Merge branch 'master' into client-allow-yaml

docs/en/engines/table-engines/mergetree-family/annindexes.md

@@ -193,6 +193,19 @@ index creation, `L2Distance` is used as default. Parameter `NumTrees` is the num
 specified: 100). Higher values of `NumTree` mean more accurate search results but slower index creation / query times (approximately
 linearly) as well as larger index sizes.
 
+`L2Distance` is also called Euclidean distance, the Euclidean distance between two points in Euclidean space is the length of a line segment between the two points.
+For example: If we have point P(p1,p2), Q(q1,q2), their distance will be d(p,q)
+![L2Distance](https://en.wikipedia.org/wiki/Euclidean_distance#/media/File:Euclidean_distance_2d.svg)
+
+`cosineDistance` also called cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. 
+![cosineDistance](https://www.tyrrell4innovation.ca/wp-content/uploads/2021/06/rsz_jenny_du_miword.png)
+
+The Euclidean distance corresponds to the L2-norm of a difference between vectors. The cosine similarity is proportional to the dot product of two vectors and inversely proportional to the product of their magnitudes.
+![compare](https://www.researchgate.net/publication/320914786/figure/fig2/AS:558221849841664@1510101868614/The-difference-between-Euclidean-distance-and-cosine-similarity.png)
+In one sentence: cosine similarity care only about the angle between them, but do not care about the "distance" we normally think.
+![L2 distance](https://www.baeldung.com/wp-content/uploads/sites/4/2020/06/4-1.png)
+![cosineDistance](https://www.baeldung.com/wp-content/uploads/sites/4/2020/06/5.png)
+
 :::note
 Indexes over columns of type `Array` will generally work faster than indexes on `Tuple` columns. All arrays **must** have same length. Use
 [CONSTRAINT](/docs/en/sql-reference/statements/create/table.md#constraints) to avoid errors. For example, `CONSTRAINT constraint_name_1

src/Storages/System/StorageSystemStackTrace.cpp

@@ -275,15 +275,6 @@ Pipe StorageSystemStackTrace::read(
 
     Block sample_block = storage_snapshot->metadata->getSampleBlock();
 
-    std::vector<UInt8> columns_mask(sample_block.columns());
-    for (size_t i = 0, size = columns_mask.size(); i < size; ++i)
-    {
-        if (names_set.contains(sample_block.getByPosition(i).name))
-        {
-            columns_mask[i] = 1;
-        }
-    }
-
     bool send_signal = names_set.contains("trace") || names_set.contains("query_id");
     bool read_thread_names = names_set.contains("thread_name");
 

tests/queries/0_stateless/00612_http_max_query_size.sh

@@ -36,7 +36,7 @@ def gen_data(q):
 
     pattern = ''' or toString(number) = '{}'\n'''
 
-    for i in range(1, 4 * 1024):
+    for i in range(0, 1024 * 2):
         yield pattern.format(str(i).zfill(1024 - len(pattern) + 2)).encode()
 
 s = requests.Session()

tests/queries/0_stateless/02790_client_max_opening_fd.reference

@@ -0,0 +1 @@
+1

tests/queries/0_stateless/02790_client_max_opening_fd.sh

@@ -0,0 +1,9 @@
+#!/usr/bin/env bash
+
+CUR_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
+# shellcheck source=../shell_config.sh
+. "$CUR_DIR"/../shell_config.sh
+
+# Ensure that clickhouse-client does not open a large number of files.
+ulimit -n 1024
+${CLICKHOUSE_CLIENT} --query "SELECT 1"