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 ems_quarterly_omd_clinical_performance_indicators table in this repository, by referencing it like:
"datahub-austintexas-gov/ems-quarterly-omd-clinical-performance-indicators-2cxe-9vbj:latest"."ems_quarterly_omd_clinical_performance_indicators"or in a full query, like:
SELECT
":id", -- Socrata column ID
"cpi_05_count_goal_met", -- Count of AMS patients who receive a blood glucose level (BGL) assessment.
"cpi_07_percent_goal_met", -- Percent of stroke patients who receive a blood glucose level assessment.
"cpi_04_count_goal_met", -- Count of ACS patients who receive aspirin (ASA).
"cpi_03_percent_goal_met_target", -- Target performance level for compliance with scene interval goal for STEMI Alert patients.
"cpi_03_percentile_90_interval_scene_target", -- Target performance level for 90th percentile scene interval for STEMI Alert patients, measured in decimal minutes.
"cpi_04_percent_goal_met_target", -- Target performance level for aspirin administration to ACS patients.
"cpi_03_percentile_90_interval_scene", -- 90th percentile scene interval for included STEMI Alert patients, measured in decimal minutes. Scene interval starts when the first ATCEMS unit arrives on scene, and ends when ATCEMS personnel depart the scene.
"cpi_06_percent_goal_met_target", -- Target performance level for blood glucose assessment of seizure patients.
"cpi_01_percent_goal_met_target", -- Target performance level for compliance with scene interval goal for Trauma Alert patients.
"cpi_01_percentile_90_interval_scene_target", -- Target performance level for 90th percentile scene interval for Trauma Alert patients, measured in decimal minutes.
"cpi_04_percent_goal_met", -- Percent of ACS patients who receive aspirin.
"cpi_05_percent_goal_met_target", -- Target performance level for blood glucose assessment of AMS patients.
"cpi_03_percent_goal_met", -- Percent of STEMI Alert patients with a scene interval less than 15 minutes.
"cpi_02_percentile_90_interval_scene_target", -- Target performance level for 90th percentile scene interval for Stroke Alert patients, measured in decimal minutes.
"cpi_03_count_goal_met", -- Count of STEMI Alert patients with a scene interval less than 15 minutes. Scene interval starts when the first ATCEMS unit arrives on scene, and ends when ATCEMS personnel depart the scene.
"fiscal_quarter_key", -- Row identifier – numeric representation of fiscal quarter in <yyyyqq> format.
"fiscal_quarter_end_date", -- Last day of fiscal quarter, presented in <mmm yyyy> format.
"cpi_01_count", -- Count of Trauma Alert patients. In this setting, “Trauma Alert” refers to injured patients who meet Physiological or Anatomical criteria for transport to a Trauma Center per City of Austin/Travis County EMS System Clinical Operating Guidelines.
"cpi_02_percent_goal_met_target", -- Target performance level for compliance with scene interval goal for Stroke Alert patients.
"cpi_01_percent_goal_met", -- Percent of Trauma Alert patients with a scene interval less than 15 minutes.
"cpi_02_percent_goal_met", -- Percent of Stroke Alert patients with a scene interval less than 15 minutes.
"cpi_02_percentile_90_interval_scene", -- 90th percentile scene interval for included Stroke Alert patients, measured in decimal minutes. Scene interval starts when the first ATCEMS unit arrives on scene, and ends when ATCEMS personnel depart the scene.
"cpi_03_count", -- Count of STEMI Alert patients. STEMI Alert patients are a subset of ACS patients; therefore, this count will be smaller than the count for CPI 4 – Aspirin Administration to ACS Patients.
"cpi_04_count", -- Count of Acute Coronary Syndrome (ACS) patients. This includes patients with a Primary Impression of “Acute Coronary Syndrome” or “STEMI,” and patients for whom a STEMI Alert is called. This group will be larger than the group for CPI 3 – Scene interval for STEMI Alerts.
"cpi_02_count_goal_met", -- Count of Stroke Alert patients with a scene interval less than 15 minutes. Scene interval starts when the first ATCEMS unit arrives on scene, and ends when ATCEMS personnel depart the scene.
"cpi_02_count", -- Count of Stroke Alert patients. Not all stroke patients meet criteria for calling a Stroke Alert, so this group will be smaller than the group for CPI 7 – Blood Glucose Test for Stroke Patients.
"cpi_07_count", -- Count of stroke patients. This definition includes all patients with Primary Impression of stroke, regardless of whether the patient qualified as a Stroke Alert, as well as those for whom a Stroke Alert is called. This group will be larger than the group for CPI 2—Scene interval for Stroke Alerts.
"cpi_06_percent_goal_met", -- Percent of seizure patients who receive a blood glucose level assessment.
"cpi_01_percentile_90_interval_scene", -- 90th percentile scene interval for included Trauma Alert patients, measured in decimal minutes. Scene interval starts when the first ATCEMS unit arrives on scene, and ends when ATCEMS personnel depart the scene.
"cpi_05_count", -- Count of patients with a primary impression suggestive of Altered Mental Status (AMS).
"cpi_07_percent_goal_met_target", -- Target performance level for blood glucose assessment of stroke patients.
"fiscal_quarter", -- Year and fiscal quarter for record in <yyyy-qq> format (e.g. 2014-Q1).
"fiscal_quarter_start_date", -- First day of fiscal quarter, presented in <mmm yyyy> format.
"cpi_01_count_goal_met", -- Count of Trauma Alert patients with a scene interval less than 15 minutes. Scene interval starts when the first ATCEMS unit arrives on scene, and ends when ATCEMS personnel depart the scene.
"cpi_06_count_goal_met", -- Count of seizure patients who receive a blood glucose level assessment.
"cpi_06_count", -- Count of patients with a primary impression of “Seizure – Other Convulsions,” “Seizure Febrile,” or “Seizure Postictal State.”
"cpi_07_count_goal_met", -- Count of stroke patients who receive a blood glucose level assessment.
"cpi_05_percent_goal_met" -- Percent of AMS patients who receive a blood glucose level assessment.
FROM
"datahub-austintexas-gov/ems-quarterly-omd-clinical-performance-indicators-2cxe-9vbj:latest"."ems_quarterly_omd_clinical_performance_indicators"
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-austintexas-gov/ems-quarterly-omd-clinical-performance-indicators-2cxe-9vbj 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-austintexas-gov/ems-quarterly-omd-clinical-performance-indicators-2cxe-9vbj: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-austintexas-gov/ems-quarterly-omd-clinical-performance-indicators-2cxe-9vbjCheckout 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-austintexas-gov/ems-quarterly-omd-clinical-performance-indicators-2cxe-9vbj:latestThis will download all the objects for the latest tag of datahub-austintexas-gov/ems-quarterly-omd-clinical-performance-indicators-2cxe-9vbj 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-austintexas-gov/ems-quarterly-omd-clinical-performance-indicators-2cxe-9vbj: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-austintexas-gov/ems-quarterly-omd-clinical-performance-indicators-2cxe-9vbj: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-austintexas-gov/ems-quarterly-omd-clinical-performance-indicators-2cxe-9vbj is just another Postgres schema.