libbigdata 1.0.1

A pure Dart library for big data manipulation with DataFrame API and query engine.

libbigdata

A pure-Dart library for big-data manipulation — a DataFrame API and an in-memory SQL query engine, inspired by pandas and DuckDB, with native readers and writers for the common columnar and table formats. No native dependencies.

Features

• DataFrame API — chainable select / filter / orderBy / groupBy / agg / join / withColumn, evaluated on a columnar engine.
• SQL engine — query DataFrames and files directly, e.g. SELECT * FROM 'data.parquet' WHERE x > 10.
• Formats — CSV, JSON / JSON Lines, Parquet, Arrow IPC, Delta Lake, and Apache Iceberg (read), with streaming/batch variants for out-of-core inputs.
• Parquet codecs — Snappy, GZIP, ZSTD, LZ4 (LZ4_RAW read+write; Hadoop LZ4 read), plus optional page CRC32 integrity checks.

Install

dependencies:
  libbigdata: ^1.0.0

dart pub get

Quick start

DataFrame API

import 'package:libbigdata/libbigdata.dart';

Future<void> main() async {
  final df = await DataFrameIO.readCsv('people.csv');

  final adults = await df
      .filter('age >= 18')
      .select(['name', 'age', 'city'])
      .orderBy('age', ascending: false)
      .limit(10)
      .execute();

  print(adults.toRows());
}

Grouping, joins, derived columns

final byCity = await df
    .groupBy(['city'])
    .agg({'age': 'avg', 'name': 'count'})
    .execute();

final joined = await orders
    .join(customers, 'customer_id', type: JoinType.left)
    .withColumn('total', 'price * quantity')
    .execute();

The filter and withColumn strings are small, deliberately narrow expression languages rather than SQL. See doc/DATAFRAME.md for the exact grammar each accepts, and use the SQL engine for anything richer.

SQL

// Query a file directly.
final result = await sql.execute("SELECT city, count(*) FROM 'people.csv' GROUP BY city");

// Or register a DataFrame and query it.
final engine = SqlEngine();
await engine.registerDataFrame('people', df);
final top = await engine.sql('SELECT name FROM people WHERE age > 30');

Formats

Format	Read	Write	Notes
CSV	Yes	Yes	auto-inference, streaming batches
JSON / JSONL	Yes	Yes	streaming batches (JSON Lines)
Parquet	Yes	Yes	predicate/column pushdown, optional CRC32
Arrow IPC	Yes	Yes	streaming batches
Delta Lake	Yes	Yes	append/overwrite, delete/update/merge, time travel
Iceberg	Yes	Yes	snapshots, time travel

For files larger than memory, use the streaming readers (readCsvBatches, readJsonBatches, readArrowBatches), the streaming Parquet writer (writeParquetStream), and SqlEngine.executeStream.

Documentation

• doc/DATAFRAME.md — the DataFrame API: operations, expression syntax, execution.
• doc/SQL.md — the SQL engine: API, supported syntax, architecture.
• doc/DESIGN.md — architecture: data model, query pipeline, spilling, commit coordination.
• doc/CLI.md — the native command-line interface.
• doc/parquet_support.md — Parquet format coverage.

Runnable examples live in example/.

Status

API under active development; the surface may change between minor versions. Contributions and bug reports welcome at github.com/libdbm/libbigdata.

License

BSD 3-Clause. See LICENSE.