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parquet: Add support for row group pruning on FixedSizeBinary #9646

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merged 2 commits into from
Mar 19, 2024
Merged

parquet: Add support for row group pruning on FixedSizeBinary #9646

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496ba9a
Add support for row group pruning on FixedSizeBinary
progval Mar 16, 2024
8e11cdf
Check statistics values are valid for their type
progval Mar 19, 2024
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1 change: 1 addition & 0 deletions datafusion/core/src/datasource/physical_plan/parquet/row_groups.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -226,6 +226,7 @@ impl PruningStatistics for BloomFilterStatistics {
match value {
ScalarValue::Utf8(Some(v)) => sbbf.check(&v.as_str()),
ScalarValue::Binary(Some(v)) => sbbf.check(v),
ScalarValue::FixedSizeBinary(_size, Some(v)) => sbbf.check(v),
ScalarValue::Boolean(Some(v)) => sbbf.check(v),
ScalarValue::Float64(Some(v)) => sbbf.check(v),
ScalarValue::Float32(Some(v)) => sbbf.check(v),
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11 changes: 9 additions & 2 deletions datafusion/core/src/datasource/physical_plan/parquet/statistics.rs
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Expand Up @@ -109,17 +109,24 @@ macro_rules! get_statistic {
}
}
}
// type not supported yet
// type not fully supported yet
ParquetStatistics::FixedLenByteArray(s) => {
match $target_arrow_type {
// just support the decimal data type
// just support specific logical data types, there are others each
// with their own ordering
Some(DataType::Decimal128(precision, scale)) => {
Some(ScalarValue::Decimal128(
Some(from_bytes_to_i128(s.$bytes_func())),
*precision,
*scale,
))
}
Some(DataType::FixedSizeBinary(size)) => {
Some(ScalarValue::FixedSizeBinary(
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@alamb alamb Mar 19, 2024 •
edited
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Should we verify that the resulting bytes have the correct size? As in verify that *size is the same as (s.$bytes_func().to_vec().len()?

If not, I suggest we ignore the error (but don't use the statistics) the same way as is done for non UTF8 values i string statistics. Perhaps we can at least debug when this happens with debug!

Since this data can come from any arbitrary parquet writer, I think it is good practice to validate what is in there

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Oh sure, good catch.

The code for strings is:

let s = std::str::from_utf8(s.$bytes_func())
    .map(|s| s.to_string())
    .ok();
Some(ScalarValue::Utf8(s))

which looks like it's treated as a NULL, rather than ignored as a statistics value. Should I do the same here?

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Yes, I think converting to NULL is the correct behavior. In this case that results in ignoring the value in terms of range analysis. More details are here if you are curious:

https://github.com/apache/arrow-datafusion/blob/8074ca1e758470319699a562074290906003b312/datafusion/core/src/physical_optimizer/pruning.rs#L85-L87

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I see, done. I also added a log statement for strings, for consistency.

*size,
Some(s.$bytes_func().to_vec()),
))
}
_ => None,
}
}
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101 changes: 101 additions & 0 deletions datafusion/core/tests/parquet/row_group_pruning.rs
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Expand Up @@ -948,6 +948,107 @@ async fn prune_binary_lt() {
.await;
}

#[tokio::test]
async fn prune_fixedsizebinary_eq_match() {
RowGroupPruningTest::new()
.with_scenario(Scenario::ByteArray)
.with_query(
"SELECT name, service_fixedsize FROM t WHERE service_fixedsize = ARROW_CAST(CAST('fe6' AS bytea), 'FixedSizeBinary(3)')",
)
.with_expected_errors(Some(0))
// false positive on 'all frontends' batch: 'fe1' < 'fe6' < 'fe7'
.with_matched_by_stats(Some(2))
.with_pruned_by_stats(Some(1))
.with_matched_by_bloom_filter(Some(1))
.with_pruned_by_bloom_filter(Some(1))
.with_expected_rows(1)
.test_row_group_prune()
.await;

RowGroupPruningTest::new()
.with_scenario(Scenario::ByteArray)
.with_query(
"SELECT name, service_fixedsize FROM t WHERE service_fixedsize = ARROW_CAST(CAST('fe6' AS bytea), 'FixedSizeBinary(3)')",
)
.with_expected_errors(Some(0))
// false positive on 'all frontends' batch: 'fe1' < 'fe6' < 'fe7'
.with_matched_by_stats(Some(2))
.with_pruned_by_stats(Some(1))
.with_matched_by_bloom_filter(Some(1))
.with_pruned_by_bloom_filter(Some(1))
.with_expected_rows(1)
.test_row_group_prune()
.await;
}

#[tokio::test]
async fn prune_fixedsizebinary_eq_no_match() {
RowGroupPruningTest::new()
.with_scenario(Scenario::ByteArray)
.with_query(
"SELECT name, service_fixedsize FROM t WHERE service_fixedsize = ARROW_CAST(CAST('be9' AS bytea), 'FixedSizeBinary(3)')",
)
.with_expected_errors(Some(0))
// false positive on 'mixed' batch: 'be1' < 'be9' < 'fe4'
.with_matched_by_stats(Some(1))
.with_pruned_by_stats(Some(2))
.with_matched_by_bloom_filter(Some(0))
.with_pruned_by_bloom_filter(Some(1))
.with_expected_rows(0)
.test_row_group_prune()
.await;
}

#[tokio::test]
async fn prune_fixedsizebinary_neq() {
RowGroupPruningTest::new()
.with_scenario(Scenario::ByteArray)
.with_query(
"SELECT name, service_fixedsize FROM t WHERE service_fixedsize != ARROW_CAST(CAST('be1' AS bytea), 'FixedSizeBinary(3)')",
)
.with_expected_errors(Some(0))
.with_matched_by_stats(Some(3))
.with_pruned_by_stats(Some(0))
.with_matched_by_bloom_filter(Some(3))
.with_pruned_by_bloom_filter(Some(0))
.with_expected_rows(14)
.test_row_group_prune()
.await;
}

#[tokio::test]
async fn prune_fixedsizebinary_lt() {
RowGroupPruningTest::new()
.with_scenario(Scenario::ByteArray)
.with_query(
"SELECT name, service_fixedsize FROM t WHERE service_fixedsize < ARROW_CAST(CAST('be3' AS bytea), 'FixedSizeBinary(3)')",
)
.with_expected_errors(Some(0))
// matches 'all backends' only
.with_matched_by_stats(Some(1))
.with_pruned_by_stats(Some(2))
.with_matched_by_bloom_filter(Some(0))
.with_pruned_by_bloom_filter(Some(0))
.with_expected_rows(2)
.test_row_group_prune()
.await;

RowGroupPruningTest::new()
.with_scenario(Scenario::ByteArray)
.with_query(
"SELECT name, service_fixedsize FROM t WHERE service_fixedsize < ARROW_CAST(CAST('be9' AS bytea), 'FixedSizeBinary(3)')",
)
.with_expected_errors(Some(0))
.with_matched_by_stats(Some(2))
.with_pruned_by_stats(Some(1))
.with_matched_by_bloom_filter(Some(0))
.with_pruned_by_bloom_filter(Some(0))
// all backends from 'mixed' and 'all backends'
.with_expected_rows(8)
.test_row_group_prune()
.await;
}

#[tokio::test]
async fn prune_periods_in_column_names() {
// There are three row groups for "service.name", each with 5 rows = 15 rows total
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