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Enable reading StringViewArray by default from Parquet #12092

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Enable reading StringViewArray by default from Parquet #12092

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ce5470d
Enable reading string view by default from Parquet
alamb Aug 20, 2024
7cb94dc
Update plans
alamb Sep 12, 2024
915f14f
Support StringView in BloomFilters
alamb Sep 12, 2024
fdce8bf
Fix parquet page pruning test
alamb Sep 12, 2024
29fb559
update config
alamb Sep 12, 2024
e8f7384
remove view override flag in benchmarks
alamb Sep 13, 2024
642f137
Workaround hits_partitioned issue
alamb Sep 13, 2024
7b8cc0a
Merge remote-tracking branch 'apache/main' into alamb/enable_string_v…
alamb Sep 22, 2024
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2 changes: 1 addition & 1 deletion datafusion/common/src/config.rs
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Expand Up @@ -382,7 +382,7 @@ config_namespace! {

/// (reading) If true, parquet reader will read columns of `Utf8/Utf8Large` with `Utf8View`,
/// and `Binary/BinaryLarge` with `BinaryView`.
pub schema_force_view_types: bool, default = false
pub schema_force_view_types: bool, default = true

// The following options affect writing to parquet files
// and map to parquet::file::properties::WriterProperties
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5 changes: 5 additions & 0 deletions datafusion/common/src/scalar/mod.rs
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Expand Up @@ -978,6 +978,11 @@ impl ScalarValue {
ScalarValue::from(val.into())
}

/// Returns a [`ScalarValue::Utf8View`] representing `val`
pub fn new_utf8view(val: impl Into<String>) -> Self {
ScalarValue::Utf8View(Some(val.into()))
}

/// Returns a [`ScalarValue::IntervalYearMonth`] representing
/// `years` years and `months` months
pub fn new_interval_ym(years: i32, months: i32) -> Self {
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8 changes: 6 additions & 2 deletions datafusion/core/src/datasource/physical_plan/parquet/row_group_filter.rs
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Expand Up @@ -264,8 +264,12 @@ impl PruningStatistics for BloomFilterStatistics {
.iter()
.map(|value| {
match value {
ScalarValue::Utf8(Some(v)) => sbbf.check(&v.as_str()),
ScalarValue::Binary(Some(v)) => sbbf.check(v),
ScalarValue::Utf8(Some(v)) | ScalarValue::Utf8View(Some(v)) => {
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this needs its own ticket / test I think. Will do it shortly

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Filed #12499

sbbf.check(&v.as_str())
}
ScalarValue::Binary(Some(v)) | ScalarValue::BinaryView(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),
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5 changes: 3 additions & 2 deletions datafusion/core/tests/parquet/page_pruning.rs
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Expand Up @@ -149,8 +149,9 @@ async fn page_index_filter_one_col() {
let session_ctx = SessionContext::new();
let task_ctx = session_ctx.task_ctx();

// 5.create filter date_string_col == 1;
let filter = col("date_string_col").eq(lit("01/01/09"));
// 5.create filter date_string_col == "01/01/09"`;
// Note this test doesn't apply type coercion so the literal must match the actual view type
let filter = col("date_string_col").eq(lit(ScalarValue::new_utf8view("01/01/09")));
let parquet_exec = get_parquet_exec(&state, filter).await;
let mut results = parquet_exec.execute(0, task_ctx.clone()).unwrap();
let batch = results.next().await.unwrap().unwrap();
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4 changes: 2 additions & 2 deletions datafusion/sqllogictest/test_files/describe.slt
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Expand Up @@ -81,8 +81,8 @@ int_col Int32 YES
bigint_col Int64 YES
float_col Float32 YES
double_col Float64 YES
date_string_col Utf8 YES
string_col Utf8 YES
date_string_col Utf8View YES
string_col Utf8View YES
timestamp_col Timestamp(Nanosecond, None) YES
year Int32 YES
month Int32 YES
12 changes: 6 additions & 6 deletions datafusion/sqllogictest/test_files/explain.slt
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Expand Up @@ -308,8 +308,8 @@ initial_physical_plan
01)GlobalLimitExec: skip=0, fetch=10, statistics=[Rows=Exact(8), Bytes=Absent, [(Col[0]:),(Col[1]:),(Col[2]:),(Col[3]:),(Col[4]:),(Col[5]:),(Col[6]:),(Col[7]:),(Col[8]:),(Col[9]:),(Col[10]:)]]
02)--ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, statistics=[Rows=Exact(8), Bytes=Absent, [(Col[0]:),(Col[1]:),(Col[2]:),(Col[3]:),(Col[4]:),(Col[5]:),(Col[6]:),(Col[7]:),(Col[8]:),(Col[9]:),(Col[10]:)]]
initial_physical_plan_with_schema
01)GlobalLimitExec: skip=0, fetch=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:Binary;N, string_col:Binary;N, timestamp_col:Timestamp(Nanosecond, None);N]
02)--ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:Binary;N, string_col:Binary;N, timestamp_col:Timestamp(Nanosecond, None);N]
01)GlobalLimitExec: skip=0, fetch=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:BinaryView;N, string_col:BinaryView;N, timestamp_col:Timestamp(Nanosecond, None);N]
02)--ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:BinaryView;N, string_col:BinaryView;N, timestamp_col:Timestamp(Nanosecond, None);N]
physical_plan after OutputRequirements
01)OutputRequirementExec, statistics=[Rows=Exact(8), Bytes=Absent, [(Col[0]:),(Col[1]:),(Col[2]:),(Col[3]:),(Col[4]:),(Col[5]:),(Col[6]:),(Col[7]:),(Col[8]:),(Col[9]:),(Col[10]:)]]
02)--GlobalLimitExec: skip=0, fetch=10, statistics=[Rows=Exact(8), Bytes=Absent, [(Col[0]:),(Col[1]:),(Col[2]:),(Col[3]:),(Col[4]:),(Col[5]:),(Col[6]:),(Col[7]:),(Col[8]:),(Col[9]:),(Col[10]:)]]
Expand All @@ -331,7 +331,7 @@ physical_plan after ProjectionPushdown SAME TEXT AS ABOVE
physical_plan after LimitPushdown ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, statistics=[Rows=Exact(8), Bytes=Absent, [(Col[0]:),(Col[1]:),(Col[2]:),(Col[3]:),(Col[4]:),(Col[5]:),(Col[6]:),(Col[7]:),(Col[8]:),(Col[9]:),(Col[10]:)]]
physical_plan after SanityCheckPlan SAME TEXT AS ABOVE
physical_plan ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, statistics=[Rows=Exact(8), Bytes=Absent, [(Col[0]:),(Col[1]:),(Col[2]:),(Col[3]:),(Col[4]:),(Col[5]:),(Col[6]:),(Col[7]:),(Col[8]:),(Col[9]:),(Col[10]:)]]
physical_plan_with_schema ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:Binary;N, string_col:Binary;N, timestamp_col:Timestamp(Nanosecond, None);N]
physical_plan_with_schema ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:BinaryView;N, string_col:BinaryView;N, timestamp_col:Timestamp(Nanosecond, None);N]


statement ok
Expand All @@ -348,8 +348,8 @@ initial_physical_plan_with_stats
01)GlobalLimitExec: skip=0, fetch=10, statistics=[Rows=Exact(8), Bytes=Absent, [(Col[0]:),(Col[1]:),(Col[2]:),(Col[3]:),(Col[4]:),(Col[5]:),(Col[6]:),(Col[7]:),(Col[8]:),(Col[9]:),(Col[10]:)]]
02)--ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, statistics=[Rows=Exact(8), Bytes=Absent, [(Col[0]:),(Col[1]:),(Col[2]:),(Col[3]:),(Col[4]:),(Col[5]:),(Col[6]:),(Col[7]:),(Col[8]:),(Col[9]:),(Col[10]:)]]
initial_physical_plan_with_schema
01)GlobalLimitExec: skip=0, fetch=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:Binary;N, string_col:Binary;N, timestamp_col:Timestamp(Nanosecond, None);N]
02)--ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:Binary;N, string_col:Binary;N, timestamp_col:Timestamp(Nanosecond, None);N]
01)GlobalLimitExec: skip=0, fetch=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:BinaryView;N, string_col:BinaryView;N, timestamp_col:Timestamp(Nanosecond, None);N]
02)--ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:BinaryView;N, string_col:BinaryView;N, timestamp_col:Timestamp(Nanosecond, None);N]
physical_plan after OutputRequirements
01)OutputRequirementExec
02)--GlobalLimitExec: skip=0, fetch=10
Expand All @@ -372,7 +372,7 @@ physical_plan after LimitPushdown ParquetExec: file_groups={1 group: [[WORKSPACE
physical_plan after SanityCheckPlan SAME TEXT AS ABOVE
physical_plan ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10
physical_plan_with_stats ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, statistics=[Rows=Exact(8), Bytes=Absent, [(Col[0]:),(Col[1]:),(Col[2]:),(Col[3]:),(Col[4]:),(Col[5]:),(Col[6]:),(Col[7]:),(Col[8]:),(Col[9]:),(Col[10]:)]]
physical_plan_with_schema ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:Binary;N, string_col:Binary;N, timestamp_col:Timestamp(Nanosecond, None);N]
physical_plan_with_schema ParquetExec: file_groups={1 group: [[WORKSPACE_ROOT/parquet-testing/data/alltypes_plain.parquet]]}, projection=[id, bool_col, tinyint_col, smallint_col, int_col, bigint_col, float_col, double_col, date_string_col, string_col, timestamp_col], limit=10, schema=[id:Int32;N, bool_col:Boolean;N, tinyint_col:Int32;N, smallint_col:Int32;N, int_col:Int32;N, bigint_col:Int64;N, float_col:Float32;N, double_col:Float64;N, date_string_col:BinaryView;N, string_col:BinaryView;N, timestamp_col:Timestamp(Nanosecond, None);N]


statement ok
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4 changes: 2 additions & 2 deletions datafusion/sqllogictest/test_files/information_schema.slt
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Expand Up @@ -200,7 +200,7 @@ datafusion.execution.parquet.metadata_size_hint NULL
datafusion.execution.parquet.pruning true
datafusion.execution.parquet.pushdown_filters false
datafusion.execution.parquet.reorder_filters false
datafusion.execution.parquet.schema_force_view_types false
datafusion.execution.parquet.schema_force_view_types true
datafusion.execution.parquet.skip_metadata true
datafusion.execution.parquet.statistics_enabled page
datafusion.execution.parquet.write_batch_size 1024
Expand Down Expand Up @@ -290,7 +290,7 @@ datafusion.execution.parquet.metadata_size_hint NULL (reading) If specified, the
datafusion.execution.parquet.pruning true (reading) If true, the parquet reader attempts to skip entire row groups based on the predicate in the query and the metadata (min/max values) stored in the parquet file
datafusion.execution.parquet.pushdown_filters false (reading) If true, filter expressions are be applied during the parquet decoding operation to reduce the number of rows decoded. This optimization is sometimes called "late materialization".
datafusion.execution.parquet.reorder_filters false (reading) If true, filter expressions evaluated during the parquet decoding operation will be reordered heuristically to minimize the cost of evaluation. If false, the filters are applied in the same order as written in the query
datafusion.execution.parquet.schema_force_view_types false (reading) If true, parquet reader will read columns of `Utf8/Utf8Large` with `Utf8View`, and `Binary/BinaryLarge` with `BinaryView`.
datafusion.execution.parquet.schema_force_view_types true (reading) If true, parquet reader will read columns of `Utf8/Utf8Large` with `Utf8View`, and `Binary/BinaryLarge` with `BinaryView`.
datafusion.execution.parquet.skip_metadata true (reading) If true, the parquet reader skip the optional embedded metadata that may be in the file Schema. This setting can help avoid schema conflicts when querying multiple parquet files with schemas containing compatible types but different metadata
datafusion.execution.parquet.statistics_enabled page (writing) Sets if statistics are enabled for any column Valid values are: "none", "chunk", and "page" These values are not case sensitive. If NULL, uses default parquet writer setting
datafusion.execution.parquet.write_batch_size 1024 (writing) Sets write_batch_size in bytes
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2 changes: 1 addition & 1 deletion datafusion/sqllogictest/test_files/map.slt
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Expand Up @@ -42,7 +42,7 @@ describe data;
----
ints Map(Field { name: "entries", data_type: Struct([Field { name: "key", data_type: Utf8, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "value", data_type: Int64, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, false) NO
strings Map(Field { name: "entries", data_type: Struct([Field { name: "key", data_type: Utf8, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "value", data_type: Utf8, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, false) NO
timestamp Utf8 NO
timestamp Utf8View NO

query ??T
SELECT * FROM data ORDER by ints['bytes'] DESC LIMIT 10;
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