Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Extract parquet statistics from LargeBinary columns #10753

Closed
Tracked by #10453
alamb opened this issue Jun 1, 2024 · 0 comments · Fixed by #10775
Closed
Tracked by #10453

Extract parquet statistics from LargeBinary columns #10753

alamb opened this issue Jun 1, 2024 · 0 comments · Fixed by #10775

Comments

@alamb
Copy link
Contributor

alamb commented Jun 1, 2024

Is your feature request related to a problem or challenge?

Part of #10453, where we are filling out support for extracting statistics for all data types from parquet files

At the moment, even if statistics are extracted for a different type (like Int32) the PruningPredicate will attempt to cast these values to the correct type:

// cast statistics array to required data type (e.g. parquet
// provides timestamp statistics as "Int64")
let array = arrow::compute::cast(&array, data_type)?;

However, in order to be efficient and ensure the cast kernel doesn't add anything incorrectly, we should be extracting the parquet statistics as the correct Array type directly. It turns out we do not do this yet for several types and those types do not have good (or any) test coverage. We almost missed this in #10711 in @xinlifoobar

Thus, we need to add support and tests for other types

Describe the solution you'd like

  1. Add a new test to arrow_stastics.rs (run this with cargo test --test parquet_exec) with the relevant type
  2. Potentially add a new case to the match here
    macro_rules! get_statistic {
    ($column_statistics:expr, $func:ident, $bytes_func:ident, $target_arrow_type:expr) => {{
    if !$column_statistics.has_min_max_set() {
    return None;
    }
    match $column_statistics {
    ParquetStatistics::Boolean(s) => Some(ScalarValue::Boolean(Some(*s.$func()))),
    ParquetStatistics::Int32(s) => {
    match $target_arrow_type {
    // int32 to decimal with the precision and scale
    Some(DataType::Decimal128(precision, scale)) => {
    Some(ScalarValue::Decimal128(
    Some(*s.$func() as i128),
    *precision,
    *scale,
    ))
    }
    Some(DataType::Int8) => {
    Some(ScalarValue::Int8(Some((*s.$func()).try_into().unwrap())))
    }
    Some(DataType::Int16) => {
    Some(ScalarValue::Int16(Some((*s.$func()).try_into().unwrap())))
    }
    Some(DataType::UInt8) => {
    Some(ScalarValue::UInt8(Some((*s.$func()).try_into().unwrap())))
    }
    Some(DataType::UInt16) => {
    Some(ScalarValue::UInt16(Some((*s.$func()).try_into().unwrap())))
    }
    Some(DataType::UInt32) => {
    Some(ScalarValue::UInt32(Some((*s.$func()) as u32)))
    }
    Some(DataType::Date32) => {
    Some(ScalarValue::Date32(Some(*s.$func())))
    }
    Some(DataType::Date64) => {
    Some(ScalarValue::Date64(Some(i64::from(*s.$func()) * 24 * 60 * 60 * 1000)))
    }
    _ => Some(ScalarValue::Int32(Some(*s.$func()))),
    }
    }
    ParquetStatistics::Int64(s) => {
    match $target_arrow_type {
    // int64 to decimal with the precision and scale
    Some(DataType::Decimal128(precision, scale)) => {
    Some(ScalarValue::Decimal128(
    Some(*s.$func() as i128),
    *precision,
    *scale,
    ))
    }
    Some(DataType::UInt64) => {
    Some(ScalarValue::UInt64(Some((*s.$func()) as u64)))
    }
    _ => Some(ScalarValue::Int64(Some(*s.$func()))),
    }
    }
    // 96 bit ints not supported
    ParquetStatistics::Int96(_) => None,
    ParquetStatistics::Float(s) => Some(ScalarValue::Float32(Some(*s.$func()))),
    ParquetStatistics::Double(s) => Some(ScalarValue::Float64(Some(*s.$func()))),
    ParquetStatistics::ByteArray(s) => {
    match $target_arrow_type {
    // decimal data type
    Some(DataType::Decimal128(precision, scale)) => {
    Some(ScalarValue::Decimal128(
    Some(from_bytes_to_i128(s.$bytes_func())),
    *precision,
    *scale,
    ))
    }
    Some(DataType::Binary) => {
    Some(ScalarValue::Binary(Some(s.$bytes_func().to_vec())))
    }
    _ => {
    let s = std::str::from_utf8(s.$bytes_func())
    .map(|s| s.to_string())
    .ok();
    if s.is_none() {
    log::debug!(
    "Utf8 statistics is a non-UTF8 value, ignoring it."
    );
    }
    Some(ScalarValue::Utf8(s))
    }
    }
    }
    // type not fully supported yet
    ParquetStatistics::FixedLenByteArray(s) => {
    match $target_arrow_type {
    // just support specific logical data types, there are others each
    // with their own ordering
    Some(DataType::Decimal128(precision, scale)) => {
    Some(ScalarValue::Decimal128(
    Some(from_bytes_to_i128(s.$bytes_func())),
    *precision,
    *scale,
    ))
    }
    Some(DataType::FixedSizeBinary(size)) => {
    let value = s.$bytes_func().to_vec();
    let value = if value.len().try_into() == Ok(*size) {
    Some(value)
    } else {
    log::debug!(
    "FixedSizeBinary({}) statistics is a binary of size {}, ignoring it.",
    size,
    value.len(),
    );
    None
    };
    Some(ScalarValue::FixedSizeBinary(
    *size,
    value,
    ))
    }
    _ => None,
    }
    }
    }
    }};
    }
    to get the correct types

Here are some example PRs:

  1. Minor: Add tests for extracting dictionary parquet statistics #10729
  2. Extract Date32 parquet statistics as Date32Array rather than Int32Array #10593

Describe alternatives you've considered

No response

Additional context

No response

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging a pull request may close this issue.

1 participant