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 hhs_covid19_monthly_outcome_survey_wave_06 table in this repository, by referencing it like:
"datahub-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-06-qhxy-ktqs:latest"."hhs_covid19_monthly_outcome_survey_wave_06"or in a full query, like:
SELECT
":id", -- Socrata column ID
"cv5_2_urgent_care", -- CV5_2_Urgent_care: Urgent care facility
"cv3_5_flu", -- CV3_5_Flu: Flu symptoms
"cv3_4_senses", -- CV3_4_Senses: Decreased sense of smell and taste
"cv3_1_fever", -- CV3_1_Fever: Fever
"cv2_5_flu", -- CV2_5_Flu: Flu symptoms
"cv2_3_breath", -- CV2_3_Breath: Shortness of breath
"cv2_1_fever", -- CV2_1_Fever: Fever
"cv1", -- CV1: Physical health
"cam11_par2", -- CAM11_par2: Once they are eligible, how soon will your child(ren) under the age
"cam11_par1", -- CAM11_par1: If you have any children ages 12-17 select their vaccination status
"cam7_vaccbel_6", -- CAM7_VaccBel_6: Worse side effects
"cam7_vaccbel_5", -- CAM7_VaccBel_5: Worried side effects
"cam7_vaccbel_3", -- CAM7_VaccBel_3: Immunity from exposure
"cam7_vaccbel_2", -- CAM7_VaccBel_2: Worried COVID from vaccine
"cam7_vaccbel_1", -- CAM7_VaccBel_1: COVID vaccine likelihood
"cam5_vaccuptake", -- CAM5_VaccUptake: Vaccine Uptake
"public_id", -- public_id: Unique Identifier
"cv8c", -- CV8c: Current insurance coverage
"cam5a_vacclike", -- CAM5a_VaccLike: Vaccine Likelihood
"cv14_1", -- CV14_1: Kept social distance from others
"cv16", -- CV16: Wash hands time
"cv11_2_worrying", -- CV11_2_Worrying: Household not able to stop worrying
"cv13", -- CV13: Time spent at home
"cv14_3", -- CV14_3: Avoided enclosed spaces
"cv6b", -- CV6b: Employment status changed since COVID pandemic
"cv5_5_video", -- CV5_5_Video: Consulted with healthcare provider using video chat
"xurbanicity", -- xurbanicity: Urbanicity
"cv9_4_none", -- CV9_4_None: None
"cv3_2_cough", -- CV3_2_Cough: Dry cough
"ppeducat", -- ppeducat: Education -- categorical
"cv10_1_children_home", -- CV10_1_Children_home: kept children home from school
"cv12_1_nervous", -- CV12_1_Nervous: Self nervous, anxious, on edge
"cv4_2_family", -- CV4_2_Family: Family COVID diagnosis
"cv2_2_cough", -- CV2_2_Cough: Dry cough
"cv2_4_senses", -- CV2_4_Senses: Decreased sense of smell and taste
"cv12_4_little_interest", -- CV12_4_Little_interest: Self little interest or pleasure
"cv10_3_work_from_home", -- CV10_3_Work_from_home: worked from home more than before the pandemic
"cv4_3_no", -- CV4_3_No: No COVID diagnosis
"weights", -- Weights: Weights
"cv5_7_none", -- CV5_7_None: None of the above
"cv4_1_self", -- CV4_1_Self: Self COVID diagnosis
"cv3_3_breath", -- CV3_3_Breath: Shortness of breath
"cv9_2_covid_enhanced", -- CV9_2_COVID_enhanced: COVID related enhanced unemployment benefits
"cv7b", -- CV7b: Healthcare worker
"cv12_2_worrying", -- CV12_2_Worrying: Self not able to stop worrying
"cv12_3_depressed", -- CV12_3_Depressed: Self feeling down, depressed, or hopeless
"cv5_1_hospital", -- CV5_1_Hospital: Hospital or emergency room
"cam6_vaccwait", -- CAM6_VaccWait: Wait to get vaccinated
"cv5_3_doctor", -- CV5_3_Doctor: Visited doctor's office
"cv5_4_phone", -- CV5_4_Phone: Consulted with healthcare provider over the phone
"cv5_6_chat", -- CV5_6_Chat: Consulted with healthcare provider using chat, text, or email
"cv6a_rec", -- CV6a_Rec: Employment status prior to COVID pandemic
"cv6c_rec", -- CV6c_Rec: Current employment status
"cv7a", -- CV7a: Essential worker
"cv8a", -- CV8a: Insurance coverage prior to COVID pandemic
"cv8b", -- CV8b: Insurance changed since COVID pandemic
"cv9_1_unemployment_benefits", -- CV9_1_Unemployment_benefits: Unemployment benefits
"cv9_3_cares", -- CV9_3_CARES: CARES Act check
"cv10_2_home_schooled", -- CV10_2_Home_schooled: home schooled children
"cv10_4_return_to_work", -- CV10_4_Return_to_work: returned to work after temporary closure
"cv10_5_none", -- CV10_5_None: None
"cv11_1_nervous", -- CV11_1_Nervous: Household nervous, anxious, on edge
"cv11_3_depressed", -- CV11_3_Depressed: Household feeling down, depressed, or hopeless
"cv11_4_little_interest", -- CV11_4_Little_interest: Household little interest or pleasure
"cv14_2", -- CV14_2: Wore a mask
"cv14_4", -- CV14_4: Washed or sanitized hands frequently
"cv14_5", -- CV14_5: None of the above
"cv15", -- CV15: Wash hands yesterday
"parent", -- parent: Parent
"agecat", -- agecat: Age category
"ppgender", -- ppgender: Gender
"income", -- income: Income
"ppreg4", -- ppreg4: U.S. Census Region 4
"race", -- race: Race
"politicalideo" -- politicalideo: Political ideology
FROM
"datahub-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-06-qhxy-ktqs:latest"."hhs_covid19_monthly_outcome_survey_wave_06"
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-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-06-qhxy-ktqs 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-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-06-qhxy-ktqs: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-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-06-qhxy-ktqsCheckout 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-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-06-qhxy-ktqs:latestThis will download all the objects for the latest tag of datahub-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-06-qhxy-ktqs 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-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-06-qhxy-ktqs: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-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-06-qhxy-ktqs: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-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-06-qhxy-ktqs is just another Postgres schema.