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_04 table in this repository, by referencing it like:
"datahub-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-04-me3u-se5h:latest"."hhs_covid19_monthly_outcome_survey_wave_04"or in a full query, like:
SELECT
":id", -- Socrata column ID
"cv3_5_flu", -- CV3_5_Flu: Flu symptoms
"cv7a", -- CV7a: Essential worker
"cv10_5_none", -- CV10_5_None: None
"cam5a_vacclike", -- CAM5a_VaccLike: Vaccine Likelihood
"cam5_vaccuptake", -- CAM5_VaccUptake: Vaccine Uptake
"cv5_1_hospital", -- CV5_1_Hospital: Hospital or emergency room
"cv8c", -- CV8c: Current insurance coverage
"cam6_vaccwait", -- CAM6_VaccWait: Wait to get vaccinated
"cv11_3_depressed", -- CV11_3_Depressed: Household feeling down, depressed, or hopeless
"cv10_4_return_to_work", -- CV10_4_Return_to_work: returned to work after temporary closure
"cv2_5_flu", -- CV2_5_Flu: Flu symptoms
"cv8b", -- CV8b: Insurance changed since COVID pandemic
"public_id", -- public_id: Unique Identifier
"cv2_2_cough", -- CV2_2_Cough: Dry cough
"cv11_4_little_interest", -- CV11_4_Little_interest: Household little interest or pleasure
"cv6b", -- CV6b: Employment status changed since COVID pandemic
"cv14_2", -- CV14_2: Wore a mask
"cv2_1_fever", -- CV2_1_Fever: Fever
"cv16", -- CV16: Wash hands time
"cv3_3_breath", -- CV3_3_Breath: Shortness of breath
"cam7_vaccbel_6", -- CAM7_VaccBel_6: Worse side effects
"cv14_5", -- CV14_5: None of the above
"cv9_2_covid_enhanced", -- CV9_2_COVID_enhanced: COVID related enhanced unemployment benefits
"cv3_2_cough", -- CV3_2_Cough: Dry cough
"cv5_7_none", -- CV5_7_None: None of the above
"cv10_1_children_home", -- CV10_1_Children_home: kept children home from school
"cv11_2_worrying", -- CV11_2_Worrying: Household not able to stop worrying
"cv10_2_home_schooled", -- CV10_2_Home_schooled: home schooled children
"cv9_3_cares", -- CV9_3_CARES: CARES Act check
"ppreg4", -- ppreg4: U.S. Census Region 4
"cv12_3_depressed", -- CV12_3_Depressed: Self feeling down, depressed, or hopeless
"cv14_1", -- CV14_1: Kept social distance from others
"cv4_3_no", -- CV4_3_No: No COVID diagnosis
"ppeducat", -- ppeducat: Education -- categorical
"cv9_1_unemployment_benefits", -- CV9_1_Unemployment_benefits: Unemployment benefits
"cv8a", -- CV8a: Insurance coverage prior to COVID pandemic
"cv7b", -- CV7b: Healthcare worker
"cv6c_rec", -- CV6c_Rec: Current employment status
"cv6a_rec", -- CV6a_Rec: Employment status prior to COVID pandemic
"cv5_6_chat", -- CV5_6_Chat: Consulted with healthcare provider using chat, text, or email
"cv5_5_video", -- CV5_5_Video: Consulted with healthcare provider using video chat
"cv5_4_phone", -- CV5_4_Phone: Consulted with healthcare provider over the phone
"cv5_3_doctor", -- CV5_3_Doctor: Visited doctor's office
"cv5_2_urgent_care", -- CV5_2_Urgent_care: Urgent care facility
"cv4_2_family", -- CV4_2_Family: Family COVID diagnosis
"cv4_1_self", -- CV4_1_Self: Self COVID diagnosis
"weights", -- Weights: Weights
"politicalideo", -- politicalideo: Political ideology
"race", -- race: Race
"agecat", -- agecat: Age category
"xurbanicity", -- xurbanicity: Urbanicity
"cv15", -- CV15: Wash hands yesterday
"cv14_4", -- CV14_4: Washed or sanitized hands frequently
"cv14_3", -- CV14_3: Avoided enclosed spaces
"cv13", -- CV13: Time spent at home
"cv12_4_little_interest", -- CV12_4_Little_interest: Self little interest or pleasure
"cv12_2_worrying", -- CV12_2_Worrying: Self not able to stop worrying
"cv12_1_nervous", -- CV12_1_Nervous: Self nervous, anxious, on edge
"cv11_1_nervous", -- CV11_1_Nervous: Household nervous, anxious, on edge
"cv10_3_work_from_home", -- CV10_3_Work_from_home: worked from home more than before the pandemic
"cv2_4_senses", -- CV2_4_Senses: Decreased sense of smell and taste
"cv2_3_breath", -- CV2_3_Breath: Shortness of breath
"cv1", -- CV1: Physical health
"cam7_vaccbel_5", -- CAM7_VaccBel_5: Worried side effects
"cam7_vaccbel_3", -- CAM7_VaccBel_3: Immunity from exposure
"income", -- income: Income
"cv3_1_fever", -- CV3_1_Fever: Fever
"parent", -- parent: Parent
"cv9_4_none", -- CV9_4_None: None
"cam7_vaccbel_1", -- CAM7_VaccBel_1: COVID vaccine likelihood
"cam7_vaccbel_2", -- CAM7_VaccBel_2: Worried COVID from vaccine
"cv3_4_senses", -- CV3_4_Senses: Decreased sense of smell and taste
"ppgender" -- ppgender: Gender
FROM
"datahub-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-04-me3u-se5h:latest"."hhs_covid19_monthly_outcome_survey_wave_04"
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-04-me3u-se5h 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-04-me3u-se5h: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-04-me3u-se5hCheckout 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-04-me3u-se5h:latestThis will download all the objects for the latest tag of datahub-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-04-me3u-se5h 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-04-me3u-se5h: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-04-me3u-se5h: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-04-me3u-se5h is just another Postgres schema.