Crate dataprof 

High-performance data profiling with ISO 8000/25012 quality metrics.

The dataprof crate is the user-facing facade for profiling CSV, JSON, JSONL, Parquet, database, DataFrame, and Arrow sources. Implementation details live in the workspace crates under crates/; this package keeps the public API compact and oriented around Profiler.

Quick Start

use dataprof::Profiler;

let report = Profiler::new().analyze_file("data.csv")?;
println!("Rows: {}", report.execution.rows_processed);
println!("Quality: {:?}", report.quality_score());

Structs

AccuracyMetrics

Accuracy metrics (ISO 25012).

AsyncSourceInfo

Metadata about an async data source for report construction and progress tracking.

AsyncStreamingProfiler

Async streaming profiler that accepts AsyncDataSource instead of file paths.

BooleanStats

Statistics for boolean columns.

BytesSource

An in-memory byte buffer that implements AsyncDataSource.

ColumnProfile

Profiling statistics for a single column.

ColumnSchema

A single column’s name and inferred data type.

CompletenessMetrics

Completeness metrics (ISO 8000-8).

ConsistencyMetrics

Consistency metrics (ISO 8000-61).

CsvDiagnostics

CsvParserConfig

Configuration for CSV parsing and analysis.

DatabaseConfig

Database configuration for connection strings and settings

DatabaseCredentials

Database credentials management with environment variable support

DataprofConfig

Main configuration structure for dataprof.

DataprofConfigBuilder

Builder for constructing DataprofConfig with a fluent API.

DateTimeStats

Statistics for date/datetime columns.

ExecutionMetadata

Metadata about the profiling execution.

FrequencyItem

A value and its frequency count within a column.

HttpParquetReader

An asynchronous reader that fetches byte ranges from an HTTP server using HTTP Range requests. Designed specifically for remote Parquet parsing.

InputValidator

Enhanced input validation with helpful error messages and suggestions

JsonParserConfig

Configuration for JSON/JSONL parsing and scanning.

MetricsCalculator

Engine for calculating comprehensive data quality metrics Supports ISO 8000/25012 configurable thresholds

MySqlConnector

MySQL/MariaDB connector

NumericStats

Statistics for numeric (integer or float) columns.

ParquetConfig

Configuration options for Parquet analysis.

ParquetMetadata

Metadata specific to Parquet files

Pattern

A detected value pattern within a column (e.g. email, phone, UUID).

PostgresConnector

PostgreSQL connector with connection pooling support

ProfileReport

Complete profiling report for a data source.

Profiler

Unified profiler with builder pattern

ProfilerConfig

Plain-data configuration for a profiler

QualityAssessment

Wraps quality metrics with confidence information.

QualityMetrics

Comprehensive data quality metrics following industry standards.

Quartiles

Quartile statistics for numeric distributions.

ReqwestSource

An HTTP response body that implements AsyncDataSource.

RetryConfig

Retry configuration for database operations

RowCountEstimate

Result of a quick row count operation.

SamplingConfig

Configuration for database table sampling

SchemaResult

Result of fast schema inference — column names paired with inferred data types.

SemanticHints

User-supplied semantic hints that affect profiling and quality metrics.

SqliteConnector

SQLite embedded database connector

SslConfig

SSL/TLS configuration for database connections

StopEvaluator

Runtime evaluator that checks a StopCondition against accumulated counters.

TextStats

Statistics for text/string columns.

TimelinessMetrics

Timeliness metrics (ISO 8000-8).

UniquenessMetrics

Uniqueness metrics (ISO 8000-110).

ValidationError

Enums

ChunkSize

ColumnStats

Type-specific statistics for a column, determined by the inferred data type.

CountMethod

Method used to obtain the row count.

DataFrameLibrary

Source library for in-memory DataFrame profiling

DataProfilerError

Enhanced error types with more descriptive messages for DataProfiler

DataSource

Source-agnostic data source metadata.

DataType

Inferred column data type.

DbSamplingStrategy

Available sampling strategies for large databases

EngineType

Which engine to use for profiling

FileFormat

Supported file formats for data profiling

JsonFormat

JSON/JSONL format hint.

MetricConfidence

Confidence level for quality metrics.

MetricPack

High-level categories of analysis that can be selectively enabled.

OutputFormat

Output format for serialized reports.

PatternCategory

Semantic category for a detected pattern.

ProgressEvent

Structured progress events emitted by profiling engines.

ProgressSink

How progress events are delivered to the consumer.

QualityDimension

ISO 25012 quality dimensions that can be selectively requested.

QueryEngine

Supported query engines for SQL-based profiling

SamplingStrategy

StopCondition

A composable condition that can trigger early termination of profiling.

TruncationReason

Reason why profiling was truncated before exhausting the source.

Traits

AsyncDataSource

A source of raw bytes that can be consumed asynchronously.

DatabaseConnector

Trait that all database connectors must implement

Functions

analyze_column

Analyze a column with full profiling (includes pattern detection and unique counts)

analyze_column_fast

Analyze a column in fast mode (skips expensive operations)

analyze_csv_file

Analyze a CSV file, returning a full ProfileReport.

analyze_csv_from_reader

Analyze CSV data from any Read source using streaming statistics.

analyze_database

High-level function to analyze a database table or query.

analyze_json_file

Analyze a JSON or JSONL file, returning a full ProfileReport.

analyze_json_from_reader

Analyze JSON/JSONL data from a buffered reader using streaming statistics.

analyze_parquet_async_http

Analyzes a remote Parquet file by fetching it via HTTP Range requests.

analyze_parquet_with_config

analyze_parquet_with_quality

analyze_parquet_with_quality_dims

calculate_datetime_stats

calculate_numeric_stats

calculate_text_stats

create_connector

Factory function to create appropriate database connector

detect_patterns

Detect common data patterns in a column.

infer_schema

Infer the schema (column names + data types) of a file.

infer_schema_async

infer_schema_stream

Infer schema from any async byte stream.

infer_type

is_parquet_file

Check if a file is a valid Parquet file by examining its magic number.

quick_quality_check

One-liner API for quick profiling with intelligent engine selection

quick_quality_check_source

One-liner API for quick profiling from a DataSource

quick_row_count

Quick row count (exact or estimated) for a file.

quick_row_count_async

quick_row_count_stream

Quick row count from any async byte stream.