Enable reading StringViewArray by default from Parquet (8% improvem…

…ent for entire ClickBench suite) (#13101)

* Read String data as `StringView` by default from Parquet

* fix benchmark

benchmarks/src/bin/external_aggr.rs

@@ -193,12 +193,7 @@ impl ExternalAggrConfig {
     ) -> Result<Vec<QueryResult>> {
         let query_name =
             format!("Q{query_id}({})", human_readable_size(mem_limit as usize));
-        let mut config = self.common.config();
-        config
-            .options_mut()
-            .execution
-            .parquet
-            .schema_force_view_types = self.common.force_view_types;
+        let config = self.common.config();
         let runtime_config = RuntimeConfig::new()
             .with_memory_pool(Arc::new(FairSpillPool::new(mem_limit as usize)))
             .build_arc()?;

benchmarks/src/clickbench.rs

@@ -119,7 +119,6 @@ impl RunOpt {
         let mut config = self.common.config();
         {
             let parquet_options = &mut config.options_mut().execution.parquet;
-            parquet_options.schema_force_view_types = self.common.force_view_types;
             // The hits_partitioned dataset specifies string columns
             // as binary due to how it was written. Force it to strings
             parquet_options.binary_as_string = true;

benchmarks/src/imdb/run.rs

@@ -305,11 +305,7 @@ impl RunOpt {
             .config()
             .with_collect_statistics(!self.disable_statistics);
         config.options_mut().optimizer.prefer_hash_join = self.prefer_hash_join;
-        config
-            .options_mut()
-            .execution
-            .parquet
-            .schema_force_view_types = self.common.force_view_types;
+
         let ctx = SessionContext::new_with_config(config);
 
         // register tables
@@ -517,7 +513,6 @@ mod tests {
             partitions: Some(2),
             batch_size: 8192,
             debug: false,
-            force_view_types: false,
         };
         let opt = RunOpt {
             query: Some(query),
@@ -551,7 +546,6 @@ mod tests {
             partitions: Some(2),
             batch_size: 8192,
             debug: false,
-            force_view_types: false,
         };
         let opt = RunOpt {
             query: Some(query),

benchmarks/src/tpch/run.rs

@@ -120,11 +120,6 @@ impl RunOpt {
             .config()
             .with_collect_statistics(!self.disable_statistics);
         config.options_mut().optimizer.prefer_hash_join = self.prefer_hash_join;
-        config
-            .options_mut()
-            .execution
-            .parquet
-            .schema_force_view_types = self.common.force_view_types;
         let ctx = SessionContext::new_with_config(config);
 
         // register tables
@@ -345,7 +340,6 @@ mod tests {
             partitions: Some(2),
             batch_size: 8192,
             debug: false,
-            force_view_types: false,
         };
         let opt = RunOpt {
             query: Some(query),
@@ -379,7 +373,6 @@ mod tests {
             partitions: Some(2),
             batch_size: 8192,
             debug: false,
-            force_view_types: false,
         };
         let opt = RunOpt {
             query: Some(query),

benchmarks/src/util/options.rs

@@ -37,11 +37,6 @@ pub struct CommonOpt {
     /// Activate debug mode to see more details
     #[structopt(short, long)]
     pub debug: bool,
-
-    /// If true, will use StringView/BinaryViewArray instead of String/BinaryArray
-    /// when reading ParquetFiles
-    #[structopt(long)]
-    pub force_view_types: bool,
 }
 
 impl CommonOpt {

datafusion/common/src/config.rs

@@ -399,7 +399,7 @@ 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
 
         /// (reading) If true, parquet reader will read columns of
         /// `Binary/LargeBinary` with `Utf8`, and `BinaryView` with `Utf8View`.

datafusion/common/src/scalar/mod.rs

@@ -980,6 +980,11 @@ 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 {

datafusion/core/tests/parquet/page_pruning.rs

@@ -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();

datafusion/sqllogictest/test_files/describe.slt

@@ -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

datafusion/sqllogictest/test_files/explain.slt

@@ -305,8 +305,8 @@ 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]:)]]
@@ -328,7 +328,7 @@ 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
@@ -345,8 +345,8 @@ 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
@@ -369,7 +369,7 @@ 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

datafusion/sqllogictest/test_files/information_schema.slt

@@ -202,7 +202,7 @@ 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
@@ -295,7 +295,7 @@ 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

datafusion/sqllogictest/test_files/map.slt

@@ -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;