Fix formatting and grammar in ReplacingMergeTree guide

docs/guides/developer/replacing-merge-tree.md

@@ -10,7 +10,7 @@
 import postgres_replacingmergetree from '@site/static/images/migrations/postgres-replacingmergetree.png';
 import Image from '@theme/IdealImage';
 
-While transactional databases are optimized for transactional update and delete workloads, OLAP databases offer reduced guarantees for such operations. Instead, they optimize for immutable data inserted in batches for the benefit of significantly faster analytical queries. While ClickHouse offers update operations through mutations, as well as a lightweight means of deleting rows, its column-orientated structure means these operations should be scheduled with care, as described above. These operations are handled asynchronously, processed with a single thread, and require (in the case of updates) data to be rewritten on disk. They should thus not be used for high numbers of small changes.
+While transactional databases are optimized for transactional update and delete workloads, OLAP databases offer reduced guarantees for such operations. Instead, they optimize for immutable data inserted in batches for the benefit of significantly faster analytical queries. While ClickHouse offers update operations through mutations, as well as a lightweight means of deleting rows, its column-oriented structure means these operations should be scheduled with care, as described above. These operations are handled asynchronously, processed with a single thread, and require (in the case of updates) data to be rewritten on disk. They should thus not be used for high numbers of small changes.
 In order to process a stream of update and delete rows while avoiding the above usage patterns, we can use the ClickHouse table engine ReplacingMergeTree.
 
 ## Automatic upserts of inserted rows {#automatic-upserts-of-inserted-rows}
@@ -201,7 +201,7 @@
 0 rows in set. Elapsed: 0.166 sec. Processed 135.53 thousand rows, 212.65 MB (816.30 thousand rows/s., 1.28 GB/s.)
 ```
 
-The result of the above operations will be 16,000 rows i.e. 10,000 + 5000 + 1000. The correct total here is, reality we should have only 1000 rows less than our original total i.e. 10,000 - 1000 = 9000.
+The result of the above operations will be 16,000 rows i.e. 10,000 + 5000 + 1000. In reality, we should have only 1000 fewer rows than our original total i.e. 10,000 - 1000 = 9000.
 
 ```sql
 SELECT count()
@@ -215,7 +215,7 @@
 1 row in set. Elapsed: 0.002 sec.
 ```
 
-Your results will vary here depending on the merges that have occurred. We can see the total here is different as we have duplicate rows. Applying `FINAL` to the table delivers the correct result.
+Your results will vary depending on the merges that have occurred. We can see the total differs because of duplicate rows. Applying `FINAL` to the table delivers the correct result.
 
 ```sql
 SELECT count()
@@ -346,7 +346,7 @@
 
 ### Merging behavior on large parts {#merging-behavior-on-large-parts}
 
-The ReplacingMergeTree engine in ClickHouse is optimized for managing duplicate rows by merging data parts, keeping only the latest version of each row based on a specified unique key. However, when a merged part reaches the max_bytes_to_merge_at_max_space_in_pool threshold, it will no longer be selected for further merging, even if min_age_to_force_merge_seconds is set. As a result, automatic merges can no longer be relied upon to remove duplicates that may accumulate with ongoing data insertion.
+The ReplacingMergeTree engine in ClickHouse is optimized for managing duplicate rows by merging data parts, keeping only the latest version of each row based on a specified unique key. However, when a merged part reaches the `max_bytes_to_merge_at_max_space_in_pool` threshold, it will no longer be selected for further merging, even if `min_age_to_force_merge_seconds` is set. As a result, automatic merges can no longer be relied upon to remove duplicates that may accumulate with ongoing data insertion.
 
 To address this, you can invoke `OPTIMIZE FINAL` to manually merge parts and remove duplicates. Unlike automatic merges, `OPTIMIZE FINAL` bypasses the `max_bytes_to_merge_at_max_space_in_pool` threshold, merging parts based solely on available resources, particularly disk space, until a single part remains in each partition. However, this approach can be memory-intensive on large tables and may require repeated execution as new data is added.
 
@@ -358,18 +358,18 @@
 
 ### Tuning merges for better query performance {#tuning-merges-for-better-query-performance}
 
-By default, min_age_to_force_merge_seconds and min_age_to_force_merge_on_partition_only are set to 0 and false, respectively, disabling these features. In this configuration, ClickHouse will apply standard merging behavior without forcing merges based on partition age.
+By default, `min_age_to_force_merge_seconds` and `min_age_to_force_merge_on_partition_only` are set to 0 and false, respectively, disabling these features. In this configuration, ClickHouse will apply standard merging behavior without forcing merges based on partition age.
 
-If a value for min_age_to_force_merge_seconds is specified, ClickHouse will ignore normal merging heuristics for parts older than the specified period. While this is generally only effective if the goal is to minimize the total number of parts, it can improve query performance in ReplacingMergeTree by reducing the number of parts needing merging at query time.
+If a value for `min_age_to_force_merge_seconds` is specified, ClickHouse will ignore normal merging heuristics for parts older than the specified period. While this is generally only effective if the goal is to minimize the total number of parts, it can improve query performance in ReplacingMergeTree by reducing the number of parts needing merging at query time.
 
-This behavior can be further tuned by setting min_age_to_force_merge_on_partition_only=true, requiring all parts in the partition to be older than min_age_to_force_merge_seconds for aggressive merging. This configuration allows older partitions to merge down to a single part over time, which consolidates data and maintains query performance.
+This behavior can be further tuned by setting `min_age_to_force_merge_on_partition_only=true`, requiring all parts in the partition to be older than `min_age_to_force_merge_seconds` for aggressive merging. This configuration allows older partitions to merge down to a single part over time, which consolidates data and maintains query performance.
 
 ### Recommended settings {#recommended-settings}
 
 :::warning
 Tuning merge behavior is an advanced operation. We recommend consulting with ClickHouse support before enabling these settings in production workloads.
 :::
 
-In most cases, setting min_age_to_force_merge_seconds to a low value—significantly less than the partition period—is preferred. This minimizes the number of parts and prevents unnecessary merging at query time with the FINAL operator.
+In most cases, setting `min_age_to_force_merge_seconds` to a low value—significantly less than the partition period—is preferred. This minimizes the number of parts and prevents unnecessary merging at query time with the `FINAL` operator.
 
-For example, consider a monthly partition that has already been merged into a single part. If a small, stray insert creates a new part within this partition, query performance can suffer because ClickHouse must read multiple parts until the merge completes. Setting min_age_to_force_merge_seconds can ensure these parts are merged aggressively, preventing a degradation in query performance.
+For example, consider a monthly partition that has already been merged into a single part. If a small, stray insert creates a new part within this partition, query performance can suffer because ClickHouse must read multiple parts until the merge completes. Setting `min_age_to_force_merge_seconds` can ensure these parts are merged aggressively, preventing a degradation in query performance.