replace dots

Author: Blargian

docs/cloud/bestpractices/avoidmutations.md

@@ -5,7 +5,7 @@ title: 'Avoid Mutations'
 description: 'Page describing why you should avoid mutations, ALTER queries that manipulate table data through deletion or updates'
 ---
 
-Mutations refers to [ALTER](/sql-reference/statements/alter/) queries that manipulate table data through deletion or updates. Most notably they are queries like ALTER TABLE … DELETE, UPDATE, etc. Performing such queries will produce new mutated versions of the data parts. This means that such statements would trigger a rewrite of whole data parts for all data that was inserted before the mutation, translating to a large amount of write requests.
+Mutations refers to [ALTER](/sql-reference/statements/alter/) queries that manipulate table data through deletion or updates. Most notably they are queries like ALTER TABLE ... DELETE, UPDATE, etc. Performing such queries will produce new mutated versions of the data parts. This means that such statements would trigger a rewrite of whole data parts for all data that was inserted before the mutation, translating to a large amount of write requests.
 
 For updates, you can avoid these large amounts of write requests by using specialised table engines like [ReplacingMergeTree](/engines/table-engines/mergetree-family/replacingmergetree.md) or [CollapsingMergeTree](/engines/table-engines/mergetree-family/collapsingmergetree.md) instead of the default MergeTree table engine.
 

docs/cloud/reference/changelog.md

@@ -1043,7 +1043,7 @@ This release brings an API for retrieving cloud endpoints, an advanced scaling c
   - Fixed server-side parameter binding of the NULL value for Nullable types
 
 ### Bug fixes {#bug-fixes-1}
-* Fixed behavior where running `INSERT INTO … SELECT …` from the SQL console incorrectly applied the same row limit as select queries
+* Fixed behavior where running `INSERT INTO ... SELECT ...` from the SQL console incorrectly applied the same row limit as select queries
 
 
 ## March 23, 2023 {#march-23-2023}

docs/cloud/reference/cloud-compatibility.md

@@ -26,7 +26,7 @@ ClickHouse Cloud provides access to a curated set of capabilities in the open so
 ### DDL syntax {#ddl-syntax}
 For the most part, the DDL syntax of ClickHouse Cloud should match what is available in self-managed installs. A few notable exceptions:
   - Support for `CREATE AS SELECT`, which is currently not available. As a workaround, we suggest using `CREATE ... EMPTY ... AS SELECT` and then inserting into that table (see [this blog](https://clickhouse.com/blog/getting-data-into-clickhouse-part-1) for an example).
-  - Some experimental syntax may be disabled, for instance, `ALTER TABLE … MODIFY QUERY` statement.
+  - Some experimental syntax may be disabled, for instance, `ALTER TABLE ... MODIFY QUERY` statement.
   - Some introspection functionality may be disabled for security purposes, for example, the `addressToLine` SQL function.
   - Do not use `ON CLUSTER` parameters in ClickHouse Cloud - these are not needed. While these are mostly no-op functions, they can still cause an error if you are trying to use [macros](/operations/server-configuration-parameters/settings#macros). Macros often do not work and are not needed in ClickHouse Cloud.
 

docs/concepts/olap.md

@@ -11,13 +11,13 @@ slug: /concepts/olap
 [OLAP](https://en.wikipedia.org/wiki/Online_analytical_processing) stands for Online Analytical Processing. It is a broad term that can be looked at from two perspectives: technical and business. At the highest level, you can just read these words backward:
 
 Processing
-:   Some source data is processed…
+:   Some source data is processed...
 
 Analytical
-:   …to produce some analytical reports and insights…
+:   ...to produce some analytical reports and insights...
 
 Online
-:   …in real-time.
+:   ...in real-time.
 
 ## OLAP from the Business Perspective {#olap-from-the-business-perspective}
 

docs/concepts/why-clickhouse-is-so-fast.md

@@ -103,7 +103,7 @@ If a single node becomes too small to hold the table data, further nodes can be
 
 <iframe width="768" height="432" src="https://www.youtube.com/embed/dccGLSuYWy0?si=rQ-Jp-z5Ik_-Rb8S" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
-> **"ClickHouse is a freak system - you guys have 20 versions of a hash table. You guys have all these amazing things where most systems will have one hash table** **…** **ClickHouse has this amazing performance because it has all these specialized components"** [Andy Pavlo, Database Professor at CMU](https://www.youtube.com/watch?v=Vy2t_wZx4Is&t=3579s)
+> **"ClickHouse is a freak system - you guys have 20 versions of a hash table. You guys have all these amazing things where most systems will have one hash table** **...** **ClickHouse has this amazing performance because it has all these specialized components"** [Andy Pavlo, Database Professor at CMU](https://www.youtube.com/watch?v=Vy2t_wZx4Is&t=3579s)
 
 What sets ClickHouse [apart](https://www.youtube.com/watch?v=CAS2otEoerM) is its meticulous attention to low-level optimization. Building a database that simply works is one thing, but engineering it to deliver speed across diverse query types, data structures, distributions, and index configurations is where the "[freak system](https://youtu.be/Vy2t_wZx4Is?si=K7MyzsBBxgmGcuGU&t=3579)" artistry shines.
 

docs/dictionary/index.md

@@ -279,7 +279,7 @@ CREATE TABLE posts_with_location
 (
     `Id` UInt32,
     `PostTypeId` Enum8('Question' = 1, 'Answer' = 2, 'Wiki' = 3, 'TagWikiExcerpt' = 4, 'TagWiki' = 5, 'ModeratorNomination' = 6, 'WikiPlaceholder' = 7, 'PrivilegeWiki' = 8),
-     …
+     ...
     `Location` MATERIALIZED dictGet(users_dict, 'Location', OwnerUserId::'UInt64')
 )
 ENGINE = MergeTree

docs/faq/general/olap.md

@@ -11,13 +11,13 @@ description: 'An explainer on what Online Analytical Processing is'
 [OLAP](https://en.wikipedia.org/wiki/Online_analytical_processing) stands for Online Analytical Processing. It is a broad term that can be looked at from two perspectives: technical and business. But at the very high level, you can just read these words backward:
 
 Processing
-:   Some source data is processed…
+:   Some source data is processed...
 
 Analytical
-:   …to produce some analytical reports and insights…
+:   ...to produce some analytical reports and insights...
 
 Online
-:   …in real-time.
+:   ...in real-time.
 
 ## OLAP from the Business Perspective {#olap-from-the-business-perspective}
 

docs/guides/developer/deduplication.md

@@ -103,7 +103,7 @@ FINAL
 The result only has 2 rows, and the last row inserted is the row that gets returned.
 
 :::note
-Using `FINAL` works OK if you have a small amount of data. If you are dealing with a large amount of data, using `FINAL` is probably not the best option. Let's discuss a better option for finding the latest value of a column…
+Using `FINAL` works OK if you have a small amount of data. If you are dealing with a large amount of data, using `FINAL` is probably not the best option. Let's discuss a better option for finding the latest value of a column...
 :::
 
 ### Avoiding FINAL {#avoiding-final}
@@ -164,7 +164,7 @@ Our [Deleting and Updating Data training module](https://learn.clickhouse.com/vi
 
 ## Using CollapsingMergeTree for Updating Columns Frequently {#using-collapsingmergetree-for-updating-columns-frequently}
 
-Updating a column involves deleting an existing row and replacing it with new values. As you have already seen, this type of mutation in ClickHouse happens _eventually_ - during merges. If you have a lot of rows to update, it can actually be more efficient to avoid `ALTER TABLE..UPDATE` and instead just insert the new data alongside the existing data. We could add a column that denotes whether or not the data is stale or new… and there is actually a table engine that already implements this behavior very nicely, especially considering that it deletes the stale data automatically for you. Let's see how it works.
+Updating a column involves deleting an existing row and replacing it with new values. As you have already seen, this type of mutation in ClickHouse happens _eventually_ - during merges. If you have a lot of rows to update, it can actually be more efficient to avoid `ALTER TABLE..UPDATE` and instead just insert the new data alongside the existing data. We could add a column that denotes whether or not the data is stale or new... and there is actually a table engine that already implements this behavior very nicely, especially considering that it deletes the stale data automatically for you. Let's see how it works.
 
 Suppose we track the number of views that a Hacker News comment has using an external system and every few hours, we push the data into ClickHouse. We want the old rows deleted and the new rows to represent the new state of each Hacker News comment. We can use a `CollapsingMergeTree` to implement this behavior.
 

docs/guides/developer/ttl.md

@@ -203,7 +203,7 @@ FROM system.disks
 └─────────────┴────────────────┴──────────────┴──────────────┘
 ```
 
-3. And…let's verify the volumes:
+3. And...let's verify the volumes:
 
 ```sql
 SELECT

docs/integrations/data-ingestion/data-formats/csv-tsv.md

@@ -217,7 +217,7 @@ FORMAT CSVWithNames
 
 ### Saving exported data to a CSV file {#saving-exported-data-to-a-csv-file}
 
-To save exported data to a file, we can use the [INTO…OUTFILE](/sql-reference/statements/select/into-outfile.md) clause:
+To save exported data to a file, we can use the [INTO...OUTFILE](/sql-reference/statements/select/into-outfile.md) clause:
 
 ```sql
 SELECT *