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_16 table in this repository, by referencing it like:
"datahub-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-16-4ta4-qa59:latest"."hhs_covid19_monthly_outcome_survey_wave_16"or in a full query, like:
SELECT
":id", -- Socrata column ID
"cv4_1_self", -- CV4_1_Self: Self COVID diagnosis
"cv2_1_fever", -- CV2_1_Fever: Fever
"mdate1a_current", -- mdate1a_current: mdate1a_current
"mdate1_req", -- MDATE1_Req: Required
"cam11_par1_grid_1217", -- CAM11_Par1_Grid_1217: 12 to 17 years old
"child_age_1217", -- Child_Age_1217: 12 to 17 years old
"child_age_04", -- Child_Age_04: 4 years old and younger
"bstr4_readiness", -- BSTR4_Readiness: Booster Readiness
"bstr3_like", -- BSTR3_Like: Booster Like
"bstr2_month", -- BSTR2_Month: Booster Month
"cam5_vaccmonth", -- CAM5_VaccMonth: Vaccine Month
"cam5_vaccuptake", -- CAM5_VaccUptake: Vaccine Uptake V2
"public_id", -- public_id: Unique Identifier
"cv4_3_no", -- CV4_3_No: No COVID diagnosis
"cv3_1_fever", -- CV3_1_Fever: Fever
"cv5_1_hospital", -- CV5_1_Hospital: Hospital or emergency room
"child_age_511", -- Child_Age_511: 5 years to 11 years old
"cv2_3_breath", -- CV2_3_Breath: Shortness of breath
"cv3_3_breath", -- CV3_3_Breath: Shortness of breath
"cv3_4_senses", -- CV3_4_Senses: Decreased sense of smell and taste
"cv3_5_flu", -- CV3_5_Flu: Flu symptoms
"cv4_2_family", -- CV4_2_Family: Family COVID diagnosis
"cv5_4_phone", -- CV5_4_Phone: Consulted with healthcare provider over the phone
"cv6a_rec", -- CV6a_Rec: Employment status prior to COVID pandemic
"cv6b", -- CV6b: Employment status changed since COVID pandemic
"cv5_6_chat", -- CV5_6_Chat: Consulted with healthcare provider using chat, text, or email
"cv5_2_urgent_care", -- CV5_2_Urgent_care: Urgent care facility
"cv14_3", -- CV14_3: Avoided enclosed spaces
"bstr1_uptake", -- BSTR1_Uptake: Booster Uptake
"cv2_5_flu", -- CV2_5_Flu: Flu symptoms
"cv14_4", -- CV14_4: Washed or sanitized hands frequently
"cv11_3_depressed", -- CV11_3_Depressed: Household feeling down, depressed, or hopeless
"cv10_5_none", -- CV10_5_None: None
"income", -- income: Income
"cv11_1_nervous", -- CV11_1_Nervous: Household nervous, anxious, on edge
"cv13", -- CV13: Time spent at home
"bstr6_par_read_1217", -- BSTR6_Par_Read_1217: 12 to 17 years old
"cv11_2_worrying", -- CV11_2_Worrying: Household not able to stop worrying
"cam11_par3_grid_6mo4", -- CAM11_Par3_Grid_6mo4: 6 months to 4 years old
"cam11_par1_grid_511", -- CAM11_Par1_Grid_511: 5 to 11 years old
"cv5_5_video", -- CV5_5_Video: Consulted with healthcare provider using video chat
"cv2_4_senses", -- CV2_4_Senses: Decreased sense of smell and taste
"cv9_1_unemployment_benefits", -- CV9_1_Unemployment_benefits: Unemployment benefits
"cv14_5", -- CV14_5: None of the above
"cam11_par2_grid_511", -- CAM11_Par2_Grid_511: 5 to 11 years old
"cv6c_rec", -- CV6c_Rec: Current employment status
"cv7a", -- CV7a: Essential worker
"cv7b", -- CV7b: Healthcare worker
"cv8a", -- CV8a: Insurance coverage prior to COVID pandemic
"cv8b", -- CV8b: Insurance changed since COVID pandemic
"cv8c", -- CV8c: Current insurance coverage
"cv9_4_none", -- CV9_4_None: None
"cv2_2_cough", -- CV2_2_Cough: Dry cough
"cv14_1", -- CV14_1: Kept social distance from others
"cv14_2", -- CV14_2: Wore a mask
"cv12_4_little_interest", -- CV12_4_Little_interest: Self little interest or pleasure
"cv12_3_depressed", -- CV12_3_Depressed: Self feeling down, depressed, or hopeless
"cv12_2_worrying", -- CV12_2_Worrying: Self not able to stop worrying
"cv12_1_nervous", -- CV12_1_Nervous: Self nervous, anxious, on edge
"cv10_3_work_from_home", -- CV10_3_Work_from_home: worked from home more than before the pandemic
"cv10_1_children_home", -- CV10_1_Children_home: kept children home from school
"cv5_7_none", -- CV5_7_None: None of the above
"cv5_3_doctor", -- CV5_3_Doctor: Visited doctor's office
"weights", -- Weights: Weights
"cam6_vaccwait", -- CAM6_VaccWait: Wait to get vaccinated V2
"cv3_2_cough", -- CV3_2_Cough: Dry cough
"cv10_4_return_to_work", -- CV10_4_Return_to_work: returned to work after temporary closure
"cv9_2_covid_enhanced", -- CV9_2_COVID_enhanced: COVID related enhanced unemployment benefits
"cv15", -- CV15: Wash hands yesterday
"cv11_4_little_interest", -- CV11_4_Little_interest: Household little interest or pleasure
"politicalideo", -- politicalideo: Political ideology
"race", -- race: Race
"ppeducat", -- ppeducat: Education -- categorical
"agecat", -- agecat: Age category
"xurbanicity", -- xurbanicity: Urbanicity
"parent", -- parent: Parent
"cv16", -- CV16: Wash hands time
"cv1", -- CV1: Physical health
"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
"cam5a_vacclike", -- CAM5a_VaccLike: Vaccine Likelihood
"cam11_par2_grid_1217", -- CAM11_Par2_Grid_1217: 12 to 17 years old
"bstr5_par_uptake_1217", -- BSTR5_Par_Uptake_1217: 12 to 17 years old
"ppgender" -- ppgender: Gender
FROM
"datahub-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-16-4ta4-qa59:latest"."hhs_covid19_monthly_outcome_survey_wave_16"
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-16-4ta4-qa59 with SQL in under 60 seconds.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at datahub.hhs.gov. When you querydatahub-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-16-4ta4-qa59:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).
We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.
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 (like this repository), 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, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Mounting Data
This repository is an external repository. It's not hosted by Splitgraph. It is hosted by datahub.hhs.gov, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.
First, install Splitgraph if you haven't already.
Mount the table with sgr mount
sgr mount socrata \
"datahub-hhs-gov/hhs-covid19-monthly-outcome-survey-wave-16-4ta4-qa59" \
--handler-options '{
"domain": "datahub.hhs.gov",
"tables": {
"hhs_covid19_monthly_outcome_survey_wave_16": "4ta4-qa59"
}
}'That's it! Now you can query the data in the mounted table like any other Postgres table.
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-16-4ta4-qa59 is just another Postgres schema.