use bingang for agg benchmark

use bingang for agg benchmark, which includes memory consumption

Output:
```
full
histogram                     Memory: 15.8 KB              Avg: 10.9322ms  (+5.44%)    Median: 10.8790ms  (+9.28%)     Min: 10.7470ms    Max: 11.3263ms
histogram_hard_bounds         Memory: 15.5 KB              Avg: 5.1939ms  (+6.61%)     Median: 5.1722ms  (+10.98%)     Min: 5.0432ms     Max: 5.3910ms
histogram_with_avg_sub_agg    Memory: 48.7 KB              Avg: 23.8165ms  (+4.57%)    Median: 23.7264ms  (+10.06%)    Min: 23.4995ms    Max: 24.8107ms
dense
histogram                     Memory: 17.3 KB              Avg: 15.6810ms  (-8.54%)    Median: 15.6174ms  (-8.89%)    Min: 15.4953ms    Max: 16.0702ms
histogram_hard_bounds         Memory: 15.4 KB              Avg: 10.0720ms  (-7.33%)    Median: 10.0572ms  (-7.06%)    Min: 9.8500ms     Max: 10.4819ms
histogram_with_avg_sub_agg    Memory: 50.1 KB              Avg: 33.0993ms  (-7.04%)    Median: 32.9499ms  (-6.86%)    Min: 32.8284ms    Max: 34.0529ms
sparse
histogram                     Memory: 16.3 KB              Avg: 19.2325ms  (-0.44%)    Median: 19.1211ms  (-1.26%)    Min: 19.0348ms    Max: 19.7902ms
histogram_hard_bounds         Memory: 16.1 KB              Avg: 18.5179ms  (-0.61%)    Median: 18.4552ms  (-0.90%)    Min: 18.3799ms    Max: 19.0535ms
histogram_with_avg_sub_agg    Memory: 34.7 KB              Avg: 21.2589ms  (-0.69%)    Median: 21.1867ms  (-1.05%)    Min: 21.0342ms    Max: 21.9900ms
```

Cargo.toml

@@ -67,6 +67,7 @@ fnv = "1.0.7"
 winapi = "0.3.9"
 
 [dev-dependencies]
+binggan = "0.3.0"
 rand = "0.8.5"
 maplit = "1.0.2"
 matches = "0.1.9"
@@ -143,3 +144,8 @@ harness = false
 [[bench]]
 name = "index-bench"
 harness = false
+
+[[bench]]
+name = "agg_bench"
+harness = false
+

benches/agg_bench.rs

@@ -0,0 +1,368 @@
+use binggan::{black_box, BenchGroup, PeakMemAlloc, INSTRUMENTED_SYSTEM};
+use rand::prelude::SliceRandom;
+use rand::rngs::StdRng;
+use rand::{Rng, SeedableRng};
+use rand_distr::Distribution;
+use serde_json::json;
+use tantivy::aggregation::agg_req::Aggregations;
+use tantivy::aggregation::AggregationCollector;
+use tantivy::query::{AllQuery, TermQuery};
+use tantivy::schema::{IndexRecordOption, Schema, TextFieldIndexing, FAST, STRING};
+use tantivy::{doc, Index, Term};
+
+#[global_allocator]
+pub static GLOBAL: &PeakMemAlloc<std::alloc::System> = &INSTRUMENTED_SYSTEM;
+
+/// Mini macro to register a function via its name
+/// runner.register("average_u64", move |index| average_u64(index));
+macro_rules! register {
+    ($runner:expr, $func:ident) => {
+        $runner.register(stringify!($func), move |index| $func(index))
+    };
+}
+
+fn main() {
+    let inputs = vec![
+        ("full", get_test_index_bench(Cardinality::Full).unwrap()),
+        (
+            "dense",
+            get_test_index_bench(Cardinality::OptionalDense).unwrap(),
+        ),
+        (
+            "sparse",
+            get_test_index_bench(Cardinality::OptionalSparse).unwrap(),
+        ),
+        (
+            "multivalue",
+            get_test_index_bench(Cardinality::Multivalued).unwrap(),
+        ),
+    ];
+
+    bench_agg(BenchGroup::new_with_inputs(inputs));
+}
+
+fn bench_agg(mut group: BenchGroup<Index>) {
+    group.set_alloc(GLOBAL); // Set the peak mem allocator. This will enable peak memory reporting.
+    register!(group, average_u64);
+    register!(group, average_f64);
+    register!(group, average_f64_u64);
+    register!(group, stats_f64);
+    register!(group, percentiles_f64);
+    register!(group, terms_few);
+    register!(group, terms_many);
+    register!(group, terms_many_order_by_term);
+    register!(group, terms_many_with_top_hits);
+    register!(group, terms_many_with_avg_sub_agg);
+    register!(group, terms_many_json_mixed_type_with_sub_agg_card);
+    register!(group, range_agg);
+    register!(group, range_agg_with_avg_sub_agg);
+    register!(group, histogram);
+    register!(group, histogram_hard_bounds);
+    register!(group, histogram_with_avg_sub_agg);
+    register!(group, avg_and_range_with_avg_sub_agg);
+
+    group.run();
+}
+
+fn exec_term_with_agg(index: &Index, agg_req: serde_json::Value) {
+    let agg_req: Aggregations = serde_json::from_value(agg_req).unwrap();
+
+    let reader = index.reader().unwrap();
+    let text_field = reader.searcher().schema().get_field("text").unwrap();
+    let term_query = TermQuery::new(
+        Term::from_field_text(text_field, "cool"),
+        IndexRecordOption::Basic,
+    );
+    let collector = get_collector(agg_req);
+    let searcher = reader.searcher();
+    black_box(searcher.search(&term_query, &collector).unwrap());
+}
+
+fn average_u64(index: &Index) {
+    let agg_req = json!({
+        "average": { "avg": { "field": "score", } }
+    });
+    exec_term_with_agg(index, agg_req)
+}
+fn average_f64(index: &Index) {
+    let agg_req = json!({
+        "average": { "avg": { "field": "score_f64", } }
+    });
+    exec_term_with_agg(index, agg_req)
+}
+fn average_f64_u64(index: &Index) {
+    let agg_req = json!({
+        "average_f64": { "avg": { "field": "score_f64" } },
+        "average": { "avg": { "field": "score" } },
+    });
+    exec_term_with_agg(index, agg_req)
+}
+fn stats_f64(index: &Index) {
+    let agg_req = json!({
+        "average_f64": { "stats": { "field": "score_f64", } }
+    });
+    exec_term_with_agg(index, agg_req)
+}
+
+fn percentiles_f64(index: &Index) {
+    let agg_req = json!({
+      "mypercentiles": {
+        "percentiles": {
+          "field": "score_f64",
+          "percents": [ 95, 99, 99.9 ]
+        }
+      }
+    });
+    execute_agg(index, agg_req);
+}
+fn terms_few(index: &Index) {
+    let agg_req = json!({
+        "my_texts": { "terms": { "field": "text_few_terms" } },
+    });
+    execute_agg(index, agg_req);
+}
+fn terms_many(index: &Index) {
+    let agg_req = json!({
+        "my_texts": { "terms": { "field": "text_many_terms" } },
+    });
+    execute_agg(index, agg_req);
+}
+fn terms_many_order_by_term(index: &Index) {
+    let agg_req = json!({
+        "my_texts": { "terms": { "field": "text_many_terms", "order": { "_key": "desc" } } },
+    });
+    execute_agg(index, agg_req);
+}
+fn terms_many_with_top_hits(index: &Index) {
+    let agg_req = json!({
+        "my_texts": {
+            "terms": { "field": "text_many_terms" },
+            "aggs": {
+                "top_hits": { "top_hits":
+                    {
+                        "sort": [
+                            { "score": "desc" }
+                        ],
+                        "size": 2,
+                        "doc_value_fields": ["score_f64"]
+                    }
+                }
+            }
+        },
+    });
+    execute_agg(index, agg_req);
+}
+fn terms_many_with_avg_sub_agg(index: &Index) {
+    let agg_req = json!({
+        "my_texts": {
+            "terms": { "field": "text_many_terms" },
+            "aggs": {
+                "average_f64": { "avg": { "field": "score_f64" } }
+            }
+        },
+    });
+    execute_agg(index, agg_req);
+}
+fn terms_many_json_mixed_type_with_sub_agg_card(index: &Index) {
+    let agg_req = json!({
+        "my_texts": {
+            "terms": { "field": "json.mixed_type" },
+            "aggs": {
+                "average_f64": { "avg": { "field": "score_f64" } }
+            }
+        },
+    });
+    execute_agg(index, agg_req);
+}
+
+fn execute_agg(index: &Index, agg_req: serde_json::Value) {
+    let agg_req: Aggregations = serde_json::from_value(agg_req).unwrap();
+    let collector = get_collector(agg_req);
+
+    let reader = index.reader().unwrap();
+    let searcher = reader.searcher();
+    black_box(searcher.search(&AllQuery, &collector).unwrap());
+}
+fn range_agg(index: &Index) {
+    let agg_req = json!({
+        "range_f64": { "range": { "field": "score_f64", "ranges": [
+            { "from": 3, "to": 7000 },
+            { "from": 7000, "to": 20000 },
+            { "from": 20000, "to": 30000 },
+            { "from": 30000, "to": 40000 },
+            { "from": 40000, "to": 50000 },
+            { "from": 50000, "to": 60000 }
+        ] } },
+    });
+    execute_agg(index, agg_req);
+}
+fn range_agg_with_avg_sub_agg(index: &Index) {
+    let agg_req = json!({
+        "rangef64": {
+            "range": {
+                "field": "score_f64",
+                "ranges": [
+                    { "from": 3, "to": 7000 },
+                    { "from": 7000, "to": 20000 },
+                    { "from": 20000, "to": 30000 },
+                    { "from": 30000, "to": 40000 },
+                    { "from": 40000, "to": 50000 },
+                    { "from": 50000, "to": 60000 }
+                ]
+            },
+            "aggs": {
+                "average_f64": { "avg": { "field": "score_f64" } }
+            }
+        },
+    });
+    execute_agg(index, agg_req);
+}
+fn histogram(index: &Index) {
+    let agg_req = json!({
+        "rangef64": {
+            "histogram": {
+                "field": "score_f64",
+                "interval": 100 // 1000 buckets
+            },
+        }
+    });
+    execute_agg(index, agg_req);
+}
+fn histogram_hard_bounds(index: &Index) {
+    let agg_req = json!({
+        "rangef64": { "histogram": { "field": "score_f64", "interval": 100, "hard_bounds": { "min": 1000, "max": 300000 } } },
+    });
+    execute_agg(index, agg_req);
+}
+fn histogram_with_avg_sub_agg(index: &Index) {
+    let agg_req = json!({
+        "rangef64": {
+            "histogram": { "field": "score_f64", "interval": 100 },
+            "aggs": {
+                "average_f64": { "avg": { "field": "score_f64" } }
+            }
+        }
+    });
+    execute_agg(index, agg_req);
+}
+fn avg_and_range_with_avg_sub_agg(index: &Index) {
+    let agg_req = json!({
+        "rangef64": {
+            "range": {
+                "field": "score_f64",
+                "ranges": [
+                    { "from": 3, "to": 7000 },
+                    { "from": 7000, "to": 20000 },
+                    { "from": 20000, "to": 60000 }
+                ]
+            },
+            "aggs": {
+                "average_in_range": { "avg": { "field": "score" } }
+            }
+        },
+        "average": { "avg": { "field": "score" } }
+    });
+    execute_agg(index, agg_req);
+}
+
+#[derive(Clone, Copy, Hash, Default, Debug, PartialEq, Eq, PartialOrd, Ord)]
+enum Cardinality {
+    /// All documents contain exactly one value.
+    /// `Full` is the default for auto-detecting the Cardinality, since it is the most strict.
+    #[default]
+    Full = 0,
+    /// All documents contain at most one value.
+    OptionalDense = 1,
+    /// All documents may contain any number of values.
+    Multivalued = 2,
+    /// 1 / 20 documents has a value
+    OptionalSparse = 3,
+}
+
+fn get_collector(agg_req: Aggregations) -> AggregationCollector {
+    AggregationCollector::from_aggs(agg_req, Default::default())
+}
+
+fn get_test_index_bench(cardinality: Cardinality) -> tantivy::Result<Index> {
+    let mut schema_builder = Schema::builder();
+    let text_fieldtype = tantivy::schema::TextOptions::default()
+        .set_indexing_options(
+            TextFieldIndexing::default().set_index_option(IndexRecordOption::WithFreqs),
+        )
+        .set_stored();
+    let text_field = schema_builder.add_text_field("text", text_fieldtype);
+    let json_field = schema_builder.add_json_field("json", FAST);
+    let text_field_many_terms = schema_builder.add_text_field("text_many_terms", STRING | FAST);
+    let text_field_few_terms = schema_builder.add_text_field("text_few_terms", STRING | FAST);
+    let score_fieldtype = tantivy::schema::NumericOptions::default().set_fast();
+    let score_field = schema_builder.add_u64_field("score", score_fieldtype.clone());
+    let score_field_f64 = schema_builder.add_f64_field("score_f64", score_fieldtype.clone());
+    let score_field_i64 = schema_builder.add_i64_field("score_i64", score_fieldtype);
+    let index = Index::create_from_tempdir(schema_builder.build())?;
+    let few_terms_data = ["INFO", "ERROR", "WARN", "DEBUG"];
+
+    let lg_norm = rand_distr::LogNormal::new(2.996f64, 0.979f64).unwrap();
+
+    let many_terms_data = (0..150_000)
+        .map(|num| format!("author{}", num))
+        .collect::<Vec<_>>();
+    {
+        let mut rng = StdRng::from_seed([1u8; 32]);
+        let mut index_writer = index.writer_with_num_threads(1, 200_000_000)?;
+        // To make the different test cases comparable we just change one doc to force the
+        // cardinality
+        if cardinality == Cardinality::OptionalDense {
+            index_writer.add_document(doc!())?;
+        }
+        if cardinality == Cardinality::Multivalued {
+            index_writer.add_document(doc!(
+                json_field => json!({"mixed_type": 10.0}),
+                json_field => json!({"mixed_type": 10.0}),
+                text_field => "cool",
+                text_field => "cool",
+                text_field_many_terms => "cool",
+                text_field_many_terms => "cool",
+                text_field_few_terms => "cool",
+                text_field_few_terms => "cool",
+                score_field => 1u64,
+                score_field => 1u64,
+                score_field_f64 => lg_norm.sample(&mut rng),
+                score_field_f64 => lg_norm.sample(&mut rng),
+                score_field_i64 => 1i64,
+                score_field_i64 => 1i64,
+            ))?;
+        }
+        let mut doc_with_value = 1_000_000;
+        if cardinality == Cardinality::OptionalSparse {
+            doc_with_value /= 20;
+        }
+        let _val_max = 1_000_000.0;
+        for _ in 0..doc_with_value {
+            let val: f64 = rng.gen_range(0.0..1_000_000.0);
+            let json = if rng.gen_bool(0.1) {
+                // 10% are numeric values
+                json!({ "mixed_type": val })
+            } else {
+                json!({"mixed_type": many_terms_data.choose(&mut rng).unwrap().to_string()})
+            };
+            index_writer.add_document(doc!(
+                text_field => "cool",
+                json_field => json,
+                text_field_many_terms => many_terms_data.choose(&mut rng).unwrap().to_string(),
+                text_field_few_terms => few_terms_data.choose(&mut rng).unwrap().to_string(),
+                score_field => val as u64,
+                score_field_f64 => lg_norm.sample(&mut rng),
+                score_field_i64 => val as i64,
+            ))?;
+            if cardinality == Cardinality::OptionalSparse {
+                for _ in 0..20 {
+                    index_writer.add_document(doc!(text_field => "cool"))?;
+                }
+            }
+        }
+        // writing the segment
+        index_writer.commit()?;
+    }
+
+    Ok(index)
+}