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import asyncInsert01 from '@site/static/images/cloud/bestpractices/async-01.png';
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import asyncInsert02 from '@site/static/images/cloud/bestpractices/async-02.png';
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import asyncInsert03 from '@site/static/images/cloud/bestpractices/async-03.png';
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Inserting data into ClickHouse in large batches is a best practice. It saves compute cycles and disk I/O, and therefore it saves money. If your use case allows you to batch your inserts external to ClickHouse, then that is one option. If you would like ClickHouse to create the batches, then you can use the asynchronous INSERT mode described here.
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Use asynchronous inserts as an alternative to both batching data on the client-side and keeping the insert rate at around one insert query per second by enabling the [async_insert](/operations/settings/settings.md/#async_insert) setting. This causes ClickHouse to handle the batching on the server-side.
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Each insert sent to ClickHouse causes ClickHouse to immediately create a part containing the data from the insert.
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This is the default behavior when the async_insert setting is set to its default value of 0:
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<img src={asyncInsert01}
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class="image"
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alt="Asynchronous insert process - default synchronous inserts"
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style={{width: '100%', background: 'none'}} />
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By setting async_insert to 1, ClickHouse first stores the incoming inserts into an in-memory buffer before flushing them regularly to disk.
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@@ -30,10 +37,15 @@ With the [wait_for_async_insert](/operations/settings/settings.md/#wait_for_asyn
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The following two diagrams illustrate the two settings for async_insert and wait_for_async_insert:
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<img src={asyncInsert02}
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class="image"
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alt="Asynchronous insert process - async_insert=1, wait_for_async_insert=1"
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style={{width: '100%', background: 'none'}} />
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<img src={asyncInsert03}
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class="image"
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alt="Asynchronous insert process - async_insert=1, wait_for_async_insert=0"
import partitioning01 from '@site/static/images/cloud/bestpractices/partitioning-01.png';
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import partitioning02 from '@site/static/images/cloud/bestpractices/partitioning-02.png';
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When you send an insert statement (that should contain many rows - see [section above](/optimize/bulk-inserts)) to a table in ClickHouse Cloud, and that
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table is not using a [partitioning key](/engines/table-engines/mergetree-family/custom-partitioning-key.md) then all row data from that insert is written into a new part on storage:
alt="Insert with partitioning key - multiple parts created based on partitioning key values"
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style={{width: '100%', background: 'none'}} />
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Therefore, to minimize the number of write requests to the ClickHouse Cloud object storage, use a low cardinality partitioning key or avoid using any partitioning key for your table.
description: A dictionary provides a key-value representation of data for fast lookups.
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---
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import dictionaryUseCases from '@site/static/images/dictionary/dictionary-use-cases.png';
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import dictionaryLeftAnyJoin from '@site/static/images/dictionary/dictionary-left-any-join.png';
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# Dictionary
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A dictionary in ClickHouse provides an in-memory [key-value](https://en.wikipedia.org/wiki/Key%E2%80%93value_database) representation of data from various [internal and external sources](/sql-reference/dictionaries#dictionary-sources), optimizing for super-low latency lookup queries.
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- Improving the performance of queries, especially when used with `JOIN`s
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- Enriching ingested data on the fly without slowing down the ingestion process
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<img src={dictionaryUseCases}
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class="image"
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alt="Use cases for Dictionary in ClickHouse"
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style={{width: '100%', background: 'none'}} />
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## Speeding up joins using a Dictionary {#speeding-up-joins-using-a-dictionary}
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Dictionaries can be used to speed up a specific type of `JOIN`: the [`LEFT ANY` type](/sql-reference/statements/select/join#supported-types-of-join) where the join key needs to match the key attribute of the underlying key-value storage.
If this is the case, ClickHouse can exploit the dictionary to perform a [Direct Join](https://clickhouse.com/blog/clickhouse-fully-supports-joins-direct-join-part4#direct-join). This is ClickHouse's fastest join algorithm and is applicable when the underlying [table engine](/engines/table-engines) for the right-hand side table supports low-latency key-value requests. ClickHouse has three table engines providing this: [Join](/engines/table-engines/special/join) (that is basically a pre-calculated hash table), [EmbeddedRocksDB](/engines/table-engines/integrations/embedded-rocksdb) and [Dictionary](/engines/table-engines/special/dictionary). We will describe the dictionary-based approach, but the mechanics are the same for all three engines.
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Title,
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UpVotes,
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DownVotes,
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abs(UpVotes - DownVotes) AS Controversial_ratio
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abs(UpVotes - DownVotes) AS Controversial_ratio
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FROM posts
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INNER JOIN
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(
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>**Use smaller datasets on the right side of `JOIN`**: This query may seem more verbose than is required, with the filtering on `PostId`s occurring in both the outer and sub queries. This is a performance optimization which ensures the query response time is fast. For optimal performance, always ensure the right side of the `JOIN` is the smaller set and as small as possible. For tips on optimizing JOIN performance and understanding the algorithms available, we recommend [this series of blog articles](https://clickhouse.com/blog/clickhouse-fully-supports-joins-part1).
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While this query is fast, it relies on us to write the `JOIN` carefully to achieve good performance. Ideally, we would simply filter the posts to those containing "SQL", before looking at the `UpVote` and `DownVote` counts for the subset of blogs to compute our metric.
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While this query is fast, it relies on us to write the `JOIN` carefully to achieve good performance. Ideally, we would simply filter the posts to those containing "SQL", before looking at the `UpVote` and `DownVote` counts for the subset of blogs to compute our metric.
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#### Applying a dictionary {#applying-a-dictionary}
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GROUP BY PostId
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```
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To create our dictionary requires the following DDL - note the use of our above query:
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To create our dictionary requires the following DDL - note the use of our above query:
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```sql
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CREATE DICTIONARY votes_dict
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### Other dictionary types {#other-dictionary-types}
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ClickHouse also supports [Hierarchical](/sql-reference/dictionaries#hierarchical-dictionaries), [Polygon](/sql-reference/dictionaries#polygon-dictionaries) and [Regular Expression](/sql-reference/dictionaries#regexp-tree-dictionary) dictionaries.
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ClickHouse also supports [Hierarchical](/sql-reference/dictionaries#hierarchical-dictionaries), [Polygon](/sql-reference/dictionaries#polygon-dictionaries) and [Regular Expression](/sql-reference/dictionaries#regexp-tree-dictionary) dictionaries.
import SelfManaged from '@site/docs/_snippets/_self_managed_only_automated.md';
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import configuringSsl01 from '@site/static/images/guides/sre/configuring-ssl_01.png';
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# Configuring SSL-TLS
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5. Log into the Play UI using the `https` interface at `https://chnode1.marsnet.local:8443/play`.
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<img src={configuringSsl01}
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alt="Configuring SSL" />
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:::note
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the browser will show an untrusted certificate since it is being reached from a workstation and the certificates are not in the root CA stores on the client machine.
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