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 synthesis_of_environmental_mercury_loads_in_new table in this repository, by referencing it like:
"ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka:latest"."synthesis_of_environmental_mercury_loads_in_new"or in a full query, like:
SELECT
":id", -- Socrata column ID
"chem_units", -- Units for chemical data value; either micrograms per gram (µg/g), micrograms per kilogram (µg/kg), micrograms per liter (µg/l), micrograms per deciliter (µg/dl), parts per million (ppm), milligram per kilogram (mg/kg), nanogram per gram (ng/g), or nanogram per liter (ng/l).
"chem_value", -- Chemical data value for the sample analyzed
"convert_chem_value_fishadj", -- Chemical value standardized for species-specific fish length. Blank cells represent data that were not required or are not currently available.
"analysis_tissue_wgt_g", -- Weight of the tissue sample analyzed, measured in grams. Blank cells represent data that were not required or are not currently available.
"chemical_type", -- Chemical analyzed; either methylmercury (MeHg), selenium (Se), or total mercury (THg).
"analysis_tissue", -- Type of tissue or subject analyzed (ex. Blood, Egg, Fat, Sediment, Water, etc.).
"analysis_tissue_moist", -- Moisture percentage of tissue sample analyzed. Blank cells represent data that were not required or are not currently available.
"tissuecollected", -- Type of tissue or subject sampled (ex. Blood, Egg, Fat, Sediment, Water, etc.).
"band_number", -- Number of band placed on sample
"year", -- Year sample was collected. Blank cells represent data that were not required or are not currently available.
"bdate", -- Date of sample collection
"project_id", -- Unique project identifier
"site_id", -- Unique site identifier
"org_hg_id", -- Original sample ID assigned by the researcher. Blank cells represent data that were not required or are not currently available.
"longitude", -- Longitude of data collection point. Blank cells represent data that were not required or are not currently available.
"capture_event", -- Unique spatial and temporal collection point identifier
"hg_comments", -- Relevant comments related to the chemical analysis. Blank cells represent data that were not required or are not currently available.
"final_convertedtissue", -- The equivalent tissue type that each chemical value was converted into for standardized analysis. Blank cells represent data that were not required or are not currently available.
"rmk", -- Any remarks on the chemical value or the laboratory analysis. Blank cells represent data that were not required or are not currently available.
"ww_dw_fw", -- Chemical analysis tissue preparation method (ex. dry weight (dw), fresh weight (fw), wet weight (ww), etc.). Blank cells represent data that were not required or are not currently available.
"lab_code", -- This is an additional identifier used in some datasets to track lab samples assigned by the lab. Blank cells represent data that were not required or are not currently available.
"sample_id", -- Unique sample identifier
"org_id", -- Unique identifier for each individual organism from which a sample was collected.
"capture_loc", -- Unique spatial collection point identifier
"latitude", -- Latitude of data collection point. Blank cells represent data that were not required or are not currently available.
"duplicate1", -- Specifies chemical value if a sample duplicate was run. Blank cells represent data that were not required or are not currently available.
"limit_desc", -- This field defines the type of LIMIT that was quantified. Blank cells represent data that were not required or are not currently available.
"lab_abbrev", -- Abbreviation of lab where sample was processed. Blank cells represent data that were not required or are not currently available.
"rmk_desc", -- Further explanation or definitions of remarks on the chemical value or the laboratory analysis, if needed. Blank cells represent data that were not required or are not currently available.
"pcode", -- Code for the medium, type of sample (wet, dry or fresh weight), type of Hg measured, and measurement units. Blank cells represent data that were not required or are not currently available.
"method", -- Code relating further information on methodology used for chemical analysis. Blank cells represent data that were not required or are not currently available.
"steps_reasonnotconverted", -- The steps taken to convert the original chemical value to the final standardized value or reasoning for not converting the original value
"limit", -- Value of the lower detection limit, average detection limit, reporting limit, method limit, or other limit reported by the lab conducting the sample analysis. Blank cells represent data that were not required or are not currently available.
"final_chem_standardized" -- Final chemical value used in analysis after converting to standardized units (parts per million; ppm) with an appropriate wet, dry, or fresh weight (ww, dw, and fw, respectively) Hg measurement for the converted tissue type. Blank cells represent data that were not required or are not currently available.
FROM
"ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka:latest"."synthesis_of_environmental_mercury_loads_in_new"
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 ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka with SQL in under 60 seconds.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.ny.gov. When you queryny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka: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 data.ny.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 \
"ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka" \
--handler-options '{
"domain": "data.ny.gov",
"tables": {
"synthesis_of_environmental_mercury_loads_in_new": "2ei4-24ka"
}
}'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, ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka is just another Postgres schema.