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Repalce img element with image component for server management
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docs/deployment-guides/horizontal-scaling.md

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@@ -6,6 +6,7 @@ title: 'Scaling out'
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description: 'Page describing an example architecture designed to provide scalability'
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---
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import Image from '@theme/IdealImage';
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import ReplicationShardingTerminology from '@site/docs/_snippets/_replication-sharding-terminology.md';
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import ConfigFileNote from '@site/docs/_snippets/_config-files.md';
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import scalingOut1 from '@site/static/images/deployment-guides/scaling-out-1.png';
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## Environment {#environment}
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### Architecture Diagram {#architecture-diagram}
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<img src={scalingOut1} alt="Architecture diagram for 2 shards and 1 replica" />
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<Image img={scalingOut1} size='md' alt='Architecture diagram for 2 shards and 1 replica' />
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|Node|Description|
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|----|-----------|
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└────┴─────────┘
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```
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## More information about: {#more-information-about}
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- The [Distributed Table Engine](/engines/table-engines/special/distributed.md)

docs/deployment-guides/parallel-replicas.mdx

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@@ -5,6 +5,7 @@ keywords: ['parallel replica']
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description: 'In this guide, we will first discuss how ClickHouse distributes a query across multiple shards via distributed tables, and then how a query can leverage multiple replicas for its execution.'
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---
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import Image from '@theme/IdealImage';
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import BetaBadge from '@theme/badges/BetaBadge';
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import image_1 from '@site/static/images/deployment-guides/parallel-replicas-1.png'
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import image_2 from '@site/static/images/deployment-guides/parallel-replicas-2.png'
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case, each shard will execute the given queries. The server where the distributed
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table was queried will aggregate the data and respond to the client:
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<img src={image_1} alt="sharded archtiecture"/>
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<Image img={image_1} size="md" alt="sharded archtiecture" />
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The figure above visualizes what happens when a client queries a distributed table:
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The figure below shows the ClickHouse Cloud architecture:
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<img src={image_2} alt="non sharded architecture"/>
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<Image img={image_2} size="md" alt="non sharded architecture" />
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This architecture allows us to be able to add and remove replicas nearly
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instantaneously, ensuring a very high cluster scalability. The ClickHouse
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Now, let's see how it works in practice with the help of the figure below:
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<img src={image_3} alt="Parallel replicas"/>
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<Image img={image_3} size="md" alt="Parallel replicas" />
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With parallel replicas:
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To address (1) and (2) from the list above, we introduced the concept of an
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announcement. Let's visualize how it works using the figure below:
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<img src={image_4} alt="Announcements"/>
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<Image img={image_4} size="md" alt="Announcements" />
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<ol className="docs-ordered-list">
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<li>
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The figure below visualizes how dynamic coordination works:
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<img src={image_5} alt="Dynamic Coordination - part 1"/>
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<Image img={image_5} size="md" alt="Dynamic Coordination - part 1" />
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<ol className="docs-ordered-list">
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<li>
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</li>
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</ol>
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<img src={image_6} alt="Dynamic Coordination - part 2"/>
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<Image img={image_6} size="md" alt="Dynamic Coordination - part 2" />
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<ol className="docs-ordered-list">
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<li>
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</li>
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</ol>
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<img src={image_7} alt="Dynamic Coordination - part 3"/>
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<Image img={image_7} size="md" alt="Dynamic Coordination - part 3" />
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<ol className="docs-ordered-list">
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<li>
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Finally, you can also use the `EXPLAIN PIPELINE`. It highlights how ClickHouse
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is going to execute a query and what resources are going to be used for the
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execution of the query. Lets take the following query for example:
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execution of the query. Let's take the following query for example:
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```sql
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SELECT count(), uniq(pageId) , min(timestamp), max(timestamp)
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GROUP BY toYear(timestamp) LIMIT 10
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```
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Lets have a look at the query pipeline without parallel replica:
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Let's have a look at the query pipeline without parallel replica:
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```sql title="EXPLAIN PIPELINE (without parallel replica)"
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EXPLAIN PIPELINE graph = 1, compact = 0
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FORMAT TSV;
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```
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<img src={image_8} alt="EXPLAIN without parallel_replica"/>
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<Image img={image_8} size="lg" alt="EXPLAIN without parallel_replica" />
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And now with parallel replica:
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FORMAT TSV;
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```
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<img src={image_9} alt="EXPLAIN with parallel_replica"/>
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<Image img={image_9} size="lg" alt="EXPLAIN with parallel_replica"/>

docs/deployment-guides/replicated.md

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description: 'Page describing an example architecture with five servers configured. Two are used to host copies of the data and the rest are used to coordinate the replication of data'
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---
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import Image from '@theme/IdealImage';
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import ReplicationShardingTerminology from '@site/docs/_snippets/_replication-sharding-terminology.md';
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import ConfigFileNote from '@site/docs/_snippets/_config-files.md';
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import KeeperConfigFileNote from '@site/docs/_snippets/_keeper-config-files.md';
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## Environment {#environment}
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### Architecture Diagram {#architecture-diagram}
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<img src={ReplicationArchitecture} alt="Architecture diagram for 1 shard and 2 replicas with ReplicatedMergeTree" />
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<Image img={ReplicationArchitecture} size="md" alt="Architecture diagram for 1 shard and 2 replicas with ReplicatedMergeTree" />
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|Node|Description|
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|----|-----------|

docs/guides/separation-storage-compute.md

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@@ -6,6 +6,7 @@ title: 'Separation of Storage and Compute'
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description: 'This guide explores how you can use ClickHouse and S3 to implement an architecture with separated storage and compute.'
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import Image from '@theme/IdealImage';
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import BucketDetails from '@site/docs/_snippets/_S3_authentication_and_bucket.md';
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import s3_bucket_example from '@site/static/images/guides/s3_bucket_example.png';
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If everything worked successfully, you are now using ClickHouse with separated storage and compute!
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<img src={s3_bucket_example}
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alt="S3 bucket example using separation of compute and storage"
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class="image"
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/>
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<Image img={s3_bucket_example} size="md" alt="S3 bucket example using separation of compute and storage" border/>
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## 3. Implementing replication for fault tolerance (optional) {#3-implementing-replication-for-fault-tolerance-optional}
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