Fixed broken links

.github/workflows/deploy-docs.yml

@@ -30,8 +30,9 @@ jobs:
         run:  |
           mkdir tmp
           curl https://codeload.github.com/ClickHouse/ClickHouse/tar.gz/docs-staging | tar -xz -C tmp/ --strip=2 "ClickHouse-docs-staging/docs/"
-          mv tmp/en/ docs/en/reference/
-          mv tmp/ru/ docs/ru/
+          mv tmp/en/ docs/
+          mv tmp/ru/ docs/
+          mv tmp/zh/ docs/
       # Install and build Docusaurus website
       - name: Build Docusaurus website
         run: |
@@ -45,7 +46,7 @@ jobs:
           deploy_key: ${{ secrets.ROBOT_CLICKHOUSE_SSH_KEY }}
           external_repository: ClickHouse/clickhouse-docs-content
           publish_branch: main
-          publish_dir: build      
+          publish_dir: docs
         # uses: JamesIves/[email protected]
         # with:
         #   repository-name: ClickHouse/clickhouse-docs-content

docs/en/about-us/_category_.yml

@@ -4,5 +4,4 @@ collapsible: true
 collapsed: true
 link:
   type: generated-index
-  title: About Us
-  slug: /about-us
+  title: About Us

docs/en/about-us/distinctive-features.md

@@ -18,7 +18,7 @@ It’s also worth noting that ClickHouse is a database management system, not a
 
 Some column-oriented DBMSs do not use data compression. However, data compression does play a key role in achieving excellent performance.
 
-In addition to efficient general-purpose compression codecs with different trade-offs between disk space and CPU consumption, ClickHouse provides [specialized codecs](../reference/sql-reference/statements/create/table.md#create-query-specialized-codecs) for specific kinds of data, which allow ClickHouse to compete with and outperform more niche databases, like time-series ones.
+In addition to efficient general-purpose compression codecs with different trade-offs between disk space and CPU consumption, ClickHouse provides [specialized codecs](../sql-reference/statements/create/table.md#create-query-specialized-codecs) for specific kinds of data, which allow ClickHouse to compete with and outperform more niche databases, like time-series ones.
 
 ## Disk Storage of Data {#disk-storage-of-data}
 
@@ -38,9 +38,9 @@ In ClickHouse, data can reside on different shards. Each shard can be a group of
 
 ## SQL Support {#sql-support}
 
-ClickHouse supports a [declarative query language based on SQL](/en/sql-reference/) that is identical to the ANSI SQL standard in [many cases](../reference/sql-reference/ansi.md).
+ClickHouse supports a [declarative query language based on SQL](/en/sql-reference/) that is identical to the ANSI SQL standard in [many cases](../sql-reference/ansi.md).
 
-Supported queries include [GROUP BY](../reference/sql-reference/statements/select/group-by.md), [ORDER BY](../reference/sql-reference/statements/select/order-by.md), subqueries in [FROM](../reference/sql-reference/statements/select/from.md), [JOIN](../reference/sql-reference/statements/select/join.md) clause, [IN](../reference/sql-reference/operators/in.md) operator, [window functions](../reference/sql-reference/window-functions/) and scalar subqueries.
+Supported queries include [GROUP BY](../sql-reference/statements/select/group-by.md), [ORDER BY](../sql-reference/statements/select/order-by.md), subqueries in [FROM](../sql-reference/statements/select/from.md), [JOIN](../sql-reference/statements/select/join.md) clause, [IN](../sql-reference/operators/in.md) operator, [window functions](../sql-reference/window-functions/) and scalar subqueries.
 
 Correlated (dependent) subqueries are not supported at the time of writing but might become available in the future.
 
@@ -58,7 +58,7 @@ Having a data physically sorted by primary key makes it possible to extract data
 
 ## Secondary Indexes {#secondary-indexes}
 
-Unlike other database management systems, secondary indexes in ClickHouse does not point to specific rows or row ranges. Instead, they allow the database to know in advance that all rows in some data parts wouldn’t match the query filtering conditions and do not read them at all, thus they are called [data skipping indexes](../reference/engines/table-engines/mergetree-family/mergetree.md#table_engine-mergetree-data_skipping-indexes).
+Unlike other database management systems, secondary indexes in ClickHouse does not point to specific rows or row ranges. Instead, they allow the database to know in advance that all rows in some data parts wouldn’t match the query filtering conditions and do not read them at all, thus they are called [data skipping indexes](../engines/table-engines/mergetree-family/mergetree.md#table_engine-mergetree-data_skipping-indexes).
 
 ## Suitable for Online Queries {#suitable-for-online-queries}
 
@@ -76,17 +76,17 @@ ClickHouse provides various ways to trade accuracy for performance:
 
 ## Adaptive Join Algorithm {#adaptive-join-algorithm}
 
-ClickHouse adaptively chooses how to [JOIN](../reference/sql-reference/statements/select/join.md) multiple tables, by preferring hash-join algorithm and falling back to the merge-join algorithm if there’s more than one large table.
+ClickHouse adaptively chooses how to [JOIN](../sql-reference/statements/select/join.md) multiple tables, by preferring hash-join algorithm and falling back to the merge-join algorithm if there’s more than one large table.
 
 ## Data Replication and Data Integrity Support {#data-replication-and-data-integrity-support}
 
 ClickHouse uses asynchronous multi-master replication. After being written to any available replica, all the remaining replicas retrieve their copy in the background. The system maintains identical data on different replicas. Recovery after most failures is performed automatically, or semi-automatically in complex cases.
 
-For more information, see the section [Data replication](../reference/engines/table-engines/mergetree-family/replication.md).
+For more information, see the section [Data replication](../engines/table-engines/mergetree-family/replication.md).
 
 ## Role-Based Access Control {#role-based-access-control}
 
-ClickHouse implements user account management using SQL queries and allows for [role-based access control configuration](../reference/operations/access-rights.md) similar to what can be found in ANSI SQL standard and popular relational database management systems.
+ClickHouse implements user account management using SQL queries and allows for [role-based access control configuration](../operations/access-rights.md) similar to what can be found in ANSI SQL standard and popular relational database management systems.
 
 ## Features that Can Be Considered Disadvantages {#clickhouse-features-that-can-be-considered-disadvantages}
 

docs/en/connect-a-ui/_category_.yml

@@ -4,5 +4,4 @@ collapsible: true
 collapsed: true
 link:
   type: generated-index
-  title: Connect a UI
-  slug: /en/connect-a-ui
+  title: Connect a UI

docs/en/faq/general/columnar-database.md

@@ -22,4 +22,4 @@ Here is the illustration of the difference between traditional row-oriented syst
 **Columnar**
 ![Columnar](https://clickhouse.com/docs/en/images/column-oriented.gif#)
 
-A columnar database is a preferred choice for analytical applications because it allows to have many columns in a table just in case, but do not pay the cost for unused columns on read query execution time. Column-oriented databases are designed for big data processing because and data warehousing, they often natively scale using distributed clusters of low-cost hardware to increase throughput. ClickHouse does it with combination of [distributed](../../en/engines/table-engines/special/distributed.md) and [replicated](../../en/engines/table-engines/mergetree-family/replication.md) tables.
+A columnar database is a preferred choice for analytical applications because it allows to have many columns in a table just in case, but do not pay the cost for unused columns on read query execution time. Column-oriented databases are designed for big data processing because and data warehousing, they often natively scale using distributed clusters of low-cost hardware to increase throughput. ClickHouse does it with combination of [distributed](../../engines/table-engines/special/distributed.md) and [replicated](../../engines/table-engines/mergetree-family/replication.md) tables.

docs/en/faq/general/why-clickhouse-is-so-fast.md

@@ -8,7 +8,7 @@ toc_priority: 8
 
 It was designed to be fast. Query execution performance has always been a top priority during the development process, but other important characteristics like user-friendliness, scalability, and security were also considered so ClickHouse could become a real production system.
 
-ClickHouse was initially built as a prototype to do just a single task well: to filter and aggregate data as fast as possible. That’s what needs to be done to build a typical analytical report and that’s what a typical [GROUP BY](../../en/sql-reference/statements/select/group-by/) query does. ClickHouse team has made several high-level decisions that combined made achieving this task possible:
+ClickHouse was initially built as a prototype to do just a single task well: to filter and aggregate data as fast as possible. That’s what needs to be done to build a typical analytical report and that’s what a typical [GROUP BY](../../sql-reference/statements/select/group-by/) query does. ClickHouse team has made several high-level decisions that combined made achieving this task possible:
 
 Column-oriented storage
 :   Source data often contain hundreds or even thousands of columns, while a report can use just a few of them. The system needs to avoid reading unnecessary columns, or most expensive disk read operations would be wasted.
@@ -17,7 +17,7 @@ Indexes
 :   ClickHouse keeps data structures in memory that allows reading not only used columns but only necessary row ranges of those columns.
 
 Data compression
-:   Storing different values of the same column together often leads to better compression ratios (compared to row-oriented systems) because in real data column often has the same or not so many different values for neighboring rows. In addition to general-purpose compression, ClickHouse supports [specialized codecs](../../en/sql-reference/statements/create/table/#create-query-specialized-codecs) that can make data even more compact.
+:   Storing different values of the same column together often leads to better compression ratios (compared to row-oriented systems) because in real data column often has the same or not so many different values for neighboring rows. In addition to general-purpose compression, ClickHouse supports [specialized codecs](../../sql-reference/statements/create/table/#create-query-specialized-codecs) that can make data even more compact.
 
 Vectorized query execution
 :   ClickHouse not only stores data in columns but also processes data in columns. It leads to better CPU cache utilization and allows for [SIMD](https://en.wikipedia.org/wiki/SIMD) CPU instructions usage.

docs/en/faq/integration/file-export.md

@@ -8,15 +8,15 @@ toc_priority: 10
 
 ## Using INTO OUTFILE Clause {#using-into-outfile-clause}
 
-Add an [INTO OUTFILE](../../en/sql-reference/statements/select/into-outfile.md) clause to your query.
+Add an [INTO OUTFILE](../../sql-reference/statements/select/into-outfile) clause to your query.
 
 For example:
 
 ``` sql
 SELECT * FROM table INTO OUTFILE 'file'
 ```
 
-By default, ClickHouse uses the [TabSeparated](../../en/interfaces/formats.md) format for output data. To select the [data format](../../en/interfaces/formats.md), use the [FORMAT clause](../../en/sql-reference/statements/select/format.md).
+By default, ClickHouse uses the [TabSeparated](../../interfaces/formats.md) format for output data. To select the [data format](../../interfaces/formats.md), use the [FORMAT clause](../../sql-reference/statements/select/format.md).
 
 For example:
 
@@ -26,12 +26,12 @@ SELECT * FROM table INTO OUTFILE 'file' FORMAT CSV
 
 ## Using a File-Engine Table {#using-a-file-engine-table}
 
-See [File](../../en/engines/table-engines/special/file.md) table engine.
+See [File](../../engines/table-engines/special/file.md) table engine.
 
 ## Using Command-Line Redirection {#using-command-line-redirection}
 
 ``` bash
 $ clickhouse-client --query "SELECT * from table" --format FormatName > result.txt
 ```
 
-See [clickhouse-client](../../en/interfaces/cli.md).
+See [clickhouse-client](../../interfaces/cli.md).

docs/en/faq/integration/index.md

@@ -15,7 +15,7 @@ keywords: [clickhouse, faq, questions, integrations]
 -   [What if I have a problem with encodings when connecting to Oracle via ODBC?](../../faq/integration/oracle-odbc.md)
 
 :::info Don’t see what you're looking for?
-Check out our [other FAQ categories](../../faq/index.md) and also browse the many helpful articles found here in the documentation.
+Check out our [other FAQ categories](../../faq/) and also browse the many helpful articles found here in the documentation.
 :::
 
 [Original article](https://clickhouse.com/docs/faq/integration/)

docs/en/faq/integration/json-import.md

@@ -6,28 +6,28 @@ toc_priority: 11
 
 # How to Import JSON Into ClickHouse? {#how-to-import-json-into-clickhouse}
 
-ClickHouse supports a wide range of [data formats for input and output](../../en/interfaces/formats/). There are multiple JSON variations among them, but the most commonly used for data ingestion is [JSONEachRow](../../en/interfaces/formats/#jsoneachrow). It expects one JSON object per row, each object separated by a newline.
+ClickHouse supports a wide range of [data formats for input and output](../../interfaces/formats/). There are multiple JSON variations among them, but the most commonly used for data ingestion is [JSONEachRow](../../interfaces/formats/#jsoneachrow). It expects one JSON object per row, each object separated by a newline.
 
 ## Examples {#examples}
 
-Using [HTTP interface](../../en/interfaces/http/):
+Using [HTTP interface](../../interfaces/http/):
 
 ``` bash
 $ echo '{"foo":"bar"}' | curl 'http://localhost:8123/?query=INSERT%20INTO%20test%20FORMAT%20JSONEachRow' --data-binary @-
 ```
 
-Using [CLI interface](../../en/interfaces/cli/):
+Using [CLI interface](../../interfaces/cli/):
 
 ``` bash
 $ echo '{"foo":"bar"}'  | clickhouse-client --query="INSERT INTO test FORMAT JSONEachRow"
 ```
 
-Instead of inserting data manually, you might consider to use one of [client libraries](../../en/interfaces/) instead.
+Instead of inserting data manually, you might consider to use one of [client libraries](../../interfaces/) instead.
 
 ## Useful Settings {#useful-settings}
 
 -   `input_format_skip_unknown_fields` allows to insert JSON even if there were additional fields not present in table schema (by discarding them).
--   `input_format_import_nested_json` allows to insert nested JSON objects into columns of [Nested](../../en/sql-reference/data-types/nested-data-structures/nested/) type.
+-   `input_format_import_nested_json` allows to insert nested JSON objects into columns of [Nested](../../sql-reference/data-types/nested-data-structures/nested/) type.
 
 :::note    
 Settings are specified as `GET` parameters for the HTTP interface or as additional command-line arguments prefixed with `--` for the `CLI` interface.

docs/en/faq/operations/delete-old-data.md

@@ -18,7 +18,7 @@ The key advantage of this approach is that it does not need any external system
 TTL can also be used to move data not only to [/dev/null](https://en.wikipedia.org/wiki/Null_device), but also between different storage systems, like from SSD to HDD.
 :::
 
-More details on [configuring TTL](../../en/engines/table-engines/mergetree-family/mergetree/#table_engine-mergetree-ttl).
+More details on [configuring TTL](../../engines/table-engines/mergetree-family/mergetree/#table_engine-mergetree-ttl).
 
 ## ALTER DELETE {#alter-delete}
 
@@ -28,16 +28,16 @@ ClickHouse does not have real-time point deletes like in [OLTP](https://en.wikip
 
 This is the most common approach to make your system based on ClickHouse [GDPR](https://gdpr-info.eu)-compliant.
 
-More details on [mutations](../../en/sql-reference/statements/alter/#alter-mutations).
+More details on [mutations](../../sql-reference/statements/alter/#alter-mutations).
 
 ## DROP PARTITION {#drop-partition}
 
 `ALTER TABLE ... DROP PARTITION` provides a cost-efficient way to drop a whole partition. It’s not that flexible and needs proper partitioning scheme configured on table creation, but still covers most common cases. Like mutations need to be executed from an external system for regular use.
 
-More details on [manipulating partitions](../../en/sql-reference/statements/alter/partition/#alter_drop-partition).
+More details on [manipulating partitions](../../sql-reference/statements/alter/partition/#alter_drop-partition).
 
 ## TRUNCATE {#truncate}
 
 It’s rather radical to drop all data from a table, but in some cases it might be exactly what you need.
 
-More details on [table truncation](../../en/sql-reference/statements/truncate/).
+More details on [table truncation](../../sql-reference/statements/truncate/).

docs/en/faq/operations/index.md

@@ -14,7 +14,7 @@ sidebar_label:  Question about Operating ClickHouse Servers and Clusters
 -   [Does ClickHouse support multi-region replication?](../../faq/operations/multi-region-replication.md)
 
 :::info Don’t see what you're looking for?
-Check out our [other FAQ categories](../../faq/index.md) and also browse the many helpful articles found here in the documentation.
+Check out our [other FAQ categories](../../faq/) and also browse the many helpful articles found here in the documentation.
 :::
 
 [Original article](https://clickhouse.com/docs/faq/production/)

docs/en/faq/operations/multi-region-replication.md

@@ -10,4 +10,4 @@ The short answer is "yes". However, we recommend keeping latency between all reg
 
 Configuration-wise there's no difference compared to single-region replication, simply use hosts that are located in different locations for replicas.
 
-For more information, see [full article on data replication](../../en/engines/table-engines/mergetree-family/replication/).
+For more information, see [full article on data replication](../../engines/table-engines/mergetree-family/replication/).