qvdrs 0.4.1

High-performance Qlik QVD file reader/writer with Parquet/Arrow/DataFusion support — Rust-powered Python bindings (PyArrow, pandas, Polars)

qvd

High-performance Rust library for reading, writing and converting Qlik QVD files. With Parquet/Arrow interop, DataFusion SQL, 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, zero-copy where possible
• Parquet ↔ QVD — convert in both directions with compression support (snappy, zstd, gzip, lz4)
• Arrow RecordBatch — convert QVD to/from Arrow for integration with DataFusion, DuckDB, Polars
• DataFusion SQL — register QVD files as tables and query them with SQL
• DuckDB integration — use QVD data in DuckDB via Arrow bridge (Rust and Python)
• Streaming reader — read QVD files in chunks without loading everything into memory
• EXISTS() index — O(1) hash lookup, like Qlik's EXISTS() function. Streaming filtered reads — 2.5x faster than Qlik Sense
• CLI tool — qvd-cli convert, inspect, head, schema, filter
• Python bindings — PyArrow, pandas, Polars support via zero-copy Arrow bridge. 20-35x faster than PyQvd
• Zero dependencies for core QVD read/write (Parquet/Arrow/DataFusion/Python are optional features)

Performance

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

Selected benchmarks:

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

vs PyQvd (Pure Python)

File	PyQvd	qvd (Rust)	Speedup
10 MB, 1.4M rows	5.0s	0.17s	29x
41 MB, 466K rows	8.5s	0.5s	16x
480 MB, 12M rows	79.4s	2.3s	35x
1.7 GB, 87M rows	>10 min	29.6s	>20x

Streaming EXISTS() filter — vs Qlik Sense

Filtered read with EXISTS() + column selection — 2.5x faster than Qlik Sense.

The streaming reader loads only symbol tables (small, unique values) into memory, then scans the index table in chunks. For each row, only the filter column is decoded first. If the row matches, the selected columns are decoded. Non-matching rows are skipped entirely — no memory allocated.

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

Qlik Sense script:

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;

qvdrs CLI equivalent:

qvd-cli filter large_table.qvd result.qvd \
    --column %Type_ID --values 7,9 \
    --select "%Key_ID,DateField_BK,%Type_ID"

	Qlik Sense	qvdrs (streaming)
Read + filter	~28s	7.1s
Total (→ QVD)	~28s	11.4s
Total (→ Parquet)	—	15.5s
Speedup	1×	2.5× (QVD) / 1.8× (Parquet)

Recommendation: For large QVD files, always use read_filtered() (or qvd-cli filter) instead of loading the full file and filtering afterwards. The streaming approach uses dramatically less memory (only matched rows are held) and is significantly faster because non-matching rows are never fully decoded.

Installation

Rust

# Core QVD read/write (zero dependencies)
[dependencies]
qvd = "0.4"

# With Parquet/Arrow support
[dependencies]
qvd = { version = "0.4", features = ["parquet_support"] }

# With DataFusion SQL support
[dependencies]
qvd = { version = "0.4", features = ["datafusion_support"] }

CLI

cargo install qvd --features cli

Python

pip install qvdrs

Or with uv:

uv pip install qvdrs

Quick Start — Rust

Read/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());

// 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
convert_parquet_to_qvd("input.parquet", "output.qvd")?;

// QVD → Parquet (with zstd compression)
convert_qvd_to_parquet("input.qvd", "output.parquet", ParquetCompression::Zstd)?;

Arrow RecordBatch

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

let table = read_qvd_file("data.qvd")?;
let batch = qvd_to_record_batch(&table)?;
// Use with DataFusion, DuckDB, Polars, etc.

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

DataFusion SQL (feature datafusion_support)

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

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

    // Register QVD file as a table
    register_qvd(&ctx, "sales", "sales.qvd")?;

    // Run SQL queries directly on QVD data
    let df = ctx.sql("SELECT Region, SUM(Amount) as total
                      FROM sales
                      GROUP BY Region
                      ORDER BY total DESC").await?;
    df.show().await?;

    Ok(())
}

You can also register multiple QVD files and JOIN them:

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

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

DuckDB via Arrow (Rust)

DuckDB can ingest Arrow RecordBatches directly — no file conversion needed:

use qvd::{read_qvd_file, qvd_to_record_batch};

let table = read_qvd_file("data.qvd")?;
let batch = qvd_to_record_batch(&table)?;

// Pass the Arrow RecordBatch to DuckDB via its Arrow interface
// See: https://docs.rs/duckdb/latest/duckdb/

Streaming Reader

use qvd::open_qvd_stream;

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

while let Some(chunk) = reader.next_chunk(65536)? {
    // Process 65K rows at a time
    println!("Chunk: {} rows starting at {}", chunk.num_rows, chunk.start_row);
}

EXISTS() — O(1) Lookup

Like Qlik's EXISTS() function — build an index of unique values from one table and use it to check or filter another table in O(1) per row.

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

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

// O(1) lookup — does this value exist?
assert!(index.exists("12345"));
println!("Unique clients: {}", index.len());

// Filter another table — get row indices where ClientID exists in the clients table
let facts = read_qvd_file("facts.qvd")?;
let col_idx = 0; // index of "ClientID" column in facts table
let matching_rows = filter_rows_by_exists_fast(&facts, col_idx, &index);
println!("Matching rows: {}", matching_rows.len());

Streaming EXISTS() — Filtered Read (recommended for large files)

For large QVD files, use streaming read_filtered() instead of loading everything into memory. Only matching rows are loaded — 2.5x faster than Qlik Sense, uses dramatically less memory.

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

// 1. Build EXISTS index — from another table or from explicit values
let index = ExistsIndex::from_values(&["7", "9"]);

// 2. Open streaming reader (loads only symbol tables, not the full index table)
let mut stream = open_qvd_stream("large_table.qvd")?;

// 3. Stream + filter + select columns — only matching rows loaded into memory
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 x {} cols", filtered.num_rows(), filtered.num_cols());

// 4. Save result
write_qvd_file(&filtered, "output.qvd")?;

You can also build an EXISTS index from another QVD table's column:

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

let mut stream = open_qvd_stream("transactions.qvd")?;
let filtered = stream.read_filtered("ClientID", &index, None, 65536)?;

Quick Start — Python

Basic usage

import qvd

# Read QVD
table = qvd.read_qvd("data.qvd")
print(table.columns, table.num_rows)
print(table.head(5))

# Save QVD
table.save("output.qvd")

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

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

# EXISTS — O(1) lookup (like Qlik's EXISTS() function)
clients = qvd.read_qvd("clients.qvd")
idx = qvd.ExistsIndex(clients, "ClientID")

# Check if a value exists
print("12345" in idx)           # True/False
print(idx.exists("12345"))      # same thing
print(len(idx))                 # number of unique values

# Check multiple values at once
results = idx.exists_many(["12345", "67890", "99999"])
print(results)  # [True, True, False]

# Filter rows from another table — returns list of matching row indices
facts = qvd.read_qvd("facts.qvd")
matching_rows = qvd.filter_exists(facts, "ClientID", idx)
print(f"Matched {len(matching_rows)} rows out of {facts.num_rows}")

PyArrow

import qvd

# QVD → PyArrow RecordBatch (zero-copy via Arrow C Data Interface)
table = qvd.read_qvd("data.qvd")
batch = table.to_arrow()

# Or directly:
batch = qvd.read_qvd_to_arrow("data.qvd")

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

pandas

import qvd

# QVD → pandas DataFrame (via Arrow, zero-copy where possible)
df = qvd.read_qvd("data.qvd").to_pandas()

# Or directly:
df = qvd.read_qvd_to_pandas("data.qvd")

# pandas → QVD (via PyArrow round-trip)
import pyarrow as pa
batch = pa.RecordBatch.from_pandas(df)
table = qvd.QvdTable.from_arrow(batch, table_name="my_table")
table.save("output.qvd")

Polars

import qvd

# QVD → Polars DataFrame
df = qvd.read_qvd("data.qvd").to_polars()

# Or directly:
df = qvd.read_qvd_to_polars("data.qvd")

# Polars → QVD (via PyArrow round-trip)
batch = df.to_arrow()
table = qvd.QvdTable.from_arrow(batch, table_name="my_table")
table.save("output.qvd")

DuckDB (Python)

import qvd
import duckdb

# QVD → DuckDB (via Arrow, zero-copy)
batch = qvd.read_qvd_to_arrow("data.qvd")
result = duckdb.sql("SELECT * FROM batch WHERE amount > 100")

# Or query multiple QVD files:
sales = qvd.read_qvd_to_arrow("sales.qvd")
customers = qvd.read_qvd_to_arrow("customers.qvd")
result = duckdb.sql("""
    SELECT c.Name, SUM(s.Amount) as total
    FROM sales s
    JOIN customers c ON s.CustomerID = c.CustomerID
    GROUP BY c.Name
""")

CLI

Install:

cargo install qvd --features cli

Convert between formats

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

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

# QVD → Parquet with specific 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

Output example:

File:       data.qvd
Size:       41.3 MB
Table:      SalesData
Rows:       465,810
Columns:    12
Created:    2024-01-15 10:30:00
Build:      14.0
RecordSize: 89 bytes
Read time:  0.50s

Column                         Symbols BitWidth   Bias FmtType  Tags
--------------------------------------------------------------------------------
OrderID                         465810        20      0      0  $numeric, $integer
CustomerID                       12500        14      0      0  $numeric, $integer
Region                               5         3      0      0  $text
Amount                          389201        19      0      2  $numeric

Preview rows

# Show first 10 rows (default)
qvd-cli head data.qvd

# Show first 50 rows
qvd-cli head data.qvd --rows 50

Filter rows with EXISTS() (streaming)

# Filter by column value(s) — streaming, memory-efficient
qvd-cli filter large.qvd output.qvd --column %Type_ID --values 7,9

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

# Filter and save as Parquet
qvd-cli filter large.qvd output.parquet --column %Type_ID --values 7,9 \
    --select "%Key_ID,DateField_BK,%Type_ID" --compression zstd

Show Arrow schema

qvd-cli schema data.qvd

Output example:

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 (custom, zero-dep)
├── value.rs        — QVD data types (QvdSymbol, QvdValue)
├── symbol.rs       — symbol table binary reader/writer
├── index.rs        — index table bit-stuffing reader/writer
├── reader.rs       — high-level QVD reader
├── writer.rs       — high-level QVD writer + QvdTableBuilder
├── exists.rs       — ExistsIndex with HashSet + filter functions
├── streaming.rs    — streaming chunk-based QVD reader
├── parquet.rs      — Parquet/Arrow ↔ QVD conversion (optional)
├── datafusion.rs   — DataFusion TableProvider for SQL on QVD (optional)
├── python.rs       — PyO3 bindings with PyArrow/pandas/Polars (optional)
└── bin/qvd.rs      — CLI binary (optional)

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 on QVD via DataFusion
cli	+ clap	CLI binary
python	+ pyo3, arrow/pyarrow	Python bindings with PyArrow/pandas/Polars

Author

Stanislav Chernov (@bintocher)

License

MIT — see LICENSE