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
"project_id", -- Unique project identifier
"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.
"lab_abbrev", -- Abbreviation of lab where sample was processed. Blank cells represent data that were not required or are not currently available.
"year", -- Year sample was collected. Blank cells represent data that were not required or are not currently available.
"bdate", -- Date of sample collection
"site_id", -- Unique site identifier
"longitude", -- Longitude of data collection point. Blank cells represent data that were not required or are not currently available.
"latitude", -- Latitude of data collection point. Blank cells represent data that were not required or are not currently available.
"band_number", -- Number of band placed on sample
"analysis_tissue_moist", -- Moisture percentage of tissue sample analyzed. Blank cells represent data that were not required or are not currently available.
"capture_loc", -- Unique spatial collection point identifier
"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.
"chem_value", -- Chemical data value for the sample analyzed
"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.
"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.
"analysis_tissue", -- Type of tissue or subject analyzed (ex. Blood, Egg, Fat, Sediment, Water, etc.).
"tissuecollected", -- Type of tissue or subject sampled (ex. Blood, Egg, Fat, Sediment, Water, etc.).
"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_hg_id", -- Original sample ID assigned by the researcher. Blank cells represent data that were not required or are not currently available.
"org_id", -- Unique identifier for each individual organism from which a sample was collected.
"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.
"chemical_type", -- Chemical analyzed; either methylmercury (MeHg), selenium (Se), or total mercury (THg).
"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_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.
"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.
"steps_reasonnotconverted", -- The steps taken to convert the original chemical value to the final standardized value or reasoning for not converting the original value
"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.
"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.
"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).
"duplicate1" -- Specifies chemical value if a sample duplicate was run. 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.
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 ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka: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 ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24kaCheckout 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 ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka:latestThis will download all the objects for the latest tag of ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka 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 ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka: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 ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka: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, ny-gov/synthesis-of-environmental-mercury-loads-in-new-2ei4-24ka is just another Postgres schema.