qvd

High-performance Rust library for reading, writing and converting Qlik QVD files. Parquet/Arrow interop, DataFusion SQL, DuckDB integration, streaming reader, CLI tool, and Python bindings (PyArrow, pandas, Polars).

First and only QVD crate on crates.io.

Features

Read/Write QVD — byte-identical roundtrip, Qlik Sense-compatible output
Parquet ↔ QVD — bidirectional conversion with compression (snappy, zstd, gzip, lz4). Binary-identical to Qlik Sense output
Arrow RecordBatch — zero-copy QVD ↔ Arrow for DataFusion, DuckDB, Polars
DuckDB integration — register QVD files as SQL tables, query with JOINs, batch folder registration
DataFusion SQL — register QVD files as tables, run SQL queries
Streaming reader — read QVD in chunks without loading everything into memory
EXISTS() index — O(1) hash lookup like Qlik's EXISTS(). Streaming filtered reads — 2.5× faster than Qlik Sense
normalize() — auto-detect and set proper symbol types, NumberFormat, Tags, BitWidth for Qlik compatibility
CLI tool — qvd-cli convert, inspect, head, schema, filter
Python bindings — PyArrow, pandas, Polars via zero-copy Arrow bridge
Zero dependencies for core QVD read/write (Parquet/Arrow/DataFusion/Python are optional features)

Performance

Tested on 399 real QVD files (11 KB — 2.8 GB), all byte-identical roundtrip (MD5 match).

File	Size	Rows	Columns	Read	Write
sample_tiny.qvd	11 KB	12	5	0.0s	0.0s
sample_small.qvd	418 KB	2,746	8	0.0s	0.0s
sample_medium.qvd	41 MB	465,810	12	0.5s	0.0s
sample_large.qvd	587 MB	5,458,618	15	6.1s	0.4s
sample_xlarge.qvd	1.7 GB	87,617,047	8	23.6s	1.6s
sample_huge.qvd	2.8 GB	11,907,648	42	24.3s	2.4s

Streaming EXISTS() filter — vs Qlik Sense

1.7 GB QVD, 87.6M rows × 8 columns → filter by 2 values, select 3 columns → 20.4M rows × 3 columns

Qlik Sense script equivalent:

types:
LOAD * INLINE [%Type_ID
7
9];

filtered:
LOAD %Key_ID, DateField_BK, %Type_ID
FROM [lib://data/large_table.qvd](qvd)
WHERE EXISTS(%Type_ID);

STORE filtered INTO [lib://data/result.qvd](qvd);
DROP TABLE filtered;

	Qlik Sense	qvdrs
Total (→ QVD)	~28s	11.4s
Total (→ Parquet)	—	15.5s
Speedup	1×	2.5×

The streaming reader loads only symbol tables into memory, then scans the index table in chunks. For each row, only the filter column is decoded first. Matching rows get selected columns decoded. Non-matching rows are skipped entirely.

Parquet → QVD conversion

QVD files generated from Parquet are binary-identical to those created by Qlik Sense (same symbol types, NumberFormat, Tags, BitWidth, BitOffset ordering). Verified by MD5 hash comparison of the binary section.

Installation

Rust

# Core QVD read/write (zero dependencies)
[dependencies]
qvd = "0.5.0"

# With Parquet/Arrow support
[dependencies]
qvd = { version = "0.5.0", features = ["parquet_support"] }

# With DataFusion SQL support
[dependencies]
qvd = { version = "0.5.0", features = ["datafusion_support"] }

Python

pip install qvdrs

uv pip install qvdrs

CLI

cargo install qvd --features cli

Or run without installing via uvx:

uvx --from qvdrs qvd-cli inspect data.qvd
uvx --from qvdrs qvd-cli convert input.qvd output.parquet
uvx --from qvdrs qvd-cli filter large.qvd output.qvd --column %Type_ID --values 7,9

Rust Examples

Read and write QVD

use qvd::{read_qvd_file, write_qvd_file};

let table = read_qvd_file("data.qvd")?;
println!("Rows: {}, Cols: {}", table.num_rows(), table.num_cols());
println!("Columns: {:?}", table.column_names());

// Access individual values
for row in 0..5 {
    let val = table.get(row, 0);
    println!("Row {}: {:?}", row, val.as_string());
}

// Byte-identical roundtrip
write_qvd_file(&table, "output.qvd")?;

Convert Parquet ↔ QVD

use qvd::{convert_parquet_to_qvd, convert_qvd_to_parquet, ParquetCompression};

// Parquet → QVD (Qlik Sense-compatible output)
convert_parquet_to_qvd("input.parquet", "output.qvd")?;

// QVD → Parquet
convert_qvd_to_parquet("input.qvd", "output.parquet", ParquetCompression::Zstd)?;
convert_qvd_to_parquet("input.qvd", "output.parquet", ParquetCompression::Snappy)?;
convert_qvd_to_parquet("input.qvd", "output.parquet", ParquetCompression::Gzip)?;
convert_qvd_to_parquet("input.qvd", "output.parquet", ParquetCompression::Lz4)?;
convert_qvd_to_parquet("input.qvd", "output.parquet", ParquetCompression::None)?;

Arrow RecordBatch

use qvd::{read_qvd_file, qvd_to_record_batch, record_batch_to_qvd, write_qvd_file};

// QVD → Arrow
let table = read_qvd_file("data.qvd")?;
let batch = qvd_to_record_batch(&table)?;
println!("Schema: {:?}", batch.schema());

// Arrow → QVD
let qvd_table = record_batch_to_qvd(&batch, "my_table")?;
write_qvd_file(&qvd_table, "output.qvd")?;

Normalize — Qlik Sense compatibility

normalize() auto-detects and sets proper Qlik-compatible metadata on any QvdTable.

let mut table = qvd::read_qvd_file("data.qvd")?;

// Filter or modify the table
let matching = table.filter_by_values("Region", &["East", "West"]);
let mut subset = table.subset_rows(&matching);

// Normalize for Qlik compatibility before saving
subset.normalize();
qvd::write_qvd_file(&subset, "filtered.qvd")?;

What normalize() does:

Converts DualInt → Int, DualDouble → Double (removes redundant string representations)
Uses Int for float values that are exact integers (like Qlik does)
Sets NumberFormat: INTEGER (###0), REAL (14 decimals), ASCII
Sets Tags: $numeric, $integer, $ascii, $text
Reserves NULL sentinel in BitWidth (bits_needed(num_symbols + 1))
Sorts BitOffsets by descending width (optimal packing)

normalize() is called automatically during Parquet/Arrow → QVD conversion. Call it manually only when modifying existing tables.

Streaming reader

use qvd::open_qvd_stream;

let mut reader = open_qvd_stream("huge_file.qvd")?;
println!("Total rows: {}", reader.total_rows());
println!("Columns: {:?}", reader.column_names());

// Process in chunks of 64K rows
while let Some(chunk) = reader.next_chunk(65536)? {
    println!("Chunk: {} rows starting at {}", chunk.num_rows, chunk.start_row);
    // chunk.values[col_idx][row_idx] — access individual values
}

EXISTS() — O(1) lookup

Like Qlik's EXISTS() function — build an index of unique values and filter another table.

use qvd::{read_qvd_file, ExistsIndex, filter_rows_by_exists_fast};

// Build index from a table column
let clients = read_qvd_file("clients.qvd")?;
let index = ExistsIndex::from_column(&clients, "ClientID").unwrap();

// O(1) lookup
assert!(index.exists("12345"));
println!("Unique clients: {}", index.len());

// Filter another table
let facts = read_qvd_file("facts.qvd")?;
let col_idx = facts.column_index("ClientID").unwrap();
let matching_rows = filter_rows_by_exists_fast(&facts, col_idx, &index);
println!("Matching rows: {}", matching_rows.len());

// Create subset and save
let filtered = facts.subset_rows(&matching_rows);
qvd::write_qvd_file(&filtered, "filtered_facts.qvd")?;

Streaming EXISTS() — filtered read (recommended for large files)

For large QVD files, read_filtered() streams the index table and only loads matching rows into memory.

use qvd::{open_qvd_stream, ExistsIndex, write_qvd_file};

// Build index from explicit values
let index = ExistsIndex::from_values(&["7", "9"]);

// Or from another table
let clients = read_qvd_file("clients.qvd")?;
let index = ExistsIndex::from_column(&clients, "ClientID").unwrap();
drop(clients); // free memory before opening the large file

// Stream + filter + select columns
let mut stream = open_qvd_stream("large_table.qvd")?;
let filtered = stream.read_filtered(
    "%Type_ID",                                     // filter column
    &index,                                         // EXISTS index
    Some(&["%Key_ID", "DateField_BK", "%Type_ID"]), // select columns (None = all)
    65536,                                          // chunk size
)?;
println!("Matched: {} rows × {} cols", filtered.num_rows(), filtered.num_cols());

// Save as QVD or Parquet
write_qvd_file(&filtered, "output.qvd")?;

DataFusion SQL

use datafusion::prelude::*;
use qvd::register_qvd;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let ctx = SessionContext::new();

    register_qvd(&ctx, "sales", "sales.qvd")?;
    register_qvd(&ctx, "customers", "customers.qvd")?;

    // Aggregation
    let df = ctx.sql("SELECT Region, SUM(Amount) as total
                      FROM sales GROUP BY Region ORDER BY total DESC").await?;
    df.show().await?;

    // JOIN across QVD tables
    let df = ctx.sql("SELECT c.Name, COUNT(o.OrderID) as orders
                       FROM sales o
                       JOIN customers c ON o.CustomerID = c.CustomerID
                       GROUP BY c.Name").await?;
    df.show().await?;

    Ok(())
}

Build QVD from scratch

use qvd::{QvdTableBuilder, write_qvd_file};

let table = QvdTableBuilder::new("my_table")
    .add_column("id", vec![Some("1".into()), Some("2".into()), Some("3".into())])
    .add_column("name", vec![Some("Alice".into()), Some("Bob".into()), None])
    .add_column("score", vec![Some("95.5".into()), Some("87".into()), Some("92".into())])
    .build();

write_qvd_file(&table, "output.qvd")?;

Python Examples

Read and write QVD

import qvd

table = qvd.read_qvd("data.qvd")
print(table)              # QvdTable(table='data', rows=1000, cols=5)
print(table.columns)      # ['ID', 'Name', 'Region', 'Amount', 'Date']
print(table.num_rows)     # 1000
print(table.num_cols)     # 5
print(table.head(5))      # first 5 rows as formatted string

table.save("output.qvd")

Convert Parquet ↔ QVD

import qvd

# Parquet → QVD
qvd.convert_parquet_to_qvd("input.parquet", "output.qvd")

# QVD → Parquet
qvd.convert_qvd_to_parquet("input.qvd", "output.parquet", compression="zstd")

# Load Parquet as QvdTable, inspect, save
table = qvd.QvdTable.from_parquet("input.parquet")
print(table.columns)
table.save("output.qvd")
table.save_as_parquet("output.parquet", compression="snappy")

PyArrow

import qvd
import pyarrow as pa

# QVD → PyArrow (zero-copy via Arrow C Data Interface)
batch = qvd.read_qvd_to_arrow("data.qvd")
print(batch.schema)
print(batch.num_rows)

# Or via QvdTable
table = qvd.read_qvd("data.qvd")
batch = table.to_arrow()

# PyArrow → QVD
table = qvd.QvdTable.from_arrow(batch, table_name="my_table")
table.save("output.qvd")

# Any PyArrow RecordBatch works — from pandas, Polars, DuckDB, etc.
batch = pa.RecordBatch.from_pydict({
    "id": pa.array([1, 2, 3]),
    "name": pa.array(["Alice", "Bob", "Charlie"]),
    "score": pa.array([95.5, 87.0, 92.3]),
})
qvd.QvdTable.from_arrow(batch, "scores").save("scores.qvd")

pandas

import qvd

# QVD → pandas DataFrame
df = qvd.read_qvd_to_pandas("data.qvd")
print(df.head())
print(df.dtypes)

# Or via QvdTable
df = qvd.read_qvd("data.qvd").to_pandas()

# pandas → QVD
import pyarrow as pa
batch = pa.RecordBatch.from_pandas(df)
qvd.QvdTable.from_arrow(batch, "my_table").save("output.qvd")

Polars

import qvd

# QVD → Polars DataFrame
df = qvd.read_qvd_to_polars("data.qvd")
print(df.head())
print(df.schema)

# Or via QvdTable
df = qvd.read_qvd("data.qvd").to_polars()

# Polars → QVD
batch = df.to_arrow()
qvd.QvdTable.from_arrow(batch, "my_table").save("output.qvd")

EXISTS() — filter and subset

import qvd

# Build index from a table column
clients = qvd.read_qvd("clients.qvd")
idx = qvd.ExistsIndex(clients, "ClientID")

# O(1) lookup
print("12345" in idx)               # True/False
print(idx.exists("12345"))          # same
print(len(idx))                     # unique values count
print(idx.exists_many(["1", "2"]))  # [True, False]

# Filter another table
facts = qvd.read_qvd("facts.qvd")
matching = qvd.filter_exists(facts, "ClientID", idx)
print(f"Matched {len(matching)} of {facts.num_rows} rows")

# Subset rows and save
filtered = facts.subset_rows(matching)
filtered.save("filtered_facts.qvd")

EXISTS() from explicit values

import qvd

# Build index from a list of values (like LOAD * INLINE in Qlik)
idx = qvd.ExistsIndex.from_values(["7", "9"])

# Streaming filtered read — memory-efficient for large files
filtered = qvd.read_qvd_filtered(
    "large_table.qvd",
    filter_col="%Type_ID",
    exists_index=idx,
    select=["%Key_ID", "DateField_BK", "%Type_ID"],  # only these columns
    chunk_size=65536
)
print(f"{filtered.num_rows} rows × {filtered.num_cols} cols")
filtered.save("output.qvd")

Normalize for Qlik compatibility

import qvd

table = qvd.read_qvd("data.qvd")

# Filter
matching = table.filter_by_values("Region", ["East", "West"])
subset = table.subset_rows(matching)

# Normalize before saving — sets proper types, tags, format
subset.normalize()
subset.save("filtered.qvd")

DuckDB — register single file

import qvd
import duckdb

conn = duckdb.connect()

# Register QVD as a DuckDB table
qvd.register_duckdb(conn, "sales", "sales.qvd")

# SQL queries
conn.sql("SELECT * FROM sales LIMIT 10").show()
conn.sql("SELECT Region, SUM(Amount) as total FROM sales GROUP BY Region").show()
conn.sql("SELECT COUNT(*), MIN(Amount), MAX(Amount), AVG(Amount) FROM sales").show()

DuckDB — register multiple files

import qvd
import duckdb

conn = duckdb.connect()

# Register multiple QVD files
qvd.register_duckdb(conn, "sales", "data/sales.qvd")
qvd.register_duckdb(conn, "customers", "data/customers.qvd")
qvd.register_duckdb(conn, "products", "data/products.qvd")

# JOIN across QVD tables
conn.sql("""
    SELECT c.Name, p.Category, SUM(s.Amount) as total
    FROM sales s
    JOIN customers c ON s.CustomerID = c.CustomerID
    JOIN products p ON s.ProductID = p.ProductID
    GROUP BY c.Name, p.Category
    ORDER BY total DESC
    LIMIT 20
""").show()

DuckDB — register folder

import qvd
import duckdb

conn = duckdb.connect()

# Register all QVD files from a folder (table name = file name without .qvd)
tables = qvd.register_duckdb_folder(conn, "data/qvd_files/")
print(f"Registered {len(tables)} tables: {tables}")

# Register from multiple folders
tables = qvd.register_duckdb_folder(conn,
    folder_paths=["data/sales/", "data/master/"])

# With glob pattern — only matching files
tables = qvd.register_duckdb_folder(conn,
    folder_paths="data/",
    glob="sales_*.qvd")

# Recursive scan of subdirectories
tables = qvd.register_duckdb_folder(conn,
    folder_paths="data/",
    recursive=True)

# Skip large files (default 500 MB limit)
tables = qvd.register_duckdb_folder(conn,
    folder_paths="data/",
    max_file_size_mb=200)

# All options together
tables = qvd.register_duckdb_folder(conn,
    folder_paths=["data/folder1/", "data/folder2/"],
    recursive=True,
    glob="*_2024.qvd",
    max_file_size_mb=500)

# Query any registered table
conn.sql("SELECT * FROM sales_2024 LIMIT 10").show()
conn.sql("SHOW TABLES").show()

DuckDB — register with EXISTS() filter

import qvd
import duckdb

conn = duckdb.connect()

# Register with streaming filter — only matching rows loaded
idx = qvd.ExistsIndex.from_values(["7", "9"])
qvd.register_duckdb_filtered(conn, "filtered", "large_table.qvd",
    filter_col="%Type_ID",
    exists_index=idx,
    select=["%Key_ID", "DateField_BK", "%Type_ID"],
    chunk_size=65536)

conn.sql("SELECT COUNT(*) FROM filtered").show()
conn.sql("SELECT %Type_ID, COUNT(*) as cnt FROM filtered GROUP BY %Type_ID").show()

DuckDB — export results to QVD

import qvd
import duckdb

conn = duckdb.connect()
qvd.register_duckdb(conn, "sales", "sales.qvd")

# Query → pandas
df = conn.sql("SELECT * FROM sales WHERE Amount > 1000").df()

# Query → PyArrow
batch = conn.sql("SELECT * FROM sales").arrow()

# Query result → QVD (aggregation, JOIN, filter — anything)
batch = conn.sql("SELECT Region, SUM(Amount) as total FROM sales GROUP BY Region").arrow()
qvd.QvdTable.from_arrow(batch, "summary").save("summary.qvd")

Database → QVD (PostgreSQL, MySQL, SQLite, Snowflake, etc.)

Any database that can return Arrow/pandas data can save to QVD. Dates, timestamps, integers, floats — all types are automatically converted.

import qvd
import pyarrow as pa

# === PostgreSQL (via connectorx — fastest) ===
import connectorx as cx
table = cx.read_sql("postgresql://user:pass@host/db", "SELECT * FROM orders", return_type="arrow")
# connectorx returns PyArrow Table, convert to RecordBatch
batch = table.to_batches()[0]  # or combine if multiple batches
qvd.QvdTable.from_arrow(batch, "orders").save("orders.qvd")

# === PostgreSQL (via psycopg + pandas) ===
import psycopg2
import pandas as pd
conn = psycopg2.connect("host=localhost dbname=mydb user=user password=pass")
df = pd.read_sql("SELECT * FROM customers WHERE country = 'DE'", conn)
batch = pa.RecordBatch.from_pandas(df)
qvd.QvdTable.from_arrow(batch, "customers_de").save("customers_de.qvd")

# === SQLite ===
import sqlite3
conn = sqlite3.connect("data.db")
df = pd.read_sql("SELECT * FROM products", conn)
batch = pa.RecordBatch.from_pandas(df)
qvd.QvdTable.from_arrow(batch, "products").save("products.qvd")

# === DuckDB (local or remote) ===
import duckdb
conn = duckdb.connect("analytics.duckdb")
batch = conn.sql("SELECT * FROM fact_sales WHERE year = 2024").arrow()
qvd.QvdTable.from_arrow(batch, "fact_sales_2024").save("fact_sales_2024.qvd")

# === Snowflake (via snowflake-connector-python) ===
# pip install snowflake-connector-python[pandas]
import snowflake.connector
conn = snowflake.connector.connect(user='...', password='...', account='...')
cur = conn.cursor()
cur.execute("SELECT * FROM WAREHOUSE.SCHEMA.TABLE")
df = cur.fetch_pandas_all()
batch = pa.RecordBatch.from_pandas(df)
qvd.QvdTable.from_arrow(batch, "snowflake_data").save("snowflake_data.qvd")

# === BigQuery ===
from google.cloud import bigquery
client = bigquery.Client()
df = client.query("SELECT * FROM dataset.table LIMIT 1000000").to_dataframe()
batch = pa.RecordBatch.from_pandas(df)
qvd.QvdTable.from_arrow(batch, "bq_data").save("bq_data.qvd")

# === Any ADBC-compatible database ===
import adbc_driver_postgresql.dbapi
conn = adbc_driver_postgresql.dbapi.connect("postgresql://user:pass@host/db")
cur = conn.cursor()
cur.execute("SELECT * FROM large_table")
batch = cur.fetch_arrow_table().to_batches()[0]
qvd.QvdTable.from_arrow(batch, "adbc_data").save("adbc_data.qvd")

CSV/Excel → QVD

import qvd
import pandas as pd
import pyarrow as pa

# CSV → QVD
df = pd.read_csv("data.csv")
batch = pa.RecordBatch.from_pandas(df)
qvd.QvdTable.from_arrow(batch, "csv_data").save("data.qvd")

# Excel → QVD
df = pd.read_excel("report.xlsx", sheet_name="Sheet1")
batch = pa.RecordBatch.from_pandas(df)
qvd.QvdTable.from_arrow(batch, "excel_data").save("report.qvd")

# Multiple sheets → multiple QVDs
for sheet in ["Sales", "Customers", "Products"]:
    df = pd.read_excel("report.xlsx", sheet_name=sheet)
    batch = pa.RecordBatch.from_pandas(df)
    qvd.QvdTable.from_arrow(batch, sheet.lower()).save(f"{sheet.lower()}.qvd")

CLI

cargo install qvd --features cli

Or via uvx (no install needed):

uvx --from qvdrs qvd-cli <command> [args]

Convert between formats

# Parquet → QVD
qvd-cli convert input.parquet output.qvd

# QVD → Parquet (default: snappy)
qvd-cli convert input.qvd output.parquet

# QVD → Parquet with compression
qvd-cli convert input.qvd output.parquet --compression zstd
qvd-cli convert input.qvd output.parquet --compression gzip
qvd-cli convert input.qvd output.parquet --compression lz4
qvd-cli convert input.qvd output.parquet --compression none

# Rewrite QVD (re-generate from internal representation)
qvd-cli convert input.qvd output.qvd

# Recompress Parquet
qvd-cli convert input.parquet output.parquet --compression zstd

Inspect QVD metadata

qvd-cli inspect data.qvd

File:       data.qvd
Size:       41.3 MB
Table:      SalesData
Rows:       465,810
Columns:    12
Created:    2024-01-15 10:30:00
Build:      50699
RecordSize: 89 bytes
Read time:  0.50s

Column                         Symbols BitWidth   Bias FmtType  Tags
--------------------------------------------------------------------------------
OrderID                         465810        20      0 INTEGER  $numeric, $integer
CustomerID                       12500        14      0 INTEGER  $numeric, $integer
Region                               5         3      0 ASCII    $ascii, $text
Amount                          389201        19      0 REAL     $numeric

Preview rows

qvd-cli head data.qvd
qvd-cli head data.qvd --rows 50

Filter with EXISTS() (streaming)

# Filter by column values
qvd-cli filter large.qvd output.qvd --column %Type_ID --values 7,9

# Filter + select columns
qvd-cli filter large.qvd output.qvd --column %Type_ID --values 7,9 \
    --select "%Key_ID,DateField_BK,%Type_ID"

# Filter → Parquet
qvd-cli filter large.qvd output.parquet --column %Type_ID --values 7,9 \
    --compression zstd

Show Arrow schema

qvd-cli schema data.qvd

Arrow Schema for 'data.qvd':

  OrderID                        Int64
  CustomerID                     Int64
  Region                         Utf8
  Amount                         Float64 (nullable)
  OrderDate                      Date32

Architecture

src/
├── lib.rs          — public API, re-exports
├── error.rs        — error types (QvdError, QvdResult)
├── header.rs       — XML header parser/writer
├── value.rs        — QVD data types (QvdSymbol, QvdValue)
├── symbol.rs       — symbol table binary reader/writer
├── index.rs        — index table bit-packed reader/writer
├── reader.rs       — QVD reader + normalize()
├── writer.rs       — QVD writer + QvdTableBuilder
├── exists.rs       — ExistsIndex + filter functions
├── streaming.rs    — streaming chunk-based reader with filtered reads
├── parquet.rs      — Parquet/Arrow ↔ QVD conversion (optional)
├── datafusion.rs   — DataFusion TableProvider (optional)
├── python.rs       — PyO3 bindings (optional)
└── bin/qvd.rs      — CLI binary (optional)

QVD file format

A QVD file consists of three sections:

XML header — metadata: table name, field definitions (name, BitOffset, BitWidth, Bias, NumberFormat, Tags), record count
Symbol tables — unique values per column, each encoded as Int (0x01), Double (0x02), Text (0x04), DualInt (0x05), or DualDouble (0x06). Dates are stored as DualDouble (Qlik serial number + formatted string)
Index table — bit-packed rows, each row is RecordByteSize bytes. Fields are packed at their BitOffset with BitWidth bits. The stored value + Bias = symbol index. Index = NoOfSymbols means NULL

NumberFormat types: UNKNOWN, ASCII, INTEGER, REAL, FIX, MONEY, DATE, TIMESTAMP. Tags: $numeric, $integer, $text, $ascii, $timestamp, $date, $key.

Feature Flags

Feature	Dependencies	Description
(default)	none	Core QVD read/write
`parquet_support`	arrow, parquet, chrono	Parquet/Arrow conversion
`datafusion_support`	+ datafusion, tokio	SQL queries via DataFusion
`cli`	+ clap	CLI binary
`python`	+ pyo3, arrow/pyarrow	Python bindings

Author

Stanislav Chernov (@bintocher)

License

MIT — see LICENSE

qvd 0.5.0

qvd

Features

Performance

Streaming EXISTS() filter — vs Qlik Sense

Parquet → QVD conversion

Installation

Rust

Python

CLI

Rust Examples

Read and write QVD

Convert Parquet ↔ QVD

Arrow RecordBatch

Normalize — Qlik Sense compatibility

Streaming reader

EXISTS() — O(1) lookup

Streaming EXISTS() — filtered read (recommended for large files)

DataFusion SQL

Build QVD from scratch

Python Examples

Read and write QVD

Convert Parquet ↔ QVD

PyArrow

pandas

Polars

EXISTS() — filter and subset

EXISTS() from explicit values

Normalize for Qlik compatibility

DuckDB — register single file

DuckDB — register multiple files

DuckDB — register folder

DuckDB — register with EXISTS() filter

DuckDB — export results to QVD

Database → QVD (PostgreSQL, MySQL, SQLite, Snowflake, etc.)

CSV/Excel → QVD

CLI

Convert between formats

Inspect QVD metadata

Preview rows

Filter with EXISTS() (streaming)

Show Arrow schema

Architecture

QVD file format

Feature Flags

Author

License