Merge branch 'main' into cloud-disaster-recovery

Author: dhtclk

docs/_snippets/_service_actions_menu.md

@@ -1,6 +1,6 @@
 import Image from '@theme/IdealImage';
 import cloud_service_action_menu from '@site/static/images/_snippets/cloud-service-actions-menu.png';
 
-Select your service, followed by `Data souces` -> `Predefined sample data`.
+Select your service, followed by `Data sources` -> `Predefined sample data`.
 
 <Image size="md" img={cloud_service_action_menu} alt="ClickHouse Cloud service Actions menu showing Data sources and Predefined sample data options" border />

docs/best-practices/partitioning_keys.mdx

@@ -23,10 +23,10 @@ For example, consider the following UK price paid dataset table with a partition
 ```sql
 CREATE TABLE uk.uk_price_paid_simple_partitioned
 (
-    date Date,
- town LowCardinality(String),
- street LowCardinality(String),
- price UInt32
+  date Date,
+  town LowCardinality(String),
+  street LowCardinality(String),
+  price UInt32
 )
 ENGINE = MergeTree
 ORDER BY (town, street)

docs/chdb/index.md

@@ -37,7 +37,7 @@ chDB has the following language bindings:
 ## How do I get started? {#how-do-i-get-started}
 
 * If you're using [Go](install/go.md), [Rust](install/rust.md), [NodeJS](install/nodejs.md), [Bun](install/bun.md) or [C and C++](install/c.md), take a look at the corresponding language pages.
-* If you're using Python, see the [getting started developer guide](getting-started.md). There are also guides showing how to do common tasks like:
+* If you're using Python, see the [getting started developer guide](getting-started.md) or the [chDB on-demand course](https://learn.clickhouse.com/user_catalog_class/show/1901178). There are also guides showing how to do common tasks like:
   * [JupySQL](guides/jupysql.md)
   * [Querying Pandas](guides/querying-pandas.md)
   * [Querying Apache Arrow](guides/querying-apache-arrow.md)
@@ -68,6 +68,7 @@ chDB delivers exceptional performance across different scenarios:
 
 - Read the full story about the birth of the chDB project on [blog](https://clickhouse.com/blog/chdb-embedded-clickhouse-rocket-engine-on-a-bicycle)
 - Read about chDB and its use cases on the [Blog](https://clickhouse.com/blog/welcome-chdb-to-clickhouse)
+- Take the [chDB on-demand course](https://learn.clickhouse.com/user_catalog_class/show/1901178)
 - Discover chDB in your browser using [codapi examples](https://antonz.org/trying-chdb/)
 - More examples see (https://github.com/chdb-io/chdb/tree/main/examples)
 

docs/cloud/features/04_infrastructure/byoc.md

@@ -357,6 +357,7 @@ Contact support to schedule maintenance windows. Please expect a minimum of a we
 ## Observability {#observability}
 
 ### Built-in monitoring tools {#built-in-monitoring-tools}
+ClickHouse BYOC provides several approaches for various use cases.
 
 #### Observability dashboard {#observability-dashboard}
 
@@ -378,8 +379,20 @@ You can customize a dashboard using metrics from system tables like `system.metr
 
 <br />
 
+#### Access the BYOC Prometheus stack {#prometheus-access}
+ClickHouse BYOC deploys a Prometheus stack on your Kubernetes cluster. You may access and scrape the metrics from there and integrate them with your own monitoring stack.
+
+Contact ClickHouse support to enable the Private Load balancer and ask for the URL. Please note that this URL is only accessible via private network and does not support authentication
+
+**Sample URL**
+```bash
+https://prometheus-internal.<subdomain>.<region>.aws.clickhouse-byoc.com/query
+```
+
 #### Prometheus Integration {#prometheus-integration}
 
+**DEPRECATED: ** Please use the Prometheus stack integration in the above section instead. Besides the ClickHouse Server metrics, it provides more metrics including the K8S metrics and metrics from other services.
+
 ClickHouse Cloud provides a Prometheus endpoint that you can use to scrape metrics for monitoring. This allows for integration with tools like Grafana and Datadog for visualization.
 
 **Sample request via https endpoint /metrics_all**
@@ -419,8 +432,22 @@ ClickHouse_CustomMetric_TotalNumberOfErrors{hostname="c-jet-ax-16-server-43d5baj
 A ClickHouse username and password pair can be used for authentication. We recommend creating a dedicated user with minimal permissions for scraping metrics. At minimum, a `READ` permission is required on the `system.custom_metrics` table across replicas. For example:
 
 ```sql
-GRANT REMOTE ON *.* TO scraping_user
-GRANT SELECT ON system.custom_metrics TO scraping_user
+GRANT REMOTE ON *.* TO scrapping_user;
+GRANT SELECT ON system._custom_metrics_dictionary_custom_metrics_tables TO scrapping_user;
+GRANT SELECT ON system._custom_metrics_dictionary_database_replicated_recovery_time TO scrapping_user;
+GRANT SELECT ON system._custom_metrics_dictionary_failed_mutations TO scrapping_user;
+GRANT SELECT ON system._custom_metrics_dictionary_group TO scrapping_user;
+GRANT SELECT ON system._custom_metrics_dictionary_shared_catalog_recovery_time TO scrapping_user;
+GRANT SELECT ON system._custom_metrics_dictionary_table_read_only_duration_seconds TO scrapping_user;
+GRANT SELECT ON system._custom_metrics_view_error_metrics TO scrapping_user;
+GRANT SELECT ON system._custom_metrics_view_histograms TO scrapping_user;
+GRANT SELECT ON system._custom_metrics_view_metrics_and_events TO scrapping_user;
+GRANT SELECT(description, metric, value) ON system.asynchronous_metrics TO scrapping_user;
+GRANT SELECT ON system.custom_metrics TO scrapping_user;
+GRANT SELECT(name, value) ON system.errors TO scrapping_user;
+GRANT SELECT(description, event, value) ON system.events TO scrapping_user;
+GRANT SELECT(description, labels, metric, value) ON system.histogram_metrics TO scrapping_user;
+GRANT SELECT(description, metric, value) ON system.metrics TO scrapping_user;
 ```
 
 **Configuring Prometheus**

docs/cloud/features/05_admin_features/upgrades.md

@@ -30,7 +30,7 @@ When you create a service, the [`compatibility`](/operations/settings/settings#c
 
 The `compatibility` setting allows you to use default values of settings from previous versions. When your service is upgraded to a new version, the version specified for the `compatibility` setting does not change. This means that default values for settings that existed when you first created your service will not change (unless you have already overridden those default values, in which case they will persist after the upgrade).
 
-You cannot manage the `compatibility` setting for your service. You must [contact support](https://clickhouse.com/support/program) if you would like to change the version set for your `compatibility` setting.
+You cannot manage the service-level default `compatibility` setting for your service. You must [contact support](https://clickhouse.com/support/program) if you would like to change the version set for your service's default `compatibility` setting. However, you can override the `compatibility` setting at the user, role, profile, query, or session level using standard ClickHouse setting mechanisms such as `SET compatibility = '22.3'` in a session or `SETTINGS compatibility = '22.3'` in a query.
 
 ## Maintenance mode {#maintenance-mode}
 

docs/cloud/guides/security/cloud_access_management/saml-sso-setup.md

@@ -39,7 +39,7 @@ We recommend setting up a **direct link to your organization** in addition to yo
 
    1. Sign in to your [ClickHouse Cloud](https://console.clickhouse.cloud) organization.
 
-      <Image img={samlOrgId} size="md" alt="Organization ID" />
+      <Image img={samlOrgId} size="md" alt="Organization ID" force/>
       
    3. In the lower left corner, click on your organization name under **Organization**.
 
@@ -197,7 +197,7 @@ You will configure two App Integrations in Okta for each ClickHouse organization
 
    12. On the **Sign On** tab for your new app, click the **View SAML setup instructions** button. 
 
-         <Image img={samlOktaSetup} size="md" alt="Okta SAML Setup Instructions" />
+         <Image img={samlOktaSetup} size="md" alt="Okta SAML Setup Instructions" force/>
 
    13. Gather these three items and go to Submit a Support Case above to complete the process.
      - Identity Provider Single Sign-On URL
@@ -215,7 +215,7 @@ You will configure one SAML app in Google for each organization and must provide
 
    1. Go to your Google Admin console (admin.google.com).
 
-   <Image img={samlGoogleApp} size="md" alt="Google SAML App" />
+   <Image img={samlGoogleApp} size="md" alt="Google SAML App" force/>
 
    2. Click **Apps**, then **Web and mobile apps** on the left.
 
@@ -272,7 +272,7 @@ Azure (Microsoft) SAML may also be referred to as Azure Active Directory (AD) or
 
    5. Enter a name and select **Integrate any other application you don't find in the gallery (Non-gallery)**, then click **Create**.
 
-      <Image img={samlAzureApp} size="md" alt="Azure Non-Gallery App" />
+      <Image img={samlAzureApp} size="md" alt="Azure Non-Gallery App" force/>
 
    6. Click **Users and groups** on the left and assign users.
 
@@ -298,7 +298,7 @@ Azure (Microsoft) SAML may also be referred to as Azure Active Directory (AD) or
        | (A) email                            | Basic         | user.mail        |
        | (U) /identity/claims/name            | Omitted       | user.mail        |
 
-         <Image img={samlAzureClaims} size="md" alt="Attributes and Claims" />
+         <Image img={samlAzureClaims} size="md" alt="Attributes and Claims" force/>
 
    12. Gather these two items and go to Submit a Support Case above to complete the process:
      - Login URL
@@ -366,4 +366,3 @@ Security is our top priority when it comes to authentication. For this reason, w
 - **All users assigned to your app via your IdP must have the same email domain.** If you have vendors, contractors or consultants you would like to have access to your ClickHouse account, they must have an email address with the same domain (e.g. user@domain.com) as your employees.
 
 - **We do not automatically link SSO and non-SSO accounts.** You may see multiple accounts for your users in your ClickHouse user list even if they are using the same email address.
-      

docs/deployment-guides/replication-sharding-examples/01_1_shard_2_replicas.md

@@ -66,7 +66,7 @@ for i in {01..02}; do
 done
 ```
 
-Add the following `docker-compose.yml` file to the `clickhouse-cluster` directory:
+Add the following `docker-compose.yml` file to the `cluster_1S_2R` directory:
 
 ```yaml title="docker-compose.yml"
 version: '3.8'

docs/dictionary/index.md

@@ -208,7 +208,7 @@ LIMIT 5
 FORMAT PrettyCompactMonoBlock
 
 ┌───────Id─┬─Title─────────────────────────────────────────────────────────┬─Location──────────────┐
-│ 52296928 │ Comparision between two Strings in ClickHouse                 │ Spain                 │
+│ 52296928 │ Comparison between two Strings in ClickHouse                  │ Spain                 │
 │ 52345137 │ How to use a file to migrate data from mysql to a clickhouse? │ 中国江苏省Nanjing Shi   │
 │ 61452077 │ How to change PARTITION in clickhouse                         │ Guangzhou, 广东省中国   │
 │ 55608325 │ Clickhouse select last record without max() on all table      │ Moscow, Russia        │

docs/getting-started/example-datasets/dbpedia.md

@@ -116,7 +116,7 @@ LIMIT 20
 ```
 
 Note down the query latency so that we can compare it with the query latency of ANN (using vector index).
-Also record the query latency with cold OS file cache and with `max_theads=1` to recognize the real compute
+Also record the query latency with cold OS file cache and with `max_threads=1` to recognize the real compute
 usage and storage bandwidth usage (extrapolate it to a production dataset with millions of vectors!)
 
 ## Build a vector similarity index {#build-vector-similarity-index}

docs/getting-started/example-datasets/laion.md

@@ -53,7 +53,7 @@ data = data[['url', 'caption', 'NSFW', 'similarity', "image_embedding", "text_em
 data['image_embedding'] = data['image_embedding'].apply(lambda x: x.tolist())
 data['text_embedding'] = data['text_embedding'].apply(lambda x: x.tolist())
 
-# this small hack is needed becase caption sometimes contains all kind of quotes
+# this small hack is needed because caption sometimes contains all kind of quotes
 data['caption'] = data['caption'].apply(lambda x: x.replace("'", " ").replace('"', " "))
 
 # export data as CSV file
@@ -132,7 +132,7 @@ For now, we can run the embedding of a random LEGO set picture as `target`.
 10 rows in set. Elapsed: 4.605 sec. Processed 100.38 million rows, 309.98 GB (21.80 million rows/s., 67.31 GB/s.)
 ```
 
-## Run an approximate vector similarity search with a vector simialrity index {#run-an-approximate-vector-similarity-search-with-a-vector-similarity-index}
+## Run an approximate vector similarity search with a vector similarity index {#run-an-approximate-vector-similarity-search-with-a-vector-similarity-index}
 
 Let's now define two vector similarity indexes on the table.