diff --git a/contribute/style-guide.md b/contribute/style-guide.md
index 3d8fc8be8dc..37ccb071d90 100644
--- a/contribute/style-guide.md
+++ b/contribute/style-guide.md
@@ -340,7 +340,7 @@ When using URL parameters to control which version of documentation is displayed
there are conventions to follow for reliable functionality.
Here's how the `?v=v08` parameter relates to the snippet selection:
-#### How It Works
+#### How it works
The URL parameter acts as a selector that matches against the `version` property
in your component configuration. For example:
diff --git a/docs/_snippets/_GCS_authentication_and_bucket.md b/docs/_snippets/_GCS_authentication_and_bucket.md
index 6e3a45cd436..546666a8049 100644
--- a/docs/_snippets/_GCS_authentication_and_bucket.md
+++ b/docs/_snippets/_GCS_authentication_and_bucket.md
@@ -19,7 +19,7 @@ import Image from '@theme/IdealImage';
FINAL;
**you should avoid this operation in most cases** as it initiates resource intensive operations which may impact cluster performance.
-## Why Avoid? {#why-avoid}
+## Why avoid? {#why-avoid}
### It's expensive {#its-expensive}
diff --git a/docs/best-practices/index.md b/docs/best-practices/index.md
index 4f672250e63..5a3ae78ab5f 100644
--- a/docs/best-practices/index.md
+++ b/docs/best-practices/index.md
@@ -1,6 +1,6 @@
---
slug: /best-practices
-keywords: ['Cloud', 'Primary key', 'Ordering key', 'Materialized Views', 'Best Practices', 'Bulk Inserts', 'Asynchronous Inserts', 'Avoid Mutations', 'Avoid Nullable Columns', 'Avoid Optimize Final', 'Partitioning Key']
+keywords: ['Cloud', 'Primary key', 'Ordering key', 'Materialized Views', 'Best Practices', 'Bulk Inserts', 'Asynchronous Inserts', 'Avoid Mutations', 'Avoid nullable Columns', 'Avoid Optimize Final', 'Partitioning Key']
title: 'Overview'
hide_title: true
description: 'Landing page for Best Practices section in ClickHouse'
diff --git a/docs/best-practices/select_data_type.md b/docs/best-practices/select_data_type.md
index d9191294453..fa55d0a8a45 100644
--- a/docs/best-practices/select_data_type.md
+++ b/docs/best-practices/select_data_type.md
@@ -18,7 +18,7 @@ Some straightforward guidelines can significantly enhance the schema:
* **Use Strict Types:** Always select the correct data type for columns. Numeric and date fields should use appropriate numeric and date types rather than general-purpose String types. This ensures correct semantics for filtering and aggregations.
-* **Avoid Nullable Columns:** Nullable columns introduce additional overhead by maintaining separate columns for tracking null values. Only use Nullable if explicitly required to distinguish between empty and null states. Otherwise, default or zero-equivalent values typically suffice. For further information on why this type should be avoided unless needed, see [Avoid Nullable Columns](/best-practices/select-data-types#avoid-nullable-columns).
+* **Avoid nullable Columns:** Nullable columns introduce additional overhead by maintaining separate columns for tracking null values. Only use Nullable if explicitly required to distinguish between empty and null states. Otherwise, default or zero-equivalent values typically suffice. For further information on why this type should be avoided unless needed, see [Avoid nullable Columns](/best-practices/select-data-types#avoid-nullable-columns).
* **Minimize Numeric Precision:** Select numeric types with minimal bit-width that still accommodate the expected data range. For instance, prefer [UInt16 over Int32](/sql-reference/data-types/int-uint) if negative values aren't needed, and the range fits within 0–65535.
@@ -136,6 +136,6 @@ ENGINE = MergeTree
ORDER BY tuple()
```
-## Avoid Nullable columns {#avoid-nullable-columns}
+## Avoid nullable columns {#avoid-nullable-columns}
-## Frequently Asked Questions {#faqs}
+## Frequently asked questions {#faqs}
### How is compute metered? {#how-is-compute-metered}
@@ -191,7 +191,7 @@ Storage costs are the same across tiers and vary by region and cloud service pro
Storage and backups are counted towards storage costs and billed separately.
All services will default to one backup, retained for a day.
-Users who need additional backups can do so by configuring additional [backups](backups/overview.md) under the settings tab of the Cloud Console.
+Users who need additional backups can do so by configuring additional [backups](backups/overview.md) under the settings tab of the Cloud console.
### How do I estimate compression? {#how-do-i-estimate-compression}
diff --git a/docs/cloud/manage/billing/payment-thresholds.md b/docs/cloud/manage/billing/payment-thresholds.md
index 97049d81c3a..0c2b6948d0e 100644
--- a/docs/cloud/manage/billing/payment-thresholds.md
+++ b/docs/cloud/manage/billing/payment-thresholds.md
@@ -6,7 +6,7 @@ description: 'Payment thresholds and automatic invoicing for ClickHouse Cloud.'
keywords: ['billing', 'payment thresholds', 'automatic invoicing', 'invoice']
---
-# Payment Thresholds
+# Payment thresholds
When your amount due in a billing period for ClickHouse Cloud reaches $10,000 USD or the equivalent value, your payment method will be automatically charged. A failed charge will result in the suspension or termination of your services after a grace period.
diff --git a/docs/cloud/manage/cloud-tiers.md b/docs/cloud/manage/cloud-tiers.md
index fc56abe89a5..1cd784431ec 100644
--- a/docs/cloud/manage/cloud-tiers.md
+++ b/docs/cloud/manage/cloud-tiers.md
@@ -5,7 +5,7 @@ title: 'ClickHouse Cloud Tiers'
description: 'Cloud tiers available in ClickHouse Cloud'
---
-# ClickHouse Cloud Tiers
+# ClickHouse Cloud tiers
There are several tiers available in ClickHouse Cloud.
Tiers are assigned at any organizational level. Services within an organization therefore belong to the same tier.
diff --git a/docs/cloud/manage/dashboards.md b/docs/cloud/manage/dashboards.md
index 1e320c5fd78..761388007b7 100644
--- a/docs/cloud/manage/dashboards.md
+++ b/docs/cloud/manage/dashboards.md
@@ -24,9 +24,9 @@ import dashboards_11 from '@site/static/images/cloud/dashboards/11_dashboards.pn
The SQL Console's dashboards feature allows you to collect and share visualizations from saved queries. Get started by saving and visualizing queries, adding query visualizations to a dashboard, and making the dashboard interactive using query parameters.
-## Core Concepts {#core-concepts}
+## Core concepts {#core-concepts}
-### Query Sharing {#query-sharing}
+### Query sharing {#query-sharing}
In order to share your dashboard with colleagues, please be sure to share the underlying saved query. To view a visualization, users must have, at a minimum, read-only access to the underlying saved query.
@@ -36,11 +36,11 @@ Use [query parameters](/sql-reference/syntax#defining-and-using-query-parameters
You can toggle the query parameter input via the **Global** filters side pane by selecting a “filter” type in the visualization settings. You can also toggle the query parameter input by linking to another object (like a table) on the dashboard. Please see the “[configure a filter](/cloud/manage/dashboards#configure-a-filter)” section of the quick start guide below.
-## Quick Start {#quick-start}
+## Quick start {#quick-start}
Let's create a dashboard to monitor our ClickHouse service using the [query\_log](/operations/system-tables/query_log) system table.
-## Quick Start {#quick-start-1}
+## Quick start {#quick-start-1}
### Create a saved query {#create-a-saved-query}
diff --git a/docs/cloud/manage/integrations.md b/docs/cloud/manage/integrations.md
index fb2c23f8453..67e562aa23d 100644
--- a/docs/cloud/manage/integrations.md
+++ b/docs/cloud/manage/integrations.md
@@ -7,7 +7,7 @@ description: 'Integrations for ClickHouse'
To see a full list of integrations for ClickHouse, please see [this page](/integrations).
-## Proprietary Integrations for ClickHouse Cloud {#proprietary-integrations-for-clickhouse-cloud}
+## Proprietary integrations for ClickHouse Cloud {#proprietary-integrations-for-clickhouse-cloud}
Besides the dozens of integrations available for ClickHouse, there are also some proprietary integrations only available for ClickHouse Cloud:
@@ -24,9 +24,9 @@ Looker Studio can be connected to ClickHouse Cloud by enabling the [MySQL interf
### MySQL Interface {#mysql-interface}
-Some applications currently do not support the ClickHouse wire protocol. To use ClickHouse Cloud with these applications, you can enable the MySQL wire protocol through the Cloud Console. Please see [this page](/interfaces/mysql#enabling-the-mysql-interface-on-clickhouse-cloud) for details on how to enable the MySQL wire protocol through the Cloud Console.
+Some applications currently do not support the ClickHouse wire protocol. To use ClickHouse Cloud with these applications, you can enable the MySQL wire protocol through the Cloud console. Please see [this page](/interfaces/mysql#enabling-the-mysql-interface-on-clickhouse-cloud) for details on how to enable the MySQL wire protocol through the Cloud console.
-## Unsupported Integrations {#unsupported-integrations}
+## Unsupported integrations {#unsupported-integrations}
The following features for integrations are not currently available for ClickHouse Cloud as they are experimental features. If you need to support these features in your application, please contact support@clickhouse.com.
diff --git a/docs/cloud/manage/jan2025_faq/backup.md b/docs/cloud/manage/jan2025_faq/backup.md
index 706435db827..579788f8dec 100644
--- a/docs/cloud/manage/jan2025_faq/backup.md
+++ b/docs/cloud/manage/jan2025_faq/backup.md
@@ -7,7 +7,7 @@ description: 'Backup policy in new tiers'
## What is the backup policy? {#what-is-the-backup-policy}
In Basic, Scale, and Enterprise tiers backups are metered and billed separately from storage.
-All services will default to one daily backup with the ability to configure more, starting with the Scale tier, via the Settings tab of the Cloud Console. Each backup will be retained for at least 24 hours.
+All services will default to one daily backup with the ability to configure more, starting with the Scale tier, via the Settings tab of the Cloud console. Each backup will be retained for at least 24 hours.
## What happens to current configurations that users have set up separate from default backups? {#what-happens-to-current-configurations-that-users-have-set-up-separate-from-default-backups}
diff --git a/docs/cloud/manage/jan2025_faq/new_tiers.md b/docs/cloud/manage/jan2025_faq/new_tiers.md
index 704e3e442f4..3e87d497bb6 100644
--- a/docs/cloud/manage/jan2025_faq/new_tiers.md
+++ b/docs/cloud/manage/jan2025_faq/new_tiers.md
@@ -15,7 +15,7 @@ description: 'Description of new tiers and features'
- **Single Sign On (SSO):** This feature is offered in Enterprise tier and requires a support ticket to be enabled for an Organization. Users who have multiple Organizations should ensure all of their organizations are on the Enterprise tier to use SSO for each organization.
-## Basic Tier {#basic-tier}
+## Basic tier {#basic-tier}
### What are the considerations for the Basic tier? {#what-are-the-considerations-for-the-basic-tier}
@@ -37,7 +37,7 @@ Yes, single replica services are supported on all three tiers. Users can scale o
No, services on this tier are meant to support workloads that are small and fixed size (single replica `1x8GiB` or `1x12GiB`). If users need to scale up/down or add replicas, they will be prompted to upgrade to Scale or Enterprise tiers.
-## Scale Tier {#scale-tier}
+## Scale tier {#scale-tier}
### Which tiers on the new plans (Basic/Scale/Enterprise) support compute-compute separation? {#which-tiers-on-the-new-plans-basicscaleenterprise-support-compute-compute-separation}
@@ -47,7 +47,7 @@ Only Scale and Enterprise tiers support compute-compute separation. Please also
Compute-compute separation is not supported on existing Development and Production services, except for users who already participated in the Private Preview and Beta. If you have additional questions, please contact [support](https://clickhouse.com/support/program).
-## Enterprise Tier {#enterprise-tier}
+## Enterprise tier {#enterprise-tier}
### What different hardware profiles are supported for the Enterprise tier? {#what-different-hardware-profiles-are-supported-for-the-enterprise-tier}
diff --git a/docs/cloud/manage/jan2025_faq/plan_migrations.md b/docs/cloud/manage/jan2025_faq/plan_migrations.md
index f69e941127d..cce2fbe0fb9 100644
--- a/docs/cloud/manage/jan2025_faq/plan_migrations.md
+++ b/docs/cloud/manage/jan2025_faq/plan_migrations.md
@@ -28,19 +28,21 @@ Yes, see below for guidance on self-serve migrations:
Users can upgrade during the trial and continue to use the trial credits to evaluate the new service tiers and the features it supports. However, if they choose to continue using the same Development and Production services, they can do so and upgrade to PAYG. They will still have to migrate before July 23, 2025.
-### Can users upgrade their tiers, i.e. Basic → Scale, Scale → Enterprise, etc? {#can-users-upgrade-their-tiers-ie-basic--scale-scale--enterprise-etc}
+### Can users upgrade their tiers {#can-users-upgrade-their-tiers-ie-basic--scale-scale--enterprise-etc}
-Yes, users can upgrade self-serve and the pricing will reflect the tier selection after upgrade.
+Can users upgrade their tiers, for example, Basic → Scale, Scale → Enterprise, etc.
+Yes, users can upgrade self-serve, and the pricing will reflect the tier selection after upgrade.
-### Can users move from a higher to a lower-cost tier, e.g., Enterprise → Scale, Scale → Basic, Enterprise → Basic self-serve? {#can-users-move-from-a-higher-to-a-lower-cost-tier-eg-enterprise--scale-scale--basic-enterprise--basic-self-serve}
+### Can users move from a higher to a lower-cost tier {#can-users-move-from-a-higher-to-a-lower-cost-tier-eg-enterprise--scale-scale--basic-enterprise--basic-self-serve}
+For example, Enterprise → Scale, Scale → Basic, Enterprise → Basic self-serve?
No, we do not permit downgrading tiers.
-### Can users with only Development services in the organization migrate to the Basic tier? {#can-users-with-only-development-services-in-the-organization-migrate-to-the-basic-tier}
+### Can users with only development services in the organization migrate to the Basic tier? {#can-users-with-only-development-services-in-the-organization-migrate-to-the-basic-tier}
Yes, this would be permitted. Users will be given a recommendation based on their past use and can select Basic `1x8GiB` or `1x12GiB`.
-### Can users with a Development and Production service in the same organization move to the Basic Tier? {#can-users-with-a-development-and-production-service-in-the-same-organization-move-to-the-basic-tier}
+### Can users with a development and production service in the same organization move to the basic tier? {#can-users-with-a-development-and-production-service-in-the-same-organization-move-to-the-basic-tier}
No, if a user has both Development and Production services in the same organization, they can self-serve and migrate only to the Scale or Enterprise tier. If they want to migrate to Basic, they should delete all existing Production services.
diff --git a/docs/cloud/manage/notifications.md b/docs/cloud/manage/notifications.md
index abc9fde3064..708c41b2274 100644
--- a/docs/cloud/manage/notifications.md
+++ b/docs/cloud/manage/notifications.md
@@ -17,7 +17,7 @@ ClickHouse Cloud sends notifications about critical events related to your servi
2. **Notification severity**: Notification severity can be `info`, `warning`, or `critical` depending on how important a notification is. This is not configurable.
3. **Notification channel**: Channel refers to the mode by which the notification is received such as UI, email, Slack etc. This is configurable for most notifications.
-## Receiving Notifications {#receiving-notifications}
+## Receiving notifications {#receiving-notifications}
Notifications can be received via various channels. For now, ClickHouse Cloud supports receiving notifications through email, ClickHouse Cloud UI, and Slack. You can click on the bell icon in the top left menu to view current notifications, which opens a flyout. Clicking the button **View All** the bottom of the flyout will take you to a page that shows an activity log of all notifications.
@@ -27,7 +27,7 @@ Notifications can be received via various channels. For now, ClickHouse Cloud su
API or `DNS Name`console taken from [Obtain Endpoint "Service name"](#obtain-endpoint-service-info).
-#### Getting Private DNS Hostname {#getting-private-dns-hostname}
+#### Getting private DNS hostname {#getting-private-dns-hostname}
##### Option 1: ClickHouse Cloud console {#option-1-clickhouse-cloud-console-3}
@@ -328,7 +328,7 @@ Please refer [here](#attention)
- Most likely Endpoint ID was not added to service allow list, please visit [step](#add-endpoint-id-to-services-allow-list)
-### Checking Endpoint filters {#checking-endpoint-filters}
+### Checking endpoint filters {#checking-endpoint-filters}
Set the following environment variables before running any commands:
diff --git a/docs/cloud/security/azure-privatelink.md b/docs/cloud/security/azure-privatelink.md
index e3d99975381..f213496c7a7 100644
--- a/docs/cloud/security/azure-privatelink.md
+++ b/docs/cloud/security/azure-privatelink.md
@@ -99,7 +99,7 @@ curl --silent --user "${KEY_ID:?}:${KEY_SECRET:?}" "https://api.clickhouse.cloud
Make a note of the `endpointServiceId`. You'll use it in the next step.
-## Create a Private Endpoint in Azure {#create-private-endpoint-in-azure}
+## Create a private endpoint in Azure {#create-private-endpoint-in-azure}
:::important
This section covers ClickHouse-specific details for configuring ClickHouse via Azure Private Link. Azure-specific steps are provided as a reference to guide you on where to look, but they may change over time without notice from the Azure cloud provider. Please consider Azure configuration based on your specific use case.
@@ -111,7 +111,7 @@ For any issues related to Azure configuration tasks, contact Azure Support direc
In this section, we're going to create a Private Endpoint in Azure. You can use either the Azure Portal or Terraform.
-### Option 1: Using Azure Portal to create a Private Endpoint in Azure {#option-1-using-azure-portal-to-create-a-private-endpoint-in-azure}
+### Option 1: Using Azure Portal to create a private endpoint in Azure {#option-1-using-azure-portal-to-create-a-private-endpoint-in-azure}
In the Azure Portal, open **Private Link Center → Private Endpoints**.
@@ -180,7 +180,7 @@ Open the network interface associated with Private Endpoint and copy the **Priva
API or `DNS name`console taken from [Obtain Azure connection alias for Private Link](#obtain-azure-connection-alias-for-private-link).
-### Obtaining the Private DNS Hostname {#obtaining-the-private-dns-hostname}
+### Obtaining the private DNS hostname {#obtaining-the-private-dns-hostname}
#### Option 1: ClickHouse Cloud console {#option-1-clickhouse-cloud-console-3}
@@ -488,7 +488,7 @@ Address: 10.0.0.4
Most likely, the Private Endpoint GUID was not added to the service allow-list. Revisit the [_Add Private Endpoint GUID to your services allow-list_ step](#add-private-endpoint-guid-to-services-allow-list).
-### Private Endpoint is in Pending state {#private-endpoint-is-in-pending-state}
+### Private Endpoint is in pending state {#private-endpoint-is-in-pending-state}
Most likely, the Private Endpoint GUID was not added to the service allow-list. Revisit the [_Add Private Endpoint GUID to your services allow-list_ step](#add-private-endpoint-guid-to-services-allow-list).
@@ -523,7 +523,7 @@ Early data was not sent
Verify return code: 0 (ok)
```
-### Checking Private Endpoint filters {#checking-private-endpoint-filters}
+### Checking private endpoint filters {#checking-private-endpoint-filters}
Set the following environment variables before running any commands:
diff --git a/docs/cloud/security/cloud-access-management/cloud-authentication.md b/docs/cloud/security/cloud-access-management/cloud-authentication.md
index 3c4b5b4f8d0..c0138a1d18e 100644
--- a/docs/cloud/security/cloud-access-management/cloud-authentication.md
+++ b/docs/cloud/security/cloud-access-management/cloud-authentication.md
@@ -8,11 +8,11 @@ description: 'This guide explains some good practices for configuring your authe
import ScalePlanFeatureBadge from '@theme/badges/ScalePlanFeatureBadge'
import EnterprisePlanFeatureBadge from '@theme/badges/EnterprisePlanFeatureBadge'
-# Cloud Authentication
+# Cloud authentication
ClickHouse Cloud provides a number of ways to authenticate. This guide explains some good practices for configuring your authentication. Always check with your security team when selecting authentication methods.
-## Password Settings {#password-settings}
+## Password settings {#password-settings}
Minimum password settings for our console and services (databases) currently comply with [NIST 800-63B](https://pages.nist.gov/800-63-3/sp800-63b.html#sec4) Authenticator Assurance Level 1:
- Minimum 12 characters
@@ -22,15 +22,15 @@ Minimum password settings for our console and services (databases) currently com
- 1 number
- 1 special character
-## Email + Password {#email--password}
+## Email and password {#email--password}
ClickHouse Cloud allows you to authenticate with an email address and password. When using this method the best way to protect your ClickHouse account use a strong password. There are many online resources to help you devise a password you can remember. Alternatively, you can use a random password generator and store your password in a password manager for increased security.
-## SSO Using Google or Microsoft Social Authentication {#sso-using-google-or-microsoft-social-authentication}
+## SSO using Google or Microsoft social authentication {#sso-using-google-or-microsoft-social-authentication}
If your company uses Google Workspace or Microsoft 365, you can leverage your current single sign-on setup within ClickHouse Cloud. To do this, simply sign up using your company email address and invite other users using their company email. The effect is that your users must login using your company's login flows, whether via your identity provider or directly through Google or Microsoft authentication, before they can authenticate into ClickHouse Cloud.
-## Multi-Factor Authentication {#multi-factor-authentication}
+## Multi-factor authentication {#multi-factor-authentication}
Users with email + password or social authentication can further secure their account using multi-factor authentication (MFA). To set up MFA:
1. Log into console.clickhouse.cloud
@@ -118,7 +118,7 @@ Users with email + password or social authentication can further secure their ac
ClickHouse Cloud also supports security assertion markup language (SAML) single sign on (SSO). For more information, see [SAML SSO Setup](/cloud/security/saml-setup).
-## Database User ID + Password {#database-user-id--password}
+## Database user ID and password {#database-user-id--password}
Use the SHA256_hash method when [creating user accounts](/sql-reference/statements/create/user.md) to secure passwords.
diff --git a/docs/cloud/security/cloud-access-management/index.md b/docs/cloud/security/cloud-access-management/index.md
index 0aa6b9e3c4c..a61339a7258 100644
--- a/docs/cloud/security/cloud-access-management/index.md
+++ b/docs/cloud/security/cloud-access-management/index.md
@@ -1,6 +1,6 @@
---
slug: /cloud/security/cloud-access-management
-title: 'Cloud Access Management'
+title: 'Cloud access management'
description: 'Cloud Access Management Table Of Contents'
---
diff --git a/docs/cloud/security/cmek.md b/docs/cloud/security/cmek.md
index a62982207da..f5be60ec17f 100644
--- a/docs/cloud/security/cmek.md
+++ b/docs/cloud/security/cmek.md
@@ -9,7 +9,7 @@ import Image from '@theme/IdealImage';
import EnterprisePlanFeatureBadge from '@theme/badges/EnterprisePlanFeatureBadge'
import cmek_performance from '@site/static/images/_snippets/cmek-performance.png';
-# ClickHouse Enhanced Encryption
+# ClickHouse enhanced encryption
@@ -129,7 +129,7 @@ For any issues related to GCP configuration tasks, contact GCP Support directly.
In this section, we're going to create a service endpoint.
-### Adding a Private Service Connection {#adding-a-private-service-connection}
+### Adding a private service connection {#adding-a-private-service-connection}
First up, we're going to create a Private Service Connection.
@@ -137,7 +137,7 @@ First up, we're going to create a Private Service Connection.
In the Google Cloud console, navigate to **Network services -> Private Service Connect**.
-API or `Service name`console from [Obtain GCP service attachment for Private Service Connect](#obtain-gcp-service-attachment-and-dns-name-for-private-service-connect) step
:::
-## Set Private DNS Name for Endpoint {#setting-up-dns}
+## Set private DNS name for endpoint {#set-private-dns-name-for-endpoint}
:::note
There are various ways to configure DNS. Please set up DNS according to your specific use case.
@@ -336,7 +336,7 @@ curl --silent --user "${KEY_ID:?}:${KEY_SECRET:?}" -X PATCH -H "Content-Type: ap
Each service with Private Link enabled has a public and private endpoint. In order to connect using Private Link, you need to use a private endpoint which will be `privateDnsHostname` taken from [Obtain GCP service attachment for Private Service Connect](#obtain-gcp-service-attachment-and-dns-name-for-private-service-connect).
-### Getting Private DNS Hostname {#getting-private-dns-hostname}
+### Getting private DNS hostname {#getting-private-dns-hostname}
#### Option 1: ClickHouse Cloud console {#option-1-clickhouse-cloud-console-3}
@@ -412,7 +412,7 @@ Early data was not sent
Verify return code: 0 (ok)
```
-### Checking Endpoint filters {#checking-endpoint-filters}
+### Checking endpoint filters {#checking-endpoint-filters}
#### REST API {#rest-api}
diff --git a/docs/cloud/security/index.md b/docs/cloud/security/index.md
index 481708bf471..b6a2d56ab1b 100644
--- a/docs/cloud/security/index.md
+++ b/docs/cloud/security/index.md
@@ -6,7 +6,7 @@ hide_title: true
description: 'Landing page for ClickHouse Cloud Security'
---
-# ClickHouse Cloud Security
+# ClickHouse Cloud security
This section delves into security in ClickHouse Cloud and contains the following pages:
diff --git a/docs/cloud/security/inviting-new-users.md b/docs/cloud/security/inviting-new-users.md
index 8af59db9301..38dc099a6cb 100644
--- a/docs/cloud/security/inviting-new-users.md
+++ b/docs/cloud/security/inviting-new-users.md
@@ -1,7 +1,7 @@
---
sidebar_label: 'Inviting new users'
slug: /cloud/security/inviting-new-users
-title: 'Inviting New Users'
+title: 'Inviting new users'
description: 'This page describes how administrators can invite new users to their organisation and assign roles to them'
---
diff --git a/docs/cloud/security/personal-data-access.md b/docs/cloud/security/personal-data-access.md
index 8682c52eb60..bcf4514b301 100644
--- a/docs/cloud/security/personal-data-access.md
+++ b/docs/cloud/security/personal-data-access.md
@@ -20,7 +20,7 @@ Depending on where you are located, applicable law may also provide you addition
Please review ClickHouse's Privacy Policy for details on personal data that ClickHouse collects and how it may be used.
-## Self Service {#self-service}
+## Self service {#self-service}
By default, ClickHouse empowers users to view their personal data directly from the ClickHouse console.
@@ -38,7 +38,7 @@ Below is a summary of the data ClickHouse collects during account setup and serv
Note: URLs with `OrgID` need to be updated to reflect the `OrgID` for your specific account.
-### Current Customers {#current-customers}
+### Current customers {#current-customers}
If you have an account with us and the self-service option has not resolved your personal data issue, you can submit a Data Subject Access Request under the Privacy Policy. To do so, log into your ClickHouse account and open a [support case](https://console.clickhouse.cloud/support). This helps us verify your identity and streamline the process to address your request.
@@ -51,11 +51,11 @@ Please be sure to include the following details in your support case:
Configure your SAML integration
- ClickHouse uses service provider initiated SAML connections. This means you can log in via https://console.clickhouse.cloud or via a direct link. We do not currently support identity provider initiated connections. Basic SAML configurations include the following:
+ ClickHouse uses service provider-initiated SAML connections. This means you can log in via https://console.clickhouse.cloud or via a direct link. We do not currently support identity provider initiated connections. Basic SAML configurations include the following:
- SSO URL or ACS URL: `https://auth.clickhouse.cloud/login/callback?connection={organizationid}`
@@ -332,33 +332,33 @@ Azure (Microsoft) SAML may also be referred to as Azure Active Directory (AD) or
-## How It Works {#how-it-works}
+## How it works {#how-it-works}
-### Service Provider Initiated SSO {#service-provider-initiated-sso}
+### Service provider-initiated SSO {#service-provider-initiated-sso}
-We only utilize service provider initiated SSO. This means users go to `https://console.clickhouse.cloud` and enter their email address to be redirected to the IdP for authentication. Users already authenticated via your IdP can use the direct link to automatically log in to your organization without entering their email address at the login page.
+We only utilize service provider-initiated SSO. This means users go to `https://console.clickhouse.cloud` and enter their email address to be redirected to the IdP for authentication. Users already authenticated via your IdP can use the direct link to automatically log in to your organization without entering their email address at the login page.
-### Assigning User Roles {#assigning-user-roles}
+### Assigning user roles {#assigning-user-roles}
Users will appear in your ClickHouse Cloud console after they are assigned to your IdP application and log in for the first time. At least one SSO user should be assigned the Admin role in your organization. Use social login or `https://console.clickhouse.cloud/?with=email` to log in with your original authentication method to update your SSO role.
-### Removing Non-SSO Users {#removing-non-sso-users}
+### Removing non-SSO users {#removing-non-sso-users}
Once you have SSO users set up and have assigned at least one user the Admin role, the Admin can remove users using other methods (e.g. social authentication or user ID + password). Google authentication will continue to work after SSO is set up. User ID + password users will be automatically redirected to SSO based on their email domain unless users use `https://console.clickhouse.cloud/?with=email`.
-### Managing Users {#managing-users}
+### Managing users {#managing-users}
ClickHouse Cloud currently implements SAML for SSO. We have not yet implemented SCIM to manage users. This means SSO users must be assigned to the application in your IdP to access your ClickHouse Cloud organization. Users must log in to ClickHouse Cloud once to appear in the **Users** area in the organization. When users are removed in your IdP, they will not be able to log in to ClickHouse Cloud using SSO. However, the SSO user will still show in your organization until and administrator manually removes the user.
-### Multi-Org SSO {#multi-org-sso}
+### Multi-org SSO {#multi-org-sso}
ClickHouse Cloud supports multi-organization SSO by providing a separate connection for each organization. Use the direct link (`https://console.clickhouse.cloud/?connection={organizationid}`) to log in to each respective organization. Be sure to log out of one organization before logging into another.
-## Additional Information {#additional-information}
+## Additional information {#additional-information}
Security is our top priority when it comes to authentication. For this reason, we made a few decisions when implementing SSO that we need you to know.
-- **We only process service provider initiated authentication flows.** Users must navigate to `https://console.clickhouse.cloud` and enter an email address to be redirected to your identity provider. Instructions to add a bookmark application or shortcut are provided for your convenience so your users don't need to remember the URL.
+- **We only process service provider-initiated authentication flows.** Users must navigate to `https://console.clickhouse.cloud` and enter an email address to be redirected to your identity provider. Instructions to add a bookmark application or shortcut are provided for your convenience so your users don't need to remember the URL.
- **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.
diff --git a/docs/cloud/security/setting-ip-filters.md b/docs/cloud/security/setting-ip-filters.md
index 0158a4d765b..5b698b4f749 100644
--- a/docs/cloud/security/setting-ip-filters.md
+++ b/docs/cloud/security/setting-ip-filters.md
@@ -9,7 +9,7 @@ import Image from '@theme/IdealImage';
import ip_filtering_after_provisioning from '@site/static/images/cloud/security/ip-filtering-after-provisioning.png';
import ip_filter_add_single_ip from '@site/static/images/cloud/security/ip-filter-add-single-ip.png';
-## Setting IP Filters {#setting-ip-filters}
+## Setting IP filters {#setting-ip-filters}
IP access lists filter traffic to ClickHouse services or API keys by specifying which source addresses are permitted to connect. These lists are configurable for each service and each API key. Lists can be configured during service or API key creation, or afterward.
diff --git a/docs/concepts/olap.md b/docs/concepts/olap.md
index f0033debb20..253bd85310b 100644
--- a/docs/concepts/olap.md
+++ b/docs/concepts/olap.md
@@ -19,7 +19,7 @@ keywords: ['OLAP']
**Online** …in real-time.
-## OLAP from the Business Perspective {#olap-from-the-business-perspective}
+## OLAP from the business perspective {#olap-from-the-business-perspective}
In recent years business people started to realize the value of data. Companies who make their decisions blindly more often than not fail to keep up with the competition. The data-driven approach of successful companies forces them to collect all data that might be even remotely useful for making business decisions, and imposes on them a need for mechanisms which allow them to analyze this data in a timely manner. Here's where OLAP database management systems (DBMS) come in.
@@ -27,7 +27,7 @@ In a business sense, OLAP allows companies to continuously plan, analyze, and re
ClickHouse is an OLAP database management system that is pretty often used as a backend for those SaaS solutions for analyzing domain-specific data. However, some businesses are still reluctant to share their data with third-party providers and so an in-house data warehouse scenario is also viable.
-## OLAP from the Technical Perspective {#olap-from-the-technical-perspective}
+## OLAP from the technical perspective {#olap-from-the-technical-perspective}
All database management systems could be classified into two groups: OLAP (Online **Analytical** Processing) and OLTP (Online **Transactional** Processing). The former focuses on building reports, each based on large volumes of historical data, but by doing it less frequently. The latter usually handles a continuous stream of transactions, constantly modifying the current state of data.
diff --git a/docs/concepts/why-clickhouse-is-so-fast.md b/docs/concepts/why-clickhouse-is-so-fast.md
index 272865cc4d4..d3fb45a5bfe 100644
--- a/docs/concepts/why-clickhouse-is-so-fast.md
+++ b/docs/concepts/why-clickhouse-is-so-fast.md
@@ -15,7 +15,7 @@ We will next explain in more detail what makes ClickHouse so fast, especially co
From an architectural perspective, databases consist (at least) of a storage layer and a query processing layer. While the storage layer is responsible for saving, loading, and maintaining the table data, the query processing layer executes user queries. Compared to other databases, ClickHouse provides innovations in both layers that enable extremely fast inserts and Select queries.
-## Storage Layer: Concurrent inserts are isolated from each other {#storage-layer-concurrent-inserts-are-isolated-from-each-other}
+## Storage layer: concurrent inserts are isolated from each other {#storage-layer-concurrent-inserts-are-isolated-from-each-other}
VIDEO
@@ -29,7 +29,7 @@ This approach has several advantages: All data processing can be [offloaded to b
🤿 Deep dive into this in the [On-Disk Format](/docs/academic_overview#3-1-on-disk-format) section of the web version of our VLDB 2024 paper.
-## Storage Layer: Concurrent inserts and selects are isolated {#storage-layer-concurrent-inserts-and-selects-are-isolated}
+## Storage layer: concurrent inserts and selects are isolated {#storage-layer-concurrent-inserts-and-selects-are-isolated}
VIDEO
@@ -37,7 +37,7 @@ Inserts are fully isolated from SELECT queries, and merging inserted data parts
🤿 Deep dive into this in the [Storage Layer](/docs/academic_overview#3-storage-layer) section of the web version of our VLDB 2024 paper.
-## Storage Layer: Merge-time computation {#storage-layer-merge-time-computation}
+## Storage layer: merge-time computation {#storage-layer-merge-time-computation}
VIDEO
@@ -57,7 +57,7 @@ On the other hand, the majority of the runtime of merges is consumed by loading
🤿 Deep dive into this in the [Merge-time Data Transformation](/docs/academic_overview#3-3-merge-time-data-transformation) section of the web version of our VLDB 2024 paper.
-## Storage Layer: Data pruning {#storage-layer-data-pruning}
+## Storage layer: data pruning {#storage-layer-data-pruning}
VIDEO
@@ -73,7 +73,7 @@ All three techniques aim to skip as many rows during full-column reads as possib
🤿 Deep dive into this in the [Data Pruning](/docs/academic_overview#3-2-data-pruning) section of the web version of our VLDB 2024 paper.
-## Storage Layer: Data compression {#storage-layer-data-compression}
+## Storage layer: data compression {#storage-layer-data-compression}
VIDEO
diff --git a/docs/data-modeling/projections.md b/docs/data-modeling/projections.md
index 44231b1795a..1f45aa49917 100644
--- a/docs/data-modeling/projections.md
+++ b/docs/data-modeling/projections.md
@@ -21,7 +21,7 @@ queries by creating a reordering of data by attributes of interest. This can be:
1. A complete reordering
2. A subset of the original table with a different order
-3. A precomputed aggregation (similar to a Materialized View) but with an ordering
+3. A precomputed aggregation (similar to a materialized view) but with an ordering
aligned to the aggregation.
**NOT** to use ClickHouse. It's an [OLAP](../../faq/general/olap.md) system after all, while there are many excellent key-value storage systems out there.
diff --git a/docs/faq/use-cases/time-series.md b/docs/faq/use-cases/time-series.md
index dc4ea344caa..1db9ac6cba3 100644
--- a/docs/faq/use-cases/time-series.md
+++ b/docs/faq/use-cases/time-series.md
@@ -6,7 +6,7 @@ toc_priority: 101
description: 'Page describing how to use ClickHouse as a time-series database'
---
-# Can I Use ClickHouse As a Time-Series Database? {#can-i-use-clickhouse-as-a-time-series-database}
+# Can I use ClickHouse as a time-series database? {#can-i-use-clickhouse-as-a-time-series-database}
_Note: Please see the blog [Working with Time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse) for additional examples of using ClickHouse for time series analysis._
diff --git a/docs/getting-started/example-datasets/brown-benchmark.md b/docs/getting-started/example-datasets/brown-benchmark.md
index bde0445dfdb..59f07d3197e 100644
--- a/docs/getting-started/example-datasets/brown-benchmark.md
+++ b/docs/getting-started/example-datasets/brown-benchmark.md
@@ -91,7 +91,7 @@ clickhouse-client --query "INSERT INTO mgbench.logs2 FORMAT CSVWithNames" < mgbe
clickhouse-client --query "INSERT INTO mgbench.logs3 FORMAT CSVWithNames" < mgbench3.csv
```
-## Run benchmark queries: {#run-benchmark-queries}
+## Run benchmark queries {#run-benchmark-queries}
```sql
USE mgbench;
diff --git a/docs/getting-started/example-datasets/cell-towers.md b/docs/getting-started/example-datasets/cell-towers.md
index 22bda6de53a..5626cb4bf89 100644
--- a/docs/getting-started/example-datasets/cell-towers.md
+++ b/docs/getting-started/example-datasets/cell-towers.md
@@ -40,7 +40,7 @@ Here is a preview of the dashboard created in this guide:
default.skip_table (933d4b2c-8cea-4bf9-8c93-c56e900eefd1) (SelectExecutor): Index `vix` has dropped 6102/6104 granules.
```
-## Skip Index Types {#skip-index-types}
+## Skip index types {#skip-index-types}
### minmax {#minmax}
@@ -130,7 +130,7 @@ an unlimited number of discrete values). This set contains all values in the bl
The cost, performance, and effectiveness of this index is dependent on the cardinality within blocks. If each block contains a large number of unique values, either evaluating the query condition against a large index set will be very expensive, or the index will not be applied because the index is empty due to exceeding max_size.
-### Bloom Filter Types {#bloom-filter-types}
+### Bloom filter types {#bloom-filter-types}
A *Bloom filter* is a data structure that allows space-efficient testing of set membership at the cost of a slight chance of false positives. A false positive is not a significant concern in the case of skip indexes because the only disadvantage is reading a few unnecessary blocks. However, the potential for false positives does mean that the indexed expression should be expected to be true, otherwise valid data may be skipped.
@@ -149,7 +149,7 @@ This index works only with String, FixedString, and Map datatypes. The input exp
```
This index can also be useful for text searches, particularly languages without word breaks, such as Chinese.
-## Skip Index Functions {#skip-index-functions}
+## Skip index functions {#skip-index-functions}
The core purpose of data-skipping indexes is to limit the amount of data analyzed by popular queries. Given the analytic nature of ClickHouse data, the pattern of those queries in most cases includes functional expressions. Accordingly, skip indexes must interact correctly with common functions to be efficient. This can happen either when:
* data is inserted and the index is defined as a functional expression (with the result of the expression stored in the index files), or
@@ -158,7 +158,7 @@ The core purpose of data-skipping indexes is to limit the amount of data analyze
Each type of skip index works on a subset of available ClickHouse functions appropriate to the index implementation listed
[here](/engines/table-engines/mergetree-family/mergetree/#functions-support). In general, set indexes and Bloom filter based indexes (another type of set index) are both unordered and therefore do not work with ranges. In contrast, minmax indexes work particularly well with ranges since determining whether ranges intersect is very fast. The efficacy of partial match functions LIKE, startsWith, endsWith, and hasToken depend on the index type used, the index expression, and the particular shape of the data.
-## Skip Index Settings {#skip-index-settings}
+## Skip index settings {#skip-index-settings}
There are two available settings that apply to skip indexes.
@@ -170,7 +170,7 @@ queries. In circumstances where querying a table is too expensive unless a skip
names will return an exception for any query that does not use the listed index. This would prevent poorly written queries from
consuming server resources.
-## Skip Best Practices {#skip-best-practices}
+## Skip best practices {#skip-best-practices}
Skip indexes are not intuitive, especially for users accustomed to secondary row-based indexes from the RDMS realm or inverted indexes from document stores. To get any benefit, applying a ClickHouse data skipping index must avoid enough granule reads to offset the cost of calculating the index. Critically, if a value occurs even once in an indexed block, it means the entire block must be read into memory and evaluated, and the index cost has been needlessly incurred.
diff --git a/docs/guides/best-practices/sparse-primary-indexes.md b/docs/guides/best-practices/sparse-primary-indexes.md
index f4565584fa6..ae246d1b11d 100644
--- a/docs/guides/best-practices/sparse-primary-indexes.md
+++ b/docs/guides/best-practices/sparse-primary-indexes.md
@@ -33,7 +33,7 @@ import sparsePrimaryIndexes15a from '@site/static/images/guides/best-practices/s
import sparsePrimaryIndexes15b from '@site/static/images/guides/best-practices/sparse-primary-indexes-15b.png';
import Image from '@theme/IdealImage';
-# A Practical Introduction to Primary Indexes in ClickHouse
+# A practical introduction to primary indexes in ClickHouse
## Introduction {#introduction}
@@ -52,7 +52,7 @@ For ClickHouse [secondary data skipping indexes](/engines/table-engines/mergetre
:::
-### Data Set {#data-set}
+### Data set {#data-set}
Throughout this guide we will use a sample anonymized web traffic data set.
@@ -66,7 +66,7 @@ With these three columns we can already formulate some typical web analytics que
- "What are the top 10 users that most frequently clicked a specific URL?"
- "What are the most popular times (e.g. days of the week) at which a user clicks on a specific URL?"
-### Test Machine {#test-machine}
+### Test machine {#test-machine}
All runtime numbers given in this document are based on running ClickHouse 22.2.1 locally on a MacBook Pro with the Apple M1 Pro chip and 16GB of RAM.
@@ -157,7 +157,7 @@ ClickHouse client's result output indicates that ClickHouse executed a full tabl
To make this (way) more efficient and (much) faster, we need to use a table with a appropriate primary key. This will allow ClickHouse to automatically (based on the primary key's column(s)) create a sparse primary index which can then be used to significantly speed up the execution of our example query.
-## ClickHouse Index Design {#clickhouse-index-design}
+## ClickHouse index design {#clickhouse-index-design}
### An index design for massive data scales {#an-index-design-for-massive-data-scales}
diff --git a/docs/guides/creating-tables.md b/docs/guides/creating-tables.md
index 924955d1c39..fe1ecf915df 100644
--- a/docs/guides/creating-tables.md
+++ b/docs/guides/creating-tables.md
@@ -47,7 +47,7 @@ The table engine determines:
There are many engines to choose from, but for a simple table on a single-node ClickHouse server, [MergeTree](/engines/table-engines/mergetree-family/mergetree.md) is your likely choice.
:::
-## A Brief Intro to Primary Keys {#a-brief-intro-to-primary-keys}
+## A brief intro to primary keys {#a-brief-intro-to-primary-keys}
Before you go any further, it is important to understand how primary keys work in ClickHouse (the implementation
of primary keys might seem unexpected!):
diff --git a/docs/guides/developer/alternative-query-languages.md b/docs/guides/developer/alternative-query-languages.md
index 0ba281b61ce..9ca05f7ac0d 100644
--- a/docs/guides/developer/alternative-query-languages.md
+++ b/docs/guides/developer/alternative-query-languages.md
@@ -24,7 +24,7 @@ Standard SQL is the default query language of ClickHouse.
SET dialect = 'clickhouse'
```
-## Pipelined Relational Query Language (PRQL) {#pipelined-relational-query-language-prql}
+## Pipelined relational query language (PRQL) {#pipelined-relational-query-language-prql}
VIDEO
@@ -54,7 +54,7 @@ You can create a materialized view on a Null table. So the data written to the t
## Monthly aggregated table and materialized view {#monthly-aggregated-table-and-materialized-view}
-For the first Materialized View, we need to create the `Target` table, for this example, it will be `analytics.monthly_aggregated_data` and we will store the sum of the views by month and domain name.
+For the first materialized view, we need to create the `Target` table, for this example, it will be `analytics.monthly_aggregated_data` and we will store the sum of the views by month and domain name.
```sql
CREATE TABLE analytics.monthly_aggregated_data
@@ -67,7 +67,7 @@ ENGINE = AggregatingMergeTree
ORDER BY (domain_name, month)
```
-The Materialized View that will forward the data on the target table will look like this:
+The materialized view that will forward the data on the target table will look like this:
```sql
CREATE MATERIALIZED VIEW analytics.monthly_aggregated_data_mv
@@ -103,7 +103,7 @@ ORDER BY (domain_name, year)
This step defines the cascade. The `FROM` statement will use the `monthly_aggregated_data` table, this means the data flow will be:
1. The data comes to the `hourly_data` table.
-2. ClickHouse will forward the data received to the first Materialized View `monthly_aggregated_data` table,
+2. ClickHouse will forward the data received to the first materialized view `monthly_aggregated_data` table,
3. Finally, the data received in step 2 will be forwarded to the `year_aggregated_data`.
```sql
diff --git a/docs/guides/developer/deduplication.md b/docs/guides/developer/deduplication.md
index af77b949b72..24ad29c694e 100644
--- a/docs/guides/developer/deduplication.md
+++ b/docs/guides/developer/deduplication.md
@@ -10,7 +10,7 @@ import deduplication from '@site/static/images/guides/developer/de_duplication.p
import Image from '@theme/IdealImage';
-# Deduplication Strategies
+# Deduplication strategies
**Deduplication** refers to the process of ***removing duplicate rows of a dataset***. In an OLTP database, this is done easily because each row has a unique primary key-but at the cost of slower inserts. Every inserted row needs to first be searched for and, if found, needs to be replaced.
@@ -164,7 +164,7 @@ Grouping as shown in the query above can actually be more efficient (in terms of
Our [Deleting and Updating Data training module](https://learn.clickhouse.com/visitor_catalog_class/show/1328954/?utm_source=clickhouse&utm_medium=docs) expands on this example, including how to use a `version` column with `ReplacingMergeTree`.
-## Using CollapsingMergeTree for Updating Columns Frequently {#using-collapsingmergetree-for-updating-columns-frequently}
+## 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.
@@ -248,7 +248,7 @@ INSERT INTO hackernews_views(id, author, sign) VALUES
```
:::
-## Real-time Updates from Multiple Threads {#real-time-updates-from-multiple-threads}
+## Real-time updates from multiple threads {#real-time-updates-from-multiple-threads}
With a `CollapsingMergeTree` table, rows cancel each other using a sign column, and the state of a row is determined by the last row inserted. But this can be problematic if you are inserting rows from different threads where rows can be inserted out of order. Using the "last" row does not work in this situation.
diff --git a/docs/guides/developer/index.md b/docs/guides/developer/index.md
index 76ac534f25b..aad644e8cda 100644
--- a/docs/guides/developer/index.md
+++ b/docs/guides/developer/index.md
@@ -5,7 +5,7 @@ description: 'Overview of the advanced guides'
title: 'Advanced Guides'
---
-# Advanced Guides
+# Advanced guides
This section contains the following advanced guides:
diff --git a/docs/guides/developer/lightweight-update.md b/docs/guides/developer/lightweight-update.md
index 06760711571..1e319dc689e 100644
--- a/docs/guides/developer/lightweight-update.md
+++ b/docs/guides/developer/lightweight-update.md
@@ -6,9 +6,13 @@ keywords: ['lightweight update']
description: 'Provides a description of lightweight updates'
---
-## Lightweight Update {#lightweight-update}
+# Lightweight update {#lightweight-update}
-When lightweight updates are enabled, updated rows are marked as updated immediately and subsequent `SELECT` queries will automatically return with the changed values. When lightweight updates are not enabled, you may have to wait for your mutations to be applied via a background process to see the changed values.
+## Introduction {#introduction}
+
+When lightweight updates are enabled, updated rows are marked as updated immediately and subsequent `SELECT` queries will
+automatically return with the changed values. When lightweight updates are not enabled, you may have to wait for your
+mutations to be applied via a background process to see the changed values.
Lightweight updates can be enabled for `MergeTree`-family tables by enabling the query-level setting `apply_mutations_on_fly`.
@@ -16,6 +20,10 @@ Lightweight updates can be enabled for `MergeTree`-family tables by enabling the
SET apply_mutations_on_fly = 1;
```
+## How lightweight updates work {#how-lightweight-updates-work}
+
+[`ALTER TABLE ... UPDATE`]() queries in ClickHouse are implemented as mutations. A mutation is a heavyweight operation that rewrites parts, either synchronously or asynchronously.
+
## Example {#example}
Let's create a table and run some mutations:
diff --git a/docs/guides/developer/mutations.md b/docs/guides/developer/mutations.md
index 04d8e24661f..6f19b0cadd7 100644
--- a/docs/guides/developer/mutations.md
+++ b/docs/guides/developer/mutations.md
@@ -2,19 +2,21 @@
slug: /guides/developer/mutations
sidebar_label: 'Updating and Deleting Data'
sidebar_position: 1
-keywords: ['UPDATE', 'DELETE']
+keywords: ['UPDATE', 'DELETE', 'mutations']
title: 'Updating and deleting ClickHouse data'
description: 'Describes how to perform update and delete operations in ClickHouse'
show_related_blogs: false
---
-# Updating and deleting ClickHouse data
+# Updating and deleting ClickHouse data with mutations
-Although ClickHouse is geared toward high volume analytic workloads, it is possible in some situations to modify or delete existing data. These operations are labeled "mutations" and are executed using the `ALTER TABLE` command. You can also `DELETE` a row using the lightweight
-delete capability of ClickHouse.
+Although ClickHouse is geared toward high volume analytic workloads, it is possible in some situations to modify or
+delete existing data. These operations are labeled "mutations" and are executed using the `ALTER TABLE` command.
:::tip
-If you need to perform frequent updates, consider using [deduplication](../developer/deduplication.md) in ClickHouse, which allows you to update and/or delete rows without generating a mutation event.
+If you need to perform frequent updates, consider using [deduplication](../developer/deduplication.md) in ClickHouse, which allows you to update
+and/or delete rows without generating a mutation event. Alternatively, use [lightweight updates](/guides/developer/lightweight-update)
+or [lightweight deletes](/guides/developer/lightweight-delete)
:::
## Updating data {#updating-data}
diff --git a/docs/guides/developer/replacing-merge-tree.md b/docs/guides/developer/replacing-merge-tree.md
index 9b8f3f79ca9..cb9d33b01ea 100644
--- a/docs/guides/developer/replacing-merge-tree.md
+++ b/docs/guides/developer/replacing-merge-tree.md
@@ -312,15 +312,15 @@ ORDER BY year ASC
As shown, partitioning has significantly improved query performance in this case by allowing the deduplication process to occur at a partition level in parallel.
-## Merge Behavior Considerations {#merge-behavior-considerations}
+## Merge behavior considerations {#merge-behavior-considerations}
ClickHouse's merge selection mechanism goes beyond simple merging of parts. Below, we examine this behavior in the context of ReplacingMergeTree, including configuration options for enabling more aggressive merging of older data and considerations for larger parts.
-### Merge Selection Logic {#merge-selection-logic}
+### Merge selection logic {#merge-selection-logic}
While merging aims to minimize the number of parts, it also balances this goal against the cost of write amplification. Consequently, some ranges of parts are excluded from merging if they would lead to excessive write amplification, based on internal calculations. This behavior helps prevent unnecessary resource usage and extends the lifespan of storage components.
-### Merging Behavior on Large Parts {#merging-behavior-on-large-parts}
+### 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.
@@ -328,11 +328,11 @@ To address this, users can invoke OPTIMIZE FINAL to manually merge parts and rem
For a more sustainable solution that maintains performance, partitioning the table is recommended. This can help prevent data parts from reaching the maximum merge size and reduces the need for ongoing manual optimizations.
-### Partitioning and Merging Across Partitions {#partitioning-and-merging-across-partitions}
+### Partitioning and merging across partitions {#partitioning-and-merging-across-partitions}
As discussed in Exploiting Partitions with ReplacingMergeTree, we recommend partitioning tables as a best practice. Partitioning isolates data for more efficient merges and avoids merging across partitions, particularly during query execution. This behavior is enhanced in versions from 23.12 onward: if the partition key is a prefix of the sorting key, merging across partitions is not performed at query time, leading to faster query performance.
-### Tuning Merges for Better Query Performance {#tuning-merges-for-better-query-performance}
+### 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.
@@ -340,7 +340,7 @@ If a value for min_age_to_force_merge_seconds is specified, ClickHouse will igno
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}
+### 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.
diff --git a/docs/guides/developer/ttl.md b/docs/guides/developer/ttl.md
index 4b29ca64cfc..49361a6c6cc 100644
--- a/docs/guides/developer/ttl.md
+++ b/docs/guides/developer/ttl.md
@@ -10,7 +10,7 @@ show_related_blogs: true
import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge';
-# Manage Data with TTL (Time-to-live)
+# Manage data with TTL (time-to-live)
## Overview of TTL {#overview-of-ttl}
@@ -24,7 +24,7 @@ TTL (time-to-live) refers to the capability of having rows or columns moved, del
TTL can be applied to entire tables or specific columns.
:::
-## TTL Syntax {#ttl-syntax}
+## TTL syntax {#ttl-syntax}
The `TTL` clause can appear after a column definition and/or at the end of the table definition. Use the `INTERVAL` clause to define a length of time (which needs to be a `Date` or `DateTime` data type). For example, the following table has two columns
with `TTL` clauses:
@@ -48,7 +48,7 @@ ORDER BY tuple()
TTL rules can be altered or deleted. See the [Manipulations with Table TTL](/sql-reference/statements/alter/ttl.md) page for more details.
:::
-## Triggering TTL Events {#triggering-ttl-events}
+## Triggering TTL events {#triggering-ttl-events}
The deleting or aggregating of expired rows is not immediate - it only occurs during table merges. If you have a table that's not actively merging (for whatever reason), there are two settings that trigger TTL events:
@@ -67,7 +67,7 @@ OPTIMIZE TABLE example1 FINAL
`OPTIMIZE` initializes an unscheduled merge of the parts of your table, and `FINAL` forces a reoptimization if your table is already a single part.
:::
-## Removing Rows {#removing-rows}
+## Removing rows {#removing-rows}
To remove entire rows from a table after a certain amount of time, define the TTL rule at the table level:
@@ -100,7 +100,7 @@ TTL time + INTERVAL 1 MONTH DELETE WHERE event != 'error',
time + INTERVAL 6 MONTH DELETE WHERE event = 'error'
```
-## Removing Columns {#removing-columns}
+## Removing columns {#removing-columns}
Instead of deleting the entire row, suppose you want just the balance and address columns to expire. Let's modify the `customers` table and add a TTL for both columns to be 2 hours:
@@ -110,7 +110,7 @@ MODIFY COLUMN balance Int32 TTL timestamp + INTERVAL 2 HOUR,
MODIFY COLUMN address String TTL timestamp + INTERVAL 2 HOUR
```
-## Implementing a Rollup {#implementing-a-rollup}
+## Implementing a rollup {#implementing-a-rollup}
Suppose we want to delete rows after a certain amount of time but hang on to some of the data for reporting purposes. We don't want all the details - just a few aggregated results of historical data. This can be implemented by adding a `GROUP BY` clause to your `TTL` expression, along with some columns in your table to store the aggregated results.
Suppose in the following `hits` table we want to delete old rows, but hang on to the sum and maximum of the `hits` columns before removing the rows. We will need a field to store those values in, and we will need to add a `GROUP BY` clause to the `TTL` clause that rolls up the sum and maximum:
diff --git a/docs/guides/developer/understanding-query-execution-with-the-analyzer.md b/docs/guides/developer/understanding-query-execution-with-the-analyzer.md
index 6b8cd015399..24620bf6eb1 100644
--- a/docs/guides/developer/understanding-query-execution-with-the-analyzer.md
+++ b/docs/guides/developer/understanding-query-execution-with-the-analyzer.md
@@ -12,7 +12,7 @@ import analyzer4 from '@site/static/images/guides/developer/analyzer4.png';
import analyzer5 from '@site/static/images/guides/developer/analyzer5.png';
import Image from '@theme/IdealImage';
-# Understanding Query Execution with the Analyzer
+# Understanding query execution with the analyzer
ClickHouse processes queries extremely quickly, but the execution of a query is not a simple story. Let's try to understand how a `SELECT` query gets executed. To illustrate it, let's add some data in a table in ClickHouse:
@@ -241,7 +241,7 @@ GROUP BY type
You can now see all the inputs, functions, aliases, and data types that are being used. You can see some of the optimizations that the planner is going to apply [here](https://github.com/ClickHouse/ClickHouse/blob/master/src/Processors/QueryPlan/Optimizations/Optimizations.h).
-## Query Pipeline {#query-pipeline}
+## Query pipeline {#query-pipeline}
A query pipeline is generated from the query plan. The query pipeline is very similar to the query plan, with the difference that it's not a tree but a graph. It highlights how ClickHouse is going to execute a query and what resources are going to be used. Analyzing the query pipeline is very useful to see where the bottleneck is in terms of inputs/outputs. Let's take our previous query and look at the query pipeline execution:
diff --git a/docs/guides/examples/aggregate_function_combinators/anyIf.md b/docs/guides/examples/aggregate_function_combinators/anyIf.md
index f3de0478ce9..daa2e4e5ab3 100644
--- a/docs/guides/examples/aggregate_function_combinators/anyIf.md
+++ b/docs/guides/examples/aggregate_function_combinators/anyIf.md
@@ -14,7 +14,7 @@ The [`If`](/sql-reference/aggregate-functions/combinators#-if) combinator can be
aggregate function to select the first encountered element from a given column
that matches the given condition.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores sales data with success flags,
and we'll use `anyIf` to select the first `transaction_id`s which are above and
diff --git a/docs/guides/examples/aggregate_function_combinators/argMaxIf.md b/docs/guides/examples/aggregate_function_combinators/argMaxIf.md
index 566f7ee29ab..9e0968a6c4d 100644
--- a/docs/guides/examples/aggregate_function_combinators/argMaxIf.md
+++ b/docs/guides/examples/aggregate_function_combinators/argMaxIf.md
@@ -18,7 +18,7 @@ The `argMaxIf` function is useful when you need to find the value associated wit
the maximum value in a dataset, but only for rows that satisfy a specific
condition.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll use a sample dataset of product sales to demonstrate how
`argMaxIf` works. We'll find the product name that has the highest price, but
diff --git a/docs/guides/examples/aggregate_function_combinators/argMinIf.md b/docs/guides/examples/aggregate_function_combinators/argMinIf.md
index 76772f77c3c..2399d51525d 100644
--- a/docs/guides/examples/aggregate_function_combinators/argMinIf.md
+++ b/docs/guides/examples/aggregate_function_combinators/argMinIf.md
@@ -18,7 +18,7 @@ The `argMinIf` function is useful when you need to find the value associated
with the minimum value in a dataset, but only for rows that satisfy a specific
condition.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores product prices and their timestamps,
and we'll use `argMinIf` to find the lowest price for each product when it's in stock.
diff --git a/docs/guides/examples/aggregate_function_combinators/avgIf.md b/docs/guides/examples/aggregate_function_combinators/avgIf.md
index 235f6150067..937f180d7cd 100644
--- a/docs/guides/examples/aggregate_function_combinators/avgIf.md
+++ b/docs/guides/examples/aggregate_function_combinators/avgIf.md
@@ -14,7 +14,7 @@ The [`If`](/sql-reference/aggregate-functions/combinators#-if) combinator can be
function to calculate the arithmetic mean of values for rows where the condition is true,
using the `avgIf` aggregate combinator function.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores sales data with success flags,
and we'll use `avgIf` to calculate the average sale amount for successful transactions.
diff --git a/docs/guides/examples/aggregate_function_combinators/avgMap.md b/docs/guides/examples/aggregate_function_combinators/avgMap.md
index 1dcdbd0e51b..51f73f3cf48 100644
--- a/docs/guides/examples/aggregate_function_combinators/avgMap.md
+++ b/docs/guides/examples/aggregate_function_combinators/avgMap.md
@@ -14,7 +14,7 @@ The [`Map`](/sql-reference/aggregate-functions/combinators#-map) combinator can
function to calculate the arithmetic mean of values in a Map according to each key, using the `avgMap`
aggregate combinator function.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores status codes and their counts for different timeslots,
where each row contains a Map of status codes to their corresponding counts. We'll use
diff --git a/docs/guides/examples/aggregate_function_combinators/avgMerge.md b/docs/guides/examples/aggregate_function_combinators/avgMerge.md
index 72fb2c9dbf5..34a9827561f 100644
--- a/docs/guides/examples/aggregate_function_combinators/avgMerge.md
+++ b/docs/guides/examples/aggregate_function_combinators/avgMerge.md
@@ -14,7 +14,7 @@ The [`Merge`](/sql-reference/aggregate-functions/combinators#-state) combinator
can be applied to the [`avg`](/sql-reference/aggregate-functions/reference/avg)
function to produce a final result by combining partial aggregate states.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
The `Merge` combinator is closely related to the `State` combinator. Refer to
["avgState example usage"](/examples/aggregate-function-combinators/avgState/#example-usage)
diff --git a/docs/guides/examples/aggregate_function_combinators/avgMergeState.md b/docs/guides/examples/aggregate_function_combinators/avgMergeState.md
index d4c132b16a3..916e21fb12a 100644
--- a/docs/guides/examples/aggregate_function_combinators/avgMergeState.md
+++ b/docs/guides/examples/aggregate_function_combinators/avgMergeState.md
@@ -18,7 +18,7 @@ can be applied to the [`avg`](/sql-reference/aggregate-functions/reference/avg)
function to merge partial aggregate states of type `AverageFunction(avg, T)` and
return a new intermediate aggregation state.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
The `MergeState` combinator is particularly useful for multi-level aggregation
scenarios where you want to combine pre-aggregated states and maintain them as
@@ -43,7 +43,7 @@ ORDER BY (region, server_id, timestamp);
```
We'll create a server-level aggregation target table and define an Incremental
-Materialized View acting as an insert trigger to it:
+materialized view acting as an insert trigger to it:
```sql
CREATE TABLE server_performance
@@ -88,7 +88,7 @@ AS SELECT
FROM server_performance
GROUP BY region, datacenter;
--- datacenter level table and Materialized View
+-- datacenter level table and materialized view
CREATE TABLE datacenter_performance
(
diff --git a/docs/guides/examples/aggregate_function_combinators/avgResample.md b/docs/guides/examples/aggregate_function_combinators/avgResample.md
index 029efdecdc4..bdbeb9f91d5 100644
--- a/docs/guides/examples/aggregate_function_combinators/avgResample.md
+++ b/docs/guides/examples/aggregate_function_combinators/avgResample.md
@@ -15,7 +15,7 @@ combinator can be applied to the [`count`](/sql-reference/aggregate-functions/re
aggregate function to count values of a specified key column in a fixed number
of intervals (`N`).
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
### Basic example {#basic-example}
diff --git a/docs/guides/examples/aggregate_function_combinators/avgState.md b/docs/guides/examples/aggregate_function_combinators/avgState.md
index fa6536ffb8e..e0e2317701d 100644
--- a/docs/guides/examples/aggregate_function_combinators/avgState.md
+++ b/docs/guides/examples/aggregate_function_combinators/avgState.md
@@ -15,7 +15,7 @@ can be applied to the [`avg`](/sql-reference/aggregate-functions/reference/avg)
function to produce an intermediate state of `AggregateFunction(avg, T)` type where
`T` is the specified type for the average.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll look at how we can use the `AggregateFunction` type,
together with the `avgState` function to aggregate website traffic data.
@@ -49,7 +49,7 @@ ENGINE = AggregatingMergeTree()
ORDER BY page_id;
```
-Create an Incremental Materialized View that will act as an insert trigger to
+Create an Incremental materialized view that will act as an insert trigger to
new data and store the intermediate state data in the target table defined above:
```sql
diff --git a/docs/guides/examples/aggregate_function_combinators/countIf.md b/docs/guides/examples/aggregate_function_combinators/countIf.md
index 1bd7c343e85..55caea9a253 100644
--- a/docs/guides/examples/aggregate_function_combinators/countIf.md
+++ b/docs/guides/examples/aggregate_function_combinators/countIf.md
@@ -14,7 +14,7 @@ The [`If`](/sql-reference/aggregate-functions/combinators#-if) combinator can be
function to count the number of rows where the condition is true,
using the `countIf` aggregate combinator function.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores user login attempts,
and we'll use `countIf` to count the number of successful logins.
diff --git a/docs/guides/examples/aggregate_function_combinators/countResample.md b/docs/guides/examples/aggregate_function_combinators/countResample.md
index c20f0aca74d..f90bb6a168c 100644
--- a/docs/guides/examples/aggregate_function_combinators/countResample.md
+++ b/docs/guides/examples/aggregate_function_combinators/countResample.md
@@ -15,7 +15,7 @@ combinator can be applied to the [`count`](/sql-reference/aggregate-functions/re
aggregate function to count values of a specified key column in a fixed number
of intervals (`N`).
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
### Basic example {#basic-example}
diff --git a/docs/guides/examples/aggregate_function_combinators/groupArrayDistinct.md b/docs/guides/examples/aggregate_function_combinators/groupArrayDistinct.md
index fd98073b543..dd7350258fc 100644
--- a/docs/guides/examples/aggregate_function_combinators/groupArrayDistinct.md
+++ b/docs/guides/examples/aggregate_function_combinators/groupArrayDistinct.md
@@ -14,7 +14,7 @@ The [`groupArrayDistinct`](/sql-reference/aggregate-functions/combinators#-forea
can be applied to the [`groupArray`](/sql-reference/aggregate-functions/reference/sum) aggregate function to create an array
of distinct argument values.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
For this example we'll make use of the `hits` dataset available in our [SQL playground](https://sql.clickhouse.com/).
diff --git a/docs/guides/examples/aggregate_function_combinators/groupArrayResample.md b/docs/guides/examples/aggregate_function_combinators/groupArrayResample.md
index 9abea133bb0..38176eaa49f 100644
--- a/docs/guides/examples/aggregate_function_combinators/groupArrayResample.md
+++ b/docs/guides/examples/aggregate_function_combinators/groupArrayResample.md
@@ -17,7 +17,7 @@ and construct the resulting array by selecting one representative value
(corresponding to the minimum key) from the data points falling into each interval.
It creates a downsampled view of the data rather than collecting all values.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
Let's look at an example. We'll create a table which contains the `name`, `age` and
`wage` of employees, and we'll insert some data into it:
diff --git a/docs/guides/examples/aggregate_function_combinators/maxMap.md b/docs/guides/examples/aggregate_function_combinators/maxMap.md
index dd2e524ac04..e1ffe4907fb 100644
--- a/docs/guides/examples/aggregate_function_combinators/maxMap.md
+++ b/docs/guides/examples/aggregate_function_combinators/maxMap.md
@@ -14,7 +14,7 @@ The [`Map`](/sql-reference/aggregate-functions/combinators#-map) combinator can
function to calculate the maximum value in a Map according to each key, using the `maxMap`
aggregate combinator function.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores status codes and their counts for different timeslots,
where each row contains a Map of status codes to their corresponding counts. We'll use
diff --git a/docs/guides/examples/aggregate_function_combinators/maxSimpleState.md b/docs/guides/examples/aggregate_function_combinators/maxSimpleState.md
index 2b758124b06..729637ce814 100644
--- a/docs/guides/examples/aggregate_function_combinators/maxSimpleState.md
+++ b/docs/guides/examples/aggregate_function_combinators/maxSimpleState.md
@@ -14,7 +14,7 @@ The [`SimpleState`](/sql-reference/aggregate-functions/combinators#-simplestate)
function to return the maximum value across all input values. It returns the
result with type `SimpleAggregateState`.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
The example given in [`minSimpleState`](/examples/aggregate-function-combinators/minSimpleState/#example-usage)
demonstrates a usage of both `maxSimpleState` and `minSimpleState`.
diff --git a/docs/guides/examples/aggregate_function_combinators/minMap.md b/docs/guides/examples/aggregate_function_combinators/minMap.md
index e72d0d86954..e8843244f64 100644
--- a/docs/guides/examples/aggregate_function_combinators/minMap.md
+++ b/docs/guides/examples/aggregate_function_combinators/minMap.md
@@ -14,7 +14,7 @@ The [`Map`](/sql-reference/aggregate-functions/combinators#-map) combinator can
function to calculate the minimum value in a Map according to each key, using the `minMap`
aggregate combinator function.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores status codes and their counts for different timeslots,
where each row contains a Map of status codes to their corresponding counts. We'll use
diff --git a/docs/guides/examples/aggregate_function_combinators/minSimpleState.md b/docs/guides/examples/aggregate_function_combinators/minSimpleState.md
index fdad0a2e374..0d7fa86a4ac 100644
--- a/docs/guides/examples/aggregate_function_combinators/minSimpleState.md
+++ b/docs/guides/examples/aggregate_function_combinators/minSimpleState.md
@@ -14,7 +14,7 @@ The [`SimpleState`](/sql-reference/aggregate-functions/combinators#-simplestate)
function to return the minimum value across all input values. It returns the
result with type [`SimpleAggregateFunction`](/docs/sql-reference/data-types/simpleaggregatefunction).
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
Let's look at a practical example using a table that tracks daily temperature
readings. For each location, we want to maintain the lowest temperature recorded.
@@ -49,7 +49,7 @@ ENGINE = AggregatingMergeTree()
ORDER BY location_id;
```
-Create an Incremental Materialized View that will act as an insert trigger
+Create an Incremental materialized view that will act as an insert trigger
for inserted data and maintains the minimum, maximum temperatures per location.
```sql
@@ -74,7 +74,7 @@ INSERT INTO raw_temperature_readings (location_id, location_name, temperature) V
(4, 'East', 8);
```
-These readings are automatically processed by the Materialized View. Let's check
+These readings are automatically processed by the materialized view. Let's check
the current state:
```sql
diff --git a/docs/guides/examples/aggregate_function_combinators/quantilesTimingArrayIf.md b/docs/guides/examples/aggregate_function_combinators/quantilesTimingArrayIf.md
index 651734121f2..3df6548f982 100644
--- a/docs/guides/examples/aggregate_function_combinators/quantilesTimingArrayIf.md
+++ b/docs/guides/examples/aggregate_function_combinators/quantilesTimingArrayIf.md
@@ -15,7 +15,7 @@ combinator can be applied to the [`quantilesTiming`](/sql-reference/aggregate-fu
function to calculate quantiles of timing values in arrays for rows where the condition is true,
using the `quantilesTimingArrayIf` aggregate combinator function.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores API response times for different endpoints,
and we'll use `quantilesTimingArrayIf` to calculate response time quantiles for successful requests.
diff --git a/docs/guides/examples/aggregate_function_combinators/quantilesTimingIf.md b/docs/guides/examples/aggregate_function_combinators/quantilesTimingIf.md
index 6d33707032c..4dc1ae5743b 100644
--- a/docs/guides/examples/aggregate_function_combinators/quantilesTimingIf.md
+++ b/docs/guides/examples/aggregate_function_combinators/quantilesTimingIf.md
@@ -14,7 +14,7 @@ The [`If`](/sql-reference/aggregate-functions/combinators#-if) combinator can be
function to calculate quantiles of timing values for rows where the condition is true,
using the `quantilesTimingIf` aggregate combinator function.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores API response times for different endpoints,
and we'll use `quantilesTimingIf` to calculate response time quantiles for successful requests.
diff --git a/docs/guides/examples/aggregate_function_combinators/sumArray.md b/docs/guides/examples/aggregate_function_combinators/sumArray.md
index 3569d914cc9..6ce78553bfd 100644
--- a/docs/guides/examples/aggregate_function_combinators/sumArray.md
+++ b/docs/guides/examples/aggregate_function_combinators/sumArray.md
@@ -18,7 +18,7 @@ aggregate combinator function.
The `sumArray` function is useful when you need to calculate the total sum of
all elements across multiple arrays in a dataset.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll use a sample dataset of daily sales across different
product categories to demonstrate how `sumArray` works. We'll calculate the total
diff --git a/docs/guides/examples/aggregate_function_combinators/sumForEach.md b/docs/guides/examples/aggregate_function_combinators/sumForEach.md
index 1b3be84900e..8184ccf01f2 100644
--- a/docs/guides/examples/aggregate_function_combinators/sumForEach.md
+++ b/docs/guides/examples/aggregate_function_combinators/sumForEach.md
@@ -15,7 +15,7 @@ can be applied to the [`sum`](/sql-reference/aggregate-functions/reference/sum)
function which operates on row values to an aggregate function which operates on
array columns, applying the aggregate to each element in the array across rows.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
For this example we'll make use of the `hits` dataset available in our [SQL playground](https://sql.clickhouse.com/).
diff --git a/docs/guides/examples/aggregate_function_combinators/sumIf.md b/docs/guides/examples/aggregate_function_combinators/sumIf.md
index 324868f6344..6161be33acf 100644
--- a/docs/guides/examples/aggregate_function_combinators/sumIf.md
+++ b/docs/guides/examples/aggregate_function_combinators/sumIf.md
@@ -14,7 +14,7 @@ The [`If`](/sql-reference/aggregate-functions/combinators#-if) combinator can be
function to calculate the sum of values for rows where the condition is true,
using the `sumIf` aggregate combinator function.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores sales data with success flags,
and we'll use `sumIf` to calculate the total sales amount for successful transactions.
diff --git a/docs/guides/examples/aggregate_function_combinators/sumMap.md b/docs/guides/examples/aggregate_function_combinators/sumMap.md
index 17829dbb144..fda6b895388 100644
--- a/docs/guides/examples/aggregate_function_combinators/sumMap.md
+++ b/docs/guides/examples/aggregate_function_combinators/sumMap.md
@@ -14,7 +14,7 @@ The [`Map`](/sql-reference/aggregate-functions/combinators#-map) combinator can
function to calculate the sum of values in a Map according to each key, using the `sumMap`
aggregate combinator function.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll create a table that stores status codes and their counts for different timeslots,
where each row contains a Map of status codes to their corresponding counts. We'll use
diff --git a/docs/guides/examples/aggregate_function_combinators/sumSimpleState.md b/docs/guides/examples/aggregate_function_combinators/sumSimpleState.md
index d048462ba85..35b8759ea35 100644
--- a/docs/guides/examples/aggregate_function_combinators/sumSimpleState.md
+++ b/docs/guides/examples/aggregate_function_combinators/sumSimpleState.md
@@ -14,7 +14,7 @@ The [`SimpleState`](/sql-reference/aggregate-functions/combinators#-simplestate)
function to return the sum across all input values. It returns the result with
type [`SimpleAggregateFunction`](/docs/sql-reference/data-types/simpleaggregatefunction).
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
### Tracking upvotes and downvotes {#tracking-post-votes}
@@ -51,7 +51,7 @@ ENGINE = AggregatingMergeTree()
ORDER BY post_id;
```
-We then create a Materialized View with `SimpleAggregateFunction` type columns:
+We then create a materialized view with `SimpleAggregateFunction` type columns:
```sql
CREATE MATERIALIZED VIEW mv_vote_processor TO vote_aggregates
@@ -79,7 +79,7 @@ INSERT INTO raw_votes VALUES
(3, 'downvote');
```
-Query the Materialized View using the `SimpleState` combinator:
+Query the materialized view using the `SimpleState` combinator:
```sql
SELECT
diff --git a/docs/guides/examples/aggregate_function_combinators/uniqArray.md b/docs/guides/examples/aggregate_function_combinators/uniqArray.md
index 8d2181e829a..89f73200e71 100644
--- a/docs/guides/examples/aggregate_function_combinators/uniqArray.md
+++ b/docs/guides/examples/aggregate_function_combinators/uniqArray.md
@@ -19,7 +19,7 @@ The `uniqArray` function is useful when you need to count unique elements across
multiple arrays in a dataset. It's equivalent to using `uniq(arrayJoin())`, where
`arrayJoin` first flattens the arrays and then `uniq` counts the unique elements.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
In this example, we'll use a sample dataset of user interests across different
categories to demonstrate how `uniqArray` works. We'll compare it with
diff --git a/docs/guides/examples/aggregate_function_combinators/uniqArrayIf.md b/docs/guides/examples/aggregate_function_combinators/uniqArrayIf.md
index 04caee8afc3..31470be7e31 100644
--- a/docs/guides/examples/aggregate_function_combinators/uniqArrayIf.md
+++ b/docs/guides/examples/aggregate_function_combinators/uniqArrayIf.md
@@ -21,7 +21,7 @@ condition is true, using the `uniqArrayIf` aggregate combinator function.
This is useful when you want to count unique elements in an array based on
specific conditions without having to use `arrayJoin`.
-## Example Usage {#example-usage}
+## Example usage {#example-usage}
### Count unique products viewed by segment type and engagement level {#count-unique-products}
diff --git a/docs/guides/inserting-data.md b/docs/guides/inserting-data.md
index 2c9a9e2fd5a..4e26d2c13ff 100644
--- a/docs/guides/inserting-data.md
+++ b/docs/guides/inserting-data.md
@@ -10,7 +10,7 @@ show_related_blogs: true
import postgres_inserts from '@site/static/images/guides/postgres-inserts.png';
import Image from '@theme/IdealImage';
-## Basic Example {#basic-example}
+## Basic example {#basic-example}
You can use the familiar `INSERT INTO TABLE` command with ClickHouse. Let's insert some data into the table that we created in the start guide ["Creating Tables in ClickHouse"](./creating-tables).
@@ -38,7 +38,7 @@ user_id message timestamp
102 Sort your data based on your commonly-used queries 2024-11-13 00:00:00 2.718
```
-## Inserting into ClickHouse vs. OLTP Databases {#inserting-into-clickhouse-vs-oltp-databases}
+## Inserting into ClickHouse vs. OLTP databases {#inserting-into-clickhouse-vs-oltp-databases}
As an OLAP (Online Analytical Processing) database, ClickHouse is optimized for high performance and scalability, allowing potentially millions of rows to be inserted per second.
This is achieved through a combination of a highly parallelized architecture and efficient column-oriented compression, but with compromises on immediate consistency.
@@ -51,7 +51,7 @@ These transactions can potentially involve a small number of rows at a time, wit
To achieve high insert performance while maintaining strong consistency guarantees, users should adhere to the simple rules described below when inserting data into ClickHouse.
Following these rules will help to avoid issues users commonly encounter the first time they use ClickHouse, and try to replicate an insert strategy that works for OLTP databases.
-## Best Practices for Inserts {#best-practices-for-inserts}
+## Best practices for Inserts {#best-practices-for-inserts}
### Insert in large batch sizes {#insert-in-large-batch-sizes}
@@ -118,7 +118,7 @@ These are optimized to ensure that inserts are performed correctly and natively
See [Clients and Drivers](/interfaces/cli) for a full list of available ClickHouse clients and drivers.
-### Prefer the Native format {#prefer-the-native-format}
+### Prefer the native format {#prefer-the-native-format}
ClickHouse supports many [input formats](/interfaces/formats) at insert (and query) time.
This is a significant difference with OLTP databases and makes loading data from external sources much easier - especially when coupled with [table functions](/sql-reference/table-functions) and the ability to load data from files on disk.
diff --git a/docs/guides/joining-tables.md b/docs/guides/joining-tables.md
index 1d588e5129b..f6e6073de5a 100644
--- a/docs/guides/joining-tables.md
+++ b/docs/guides/joining-tables.md
@@ -123,7 +123,7 @@ WHERE (VoteTypeId = 2) AND (PostId IN (
Peak memory usage: 250.66 MiB.
```
-## Choosing a join algorithm {#choosing-a-join-algorithm}
+## Choosing a JOIN algorithm {#choosing-a-join-algorithm}
ClickHouse supports a number of [join algorithms](https://clickhouse.com/blog/clickhouse-fully-supports-joins-part1). These algorithms typically trade memory usage for performance. The following provides an overview of the ClickHouse join algorithms based on their relative memory consumption and execution time:
diff --git a/docs/guides/manage-and-deploy-index.md b/docs/guides/manage-and-deploy-index.md
index 68159deef47..388bfcaa4c1 100644
--- a/docs/guides/manage-and-deploy-index.md
+++ b/docs/guides/manage-and-deploy-index.md
@@ -4,7 +4,7 @@ description: 'Overview page for Manage and Deploy'
slug: /guides/manage-and-deploy-index
---
-# Manage and Deploy
+# Manage and deploy
This section contains the following topics:
@@ -12,7 +12,7 @@ This section contains the following topics:
|-------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|
| [Deployment and Scaling](/deployment-guides/index) | Working deployment examples based on the advice provided to ClickHouse users by the ClickHouse Support and Services organization. |
| [Separation of Storage and Compute](/guides/separation-storage-compute) | Guide exploring how you can use ClickHouse and S3 to implement an architecture with separated storage and compute. |
-| [Sizing and Hardware Recommendations](/guides/sizing-and-hardware-recommendations) | Guide discussing general recommendations regarding hardware, compute, memory, and disk configurations for open-source users. |
+| [Sizing and hardware recommendations'](/guides/sizing-and-hardware-recommendations) | Guide discussing general recommendations regarding hardware, compute, memory, and disk configurations for open-source users. |
| [Configuring ClickHouse Keeper](/guides/sre/keeper/clickhouse-keeper) | Information and examples on how to configure ClickHouse Keeper. |
| [Network ports](/guides/sre/network-ports) | List of network ports used by ClickHouse. |
| [Re-balancing Shards](/guides/sre/scaling-clusters) | Recommendations on re-balancing shards. |
diff --git a/docs/guides/separation-storage-compute.md b/docs/guides/separation-storage-compute.md
index 8fe6d258ac8..b404f55e7f1 100644
--- a/docs/guides/separation-storage-compute.md
+++ b/docs/guides/separation-storage-compute.md
@@ -10,7 +10,7 @@ import Image from '@theme/IdealImage';
import BucketDetails from '@site/docs/_snippets/_S3_authentication_and_bucket.md';
import s3_bucket_example from '@site/static/images/guides/s3_bucket_example.png';
-# Separation of Storage and Compute
+# Separation of storage and compute
## Overview {#overview}
@@ -170,7 +170,7 @@ For fault tolerance, you can use multiple ClickHouse server nodes distributed ac
Replication with S3 disks can be accomplished by using the `ReplicatedMergeTree` table engine. See the following guide for details:
- [Replicating a single shard across two AWS regions using S3 Object Storage](/integrations/s3#s3-multi-region).
-## Further Reading {#further-reading}
+## Further reading {#further-reading}
- [SharedMergeTree table engine](/cloud/reference/shared-merge-tree)
- [SharedMergeTree announcement blog](https://clickhouse.com/blog/clickhouse-cloud-boosts-performance-with-sharedmergetree-and-lightweight-updates)
diff --git a/docs/guides/sre/keeper/index.md b/docs/guides/sre/keeper/index.md
index b25fa32da25..116f19cc1f0 100644
--- a/docs/guides/sre/keeper/index.md
+++ b/docs/guides/sre/keeper/index.md
@@ -160,7 +160,7 @@ If you don't have the symlink (`clickhouse-keeper`) you can create it or specify
clickhouse keeper --config /etc/your_path_to_config/config.xml
```
-### Four Letter Word Commands {#four-letter-word-commands}
+### Four letter word commands {#four-letter-word-commands}
ClickHouse Keeper also provides 4lw commands which are almost the same with Zookeeper. Each command is composed of four letters such as `mntr`, `stat` etc. There are some more interesting commands: `stat` gives some general information about the server and connected clients, while `srvr` and `cons` give extended details on server and connections respectively.
@@ -409,7 +409,7 @@ AIOWriteBytes 0 Number of bytes written with Linux or FreeBSD AIO interf
...
```
-### HTTP Control {#http-control}
+### HTTP control {#http-control}
ClickHouse Keeper provides an HTTP interface to check if a replica is ready to receive traffic. It may be used in cloud environments, such as [Kubernetes](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/#define-readiness-probes).
@@ -672,11 +672,11 @@ curl 127.0.0.1:9363/metrics
Please also see the ClickHouse Cloud [Prometheus integration](/integrations/prometheus).
-## ClickHouse Keeper User Guide {#clickhouse-keeper-user-guide}
+## ClickHouse Keeper user guide {#clickhouse-keeper-user-guide}
This guide provides simple and minimal settings to configure ClickHouse Keeper with an example on how to test distributed operations. This example is performed using 3 nodes on Linux.
-### 1. Configure Nodes with Keeper settings {#1-configure-nodes-with-keeper-settings}
+### 1. Configure nodes with Keeper settings {#1-configure-nodes-with-keeper-settings}
1. Install 3 ClickHouse instances on 3 hosts (`chnode1`, `chnode2`, `chnode3`). (View the [Quick Start](/getting-started/install/install.mdx) for details on installing ClickHouse.)
@@ -947,7 +947,7 @@ this avoids having to wait several minutes for Keeper garbage collection
to remove path entries as each time a path is created a new `uuid` is used
in that path; paths are never reused.
-### Example Environment {#example-environment}
+### Example environment {#example-environment}
A three node cluster that will be configured to have ClickHouse Keeper
on all three nodes, and ClickHouse on two of the nodes. This provides
ClickHouse Keeper with three nodes (including a tiebreaker node), and
@@ -1340,7 +1340,7 @@ Sometimes it's necessary to extend experimental keeper node into a cluster. Here
To get confident with the process, here's a [sandbox repository](https://github.com/ClickHouse/keeper-extend-cluster).
-## Unsupported Features {#unsupported-features}
+## Unsupported features {#unsupported-features}
While ClickHouse Keeper aims to be fully compatible with ZooKeeper, there are some features that are currently not implemented (although development is ongoing):
diff --git a/docs/guides/sre/scaling-clusters.md b/docs/guides/sre/scaling-clusters.md
index d2663f78f12..b9a6bfecbcd 100644
--- a/docs/guides/sre/scaling-clusters.md
+++ b/docs/guides/sre/scaling-clusters.md
@@ -6,7 +6,7 @@ description: 'ClickHouse does not support automatic shard rebalancing, so we pro
title: 'Rebalancing Data'
---
-# Rebalancing Data
+# Rebalancing data
ClickHouse does not support automatic shard rebalancing. However, there are ways to rebalance shards in order of preference:
diff --git a/docs/guides/sre/user-management/configuring-ldap.md b/docs/guides/sre/user-management/configuring-ldap.md
index e660e6ac459..e2ca9b41230 100644
--- a/docs/guides/sre/user-management/configuring-ldap.md
+++ b/docs/guides/sre/user-management/configuring-ldap.md
@@ -8,7 +8,7 @@ description: 'Describes how to configure ClickHouse to use LDAP for authenticati
import SelfManaged from '@site/docs/_snippets/_self_managed_only_no_roadmap.md';
-# Configuring ClickHouse to Use LDAP for Authentication and Role Mapping
+# Configuring ClickHouse to use LDAP for authentication and role mapping
VIDEO
diff --git a/docs/materialized-view/refreshable-materialized-view.md b/docs/materialized-view/refreshable-materialized-view.md
index 1d797380ac7..9b2362b868d 100644
--- a/docs/materialized-view/refreshable-materialized-view.md
+++ b/docs/materialized-view/refreshable-materialized-view.md
@@ -1,6 +1,6 @@
---
slug: /materialized-view/refreshable-materialized-view
-title: 'Refreshable Materialized View'
+title: 'Refreshable materialized view'
description: 'How to use materialized views to speed up queries'
keywords: ['refreshable materialized view', 'refresh', 'materialized views', 'speed up queries', 'query optimization']
---
diff --git a/docs/migrations/bigquery/equivalent-concepts.md b/docs/migrations/bigquery/equivalent-concepts.md
index f12bab2bb28..48352b93c42 100644
--- a/docs/migrations/bigquery/equivalent-concepts.md
+++ b/docs/migrations/bigquery/equivalent-concepts.md
@@ -9,7 +9,7 @@ show_related_blogs: true
import bigquery_1 from '@site/static/images/migrations/bigquery-1.png';
import Image from '@theme/IdealImage';
-# BigQuery vs ClickHouse Cloud: Equivalent and different concepts
+# BigQuery vs ClickHouse Cloud: equivalent and different concepts
## Resource organization {#resource-organization}
@@ -21,7 +21,7 @@ The way resources are organized in ClickHouse Cloud is similar to [BigQuery's re
Similar to BigQuery, organizations are the root nodes in the ClickHouse cloud resource hierarchy. The first user you set up in your ClickHouse Cloud account is automatically assigned to an organization owned by the user. The user may invite additional users to the organization.
-### BigQuery Projects vs ClickHouse Cloud Services {#bigquery-projects-vs-clickhouse-cloud-services}
+### BigQuery projects vs ClickHouse Cloud services {#bigquery-projects-vs-clickhouse-cloud-services}
Within organizations, you can create services loosely equivalent to BigQuery projects because stored data in ClickHouse Cloud is associated with a service. There are [several service types available](/cloud/manage/cloud-tiers) in ClickHouse Cloud. Each ClickHouse Cloud service is deployed in a specific region and includes:
@@ -29,15 +29,15 @@ Within organizations, you can create services loosely equivalent to BigQuery pro
2. An object storage folder where the service stores all the data.
3. An endpoint (or multiple endpoints created via ClickHouse Cloud UI console) - a service URL that you use to connect to the service (for example, `https://dv2fzne24g.us-east-1.aws.clickhouse.cloud:8443`)
-### BigQuery Datasets vs ClickHouse Cloud Databases {#bigquery-datasets-vs-clickhouse-cloud-databases}
+### BigQuery datasets vs ClickHouse Cloud databases {#bigquery-datasets-vs-clickhouse-cloud-databases}
ClickHouse logically groups tables into databases. Like BigQuery datasets, ClickHouse databases are logical containers that organize and control access to table data.
-### BigQuery Folders {#bigquery-folders}
+### BigQuery folders {#bigquery-folders}
ClickHouse Cloud currently has no concept equivalent to BigQuery folders.
-### BigQuery Slot reservations and Quotas {#bigquery-slot-reservations-and-quotas}
+### BigQuery slot reservations and quotas {#bigquery-slot-reservations-and-quotas}
Like BigQuery slot reservations, you can [configure vertical and horizontal autoscaling](/manage/scaling#configuring-vertical-auto-scaling) in ClickHouse Cloud. For vertical autoscaling, you can set the minimum and maximum size for the memory and CPU cores of the compute nodes for a service. The service will then scale as needed within those bounds. These settings are also available during the initial service creation flow. Each compute node in the service has the same size. You can change the number of compute nodes within a service with [horizontal scaling](/manage/scaling#manual-horizontal-scaling).
@@ -78,7 +78,7 @@ When presented with multiple options for ClickHouse types, consider the actual r
## Query acceleration techniques {#query-acceleration-techniques}
-### Primary and Foreign keys and Primary index {#primary-and-foreign-keys-and-primary-index}
+### Primary and foreign keys and primary index {#primary-and-foreign-keys-and-primary-index}
In BigQuery, a table can have [primary key and foreign key constraints](https://cloud.google.com/bigquery/docs/information-schema-table-constraints). Typically, primary and foreign keys are used in relational databases to ensure data integrity. A primary key value is normally unique for each row and is not `NULL`. Each foreign key value in a row must be present in the primary key column of the primary key table or be `NULL`. In BigQuery, these constraints are not enforced, but the query optimizer may use this information to optimize queries better.
diff --git a/docs/migrations/postgres/appendix.md b/docs/migrations/postgres/appendix.md
index 99e0286be0c..6e4b762cfbd 100644
--- a/docs/migrations/postgres/appendix.md
+++ b/docs/migrations/postgres/appendix.md
@@ -13,7 +13,7 @@ import Image from '@theme/IdealImage';
Users coming from OLTP systems who are used to ACID transactions should be aware that ClickHouse makes deliberate compromises in not fully providing these in exchange for performance. ClickHouse semantics can deliver high durability guarantees and high write throughput if well understood. We highlight some key concepts below that users should be familiar with prior to working with ClickHouse from Postgres.
-### Shards vs Replicas {#shards-vs-replicas}
+### Shards vs replicas {#shards-vs-replicas}
Sharding and replication are two strategies used for scaling beyond one Postgres instance when storage and/or compute become a bottleneck to performance. Sharding in Postgres involves splitting a large database into smaller, more manageable pieces across multiple nodes. However, Postgres does not support sharding natively. Instead, sharding can be achieved using extensions such as [Citus](https://www.citusdata.com/), in which Postgres becomes a distributed database capable of scaling horizontally. This approach allows Postgres to handle higher transaction rates and larger datasets by spreading the load across several machines. Shards can be row or schema-based in order to provide flexibility for workload types, such as transactional or analytical. Sharding can introduce significant complexity in terms of data management and query execution as it requires coordination across multiple machines and consistency guarantees.
diff --git a/docs/migrations/postgres/data-modeling-techniques.md b/docs/migrations/postgres/data-modeling-techniques.md
index 4a6d57a486d..9a4b17ecee8 100644
--- a/docs/migrations/postgres/data-modeling-techniques.md
+++ b/docs/migrations/postgres/data-modeling-techniques.md
@@ -71,7 +71,7 @@ PARTITION BY toYear(CreationDate)
For a full description of partitioning see ["Table partitions"](/partitions).
-### Applications of Partitions {#applications-of-partitions}
+### Applications of partitions {#applications-of-partitions}
Partitioning in ClickHouse has similar applications as in Postgres but with some subtle differences. More specifically:
@@ -114,7 +114,7 @@ Ok.
- **Query optimization** - While partitions can assist with query performance, this depends heavily on the access patterns. If queries target only a few partitions (ideally one), performance can potentially improve. This is only typically useful if the partitioning key is not in the primary key and you are filtering by it. However, queries that need to cover many partitions may perform worse than if no partitioning is used (as there may possibly be more parts as a result of partitioning). The benefit of targeting a single partition will be even less pronounced to non-existence if the partitioning key is already an early entry in the primary key. Partitioning can also be used to [optimize GROUP BY queries](/engines/table-engines/mergetree-family/custom-partitioning-key#group-by-optimisation-using-partition-key) if values in each partition are unique. However, in general, users should ensure the primary key is optimized and only consider partitioning as a query optimization technique in exceptional cases where access patterns access a specific predictable subset of the day, e.g., partitioning by day, with most queries in the last day.
-### Recommendations for Partitions {#recommendations-for-partitions}
+### Recommendations for partitions {#recommendations-for-partitions}
Users should consider partitioning a data management technique. It is ideal when data needs to be expired from the cluster when operating with time series data e.g. the oldest partition can [simply be dropped](/sql-reference/statements/alter/partition#drop-partitionpart).
diff --git a/docs/use-cases/observability/build-your-own/schema-design.md b/docs/use-cases/observability/build-your-own/schema-design.md
index 496ce09f563..0e6ec581907 100644
--- a/docs/use-cases/observability/build-your-own/schema-design.md
+++ b/docs/use-cases/observability/build-your-own/schema-design.md
@@ -225,7 +225,7 @@ Materialized columns will, by default, not be returned in a `SELECT *`. This is
[Materialized views](/materialized-views) provide a more powerful means of applying SQL filtering and transformations to logs and traces.
-Materialized Views allow users to shift the cost of computation from query time to insert time. A ClickHouse Materialized View is just a trigger that runs a query on blocks of data as they are inserted into a table. The results of this query are inserted into a second "target" table.
+Materialized Views allow users to shift the cost of computation from query time to insert time. A ClickHouse materialized view is just a trigger that runs a query on blocks of data as they are inserted into a table. The results of this query are inserted into a second "target" table.
`) and some basic cast functions and functions to interact with other data types. Also, changed the existing function's name toTime to toTimeWithFixedDate because the function toTime is required for the cast function. [#75735](https://github.com/ClickHouse/ClickHouse/pull/75735) ([Yarik Briukhovetskyi](https://github.com/yariks5s)).
+72459).
+* Hive metastore catalog for Iceberg datalake. [#77677](https://github.com/ClickHouse/ClickHouse/pull/77677) ([scanhex12](https://github.com/scanhex12)).
+* Indexes of type `full_text` were renamed to `gin`. This follows the more familiar terminology of PostgreSQL and other databases. Existing indexes of type `full_text` remain loadable but they will throw an exception (suggesting `gin` indexes instead) when one tries to use them in searches. [#79024](https://github.com/ClickHouse/ClickHouse/pull/79024) ([Robert Schulze](https://github.com/rschu1ze)).
+
+#### Performance Improvement
+* Change the Compact part format to save marks for each substream to be able to read individual subcolumns. Old Compact format is still supported for reads and can be enabled for writes using MergeTree setting `write_marks_for_substreams_in_compact_parts`. It's disabled by default for safer upgrades as it changes the compact parts storage. It will be enabled by default in one of the next releases. [#77940](https://github.com/ClickHouse/ClickHouse/pull/77940) ([Pavel Kruglov](https://github.com/Avogar)).
+* Allow moving conditions with subcolumns to prewhere. [#79489](https://github.com/ClickHouse/ClickHouse/pull/79489) ([Pavel Kruglov](https://github.com/Avogar)).
+* Speed up secondary indices by evaluating their expressions on multiple granules at once. [#64109](https://github.com/ClickHouse/ClickHouse/pull/64109) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Enable `compile_expressions` (JIT compiler for fragments of ordinary expressions) by default. This closes [#51264](https://github.com/ClickHouse/ClickHouse/issues/51264) and [#56386](https://github.com/ClickHouse/ClickHouse/issues/56386) and [#66486](https://github.com/ClickHouse/ClickHouse/issues/66486). [#79907](https://github.com/ClickHouse/ClickHouse/pull/79907) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* New setting introduced: `use_skip_indexes_in_final_exact_mode`. If a query on a `ReplacingMergeTree` table has FINAL clause, reading only table ranges based on skip indexes may produce incorrect result. This setting can ensure that correct results are returned by scanning newer parts that have overlap with primary key ranges returned by the skip index. Set to 0 to disable, 1 to enable. [#78350](https://github.com/ClickHouse/ClickHouse/pull/78350) ([Shankar Iyer](https://github.com/shankar-iyer)).
+* Object storage cluster table functions (e.g. `s3Cluster`) will now assign files to replicas for reading based on consistent hash to improve cache locality. [#77326](https://github.com/ClickHouse/ClickHouse/pull/77326) ([Andrej Hoos](https://github.com/adikus)).
+* Improve performance of `S3Queue`/`AzureQueue` by allowing INSERTs data in parallel (can be enabled with `parallel_inserts=true` queue setting). Previously S3Queue/AzureQueue can only do first part of pipeline in parallel (downloading, parsing), INSERT was single-threaded. And `INSERT`s are almost always the bottleneck. Now it will scale almost linear with `processing_threads_num`. [#77671](https://github.com/ClickHouse/ClickHouse/pull/77671) ([Azat Khuzhin](https://github.com/azat)). More fair max_processed_files_before_commit in S3Queue/AzureQueue. [#79363](https://github.com/ClickHouse/ClickHouse/pull/79363) ([Azat Khuzhin](https://github.com/azat)).
+* Introduced threshold (regulated by setting `parallel_hash_join_threshold`) to fall back to the `hash` algorithm when the size of the right table is below the threshold. [#76185](https://github.com/ClickHouse/ClickHouse/pull/76185) ([Nikita Taranov](https://github.com/nickitat)).
+* Now we use number of replicas to determine task size for reading with parallel replicas enabled. This provides better work distribution between replicas when the amount of data to read is not really big. [#78695](https://github.com/ClickHouse/ClickHouse/pull/78695) ([Nikita Taranov](https://github.com/nickitat)).
+* Allow parallel merging of `uniqExact` states during the final stage of distributed aggregation. [#78703](https://github.com/ClickHouse/ClickHouse/pull/78703) ([Nikita Taranov](https://github.com/nickitat)).
+* Fix possible performance degradation of the parallel merging of `uniqExact` states for aggregation with key. [#78724](https://github.com/ClickHouse/ClickHouse/pull/78724) ([Nikita Taranov](https://github.com/nickitat)).
+* Reduce the number of List Blobs API calls to Azure storage. [#78860](https://github.com/ClickHouse/ClickHouse/pull/78860) ([Julia Kartseva](https://github.com/jkartseva)).
+* Fix performance of the distributed INSERT SELECT with parallel replicas. [#79441](https://github.com/ClickHouse/ClickHouse/pull/79441) ([Azat Khuzhin](https://github.com/azat)).
+* Prevent `LogSeriesLimiter` from doing cleanup on every construction, avoiding lock contention and performance regressions in high-concurrency scenarios. [#79864](https://github.com/ClickHouse/ClickHouse/pull/79864) ([filimonov](https://github.com/filimonov)).
+* Speedup queries with trivial count optimization. [#79945](https://github.com/ClickHouse/ClickHouse/pull/79945) ([Raúl Marín](https://github.com/Algunenano)).
+* Better inlining for some operations with `Decimal`. [#79999](https://github.com/ClickHouse/ClickHouse/pull/79999) ([Konstantin Bogdanov](https://github.com/thevar1able)).
+* Set `input_format_parquet_bloom_filter_push_down` to true by default. Also, fix a mistake in the settings changes history. [#80058](https://github.com/ClickHouse/ClickHouse/pull/80058) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Optimized `ALTER ... DELETE` mutations for parts in which all rows should be deleted. Now, in such cases an empty part is created instead of original without executing a mutation. [#79307](https://github.com/ClickHouse/ClickHouse/pull/79307) ([Anton Popov](https://github.com/CurtizJ)).
+* Avoid extra copying of the block during insertion into Compact part when possible. [#79536](https://github.com/ClickHouse/ClickHouse/pull/79536) ([Pavel Kruglov](https://github.com/Avogar)).
+* Add setting `input_format_max_block_size_bytes` to limit blocks created in input formats in bytes. It can help to avoid high memory usage during data import when rows contains large values. [#79495](https://github.com/ClickHouse/ClickHouse/pull/79495) ([Pavel Kruglov](https://github.com/Avogar)).
+* Remove guard pages for threads and async_socket_for_remote/use_hedge_requests. Change the allocation method in `FiberStack` from `mmap` to `aligned_alloc`. Since this splits VMAs and under heavy load vm.max_map_count can be reached. [#79147](https://github.com/ClickHouse/ClickHouse/pull/79147) ([Sema Checherinda](https://github.com/CheSema)).
+* Lazy Materialization with parallel replicas. [#79401](https://github.com/ClickHouse/ClickHouse/pull/79401) ([Igor Nikonov](https://github.com/devcrafter)).
+
+#### Improvement
+* Added an ability to apply lightweight deletes on the fly (with settings `lightweight_deletes_sync = 0`, `apply_mutations_on_fly = 1`. [#79281](https://github.com/ClickHouse/ClickHouse/pull/79281) ([Anton Popov](https://github.com/CurtizJ)).
+* If data in the pretty format is displayed in the terminal, and a subsequent block has the same column widths, it can continue from the previous block, glue it to the previous block by moving the cursor up. This closes [#79333](https://github.com/ClickHouse/ClickHouse/issues/79333). The feature is controlled by the new setting, `output_format_pretty_glue_chunks`. [#79339](https://github.com/ClickHouse/ClickHouse/pull/79339) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Extend the `isIPAddressInRange` function to `String`, `IPv4`, `IPv6`, `Nullable(String)`, `Nullable(IPv4)`, and `Nullable(IPv6)` data types. [#78364](https://github.com/ClickHouse/ClickHouse/pull/78364) ([YjyJeff](https://github.com/YjyJeff)).
+* Allow changing `PostgreSQL` engine connection pooler settings dynamically. [#78414](https://github.com/ClickHouse/ClickHouse/pull/78414) ([Samay Sharma](https://github.com/samay-sharma)).
+* Allow to specify `_part_offset` in normal projection. This is the first step to build projection index. It can be used with [#58224](https://github.com/ClickHouse/ClickHouse/issues/58224) and can help improve #63207. [#78429](https://github.com/ClickHouse/ClickHouse/pull/78429) ([Amos Bird](https://github.com/amosbird)).
+* Add new columns (`create_query` and `source`) for `system.named_collections`. Closes [#78179](https://github.com/ClickHouse/ClickHouse/issues/78179). [#78582](https://github.com/ClickHouse/ClickHouse/pull/78582) ([MikhailBurdukov](https://github.com/MikhailBurdukov)).
+* Added a new field `condition` to system table `system.query_condition_cache`. It stores the plaintext condition whose hash is used as a key in the query condition cache. [#78671](https://github.com/ClickHouse/ClickHouse/pull/78671) ([Robert Schulze](https://github.com/rschu1ze)).
+* Vector similarity indexes can now be created on top of `BFloat16` columns. [#78850](https://github.com/ClickHouse/ClickHouse/pull/78850) ([Robert Schulze](https://github.com/rschu1ze)).
+* Support unix timestapms with fractional part in best effort `DateTime64` parsing. [#78908](https://github.com/ClickHouse/ClickHouse/pull/78908) ([Pavel Kruglov](https://github.com/Avogar)).
+* In the storage `DeltaLake` delta-kernel implementation, fix for column mapping mode, add tests for schema evolution. [#78921](https://github.com/ClickHouse/ClickHouse/pull/78921) ([Kseniia Sumarokova](https://github.com/kssenii)).
+* Improve insert into `Variant` column in Values format by better conversion of values. [#78923](https://github.com/ClickHouse/ClickHouse/pull/78923) ([Pavel Kruglov](https://github.com/Avogar)).
+* The `tokens` function was extended to accept an additional "tokenizer" argument plus further tokenizer-specific arguments. [#79001](https://github.com/ClickHouse/ClickHouse/pull/79001) ([Elmi Ahmadov](https://github.com/ahmadov)).
+* The `SHOW CLUSTER` statement now expands macros (if any) in its argument. [#79006](https://github.com/ClickHouse/ClickHouse/pull/79006) ([arf42](https://github.com/arf42)).
+* Hash functions now support `NULL`s inside arrays, tuples, and maps. (issues [#48365](https://github.com/ClickHouse/ClickHouse/issues/48365) and [#48623](https://github.com/ClickHouse/ClickHouse/issues/48623)). [#79008](https://github.com/ClickHouse/ClickHouse/pull/79008) ([Michael Kolupaev](https://github.com/al13n321)).
+* Update cctz to 2025a. [#79043](https://github.com/ClickHouse/ClickHouse/pull/79043) ([Raúl Marín](https://github.com/Algunenano)).
+* Change the default stderr processing for UDFs to "log_last". It's better for usability. [#79066](https://github.com/ClickHouse/ClickHouse/pull/79066) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Make tabs undo-able in the Web UI. This closes [#71284](https://github.com/ClickHouse/ClickHouse/issues/71284). [#79084](https://github.com/ClickHouse/ClickHouse/pull/79084) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Remove settings during `recoverLostReplica` same as it was done in: https://github.com/ClickHouse/ClickHouse/pull/78637. [#79113](https://github.com/ClickHouse/ClickHouse/pull/79113) ([Nikita Mikhaylov](https://github.com/nikitamikhaylov)).
+* Add profile events: `ParquetReadRowGroups` and `ParquetPrunedRowGroups` to profile parquet index prune. [#79180](https://github.com/ClickHouse/ClickHouse/pull/79180) ([flynn](https://github.com/ucasfl)).
+* Support `ALTER`ing database on cluster. [#79242](https://github.com/ClickHouse/ClickHouse/pull/79242) ([Tuan Pham Anh](https://github.com/tuanpach)).
+* Explicitly skip missed runs of statistics collection for QueryMetricLog, otherwise the log will take a long time to catch up with the current time. [#79257](https://github.com/ClickHouse/ClickHouse/pull/79257) ([Mikhail Artemenko](https://github.com/Michicosun)).
+* Some small optimizations for reading `Arrow`-based formats. [#79308](https://github.com/ClickHouse/ClickHouse/pull/79308) ([Bharat Nallan](https://github.com/bharatnc)).
+* The setting `allow_archive_path_syntax` was marked as experimental by mistake. Add a test to prevent having experimental settings enabled by default. [#79320](https://github.com/ClickHouse/ClickHouse/pull/79320) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Made page cache settings adjustable on a per-query level. This is needed for faster experimentation and for the possibility of fine-tuning for high-throughput and low-latency queries. [#79337](https://github.com/ClickHouse/ClickHouse/pull/79337) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Do not print number tips in pretty formats for numbers that look like most of the 64-bit hashes. This closes [#79334](https://github.com/ClickHouse/ClickHouse/issues/79334). [#79338](https://github.com/ClickHouse/ClickHouse/pull/79338) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Colors of graphs on the advanced dashboards will be calculated from the hash of the corresponding query. This makes it easier to remember and locate a graph while scrolling the dashboard. [#79341](https://github.com/ClickHouse/ClickHouse/pull/79341) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Add asynchronous metric, `FilesystemCacheCapacity` - total capacity in the `cache` virtual filesystem. This is useful for global infrastructure monitoring. [#79348](https://github.com/ClickHouse/ClickHouse/pull/79348) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Optimize access to system.parts (read columns/indexes size only when requested). [#79352](https://github.com/ClickHouse/ClickHouse/pull/79352) ([Azat Khuzhin](https://github.com/azat)).
+* Calculate the relevant fields for query `'SHOW CLUSTER '` instead of all fields. [#79368](https://github.com/ClickHouse/ClickHouse/pull/79368) ([Tuan Pham Anh](https://github.com/tuanpach)).
+* Allow to specify storage settings for `DatabaseCatalog`. [#79407](https://github.com/ClickHouse/ClickHouse/pull/79407) ([Kseniia Sumarokova](https://github.com/kssenii)).
+* Support local storage in `DeltaLake`. [#79416](https://github.com/ClickHouse/ClickHouse/pull/79416) ([Kseniia Sumarokova](https://github.com/kssenii)).
+* Add a query level setting to enable delta-kernel-rs: `allow_experimental_delta_kernel_rs`. [#79418](https://github.com/ClickHouse/ClickHouse/pull/79418) ([Kseniia Sumarokova](https://github.com/kssenii)).
+* Fix possible endless loop when listing blobs from Azure/S3 blob storage. [#79425](https://github.com/ClickHouse/ClickHouse/pull/79425) ([Alexander Gololobov](https://github.com/davenger)).
+* Add filesystem cache setting `max_size_ratio_to_total_space`. [#79460](https://github.com/ClickHouse/ClickHouse/pull/79460) ([Kseniia Sumarokova](https://github.com/kssenii)).
+* For `clickhouse-benchmark` reconfigure `reconnect` option to take 0, 1 or N as values for reconnecting accordingly. [#79465](https://github.com/ClickHouse/ClickHouse/pull/79465) ([Sachin Kumar Singh](https://github.com/sachinkumarsingh092)).
+* Allow `ALTER TABLE ... MOVE|REPLACE PARTITION` for tables on different `plain_rewritable` disks. [#79566](https://github.com/ClickHouse/ClickHouse/pull/79566) ([Julia Kartseva](https://github.com/jkartseva)).
+* The vector similarity index is now also used if the reference vector is of type `Array(BFloat16)`. [#79745](https://github.com/ClickHouse/ClickHouse/pull/79745) ([Shankar Iyer](https://github.com/shankar-iyer)).
+* Add last_error_message, last_error_trace and query_id to the system.error_log table. Related ticket [#75816](https://github.com/ClickHouse/ClickHouse/issues/75816). [#79836](https://github.com/ClickHouse/ClickHouse/pull/79836) ([Andrei Tinikov](https://github.com/Dolso)).
+* Enable sending crash reports by default. This can be turned off in the server's configuration file. [#79838](https://github.com/ClickHouse/ClickHouse/pull/79838) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* System table `system.functions` now shows in which ClickHouse version functions were first introduced. [#79839](https://github.com/ClickHouse/ClickHouse/pull/79839) ([Robert Schulze](https://github.com/rschu1ze)).
+* Added `access_control_improvements.enable_user_name_access_type` setting. This setting allows enabling/disabling of precise grants for users/roles, introduced in https://github.com/ClickHouse/ClickHouse/pull/72246. You may want to turn this setting off in case you have a cluster with the replicas older than 25.1. [#79842](https://github.com/ClickHouse/ClickHouse/pull/79842) ([pufit](https://github.com/pufit)).
+* Proper implementation of `ASTSelectWithUnionQuery::clone()` method now takes into account `is_normalized` field as well. This might help with [#77569](https://github.com/ClickHouse/ClickHouse/issues/77569). [#79909](https://github.com/ClickHouse/ClickHouse/pull/79909) ([Nikita Mikhaylov](https://github.com/nikitamikhaylov)).
+* Fix the inconsistent formatting of certain queries with the EXCEPT operator. If the left-hand side of the EXCEPT operator ends with `*`, the formatted query loses parentheses and is then parsed as a `*` with the `EXCEPT` modifier. These queries are found by the fuzzer and are unlikely to be found in practice. This closes [#79950](https://github.com/ClickHouse/ClickHouse/issues/79950). [#79952](https://github.com/ClickHouse/ClickHouse/pull/79952) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Small improvement in `JSON` type parsing by using cache of variants deserialization order. [#79984](https://github.com/ClickHouse/ClickHouse/pull/79984) ([Pavel Kruglov](https://github.com/Avogar)).
+* Add setting `s3_slow_all_threads_after_network_error`. [#80035](https://github.com/ClickHouse/ClickHouse/pull/80035) ([Vitaly Baranov](https://github.com/vitlibar)).
+* The logging level about the selected parts to merge was wrong (Information). Closes [#80061](https://github.com/ClickHouse/ClickHouse/issues/80061). [#80062](https://github.com/ClickHouse/ClickHouse/pull/80062) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* trace-visualizer: add runtime/share in tooltips and status messages. [#79040](https://github.com/ClickHouse/ClickHouse/pull/79040) ([Sergei Trifonov](https://github.com/serxa)).
+* trace-visualizer: load data from clickhouse server. [#79042](https://github.com/ClickHouse/ClickHouse/pull/79042) ([Sergei Trifonov](https://github.com/serxa)).
+* Add metrics on failing merges. [#79228](https://github.com/ClickHouse/ClickHouse/pull/79228) ([Miсhael Stetsyuk](https://github.com/mstetsyuk)).
+* `clickhouse-benchmark` will display percentage based on the max iterations if specified. [#79346](https://github.com/ClickHouse/ClickHouse/pull/79346) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
+* Add system.parts table visualizer. [#79437](https://github.com/ClickHouse/ClickHouse/pull/79437) ([Sergei Trifonov](https://github.com/serxa)).
+* Add tool for query latency analyzing. [#79978](https://github.com/ClickHouse/ClickHouse/pull/79978) ([Sergei Trifonov](https://github.com/serxa)).
+
+#### Bug Fix (user-visible misbehavior in an official stable release)
+* Fix renames of columns missing in part. [#76346](https://github.com/ClickHouse/ClickHouse/pull/76346) ([Anton Popov](https://github.com/CurtizJ)).
+* A materialized view can start too late, e.g. after the Kafka table that streams to it. [#72123](https://github.com/ClickHouse/ClickHouse/pull/72123) ([Ilya Golshtein](https://github.com/ilejn)).
+* Fix `SELECT` query rewriting during `VIEW` creation with enabled analyzer. closes [#75956](https://github.com/ClickHouse/ClickHouse/issues/75956). [#76356](https://github.com/ClickHouse/ClickHouse/pull/76356) ([Dmitry Novik](https://github.com/novikd)).
+* Fix applying `async_insert` from server (via `apply_settings_from_server`) (previously leads to `Unknown packet 11 from server` errors on the client). [#77578](https://github.com/ClickHouse/ClickHouse/pull/77578) ([Azat Khuzhin](https://github.com/azat)).
+* Fixed refreshable materialized view in Replicated database not working on newly added replicas. [#77774](https://github.com/ClickHouse/ClickHouse/pull/77774) ([Michael Kolupaev](https://github.com/al13n321)).
+* Fixed refreshable materialized views breaking backups. [#77893](https://github.com/ClickHouse/ClickHouse/pull/77893) ([Michael Kolupaev](https://github.com/al13n321)).
+* Fix old firing logical error for `transform`. [#78247](https://github.com/ClickHouse/ClickHouse/pull/78247) ([Yarik Briukhovetskyi](https://github.com/yariks5s)).
+* Fix some cases where secondary index was not applied with analyzer. Fixes [#65607](https://github.com/ClickHouse/ClickHouse/issues/65607) , fixes [#69373](https://github.com/ClickHouse/ClickHouse/issues/69373). [#78485](https://github.com/ClickHouse/ClickHouse/pull/78485) ([Nikolai Kochetov](https://github.com/KochetovNicolai)).
+* Fix dumping profile events (`NetworkSendElapsedMicroseconds`/`NetworkSendBytes`) for HTTP protocol with compression enabled (the error should not be more then the buffer size, usually around 1MiB). [#78516](https://github.com/ClickHouse/ClickHouse/pull/78516) ([Azat Khuzhin](https://github.com/azat)).
+* Fix analyzer producing LOGICAL_ERROR when JOIN ... USING involves ALIAS column - should produce appropriate error. [#78618](https://github.com/ClickHouse/ClickHouse/pull/78618) ([Yakov Olkhovskiy](https://github.com/yakov-olkhovskiy)).
+* Fix analyzer: CREATE VIEW ... ON CLUSTER fails if SELECT contains positional arguments. [#78663](https://github.com/ClickHouse/ClickHouse/pull/78663) ([Yakov Olkhovskiy](https://github.com/yakov-olkhovskiy)).
+* Fix `Block structure mismatch` error in case of `INSERT SELECT` into table a function with schema inference if `SELECT` has scalar subqueries. [#78677](https://github.com/ClickHouse/ClickHouse/pull/78677) ([Pervakov Grigorii](https://github.com/GrigoryPervakov)).
+* Fix analyzer: with prefer_global_in_and_join=1 for Distributed table in SELECT query `in` function should be replaced by `globalIn`. [#78749](https://github.com/ClickHouse/ClickHouse/pull/78749) ([Yakov Olkhovskiy](https://github.com/yakov-olkhovskiy)).
+* Fixed several types of `SELECT` queries that read from tables with `MongoDB` engine or `mongodb` table function: queries with implicit conversion of const value in `WHERE` clause (e.g. `WHERE datetime = '2025-03-10 00:00:00'`) ; queries with `LIMIT` and `GROUP BY`. Previously, they could return the wrong result. [#78777](https://github.com/ClickHouse/ClickHouse/pull/78777) ([Anton Popov](https://github.com/CurtizJ)).
+* Fix conversion between different JSON types. Not it's performed by simple cast through convertion to/from String. It's less effective but 100% accurate. [#78807](https://github.com/ClickHouse/ClickHouse/pull/78807) ([Pavel Kruglov](https://github.com/Avogar)).
+* Fix logical error during convertion of Dynamic type to Interval. [#78813](https://github.com/ClickHouse/ClickHouse/pull/78813) ([Pavel Kruglov](https://github.com/Avogar)).
+* Fix column rollback on JSON parsing error. [#78836](https://github.com/ClickHouse/ClickHouse/pull/78836) ([Pavel Kruglov](https://github.com/Avogar)).
+* Fix 'bad cast' error when join using constant alias column. [#78848](https://github.com/ClickHouse/ClickHouse/pull/78848) ([Vladimir Cherkasov](https://github.com/vdimir)).
+* Don't allow prewhere in materialized view on columns with different types in view and target table. [#78889](https://github.com/ClickHouse/ClickHouse/pull/78889) ([Pavel Kruglov](https://github.com/Avogar)).
+* Fix logical error during parsing of bad binary data of Variant column. [#78982](https://github.com/ClickHouse/ClickHouse/pull/78982) ([Pavel Kruglov](https://github.com/Avogar)).
+* Throw an exception when the parquet batch size is set to 0. Previously when output_format_parquet_batch_size = 0 ClickHouse would hang. Now this behavior is fixed. [#78991](https://github.com/ClickHouse/ClickHouse/pull/78991) ([daryawessely](https://github.com/daryawessely)).
+* Fix deserialization of variant discriminators with basic format in compact parts. It was introduced in https://github.com/ClickHouse/ClickHouse/pull/55518. [#79000](https://github.com/ClickHouse/ClickHouse/pull/79000) ([Pavel Kruglov](https://github.com/Avogar)).
+* Dictionaries of type `complex_key_ssd_cache` now reject zero or negative `block_size` and `write_buffer_size` parameters (issue [#78314](https://github.com/ClickHouse/ClickHouse/issues/78314)). [#79028](https://github.com/ClickHouse/ClickHouse/pull/79028) ([Elmi Ahmadov](https://github.com/ahmadov)).
+* Avoid using Field for non-aggregated columns in SummingMergeTree. It could lead to unexpected errors with Dynamic/Variant types used in SummingMergeTree. [#79051](https://github.com/ClickHouse/ClickHouse/pull/79051) ([Pavel Kruglov](https://github.com/Avogar)).
+* Fix read from materialized view with Distributed destination table and different header in analyzer. [#79059](https://github.com/ClickHouse/ClickHouse/pull/79059) ([Pavel Kruglov](https://github.com/Avogar)).
+* Fixes a bug where `arrayUnion()` returned extra (incorrect) values on tables that had batch inserts. Fixes [#75057](https://github.com/ClickHouse/ClickHouse/issues/75057). [#79079](https://github.com/ClickHouse/ClickHouse/pull/79079) ([Peter Nguyen](https://github.com/petern48)).
+* Fix segfault in `OpenSSLInitializer`. Closes [#79092](https://github.com/ClickHouse/ClickHouse/issues/79092). [#79097](https://github.com/ClickHouse/ClickHouse/pull/79097) ([Konstantin Bogdanov](https://github.com/thevar1able)).
+* Always set prefix for S3 ListObject. [#79114](https://github.com/ClickHouse/ClickHouse/pull/79114) ([Azat Khuzhin](https://github.com/azat)).
+* Fixes a bug where arrayUnion() returned extra (incorrect) values on tables that had batch inserts. Fixes [#79157](https://github.com/ClickHouse/ClickHouse/issues/79157). [#79158](https://github.com/ClickHouse/ClickHouse/pull/79158) ([Peter Nguyen](https://github.com/petern48)).
+* Fix logical error after filter pushdown. [#79164](https://github.com/ClickHouse/ClickHouse/pull/79164) ([Pervakov Grigorii](https://github.com/GrigoryPervakov)).
+* Fix DeltaLake table engine with delta-kernel implementation being used with http based endpoints, fix NOSIGN. Closes [#78124](https://github.com/ClickHouse/ClickHouse/issues/78124). [#79203](https://github.com/ClickHouse/ClickHouse/pull/79203) ([Kseniia Sumarokova](https://github.com/kssenii)).
+* Keeper fix: Avoid triggering watches on failed multi requests. [#79247](https://github.com/ClickHouse/ClickHouse/pull/79247) ([Antonio Andelic](https://github.com/antonio2368)).
+* Forbid Dynamic and JSON types in IN. With current implementation of `IN` it can lead to incorrect results. Proper support of this types in `IN` is complicated and can be done in future. [#79282](https://github.com/ClickHouse/ClickHouse/pull/79282) ([Pavel Kruglov](https://github.com/Avogar)).
+* Fix check for duplicate paths in JSON type parsing. [#79317](https://github.com/ClickHouse/ClickHouse/pull/79317) ([Pavel Kruglov](https://github.com/Avogar)).
+* Fix SecureStreamSocket connection issues. [#79383](https://github.com/ClickHouse/ClickHouse/pull/79383) ([Konstantin Bogdanov](https://github.com/thevar1able)).
+* Fix loading of plain_rewritable disks containing data. [#79439](https://github.com/ClickHouse/ClickHouse/pull/79439) ([Julia Kartseva](https://github.com/jkartseva)).
+* Fix crash in dynamic subcolumns discovery in Wide parts in MergeTree. [#79466](https://github.com/ClickHouse/ClickHouse/pull/79466) ([Pavel Kruglov](https://github.com/Avogar)).
+* Verify the table name's length only for initial create queries. Do not verify this for secondary creates to avoid backward compatibility issues. [#79488](https://github.com/ClickHouse/ClickHouse/pull/79488) ([Miсhael Stetsyuk](https://github.com/mstetsyuk)).
+* Fixed error `Block structure mismatch` in several cases with tables with sparse columns. [#79491](https://github.com/ClickHouse/ClickHouse/pull/79491) ([Anton Popov](https://github.com/CurtizJ)).
+* Fix two cases of "Logical Error: Can't set alias of * of Asterisk on alias". [#79505](https://github.com/ClickHouse/ClickHouse/pull/79505) ([Raúl Marín](https://github.com/Algunenano)).
+* Fix using incorrect paths when renaming an Atomic database. [#79569](https://github.com/ClickHouse/ClickHouse/pull/79569) ([Tuan Pham Anh](https://github.com/tuanpach)).
+* Fix order by JSON column with other columns. [#79591](https://github.com/ClickHouse/ClickHouse/pull/79591) ([Pavel Kruglov](https://github.com/Avogar)).
+* Fix result duplication when reading from remote with both `use_hedged_requests` and `allow_experimental_parallel_reading_from_replicas` disabled. [#79599](https://github.com/ClickHouse/ClickHouse/pull/79599) ([Eduard Karacharov](https://github.com/korowa)).
+* Fix crash in delta-kernel implementation when using unity catalog. [#79677](https://github.com/ClickHouse/ClickHouse/pull/79677) ([Kseniia Sumarokova](https://github.com/kssenii)).
+* Resolve macros for autodiscovery clusters. [#79696](https://github.com/ClickHouse/ClickHouse/pull/79696) ([Anton Ivashkin](https://github.com/ianton-ru)).
+* Handle incorrectly configured page_cache_limits suitably. [#79805](https://github.com/ClickHouse/ClickHouse/pull/79805) ([Bharat Nallan](https://github.com/bharatnc)).
+* Fixes the result of SQL function `formatDateTime` if a variable-size formatter (e.g. `%W` aka. weekday `Monday` `Tuesday`, etc.) is followed by a compound formatter (a formatter that prints multiple components at once, e.g. `%D` aka. the American date `05/04/25`). [#79835](https://github.com/ClickHouse/ClickHouse/pull/79835) ([Robert Schulze](https://github.com/rschu1ze)).
+* IcebergS3 supports count optimization, but IcebergS3Cluster does not. As a result, the count() result returned in cluster mode may be a multiple of the number of replicas. [#79844](https://github.com/ClickHouse/ClickHouse/pull/79844) ([wxybear](https://github.com/wxybear)).
+* Fixes AMBIGUOUS_COLUMN_NAME error with lazy materialization when no columns are used for query execution until projection. Example, SELECT * FROM t ORDER BY rand() LIMIT 5. [#79926](https://github.com/ClickHouse/ClickHouse/pull/79926) ([Igor Nikonov](https://github.com/devcrafter)).
+* Hide password for query `CREATE DATABASE datalake ENGINE = DataLakeCatalog(\'http://catalog:8181\', \'admin\', \'password\')`. [#79941](https://github.com/ClickHouse/ClickHouse/pull/79941) ([Han Fei](https://github.com/hanfei1991)).
+* Allow to specify an alias in JOIN USING. Specify this alias in case the column was renamed (e.g., because of ARRAY JOIN). Fixes [#73707](https://github.com/ClickHouse/ClickHouse/issues/73707). [#79942](https://github.com/ClickHouse/ClickHouse/pull/79942) ([Nikolai Kochetov](https://github.com/KochetovNicolai)).
+* Allow materialized views with UNIONs to work correctly on new replicas. [#80037](https://github.com/ClickHouse/ClickHouse/pull/80037) ([Samay Sharma](https://github.com/samay-sharma)).
+* Format specifier `%e` in SQL function `parseDateTime` now recognizes single-digit days (e.g. `3`), whereas it previously required space padding (e.g. ` 3`). This makes its behavior compatible with MySQL. To retain the previous behaviour, set setting `parsedatetime_e_requires_space_padding = 1`. (issue [#78243](https://github.com/ClickHouse/ClickHouse/issues/78243)). [#80057](https://github.com/ClickHouse/ClickHouse/pull/80057) ([Robert Schulze](https://github.com/rschu1ze)).
+* Fix warnings `Cannot find 'kernel' in '[...]/memory.stat'` in ClickHouse's log (issue [#77410](https://github.com/ClickHouse/ClickHouse/issues/77410)). [#80129](https://github.com/ClickHouse/ClickHouse/pull/80129) ([Robert Schulze](https://github.com/rschu1ze)).
+* Check stack size in FunctionComparison to avoid stack overflow crash. [#78208](https://github.com/ClickHouse/ClickHouse/pull/78208) ([Julia Kartseva](https://github.com/jkartseva)).
+* Fix race during SELECT from `system.workloads`. [#78743](https://github.com/ClickHouse/ClickHouse/pull/78743) ([Sergei Trifonov](https://github.com/serxa)).
+* Fix: lazy materialization in distributed queries. [#78815](https://github.com/ClickHouse/ClickHouse/pull/78815) ([Igor Nikonov](https://github.com/devcrafter)).
+* Fix `Array(Bool)` to `Array(FixedString)` conversion. [#78863](https://github.com/ClickHouse/ClickHouse/pull/78863) ([Nikita Taranov](https://github.com/nickitat)).
+* Make parquet version selection less confusing. [#78818](https://github.com/ClickHouse/ClickHouse/pull/78818) ([Michael Kolupaev](https://github.com/al13n321)).
+* Fix `ReservoirSampler` self-merging. [#79031](https://github.com/ClickHouse/ClickHouse/pull/79031) ([Nikita Taranov](https://github.com/nickitat)).
+* Fix storage of insertion table in client context. [#79046](https://github.com/ClickHouse/ClickHouse/pull/79046) ([Pervakov Grigorii](https://github.com/GrigoryPervakov)).
+* Fix the destruction order of data members of `AggregatingSortedAlgorithm` and `SummingSortedAlgorithm`. [#79056](https://github.com/ClickHouse/ClickHouse/pull/79056) ([Nikita Taranov](https://github.com/nickitat)).
+* `enable_user_name_access_type` must not affect `DEFINER` access type. [#80026](https://github.com/ClickHouse/ClickHouse/pull/80026) ([pufit](https://github.com/pufit)).
+* Query to system database can hang if system database metadata located in keeper. [#79304](https://github.com/ClickHouse/ClickHouse/pull/79304) ([Mikhail Artemenko](https://github.com/Michicosun)).
+
+#### Build/Testing/Packaging Improvement
+* Make it possible to reuse the built `chcache` binary instead of always rebuilding it. [#78851](https://github.com/ClickHouse/ClickHouse/pull/78851) ([János Benjamin Antal](https://github.com/antaljanosbenjamin)).
+* Add NATS pause waiting. [#78987](https://github.com/ClickHouse/ClickHouse/pull/78987) ([Dmitry Novikov](https://github.com/dmitry-sles-novikov)).
+* Fix for incorrectly publishing ARM build as amd64compat. [#79122](https://github.com/ClickHouse/ClickHouse/pull/79122) ([Alexander Gololobov](https://github.com/davenger)).
+* Use generated ahead of time assembly for OpenSSL. [#79386](https://github.com/ClickHouse/ClickHouse/pull/79386) ([Konstantin Bogdanov](https://github.com/thevar1able)).
+* Fixes to allow building with `clang20`. [#79588](https://github.com/ClickHouse/ClickHouse/pull/79588) ([Konstantin Bogdanov](https://github.com/thevar1able)).
+* `chcache`: Rust caching support. [#78691](https://github.com/ClickHouse/ClickHouse/pull/78691) ([Konstantin Bogdanov](https://github.com/thevar1able)).
+* Add unwind information for `zstd` assembly files. [#79288](https://github.com/ClickHouse/ClickHouse/pull/79288) ([Michael Kolupaev](https://github.com/al13n321)).
+
+
### ClickHouse release 25.4, 2025-04-22 {#254}
#### Backward Incompatible Change
diff --git a/docs/whats-new/roadmap.md b/docs/whats-new/roadmap.md
index d30f4c68a49..93f7d5313dd 100644
--- a/docs/whats-new/roadmap.md
+++ b/docs/whats-new/roadmap.md
@@ -5,13 +5,13 @@ sidebar_position: 50
description: 'Present and past ClickHouse road maps'
---
-## Current Roadmap {#current-roadmap}
+## Current roadmap {#current-roadmap}
The current roadmap is published for open discussion:
- [2025](https://github.com/ClickHouse/ClickHouse/issues/74046)
-## Previous Roadmaps {#previous-roadmaps}
+## Previous roadmaps {#previous-roadmaps}
- [2024](https://github.com/ClickHouse/ClickHouse/issues/58392)
- [2023](https://github.com/ClickHouse/ClickHouse/issues/44767)
diff --git a/docs/whats-new/security-changelog.md b/docs/whats-new/security-changelog.md
index 1c79dca658c..2f02469a7e5 100644
--- a/docs/whats-new/security-changelog.md
+++ b/docs/whats-new/security-changelog.md
@@ -1,12 +1,12 @@
---
slug: /whats-new/security-changelog
sidebar_position: 20
-sidebar_label: 'Security Changelog'
-title: 'Security Changelog'
+sidebar_label: 'Security changelog'
+title: 'Security changelog'
description: 'Security changelog detailing security related updates and changes'
---
-# Security Changelog
+# Security changelog
## Fixed in ClickHouse v25.1.5.5, 2025-01-05 {#fixed-in-clickhouse-release-2025-01-05}
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/chdb/install/bun.md b/i18n/jp/docusaurus-plugin-content-docs/current/chdb/install/bun.md
index 59e5bac14db..058cd1c3c64 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/chdb/install/bun.md
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/chdb/install/bun.md
@@ -42,7 +42,9 @@ var result = query("SELECT version()", "CSV");
console.log(result); // 23.10.1.1
```
+
### Session.Query(query, *format) {#sessionqueryquery-format}
+
```javascript
const sess = new Session('./chdb-bun-tmp');
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/get-started/cloud-quick-start.mdx b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/get-started/cloud-quick-start.mdx
index 02b9c0a8828..e1afdf787f2 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/get-started/cloud-quick-start.mdx
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/get-started/cloud-quick-start.mdx
@@ -26,8 +26,6 @@ import client_details from '@site/static/images/_snippets/client_details.png';
import new_rows_from_csv from '@site/static/images/_snippets/new_rows_from_csv.png';
import SQLConsoleDetail from '@site/i18n/jp/docusaurus-plugin-content-docs/current/_snippets/_launch_sql_console.md';
-```md
-
# ClickHouse Cloud クイックスタート
ClickHouse を始める最も迅速で簡単な方法は、[ClickHouse Cloud](https://console.clickhouse.cloud) に新しいサービスを作成することです。
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/manage/billing/marketplace/index.md b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/manage/billing/marketplace/index.md
index 41113920eed..b9c1b2dd71a 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/manage/billing/marketplace/index.md
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/manage/billing/marketplace/index.md
@@ -8,8 +8,6 @@
- 'GCP'
---
-
-
このセクションでは、マーケットプレイスに関連する請求トピックについて詳しく説明します。
| ページ | 説明 |
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
index 1dc21f9d681..a266bd73f75 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
@@ -146,7 +146,7 @@ ClickHouse Cloudの安定した使用を確保し、ベストプラクティス
[Golang](https://github.com/ClickHouse/clickhouse-go/releases/tag/v2.30.1)、[Python](https://github.com/ClickHouse/clickhouse-connect/releases/tag/v0.8.11)、および[NodeJS](https://github.com/ClickHouse/clickhouse-js/releases/tag/1.10.1)クライアントが、Dynamic、Variant、およびJSONタイプリクエストをサポートしました。
-### DBT support for Refreshable Materialized Views {#dbt-support-for-refreshable-materialized-views}
+### DBT support for refreshable materialized views {#dbt-support-for-refreshable-materialized-views}
DBTは、`1.8.7`リリースで[リフレッシュ可能なマテリアライズドビュー](https://github.com/ClickHouse/dbt-clickhouse/releases/tag/v1.8.7)をサポートしています。
@@ -297,7 +297,7 @@ ClickHouse Cloudは、いくつかの請求およびスケーリングイベン
多要素認証を使用している顧客は、電話を失ったりトークンを誤って削除した場合に使用できる回復コードを取得できるようになりました。初めてMFAに登録する顧客には、設定時にコードが提供されます。既存のMFAを持っている顧客は、既存のMFAトークンを削除し新しいトークンを追加することで回復コードを取得できます。
-### ClickPipes Update: Custom Certificates, Latency Insights, and More! {#clickpipes-update-custom-certificates-latency-insights-and-more}
+### ClickPipes update: custom certificates, latency insights, and more! {#clickpipes-update-custom-certificates-latency-insights-and-more}
ClickPipes、データをClickHouseサービスに取り込むための最も簡単な方法に関する最新の更新情報をお知らせできることを嬉しく思います！これらの新機能は、データ取り込みの制御を強化し、パフォーマンスメトリクスへの可視化を提供することを目的としています。
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/security/personal-data-access.md b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/security/personal-data-access.md
index 1ea5b38d0dd..6e72e55f9b9 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/security/personal-data-access.md
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/security/personal-data-access.md
@@ -39,7 +39,7 @@ ClickHouseが収集する個人データやその使用方法については、C
注意: `OrgID`を含むURLは、特定のアカウントの`OrgID`を反映するように更新する必要があります。
-### Current Customers {#current-customers}
+### Current customers {#current-customers}
弊社とアカウントをお持ちで、セルフサービスオプションで個人データの問題が解決しない場合、プライバシーポリシーに基づきデータ主体アクセス要求を提出できます。そのためには、ClickHouseアカウントにログインし、[サポートケース](https://console.clickhouse.cloud/support)を開いてください。これにより、あなたの身元を確認し、リクエストに対応するプロセスをスムーズに進めることができます。
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/managing-data/core-concepts/shards.md b/i18n/jp/docusaurus-plugin-content-docs/current/managing-data/core-concepts/shards.md
index 689171ef683..9072126e4e5 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/managing-data/core-concepts/shards.md
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/managing-data/core-concepts/shards.md
@@ -52,6 +52,7 @@ CREATE TABLE uk.uk_price_paid_simple_dist ON CLUSTER test_cluster
price UInt32
)
ENGINE = Distributed('test_cluster', 'uk', 'uk_price_paid_simple', rand())
+```
`ON CLUSTER` 句により、DDL ステートメントは [分散 DDL ステートメント](/sql-reference/distributed-ddl) となり、ClickHouse に `test_cluster` [クラスター定義](/architecture/horizontal-scaling#replication-and-sharding-configuration) にリストされているすべてのサーバーでテーブルを作成するよう指示します。分散 DDL には、[クラスターアーキテクチャ](/architecture/horizontal-scaling#architecture-diagram) において追加の [Keeper](https://clickhouse.com/clickhouse/keeper) コンポーネントが必要です。
diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md b/i18n/zh/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
index 567c07d020c..0d0e7992055 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
@@ -201,7 +201,7 @@ Users can schedule upgrades for their services. This feature is supported for En
[Golang](https://github.com/ClickHouse/clickhouse-go/releases/tag/v2.30.1), [Python](https://github.com/ClickHouse/clickhouse-connect/releases/tag/v0.8.11), and [NodeJS](https://github.com/ClickHouse/clickhouse-js/releases/tag/1.10.1) clients added support for Dynamic, Variant, and JSON types.
-### DBT support for Refreshable Materialized Views {#dbt-support-for-refreshable-materialized-views}
+### DBT support for refreshable materialized views {#dbt-support-for-refreshable-materialized-views}
DBT now [supports Refreshable Materialized Views](https://github.com/ClickHouse/dbt-clickhouse/releases/tag/v1.8.7) in the `1.8.7` release.
@@ -360,7 +360,7 @@ Compute-compute separation allows you to designate specific services as read-wri
Customers using multi-factor authentication can now obtain recovery codes that can be used in the event of a lost phone or accidentally deleted token. Customers enrolling in MFA for the first time will be provided the code on set up. Customers with existing MFA can obtain a recovery code by removing their existing MFA token and adding a new one.
-### ClickPipes Update: Custom Certificates, Latency Insights, and More! {#clickpipes-update-custom-certificates-latency-insights-and-more}
+### ClickPipes update: custom certificates, latency insights, and more! {#clickpipes-update-custom-certificates-latency-insights-and-more}
We're excited to share the latest updates for ClickPipes, the easiest way to ingest data into your ClickHouse service! These new features are designed to enhance your control over data ingestion and provide greater visibility into performance metrics.
@@ -414,7 +414,7 @@ ClickPipes is the easiest way to ingest data into ClickHouse Cloud. We're happy
## July 18, 2024 {#july-18-2024}
-### Prometheus Endpoint for Metrics is now Generally Available {#prometheus-endpoint-for-metrics-is-now-generally-available}
+### Prometheus endpoint for metrics is now generally available {#prometheus-endpoint-for-metrics-is-now-generally-available}
In our last cloud changelog, we announced the Private Preview for exporting [Prometheus](https://prometheus.io/) metrics from ClickHouse Cloud. This feature allows you to use the [ClickHouse Cloud API](/cloud/manage/api/api-overview) to get your metrics into tools like [Grafana](https://grafana.com/) and [Datadog](https://www.datadoghq.com/) for visualization. We're happy to announce that this feature is now **Generally Available**. Please see [our docs](/integrations/prometheus) to learn more about this feature.
@@ -449,13 +449,13 @@ This release also includes support for subscriptions via the [Microsoft Azure Ma
If you'd like any specific region to be supported, please [contact us](https://clickhouse.com/support/program).
-### Query Log Insights {#query-log-insights}
+### Query log insights {#query-log-insights}
Our new Query Insights UI in the Cloud Console makes ClickHouse's built-in query log a lot easier to use. ClickHouse's `system.query_log` table is a key source of information for query optimization, debugging, and monitoring overall cluster health and performance. There's just one caveat: with 70+ fields and multiple records per query, interpreting the query log represents a steep learning curve. This initial version of query insights provides a blueprint for future work to simplify query debugging and optimization patterns. We'd love to hear your feedback as we continue to iterate on this feature, so please reach out—your input will be greatly appreciated!