Design for Datasets

The general import philosophy is that we import the data into a PostgreSQL table in a raw form, only doing those conversions necessary to be able to access it with PostgreSQL commands and functions. We then extract information from this raw form into other tables and materialized views to enable relational queries.

Each data set’s import process follows the following steps:

  1. Initialize the database schema, with an SQL script under schemas/.

  2. Import the raw data, controlled by DVC steps under import/. This may be multiple steps; for example, OpenLibrary has a separate import step for each file. Actual import is usually handled by Rust or Python code.

  3. Index the data into relational views and tables. This is done by SQL scripts under index/.

Data integration then happens after the data sets are indexed (mostly - a few indexing steps depend on the book clustering process).

Adding a New Dataset

If you want to add a new data set, there are a few steps:

  1. Set up the initial raw database schema, with an SQL script and corresponding DVC file under schemas/. This should hold the data in a form that matches as closely as practical the raw form of the data, and should have minimal indexes and constraints. For a new schema ds-schema, you create two files:

    • ds-schema.sql, containing the CREATE SCHEMA and CREATE TABLE statements. We use PostgreSQL schemas (namespaces) to separate the data from different sources to make the whole database more manageable. Look at existing schema definitions for examples.

    • ds-schema.dvc, the DVC file running ds-schema.sql. This should contain a few things:

      # Run the schema file
      cmd: python ../ sql-script ds-schema.sql
      # Depend on the file and initial database setup
      - path: ds-schema.sql
      - path: pgstat://common-schema
      # a transcript of the script run
      - path: ds-schema.transcript
      # the status of importing this schema
      - path: pgstat://ds-schema
        cache: false

      When you run ./ repro schemas/ds-schema.dvc, it will run the schema script and fill in the other values (e.g. checksums) for the dependencies and outputs.

  2. Download the raw data files into data and register them with DVC (dvc add data/file for the simplest case), and document in this file where to download them. For files that it is reasonable to auto-download, you can create a more sophisticated setup to download them, but this is often not necessary.

  3. Identify, modify, and/or create the code needed to import the raw data files into the database. We have importers for several types of files already:

    • If the data is in CSV or similar form, suitable for PostgreSQL’s COPY FROM command, the pcat import tool in the Rust tools can copy the file, decompressing if necessary, directly to the database table.

    • If the data is in JSON, we have importers for two forms of JSON in the import-json Rust tool, the source for which is in src/commands/import_json/. Right now it supports OpenLibrary and GoodReads JSON files; the first is a tab-separated file containing object metadata and the JSON object, and the second is a simple object-per-line format. The accompanying file ( and define the data format and the destination tables. For many future JSON objects, will be the appropriate template to start with, and add support for it to the appropriate places in

    • If the data is in MARC-XML, the Rust parse-marc command is your starting place. It can process both multiple-record formats (e.g. from VIAF) or single-document formats (from the Library of Congress), and can decompress while importing.

    If you need to write new import code, you may need to make sure it properly records stage dependencies and status. At a minimum, it should record each file imported and its checksum as a file for the stage, along with the stage begin/end timestamps. Look at the meta-schema.sql file for the specific tables. The and support modules provide code for recording stage status in Python and Rust, respectively.

  4. Set up the import process with an appropriate .dvc step in import/. This step should depend on the schema (pgstat://ds-schema), and have as one of its uncached outputs the import process status (pgstat://ds-import, if the file is named ds-import.dvc). Some importers require you to explicitly provide the stage name as a command-line argument.

  5. Write SQL commands to transform and index the imported data in a script under index/. This script may do a number of things:

    • Map data set book ISBNs or other identifiers to ISBN IDs.

    • Extract relational tables from structured data such as JSON (e.g. the book author lists extracted from OpenLibrary).

    • Create summary tables or materialized views.

    See the existing index logic in index/ for examples.

  6. Create a .dvc stage to run your index script; this works like the one for the schema in (1), but is under index/ and depends on pgstat://ds-import (or whatever your import stage is named).

  7. Create or update data integrations to make use of this data, as needed and appropriate.

    If the new data contains ISBN/ID links that you want to include in book clustering, add support to and update the cluster.dvc file to also depend on your data set’s index stage (e.g. pgstat://ds-index).

  8. If appropriate, add a dependency on the last stage of your processing to Dvcfile.

All dependencies should be through the pgstat:// URLs, so that they are computed from current database status.