Merge pull request #4249 from Blargian/update_date_time_links

Blargian · web-flow · commit 62a935c62672 · 2025-08-07T23:27:45.000+02:00
Update anchors
diff --git a/docs/integrations/data-visualization/tableau/tableau-analysis-tips.md b/docs/integrations/data-visualization/tableau/tableau-analysis-tips.md
@@ -25,7 +25,7 @@ ClickHouse has a huge number of functions that can be used for data analysis —
     ███████████████  259.37 million
     ```
 - **`COUNTD_UNIQ([my_field])`** *(added in v0.2.0)* — Calculates the approximate number of different values of the argument. Equivalent of [uniq()](/sql-reference/aggregate-functions/reference/uniq/). Much faster than `COUNTD()`.
-- **`DATE_BIN('day', 10, [my_datetime_or_date])`** *(added in v0.2.1)* — equivalent of [`toStartOfInterval()`](/sql-reference/functions/date-time-functions#tostartofinterval) in ClickHouse. Rounds down a Date or Date & Time to the given interval, for example:
+- **`DATE_BIN('day', 10, [my_datetime_or_date])`** *(added in v0.2.1)* — equivalent of [`toStartOfInterval()`](/sql-reference/functions/date-time-functions#toStartOfInterval) in ClickHouse. Rounds down a Date or Date & Time to the given interval, for example:
     ```text
      == my_datetime_or_date == | == DATE_BIN('day', 10, [my_datetime_or_date]) ==
         28.07.2004 06:54:50    |              21.07.2004 00:00:00
diff --git a/docs/materialized-view/incremental-materialized-view.md b/docs/materialized-view/incremental-materialized-view.md
@@ -45,7 +45,7 @@ INSERT INTO votes SELECT * FROM s3('https://datasets-documentation.s3.eu-west-3.
 0 rows in set. Elapsed: 29.359 sec. Processed 238.98 million rows, 2.13 GB (8.14 million rows/s., 72.45 MB/s.)
 ```
 
-This is a reasonably simple query in ClickHouse thanks to the [`toStartOfDay`](/sql-reference/functions/date-time-functions#tostartofday) function:
+This is a reasonably simple query in ClickHouse thanks to the [`toStartOfDay`](/sql-reference/functions/date-time-functions#toStartOfDay) function:
 
 ```sql
 SELECT toStartOfDay(CreationDate) AS day,
diff --git a/docs/migrations/bigquery/equivalent-concepts.md b/docs/migrations/bigquery/equivalent-concepts.md
@@ -55,24 +55,24 @@ ClickHouse Cloud [controls user access](/cloud/security/cloud-access-management)
 
 ClickHouse offers more granular precision with respect to numerics. For example, BigQuery offers the numeric types [`INT64`, `NUMERIC`, `BIGNUMERIC` and `FLOAT64`](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#numeric_types). Contrast these with ClickHouse, which offers multiple precision types for decimals, floats, and integers. With these data types, ClickHouse users can optimize storage and memory overhead, resulting in faster queries and lower resource consumption. Below we map the equivalent ClickHouse type for each BigQuery type:
 
-| BigQuery | ClickHouse |
-|----------|------------|
-| [ARRAY](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#array_type)    | [Array(t)](/sql-reference/data-types/array)   |
-| [NUMERIC](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#decimal_types)  | [Decimal(P, S), Decimal32(S), Decimal64(S), Decimal128(S)](/sql-reference/data-types/decimal)    |
-| [BIG NUMERIC](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#decimal_types) | [Decimal256(S)](/sql-reference/data-types/decimal) |
-| [BOOL](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#boolean_type)     | [Bool](/sql-reference/data-types/boolean)       |
-| [BYTES](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#bytes_type)    | [FixedString](/sql-reference/data-types/fixedstring) |
-| [DATE](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#date_type)     | [Date32](/sql-reference/data-types/date32) (with narrower range) |
-| [DATETIME](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#datetime_type) | [DateTime](/sql-reference/data-types/datetime), [DateTime64](/sql-reference/data-types/datetime64) (narrow range, higher precision) |
-| [FLOAT64](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#floating_point_types)  | [Float64](/sql-reference/data-types/float)    |
-| [GEOGRAPHY](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#geography_type) | [Geo Data Types](/sql-reference/data-types/float) |
-| [INT64](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#integer_types)    | [UInt8, UInt16, UInt32, UInt64, UInt128, UInt256, Int8, Int16, Int32, Int64, Int128, Int256](/sql-reference/data-types/int-uint) |
-| [INTERVAL](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#integer_types) | NA - [supported as expression](/sql-reference/data-types/special-data-types/interval#usage-remarks) or [through functions](/sql-reference/functions/date-time-functions#addyears) |
-| [JSON](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#json_type)     | [JSON](/integrations/data-formats/json/inference)       |
-| [STRING](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#string_type)   | [String (bytes)](/sql-reference/data-types/string) |
-| [STRUCT](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#constructing_a_struct)   | [Tuple](/sql-reference/data-types/tuple), [Nested](/sql-reference/data-types/nested-data-structures/nested) |
-| [TIME](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#time_type)     | [DateTime64](/sql-reference/data-types/datetime64) |
-| [TIMESTAMP](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#timestamp_type) | [DateTime64](/sql-reference/data-types/datetime64) |
+| BigQuery | ClickHouse                                                                                                                                                                        |
+|----------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [ARRAY](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#array_type)    | [Array(t)](/sql-reference/data-types/array)                                                                                                                                       |
+| [NUMERIC](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#decimal_types)  | [Decimal(P, S), Decimal32(S), Decimal64(S), Decimal128(S)](/sql-reference/data-types/decimal)                                                                                     |
+| [BIG NUMERIC](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#decimal_types) | [Decimal256(S)](/sql-reference/data-types/decimal)                                                                                                                                |
+| [BOOL](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#boolean_type)     | [Bool](/sql-reference/data-types/boolean)                                                                                                                                         |
+| [BYTES](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#bytes_type)    | [FixedString](/sql-reference/data-types/fixedstring)                                                                                                                              |
+| [DATE](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#date_type)     | [Date32](/sql-reference/data-types/date32) (with narrower range)                                                                                                                  |
+| [DATETIME](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#datetime_type) | [DateTime](/sql-reference/data-types/datetime), [DateTime64](/sql-reference/data-types/datetime64) (narrow range, higher precision)                                               |
+| [FLOAT64](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#floating_point_types)  | [Float64](/sql-reference/data-types/float)                                                                                                                                        |
+| [GEOGRAPHY](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#geography_type) | [Geo Data Types](/sql-reference/data-types/float)                                                                                                                                 |
+| [INT64](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#integer_types)    | [UInt8, UInt16, UInt32, UInt64, UInt128, UInt256, Int8, Int16, Int32, Int64, Int128, Int256](/sql-reference/data-types/int-uint)                                                  |
+| [INTERVAL](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#integer_types) | NA - [supported as expression](/sql-reference/data-types/special-data-types/interval#usage-remarks) or [through functions](/sql-reference/functions/date-time-functions#addYears) |
+| [JSON](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#json_type)     | [JSON](/integrations/data-formats/json/inference)                                                                                                                                 |
+| [STRING](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#string_type)   | [String (bytes)](/sql-reference/data-types/string)                                                                                                                                |
+| [STRUCT](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#constructing_a_struct)   | [Tuple](/sql-reference/data-types/tuple), [Nested](/sql-reference/data-types/nested-data-structures/nested)                                                                       |
+| [TIME](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#time_type)     | [DateTime64](/sql-reference/data-types/datetime64)                                                                                                                                |
+| [TIMESTAMP](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#timestamp_type) | [DateTime64](/sql-reference/data-types/datetime64)                                                                                                                                |
 
 When presented with multiple options for ClickHouse types, consider the actual range of the data and pick the lowest required. Also, consider utilizing [appropriate codecs](https://clickhouse.com/blog/optimize-clickhouse-codecs-compression-schema) for further compression.
 
diff --git a/docs/migrations/bigquery/migrating-to-clickhouse-cloud.md b/docs/migrations/bigquery/migrating-to-clickhouse-cloud.md
@@ -176,7 +176,7 @@ In ClickHouse, partitioning is specified on a table when it is initially defined
 
 <Image img={bigquery_6} size="md" alt="Partitions"/>
 
-The data parts are logically associated with each partition on disk and can be queried in isolation. For the example below, we partition the posts table by year using the expression [`toYear(CreationDate)`](/sql-reference/functions/date-time-functions#toyear). As rows are inserted into ClickHouse, this expression will be evaluated against each row – rows are then routed to the resulting partition in the form of new data parts belonging to that partition.
+The data parts are logically associated with each partition on disk and can be queried in isolation. For the example below, we partition the posts table by year using the expression [`toYear(CreationDate)`](/sql-reference/functions/date-time-functions#toYear). As rows are inserted into ClickHouse, this expression will be evaluated against each row – rows are then routed to the resulting partition in the form of new data parts belonging to that partition.
 
 ```sql
 CREATE TABLE posts
diff --git a/docs/use-cases/time-series/basic-operations.md b/docs/use-cases/time-series/basic-operations.md
@@ -86,12 +86,12 @@ LIMIT 5;
 └─────────────────────┴────────┘
 ```
 
-The [`toStartOfHour()`](/docs/sql-reference/functions/date-time-functions#tostartofhour) function used here converts the given time to the start of the hour. 
+The [`toStartOfHour()`](/docs/sql-reference/functions/date-time-functions#toStartOfHour) function used here converts the given time to the start of the hour. 
 You can also group by year, quarter, month, or day.
 
 ## Custom grouping intervals {#time-series-custom-grouping-intervals}
 
-We can even group by arbitrary intervals, e.g., 5 minutes using the [`toStartOfInterval()`](/docs/sql-reference/functions/date-time-functions#tostartofinterval) function. 
+We can even group by arbitrary intervals, e.g., 5 minutes using the [`toStartOfInterval()`](/docs/sql-reference/functions/date-time-functions#toStartOfInterval) function. 
 
 Let's say we want to group by 4-hour intervals.
 We can specify the grouping interval using the [`INTERVAL`](/docs/sql-reference/data-types/special-data-types/interval) clause:
@@ -218,7 +218,7 @@ ORDER BY hour ASC WITH FILL STEP toIntervalHour(1);
 Sometimes, we don't want to deal with the start of intervals (like the start of the day or an hour) but window intervals. 
 Let's say we want to understand the total hits for a window, not based on days but on a 24-hour period offset from 6 pm. 
 
-We can use the [`date_diff()`](/docs/sql-reference/functions/date-time-functions#date_diff) function to calculate the difference between a reference time and each record's time. 
+We can use the [`date_diff()`](/docs/sql-reference/functions/date-time-functions#timeDiff) function to calculate the difference between a reference time and each record's time. 
 In this case, the `day` column will represent the difference in days (e.g., 1 day ago, 2 days ago, etc.):
 
 ```sql
diff --git a/docs/whats-new/changelog/2023.md b/docs/whats-new/changelog/2023.md
@@ -1900,7 +1900,7 @@ description: 'Changelog for 2023'
 * Functions "multi[Fuzzy]Match(Any|AnyIndex|AllIndices}" now reject regexes which will likely evaluate very slowly in vectorscan. [#46167](https://github.com/ClickHouse/ClickHouse/pull/46167) ([Robert Schulze](https://github.com/rschu1ze)).
 * When `insert_null_as_default` is enabled and column doesn't have defined default value, the default of column type will be used. Also this PR fixes using default values on nulls in case of LowCardinality columns. [#46171](https://github.com/ClickHouse/ClickHouse/pull/46171) ([Kruglov Pavel](https://github.com/Avogar)).
 * Prefer explicitly defined access keys for S3 clients. If `use_environment_credentials` is set to `true`, and the user has provided the access key through query or config, they will be used instead of the ones from the environment variable. [#46191](https://github.com/ClickHouse/ClickHouse/pull/46191) ([Antonio Andelic](https://github.com/antonio2368)).
-* Add an alias "DATE_FORMAT()" for function "formatDateTime()" to improve compatibility with MySQL's SQL dialect, extend function `formatDateTime` with substitutions "a", "b", "c", "h", "i", "k", "l" "r", "s", "W". ### Documentation entry for user-facing changes User-readable short description: `DATE_FORMAT` is an alias of `formatDateTime`. Formats a Time according to the given Format string. Format is a constant expression, so you cannot have multiple formats for a single result column. (Provide link to [formatDateTime](/sql-reference/functions/date-time-functions/#formatdatetime)). [#46302](https://github.com/ClickHouse/ClickHouse/pull/46302) ([Jake Bamrah](https://github.com/JakeBamrah)).
+* Add an alias "DATE_FORMAT()" for function "formatDateTime()" to improve compatibility with MySQL's SQL dialect, extend function `formatDateTime` with substitutions "a", "b", "c", "h", "i", "k", "l" "r", "s", "W". ### Documentation entry for user-facing changes User-readable short description: `DATE_FORMAT` is an alias of `formatDateTime`. Formats a Time according to the given Format string. Format is a constant expression, so you cannot have multiple formats for a single result column. (Provide link to [formatDateTime](/sql-reference/functions/date-time-functions/#formatDateTime)). [#46302](https://github.com/ClickHouse/ClickHouse/pull/46302) ([Jake Bamrah](https://github.com/JakeBamrah)).
 * Add `ProfileEvents` and `CurrentMetrics` about the callback tasks for parallel replicas (`s3Cluster` and `MergeTree` tables). [#46313](https://github.com/ClickHouse/ClickHouse/pull/46313) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
 * Add support for `DELETE` and `UPDATE` for tables using `KeeperMap` storage engine. [#46330](https://github.com/ClickHouse/ClickHouse/pull/46330) ([Antonio Andelic](https://github.com/antonio2368)).
 * Allow writing RENAME queries with query parameters. Resolves [#45778](https://github.com/ClickHouse/ClickHouse/issues/45778). [#46407](https://github.com/ClickHouse/ClickHouse/pull/46407) ([Nikolay Degterinsky](https://github.com/evillique)).
