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Description:

graphique 1.7

GraphQL service for arrow tables and parquet data sets. The schema for a query API is derived automatically.
Usage
% env PARQUET_PATH=... uvicorn graphique.service:app

Open http://localhost:8000/ to try out the API in GraphiQL. There is a test fixture at ./tests/fixtures/zipcodes.parquet.
% env PARQUET_PATH=... strawberry export-schema graphique.service:app.schema

outputs the graphql schema for a parquet data set.
Configuration
Graphique uses Starlette's config: in environment variables or a .env file. Config variables are used as input to a parquet dataset.

PARQUET_PATH: path to the parquet directory or file
FEDERATED = '': field name to extend type Query with a federated Table
DEBUG = False: run service in debug mode, which includes metrics
COLUMNS = None: list of names, or mapping of aliases, of columns to select
FILTERS = None: json filter query for which rows to read at startup

For more options create a custom ASGI app. Call graphique's GraphQL on an arrow Dataset, Scanner, or Table. The GraphQL Table type will be the root Query type.
Supply a mapping of names to datasets for multiple roots, and to enable federation.
import pyarrow.dataset as ds
from graphique import GraphQL

app = GraphQL(ds.dataset(...)) # Table is root query type
app = GraphQL.federated({<name>: ds.dataset(...), ...}, keys={...}) # Tables on federated fields

Start like any ASGI app.
uvicorn <module>:app

Configuration options exist to provide a convenient no-code solution, but are subject to change in the future. Using a custom app is recommended for production usage.
API
types

Dataset: interface for an arrow dataset, scanner, or table.
Table: implements the Dataset interface. Adds typed row, columns, and filter fields from introspecting the schema.
Column: interface for an arrow column (a.k.a. ChunkedArray). Each arrow data type has a corresponding column implementation: Boolean, Int, Long, Float, Decimal, Date, Datetime, Time, Duration, Base64, String, List, Struct. All columns have a values field for their list of scalars. Additional fields vary by type.
Row: scalar fields. Arrow tables are column-oriented, and graphique encourages that usage for performance. A single row field is provided for convenience, but a field for a list of rows is not. Requesting parallel columns is far more efficient.

selection

slice: contiguous selection of rows
filter: select rows with simple predicates
scan: select rows and project columns with expressions

projection

columns: provides a field for every Column in the schema
column: access a column of any type by name
row: provides a field for each scalar of a single row
apply: transform columns by applying a function
join: join tables by key columns

aggregation

group: group by given columns, and aggregate the others
runs: partition on adjacent values in given columns, transforming the others into list columns
tables: return a list of tables by splitting on the scalars in list columns
flatten: flatten list columns with repeated scalars

ordering

sort: sort table by given columns
rank: select rows with smallest or largest values

Performance
Graphique relies on native PyArrow routines wherever possible. Otherwise it falls back to using NumPy or custom optimizations.
By default, datasets are read on-demand, with only the necessary rows and columns scanned. Although graphique is a running service, parquet is performant at reading a subset of data. Optionally specify FILTERS in the json filter format to read a subset of rows at startup, trading-off memory for latency. An empty filter ({}) will read the whole table.
Specifying COLUMNS will limit memory usage when reading at startup (FILTERS). There is little speed difference as unused columns are inherently ignored. Optional aliasing can also be used for camel casing.
If index columns are detected in the schema metadata, then an initial filter will also attempt a binary search on tables.
Installation
% pip install graphique[server]

Dependencies

pyarrow
strawberry-graphql[asgi,cli]
uvicorn (or other ASGI server)

Tests
100% branch coverage.
% pytest [--cov]