Advanced Data Dictionary

Abierto
  • ¿Algo no está bien?
#18 13 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 1 día

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
8/100
Tipo de issue
Nueva funcionalidad
Claridad
Necesita aclaración
Estado de actividad
Tranquilo
Stack tecnológico
postgresql, python
Área
data, database

Línea de trabajo

This is an open design question, not a code task: the issue lists six storage options for qsv statistics and frequency tables and does not choose one. No files or tests are named. Before any work, a maintainer (the issue asks @wardi) needs to pick an option. Done would mean a recorded decision and a scoped first step, not an implementation.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

enhancement

Currently, CKAN's Data Dictionary is limited to data type, label and description.

With qsv stats we collect descriptive statistics when we infer each column's data type during the Analysis phase of a DP+ job.

Currently - sum, min/max, min/max length, mean, stddev, variance, quartiles, median, modes, cardinality & nullcount.

When cardinality = rowcount and nullcount = 0, we can infer that a column can be a primary key and be a unique index, and annotate its data dictionary accordingly (and going further, create a unique index on it after the Copy phase).

When nullcount = rowcount, we can infer that a column is empty, and note it in the data dictionary as well.

And with qsv frequency - we can also compile frequency tables for the top N values of a column, and if the cardinality of a column is below a given N threshold, we can even infer the domain of a column as enumerated values.

Since we paid for compiling the statistics when we inferred the column data types already, we can store these statistics in the data dictionary as well as "schemata" (a term I coined for schema metadata) for "free" ( or nearly free, as running qsv frequency is not currently done by DP+, but even against a large file like the 1m row, 500mb NYC 311 benchmark data, it only takes 2.8 seconds).

We have several options:

  1. add additional properties to the existing Data Dictionary JSON which is stored as a table comment
  2. keep the existing Data Dictionary JSON as is, and store RESOURCE_NAME-stats.csv and RESOURCE_NAME-freq.csv as "system resources", that can be downloaded and queried with the CKAN API.
  3. extend the Data Dictionary with additional properties, and also store the -stats and -freq CSVs as system resources.
  4. alternatively, instead of using the original Data Dictionary JSON, we can instead insert the jsonschema file produced by the qsv schema command. The added benefit of doing so is that we use the jsonschema file with qsv validate to check if an external file conforms to the schema. And since qsv validate accepts a jsonschema URL, you can even validate an external file against the CKAN hosted jsonschema.
  5. Do 4, and add the "system resources" like 3.
  6. Store all these schemata data in a "schemata catalog" in the datastore database or a dedicated schemata database as native PostgreSQL objects. Perhaps, by using a resource's ID and adding a special prefix and/or suffix to it (e.g. RESOURCEID_datadict, RESOURCEID_stats, RESOURCEID_freq). Doing so has the added benefit of being able to query all the data dictionaries - e.g. columns with the same name, infer related resources, suggest joins, etc. and other "Linked Data" like queries and inferences in a performant manner.

@wardi , since you originally implemented the Data Dictionary, would be keen to get your opinion.

Lenguaje dominante
Python
Estrellas
47
Forks
33
Merge medio
2 h 28 min
PR fusionados (30 d)
10

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de dathere/datapusher-plus

Todos los issues de dathere/datapusher-plus

Issues similares

Más issues de Python

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.