more spelling fixes!

Author: gingerwizard

docs/en/chdb/guides/jupysql.md

@@ -3,7 +3,7 @@ title: JupySQL and chDB
 sidebar_label: JupySQL
 slug: /en/chdb/guides/jupysql
 description: How to install chDB for Bun
-keywords: [chdb, jupysql]
+keywords: [chdb, JupySQL]
 ---
 
 [JupySQL](https://jupysql.ploomber.io/en/latest/quick-start.html) is a Python library that lets you run SQL in Jupyter notebooks and the IPython shell.
@@ -65,7 +65,7 @@ for file in files:
 
 ## Configuring chDB and JupySQL
 
-Next, let's import chDB's `dbapi` module:
+Next, let's import the `dbapi` module for chDB:
 
 ```python
 from chdb import dbapi
@@ -168,7 +168,7 @@ The default database doesn't persist data on disk, so we need to create another
 %sql CREATE DATABASE atp
 ```
 
-And now we're going to create a table called `rankings` whos schema will be derived from the structure of the data in the CSV files:
+And now we're going to create a table called `rankings` whose schema will be derived from the structure of the data in the CSV files:
 
 ```python
 %%sql

docs/en/chdb/guides/query-remote-clickhouse.md

@@ -150,7 +150,7 @@ df.head(n=5)
 4  2018-03-02         5      23842
 ```
 
-We can then compute the ratio of Open AI downloads to scikit-learn downloads like this:
+We can then compute the ratio of Open AI downloads to `scikit-learn` downloads like this:
 
 ```python
 df['ratio'] = df['y_openai'] / df['y_sklearn']

docs/en/chdb/install/python.md

@@ -165,7 +165,7 @@ Some notes on the chDB Python UDF (User Defined Function) decorator.
         import json
         ...
     ```
-6. The Python interpertor used is the same as the one used to run the script. You can get it from `sys.executable`.
+6. The Python interpreter used is the same as the one used to run the script. You can get it from `sys.executable`.
 
 see also: [test_udf.py](https://github.com/chdb-io/chdb/blob/main/tests/test_udf.py).
 

docs/en/chdb/reference/data-formats.md

@@ -13,7 +13,7 @@ Output formats are used to arrange the results of a `SELECT`, and to perform `IN
 As well as the data formats that ClickHouse supports, chDB also supports:
 
 - `ArrowTable` as an output format, the type is Python `pyarrow.Table`
-- `DataFrame` as an input and output format, the type is Python `pandas.DataFrame`. For examples, see [test_joindf.py](https://github.com/chdb-io/chdb/blob/main/tests/test_joindf.py)
+- `DataFrame` as an input and output format, the type is Python `pandas.DataFrame`. For examples, see [`test_joindf.py`](https://github.com/chdb-io/chdb/blob/main/tests/test_joindf.py)
 - `Debug` as ab output (as an alias of `CSV`), but with enabled debug verbose output from ClickHouse.
 
 The supported data formats from ClickHouse are:

docs/en/cloud/bestpractices/asyncinserts.md

@@ -20,7 +20,7 @@ There are two possible conditions that can cause ClickHouse to flush the buffer
 - buffer size has reached N bytes in size (N is configurable via [async_insert_max_data_size](/docs/en/operations/settings/settings.md/#async_insert_max_data_size))
 - at least N ms has passed since the last buffer flush (N is configurable via [async_insert_busy_timeout_max_ms](/docs/en/operations/settings/settings.md/#async_insert_busy_timeout_max_ms))
 
-Everytime any of the conditions above are met, ClickHouse will flush its in-memory buffer to disk.
+Any time any of the conditions above are met, ClickHouse will flush its in-memory buffer to disk.
 
 :::note
 Your data is available for read queries once the data is written to a part on storage. Keep this in mind for when you want to modify the `async_insert_busy_timeout_ms` (set as 1 second by default) or the `async_insert_max_data_size` (set as 10 MiB by default) settings.

docs/en/cloud/changelogs/changelog-24-10.md

@@ -9,7 +9,7 @@ Relevant changes for ClickHouse Cloud services based on the v24.10 release.
 
 ## Backward Incompatible Change
 - Allow to write `SETTINGS` before `FORMAT` in a chain of queries with `UNION` when subqueries are inside parentheses. This closes [#39712](https://github.com/ClickHouse/ClickHouse/issues/39712). Change the behavior when a query has the SETTINGS clause specified twice in a sequence. The closest SETTINGS clause will have a preference for the corresponding subquery. In the previous versions, the outermost SETTINGS clause could take a preference over the inner one. [#60197](https://github.com/ClickHouse/ClickHouse/pull/60197)[#68614](https://github.com/ClickHouse/ClickHouse/pull/68614) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
-- Reimplement Dynamic type. Now when the limit of dynamic data types is reached new types are not casted to String but stored in a special data structure in binary format with binary encoded data type. Now any type ever inserted into Dynamic column can be read from it as subcolumn. [#68132](https://github.com/ClickHouse/ClickHouse/pull/68132) ([Pavel Kruglov](https://github.com/Avogar)).
+- Reimplement Dynamic type. Now when the limit of dynamic data types is reached new types are not cast to String but stored in a special data structure in binary format with binary encoded data type. Now any type ever inserted into Dynamic column can be read from it as subcolumn. [#68132](https://github.com/ClickHouse/ClickHouse/pull/68132) ([Pavel Kruglov](https://github.com/Avogar)).
 - Expressions like `a[b].c` are supported for named tuples, as well as named subscripts from arbitrary expressions, e.g., `expr().name`. This is useful for processing JSON. This closes [#54965](https://github.com/ClickHouse/ClickHouse/issues/54965). In previous versions, an expression of form `expr().name` was parsed as `tupleElement(expr(), name)`, and the query analyzer was searching for a column `name` rather than for the corresponding tuple element; while in the new version, it is changed to `tupleElement(expr(), 'name')`. In most cases, the previous version was not working, but it is possible to imagine a very unusual scenario when this change could lead to incompatibility: if you stored names of tuple elements in a column or an alias, that was named differently than the tuple element's name: `SELECT 'b' AS a, CAST([tuple(123)] AS 'Array(Tuple(b UInt8))') AS t, t[1].a`. It is very unlikely that you used such queries, but we still have to mark this change as potentially backward incompatible. [#68435](https://github.com/ClickHouse/ClickHouse/pull/68435) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
 - When the setting `print_pretty_type_names` is enabled, it will print `Tuple` data type in a pretty form in `SHOW CREATE TABLE` statements, `formatQuery` function, and in the interactive mode in `clickhouse-client` and `clickhouse-local`. In previous versions, this setting was only applied to `DESCRIBE` queries and `toTypeName`. This closes [#65753](https://github.com/ClickHouse/ClickHouse/issues/65753). [#68492](https://github.com/ClickHouse/ClickHouse/pull/68492) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
 - Reordering of filter conditions from `[PRE]WHERE` clause is now allowed by default. It could be disabled by setting `allow_reorder_prewhere_conditions` to `false`. [#70657](https://github.com/ClickHouse/ClickHouse/pull/70657) ([Nikita Taranov](https://github.com/nickitat)).

docs/en/cloud/get-started/query-insights.md

@@ -27,13 +27,13 @@ Beneath the top-level metrics, a table displays query log entries (grouped by no
 
 ![Query Insights UI Recent Queries Table](@site/docs/en/cloud/images/sqlconsole/insights_recent.png)
 
-Recent queries can be filtered and sorted by any available field. The table can also be configured to display orhide additional fields such as tables, p90, and p99 latencies.
+Recent queries can be filtered and sorted by any available field. The table can also be configured to display or hide additional fields such as tables, p90, and p99 latencies.
 
 ## Query drill-down
 
 Selecting a query from the recent queries table will open a flyout containing metrics and information specific to the selected query:
 
-![Query Insights UI Query Drilldown](@site/docs/en/cloud/images/sqlconsole/insights_drilldown.png)
+![Query Insights UI Query Drill down](@site/docs/en/cloud/images/sqlconsole/insights_drilldown.png)
 
 As we can see from the flyout, this particular query has been run more than 3000 times in the last 24 hours. All metrics in the **Query info** tab are aggregated metrics, but we can also view metrics from individual runs by selecting the **Query history** tab:
 

docs/en/cloud/manage/backups.md

@@ -17,7 +17,7 @@ Database backups provide a safety net by ensuring that if data is lost for any u
 
 ## How backups work in ClickHouse Cloud
 
-ClickHouse Cloud backups are a combination of "full" and "incremental" backups that constitute a backup chain. The chain starts with a full backup, and incremental backups are then taken over the next several scheduled time periods to create a sequence of backups. Once a backup chain reaches a certain length, a new chain is started. This entire chain of backups can then be utilized to restore data to a new service if needed. Once all backups included in a specific chain are past the retention timeframe set for the service (more on retention below), the chain is discarded.
+ClickHouse Cloud backups are a combination of "full" and "incremental" backups that constitute a backup chain. The chain starts with a full backup, and incremental backups are then taken over the next several scheduled time periods to create a sequence of backups. Once a backup chain reaches a certain length, a new chain is started. This entire chain of backups can then be utilized to restore data to a new service if needed. Once all backups included in a specific chain are past the retention time frame set for the service (more on retention below), the chain is discarded.
 
 In the screenshot below, the solid line squares show full backups and the dotted line squares show incremental backups. The solid line rectangle around the squares denotes the retention period and the backups that are visible to the end user, which can be used for a backup restore. In the scenario below, backups are being taken every 24 hours and are retained for 2 days.
 

docs/en/cloud/manage/billing/marketplace/gcp-marketplace-payg.md

@@ -41,7 +41,7 @@ Get started with ClickHouse Cloud on the [GCP Marketplace](https://console.cloud
 3. On the next screen, configure the subscription:
 
 - The plan will default to "ClickHouse Cloud"
-- Subscription timeframe is "Monthly"
+- Subscription time frame is "Monthly"
 - Choose the appropriate billing account
 - Accept the terms and click **Subscribe**
 

docs/en/cloud/reference/byoc.md

@@ -97,7 +97,7 @@ This section is focused on different network traffic to and from the customer BY
 *Inbound, Public (can be Private)*
 
 The Istio ingress gateway terminates TLS. The certificate is provisioned by CertManager with Let's Encrypt and is stored as a secret within the EKS cluster. Traffic between Istio and ClickHouse is [encrypted by AWS](https://docs.aws.amazon.com/whitepapers/latest/logical-separation/encrypting-data-at-rest-and--in-transit.html#:~:text=All%20network%20traffic%20between%20AWS,supported%20Amazon%20EC2%20instance%20types) as they are in the same VPC.
-By default, ingress is available to the public internet with IP allowlist filtering. The customer has the option to set up VPC peering to make it private and disable public connections. We highly recommend you configure an [IP filter](/en/cloud/security/setting-ip-filters) to restrict access.
+By default, ingress is available to the public internet with IP allow list filtering. The customer has the option to set up VPC peering to make it private and disable public connections. We highly recommend you configure an [IP filter](/en/cloud/security/setting-ip-filters) to restrict access.
 
 **Troubleshooting access**