card-assessment-data-201819-school-year-5y3z-mgxd:latest"."report_card_assessment_data_201819_school_year"
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 wa-gov/report-card-assessment-data-201819-school-year-5y3z-mgxd 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 wa-gov/report-card-assessment-data-201819-school-year-5y3z-mgxd: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 wa-gov/report-card-assessment-data-201819-school-year-5y3z-mgxdCheckout 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 wa-gov/report-card-assessment-data-201819-school-year-5y3z-mgxd:latestThis will download all the objects for the latest tag of wa-gov/report-card-assessment-data-201819-school-year-5y3z-mgxd 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 wa-gov/report-card-assessment-data-201819-school-year-5y3z-mgxd: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 wa-gov/report-card-assessment-data-201819-school-year-5y3z-mgxd: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, wa-gov/report-card-assessment-data-201819-school-year-5y3z-mgxd is just another Postgres schema.