id stringlengths 36 36 | document stringlengths 3 3k | metadata stringlengths 23 69 | embeddings listlengths 384 384 |
|---|---|---|---|
27f7b894-e6d8-4bc1-b9a8-0e425b961ffa | |
Compliance
| ClickHouse Cloud compliance includes CCPA, EU-US DPF, GDPR, HIPAA, ISO 27001, ISO 27001 SoA, PCI DSS, SOC2. ClickHouse Cloud's security, availability, processing integrity, and confidentiality processes are all independently audited. Details: trust.clickhouse.com. ... | {"source_file": "01_what_is.md"} | [
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9d4b8897-7786-42dd-96cd-05b86a83f1d5 | sidebar_label: 'Overview'
sidebar_position: 1
slug: /integrations/migration/overview
keywords: ['clickhouse', 'migrate', 'migration', 'migrating', 'data']
title: 'Migrating Data into ClickHouse'
description: 'Page describing the options available for migrating data into ClickHouse'
doc_type: 'guide'
Migrating data ... | {"source_file": "01_overview.md"} | [
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3a94f4b9-cba0-4d6e-9303-5664aeacbe0b | title: 'BigQuery vs ClickHouse Cloud'
slug: /migrations/bigquery/biquery-vs-clickhouse-cloud
description: 'How BigQuery differs from ClickHouse Cloud'
keywords: ['BigQuery']
show_related_blogs: true
sidebar_label: 'Overview'
doc_type: 'guide'
import bigquery_1 from '@site/static/images/migrations/bigquery-1.png';
i... | {"source_file": "01_overview.md"} | [
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ab13b3ca-783a-4818-937d-1623410f8fb1 | Furthermore, similar to BigQuery quotas, ClickHouse Cloud offers concurrency control, memory usage limits, and I/O scheduling, enabling users to isolate queries into workload classes. By setting limits on shared resources (CPU cores, DRAM, disk and network I/O) for specific workload classes, it ensures these queries do... | {"source_file": "01_overview.md"} | [
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6501c3af-73d8-4605-bbe0-1ba0d6e12bdd | | BigQuery | ClickHouse |
|----------|-------------------------------------------------------------------------------------------------------------------... | {"source_file": "01_overview.md"} | [
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292e1b2d-64ce-438b-85be-c4511d3a085d | When presented with multiple options for ClickHouse types, consider the actual range of the data and pick the lowest required. Also, consider utilizing
appropriate codecs
for further compression.
Query acceleration techniques {#query-acceleration-techniques}
Primary and Foreign keys and Primary index {#primary-an... | {"source_file": "01_overview.md"} | [
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1478489c-2d67-4785-b4c5-73f1f531ccce | BigQuery recently introduced
vector indexes
as a Pre-GA feature. Likewise, ClickHouse has experimental support for
indexes to speed up
vector search use cases.
Partitioning {#partitioning}
Like BigQuery, ClickHouse uses table partitioning to enhance the performance and manageability of large tables by dividing ... | {"source_file": "01_overview.md"} | [
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367296d3-45c1-4dbe-9de1-c709b5bb2465 | In ClickHouse, materialized views are incrementally updated. This incremental update mechanism provides high scalability and low computing costs: incrementally updated materialized views are engineered especially for scenarios where base tables contain billions or trillions of rows. Instead of querying the ever-growing... | {"source_file": "01_overview.md"} | [
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8959c29f-57f6-4f52-9160-03c1d2ae5119 | Arrays {#arrays}
Compared to BigQuery's 8 array functions, ClickHouse has over 80
built-in array functions
for modeling and solving a wide range of problems elegantly and simply.
A typical design pattern in ClickHouse is to use the
groupArray
aggregate function to (temporarily) transform specific row values of ... | {"source_file": "01_overview.md"} | [
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e2026e9a-9387-44f7-8d20-73e7b24ce9a9 | BigQuery
GENERATE_DATE_ARRAY
function
```sql
SELECT GENERATE_DATE_ARRAY('2016-10-05', '2016-10-08') AS example;
/
--------------------------------------------------
| example |
+--------------------------------------------------+
| [2016-10-05, 2016-10-06, 2016-10-07,... | {"source_file": "01_overview.md"} | [
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... |
29465844-6cc7-4f39-ac09-f6e6721cd6a8 | ClickHouse
arrayFilter
function
sql
WITH Sequences AS
(
SELECT [0, 1, 1, 2, 3, 5] AS some_numbers
UNION ALL
SELECT [2, 4, 8, 16, 32] AS some_numbers
UNION ALL
SELECT [5, 10] AS some_numbers
)
SELECT arrayMap(x -> (x * 2), arrayFilter(x -> (x < 5), some_numbers)) AS d... | {"source_file": "01_overview.md"} | [
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4ba1bfa7-6efe-4ff0-aa68-a8fe7b532112 | sidebar_label: 'Loading data'
title: 'Loading data from BigQuery to ClickHouse'
slug: /migrations/bigquery/loading-data
description: 'How to load data from BigQuery to ClickHouse'
keywords: ['migrate', 'migration', 'migrating', 'data', 'etl', 'elt', 'BigQuery']
doc_type: 'guide'
This guide is compatible with ClickH... | {"source_file": "03_loading-data.md"} | [
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a743e39c-b1c2-4278-90e2-404e18a25013 | In the above query, we export our BigQuery table to the
Parquet data format
. We also have a
*
character in our
uri
parameter. This ensures the output is sharded into multiple files, with a numerically increasing suffix, should the export exceed 1GB of data.
This approach has a number of advantages:
Google a... | {"source_file": "03_loading-data.md"} | [
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fa1d7350-4657-4d3a-af54-f857b6a30036 | Alternatively, you can
SET input_format_null_as_default=1
and any missing or NULL values will be replaced by default values for their respective columns, if those defaults are specified.
:::
Testing successful data export {#3-testing-successful-data-export}
To test whether your data was properly inserted, simply ... | {"source_file": "03_loading-data.md"} | [
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e4169392-cb75-4be3-b57f-2eee1370b0f2 | title: 'Migrating from BigQuery to ClickHouse Cloud'
slug: /migrations/bigquery/migrating-to-clickhouse-cloud
description: 'How to migrate your data from BigQuery to ClickHouse Cloud'
keywords: ['BigQuery']
show_related_blogs: true
sidebar_label: 'Migration guide'
doc_type: 'guide'
import bigquery_2 from '@site/sta... | {"source_file": "02_migrating-to-clickhouse-cloud.md"} | [
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281e6c88-df31-4956-b9e7-d8b622da9842 | Bulk loading via Google Cloud Storage (GCS) {#bulk-loading-via-google-cloud-storage-gcs}
BigQuery supports exporting data to Google's object store (GCS). For our example data set:
Export the 7 tables to GCS. Commands for that are available
here
.
Import the data into ClickHouse Cloud. For that we can use... | {"source_file": "02_migrating-to-clickhouse-cloud.md"} | [
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da2eb1e0-2b96-4cd5-8ad6-c487d2e3de55 | Adhering to this principle, we focus on the main
posts
table. The BigQuery schema for this is shown below:
sql
CREATE TABLE stackoverflow.posts (
id INTEGER,
posttypeid INTEGER,
acceptedanswerid STRING,
creationdate TIMESTAMP,
score INTEGER,
viewcount INTEGER,
body STRING,
owneruseri... | {"source_file": "02_migrating-to-clickhouse-cloud.md"} | [
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293ead35-0ae2-4be5-b20b-ec799de25bbf | As described
here
, like in BigQuery, ClickHouse doesn't enforce uniqueness for a table's primary key column values.
Similar to clustering in BigQuery, a ClickHouse table's data is stored on disk ordered by the primary key column(s). This sort order is utilized by the query optimizer to prevent resorting, minimize m... | {"source_file": "02_migrating-to-clickhouse-cloud.md"} | [
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494a0d6f-7a10-4196-9983-65212c442ce3 | For the considerations and steps in choosing an ordering key, using the posts table as an example, see
here
.
Data modeling techniques {#data-modeling-techniques}
We recommend users migrating from BigQuery read
the guide for modeling data in ClickHouse
. This guide uses the same Stack Overflow dataset and explore... | {"source_file": "02_migrating-to-clickhouse-cloud.md"} | [
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e2a5c90d-2997-41c5-bfbb-828589df6d5a | Applications {#applications}
Partitioning in ClickHouse has similar applications as in BigQuery but with some subtle differences. More specifically:
Data management
- In ClickHouse, users should principally consider partitioning to be a data management feature, not a query optimization technique. By separating d... | {"source_file": "02_migrating-to-clickhouse-cloud.md"} | [
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361fa44d-6518-4181-ab6e-c322c92c6ec5 | Internally, ClickHouse
creates parts
for inserted data. As more data is inserted, the number of parts increases. In order to prevent an excessively high number of parts, which will degrade query performance (because there are more files to read), parts are merged together in a background asynchronous process. If the ... | {"source_file": "02_migrating-to-clickhouse-cloud.md"} | [
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3bd5e2c7-b797-4079-b9f4-f3a62314f698 | If the projection is created via an
ALTER
command, the creation is asynchronous
when the
MATERIALIZE PROJECTION
command is issued. Users can confirm the progress
of this operation with the following query, waiting for
is_done=1
.
``sql
SELECT
parts_to_do,
is_done,
latest_fail_reason
FROM system.muta... | {"source_file": "02_migrating-to-clickhouse-cloud.md"} | [
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cf8085df-f959-409d-b425-8086b1593abe | We recommend using projections when:
A complete reordering of the data is required. While the expression in the projection can, in theory, use a
GROUP BY,
materialized views are more effective for maintaining aggregates. The query optimizer is also more likely to exploit projections that use a simple reordering, ... | {"source_file": "02_migrating-to-clickhouse-cloud.md"} | [
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bc711afd-7702-4ba4-b044-55c45c0c562b | Row 1:
──────
Year: 2008
MostViewedQuestionTitle: How to find the index for a given item in a list?
MaxViewCount: 6316987
Row 2:
──────
Year: 2009
MostViewedQuestionTitle: How do I undo the most recent local commits in Git?
MaxViewCount: 13962748
...
Row... | {"source_file": "02_migrating-to-clickhouse-cloud.md"} | [
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c8af21f5-828e-4d67-b64f-9780079282a5 | slug: /migrations/bigquery
title: 'BigQuery'
pagination_prev: null
pagination_next: null
description: 'Landing page for the BigQuery migrations section'
keywords: ['BigQuery', 'migration']
doc_type: 'landing-page'
In this section of the docs, learn more about the similarities and differences between BigQuery and Cl... | {"source_file": "index.md"} | [
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e679d561-90f1-4601-9d33-d2e25bdda080 | sidebar_label: 'Overview'
slug: /migrations/redshift-overview
description: 'Migrating from Amazon Redshift to ClickHouse'
keywords: ['Redshift']
title: 'Comparing ClickHouse Cloud and Amazon Redshift'
doc_type: 'guide'
Amazon Redshift to ClickHouse migration
This document provides an introduction to migrating d... | {"source_file": "01_overview.md"} | [
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ce2ae813-e4dc-43f5-a62c-58d50907af24 | | Advantage | Description ... | {"source_file": "01_overview.md"} | [
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59347e2b-9065-4af1-9304-f02b45f03dae | sidebar_label: 'Migration guide'
slug: /migrations/redshift/migration-guide
description: 'Migrating from Amazon Redshift to ClickHouse'
keywords: ['Redshift']
title: 'Amazon Redshift to ClickHouse migration guide'
doc_type: 'guide'
import MigrationGuide from '@site/docs/integrations/data-ingestion/redshift/_snippet... | {"source_file": "02_migration_guide.md"} | [
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5287aa8f-19dd-40a3-8369-0aca511f9c02 | sidebar_label: 'SQL translation reference'
slug: /migrations/redshift/sql-translation-reference
description: 'SQL translation reference for Amazon Redshift to ClickHouse'
keywords: ['Redshift']
title: 'Amazon Redshift SQL translation guide'
doc_type: 'reference'
Amazon Redshift SQL translation guide
Data types {#... | {"source_file": "03_sql_translation_reference.md"} | [
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4c3e2908-a191-431d-968f-056cd3ee86d5 | | Redshift | ClickHouse ... | {"source_file": "03_sql_translation_reference.md"} | [
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2954b475-ef13-4cd0-acd7-d7473d096bfb | |
REAL
|
Float32
... | {"source_file": "03_sql_translation_reference.md"} | [
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0.03887874633073807,
0.005223080... |
d15830ae-6eb0-4771-bb1a-5896819a44e4 | |
TIMESTAMPTZ
|
DateTime
,
DateTime64
... | {"source_file": "03_sql_translation_reference.md"} | [
0.0033434289507567883,
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0.024028053507208824,
... |
728da327-4524-4272-99d9-daf4a84f8db6 | ClickHouse additionally supports unsigned integers with extended ranges i.e.
UInt8
,
UInt32
,
UInt32
and
UInt64
.
*
ClickHouse’s String type is unlimited by default but can be constrained to specific lengths using
Constraints
.
DDL syntax {#compression}
Sorting keys {#sorting-keys}
Both ClickHouse and Red... | {"source_file": "03_sql_translation_reference.md"} | [
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... |
04a99cb8-c36f-430a-8a58-857094721b58 | sidebar_label: 'Using clickhouse-local'
keywords: ['clickhouse', 'migrate', 'migration', 'migrating', 'data', 'etl', 'elt', 'clickhouse-local', 'clickhouse-client']
slug: /cloud/migration/clickhouse-local
title: 'Migrating to ClickHouse using clickhouse-local'
description: 'Guide showing how to migrate to ClickHouse us... | {"source_file": "01_clickhouse-local-etl.md"} | [
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0.028284376487135887,
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77b6fe52-6b0f-46de-b3e5-25b57384e687 | Installing clickhouse-local {#installing-clickhouse-local}
You need a host machine for
clickhouse-local
that has network access to both your current source database system and your ClickHouse Cloud target service.
On that host machine, download the appropriate build of
clickhouse-local
based on your computer's ... | {"source_file": "01_clickhouse-local-etl.md"} | [
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0.003978286404162645,
-0.0... |
78321538-569d-4282-9701-031be09ebf70 | Example 2: Migrating from MySQL to ClickHouse Cloud with the JDBC bridge {#example-2-migrating-from-mysql-to-clickhouse-cloud-with-the-jdbc-bridge}
We will use the
JDBC integration table engine
(created on-the-fly by the
jdbc table function
) together with the
ClickHouse JDBC Bridge
and the MySQL JDBC driver for... | {"source_file": "01_clickhouse-local-etl.md"} | [
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0.12385980039834976,
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054e6abb-417a-445b-a441-d1f5734ec003 | title: 'Using object storage'
description: 'Moving data from object storage to ClickHouse Cloud'
keywords: ['object storage', 's3', 'azure blob', 'gcs', 'migration']
slug: /integrations/migration/object-storage-to-clickhouse
doc_type: 'guide'
import Image from '@theme/IdealImage';
import object_storage_01 from '@si... | {"source_file": "03_object-storage-to-clickhouse.md"} | [
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ae9fa39c-46af-47df-942f-95868e63764f | sidebar_label: 'Using a third-party ETL tool'
keywords: ['clickhouse', 'migrate', 'migration', 'migrating', 'data', 'etl', 'elt', 'clickhouse-local', 'clickhouse-client']
slug: /cloud/migration/etl-tool-to-clickhouse
title: 'Using a third-party ETL Tool'
description: 'Page describing how to use a 3rd-party ETL tool wit... | {"source_file": "02_etl-tool-to-clickhouse.md"} | [
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f162ffcf-dae3-4a06-8e0b-a3e8635ab08a | sidebar_label: 'ClickHouse OSS'
slug: /cloud/migration/clickhouse-to-cloud
title: 'Migrating between self-managed ClickHouse and ClickHouse Cloud'
description: 'Page describing how to migrate between self-managed ClickHouse and ClickHouse Cloud'
doc_type: 'guide'
keywords: ['migration', 'ClickHouse Cloud', 'OSS', 'Migr... | {"source_file": "01_clickhouse-to-cloud.md"} | [
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9a906230-4605-4e80-9873-cdf073338241 | On the source ClickHouse system (the system that currently hosts the data) {#on-the-source-clickhouse-system-the-system-that-currently-hosts-the-data}
Add a read only user that can read the source table (
db.table
in this example)
sql
CREATE USER exporter
IDENTIFIED WITH SHA256_PASSWORD BY 'password-here'
SETTING... | {"source_file": "01_clickhouse-to-cloud.md"} | [
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0.12741538882255554,
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9d8d3602-a72c-40b6-b955-14a249311750 | There are a few steps in the migration:
1. Identify one ClickHouse Cloud service to be the
source
, and the other as the
destination
1. Add a read-only user to the source service
1. Duplicate the source table structure on the destination service
1. Temporarily allow IP access to the source service
1. Copy the data f... | {"source_file": "01_clickhouse-to-cloud.md"} | [
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-0... |
cd3cb540-c82e-4de1-bfed-16a7f1f8420e | sidebar_label: 'Overview'
slug: /migrations/elastic-overview
description: 'Migrating from Snowflake to ClickHouse'
keywords: ['Snowflake']
title: 'Migrate from Snowflake to ClickHouse'
show_related_blogs: true
doc_type: 'landing-page'
Elasticsearch to ClickHouse migration
For observability use cases, see the
Ela... | {"source_file": "01_overview.md"} | [
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d8295baf-07ec-4426-8539-ccd2bd459881 | sidebar_label: 'Overview'
slug: /migrations/snowflake-overview
description: 'Migrating from Snowflake to ClickHouse'
keywords: ['Snowflake']
title: 'Migrate from Snowflake to ClickHouse'
show_related_blogs: true
doc_type: 'guide'
import snowflake_architecture from '@site/static/images/cloud/onboard/discover/use_cas... | {"source_file": "01_overview.md"} | [
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-0.0... |
05e0608d-7086-49e0-90a2-645752221237 | For this reason, ClickHouse Cloud utilizes a shared-storage architecture that is
conceptually similar to Snowflake. Data is stored once in an object store
(single copy), such as S3 or GCS, providing virtually infinite storage with
strong redundancy guarantees. Each node has access to this single copy of the
data as ... | {"source_file": "01_overview.md"} | [
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-0.070... |
7ff13216-19b4-453a-948e-84507d85074e | Snowflake and ClickHouse Cloud take different approaches to scaling to
increase query concurrency. Snowflake addresses this through a feature
known as
multi-cluster warehouses
.
This feature allows users to add clusters to a warehouse. While this offers no
improvement to query latency, it does provide addit... | {"source_file": "01_overview.md"} | [
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0.020342836156487465,
... |
624c3d42-257a-4119-8c26-92183db9d088 | Lower cost
: Snowflake warehouses can be configured to suspend after
a period of query inactivity. Once suspended, charges are not incurred.
Practically, this inactivity check can
only be lowered to 60s
.
Warehouses will automatically resume, within several seconds, once a query
is received. With Snowflake... | {"source_file": "01_overview.md"} | [
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0.02002188377082348,
-0.016... |
0fe13552-d904-4c13-b894-3d4ab9e00662 | sidebar_label: 'Migration guide'
slug: /migrations/snowflake
description: 'Migrating from Snowflake to ClickHouse'
keywords: ['Snowflake']
title: 'Migrating from Snowflake to ClickHouse'
show_related_blogs: false
doc_type: 'guide'
import migrate_snowflake_clickhouse from '@site/static/images/migrations/migrate_snow... | {"source_file": "02_migration_guide.md"} | [
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0.0492931567132473,
-0.069... |
78dcf717-25d4-484d-80ed-f8d868a174ff | Assuming the following table target schema:
sql
CREATE TABLE default.mydataset
(
`timestamp` DateTime64(6),
`some_text` String,
`some_file` Tuple(filename String, version String),
`complex_data` Tuple(name String, description String),
)
ENGINE = MergeTree
ORDER BY (timestamp)
We can then use the
INSERT INT... | {"source_file": "02_migration_guide.md"} | [
0.003799623576924205,
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0.012191088870167732,
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0.02814248576760292,
0.07580247521400452,
-0.046565353870391846,
0.0582844577729702,
-0.08... |
67eb883f-75aa-4947-965c-9c8cb564c5ef | sidebar_label: 'SQL translation reference'
slug: /migrations/snowflake-translation-reference
description: 'SQL translation reference'
keywords: ['Snowflake']
title: 'Migrating from Snowflake to ClickHouse'
show_related_blogs: true
doc_type: 'guide'
Snowflake SQL translation guide
Data types {#data-types}
Numeri... | {"source_file": "03_sql_translation_reference.md"} | [
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0.021351667121052742,
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0.03182229772210121,
0.054183099418878555,
-0.023091521114110947,
-0.09850839525461197,
-0.07721864432096481,
0.003995484672486782,
0.025552498176693916,
... |
fe265868-d940-4831-88e7-be6c1c901fef | ClickHouse offers the equivalent
Variant
,
Object
(now deprecated in favor of the native
JSON
type) and
Array
types. Additionally, ClickHouse has the
JSON
type which replaces the now deprecated
Object('json')
type and is particularly
performant and storage efficient in
comparison to other native JSON types... | {"source_file": "03_sql_translation_reference.md"} | [
-0.06494662910699844,
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0.03400587663054466,
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0.03174262493848801,
-0.04240535572171211,
0.041... |
9fee1c8f-6347-49d3-a78f-caa67da447b8 | | Snowflake ... | {"source_file": "03_sql_translation_reference.md"} | [
-0.01001453585922718,
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0.01699129492044449,
-0.013975062407553196,
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0.00853007473051548,
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-0.040425363928079605,
-0.008059924468398094,
-0.014858197420835495... |
a8fe61ab-ca5b-462f-b813-611456242622 | |
FLOAT
,
FLOAT4
,
FLOAT8
... | {"source_file": "03_sql_translation_reference.md"} | [
0.11574121564626694,
0.01814737543463707,
-0.04348642751574516,
-0.0018007007893174887,
0.0008765647653490305,
-0.00956083182245493,
-0.05797484144568443,
0.004443679004907608,
-0.04481865093111992,
-0.049074821174144745,
-0.03656693920493126,
-0.10284093767404556,
-0.005949890706688166,
-... |
ef2b6e77-946c-445f-aaf7-ac831fc3bb0b | |
BOOLEAN
... | {"source_file": "03_sql_translation_reference.md"} | [
0.006691169459372759,
0.03231026604771614,
-0.08970007300376892,
0.022136257961392403,
0.04007498174905777,
-0.012948191724717617,
-0.058382075279951096,
0.021945061162114143,
-0.010757984593510628,
-0.015339026227593422,
-0.04877440631389618,
-0.01613158918917179,
-0.02764412946999073,
-0... |
0312a1db-8e25-496e-81a3-e7e202707f3a | DateTime
and
DateTime64
can optionally have a TZ parameter defined for the column. If not present, the server's timezone is used. Additionally a
--use_client_time_zone
parameter is available for the client. ... | {"source_file": "03_sql_translation_reference.md"} | [
0.013304811902344227,
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0.025990009307861328,
0.020609967410564423,
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0.03073187917470932,
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-0.04474548622965813,
-0.08156124502420425,
0.00564936175942421,
-0.04539353772997856,
0.... |
da817ff1-df7d-4fe6-b61b-b09176fc631f | |
GEOGRAPHY
... | {"source_file": "03_sql_translation_reference.md"} | [
0.05468246340751648,
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0.017229869961738586,
-0.08966635912656784,
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-0.011737214401364326,
0.014218559488654137,
-... |
4588490e-493a-47b1-9963-c75c01e98cf7 | | ClickHouse Type | Description |
|-------------------|-----------------------------------------------------------------------------------------------------|
|
IPv4
and
IPv6
| IP-specific types, potentially allowing more effic... | {"source_file": "03_sql_translation_reference.md"} | [
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0.027137598022818565,... |
54032b84-1923-449e-8016-9d1aff1d568e | slug: /migrations/postgresql/overview
title: 'Comparing PostgreSQL and ClickHouse'
description: 'A guide to migrating from PostgreSQL to ClickHouse'
keywords: ['postgres', 'postgresql', 'migrate', 'migration']
sidebar_label: 'Overview'
doc_type: 'guide'
Comparing ClickHouse and PostgreSQL
Why use ClickHouse over ... | {"source_file": "01_overview.md"} | [
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88d8e42d-06cc-4e52-b74f-f8888648a290 | Real-time Change Data Capture (CDC) can be implemented in ClickHouse using
ClickPipes
, if you're using ClickHouse Cloud, or
PeerDB
in case you're running ClickHouse on-prem. Those solutions handles the complexities of real-time data synchronization, including initial load, by capturing inserts, updates, and deletes... | {"source_file": "01_overview.md"} | [
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... |
5353ba51-0e03-41b7-bef1-bc3653a33b13 | slug: /migrations/postgresql
pagination_prev: null
pagination_next: null
title: 'PostgreSQL'
description: 'Landing page for the PostgreSQL migrations section'
doc_type: 'landing-page'
keywords: ['PostgreSQL migration', 'database migration', 'ClickHouse migration', 'CDC replication', 'data migration']
| Page ... | {"source_file": "index.md"} | [
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470d20f9-d170-4832-8e8e-9e234fd235f0 | slug: /migrations/postgresql/appendix
title: 'Appendix'
keywords: ['postgres', 'postgresql', 'data types', 'types']
description: 'Additional information relative to migrating from PostgreSQL'
doc_type: 'reference'
import postgresReplicas from '@site/static/images/integrations/data-ingestion/dbms/postgres-replicas.p... | {"source_file": "appendix.md"} | [
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-... |
d46e2008-0f2c-4606-a621-dde470300737 | In contrast, ClickHouse shards and replicas are two key concepts related to data distribution and redundancy
. ClickHouse replicas can be thought of as analogous to Postgres replicas, although replication is eventually consistent with no notion of a primary. Sharding, unlike Postgres, is supported natively.
A shard i... | {"source_file": "appendix.md"} | [
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a3b8568c-0cac-42a2-83f4-005a8727c4ee | The replication process in ClickHouse (1) starts when data is inserted into any replica. This data, in its raw insert form, is (2) written to disk along with its checksums. Once written, the replica (3) attempts to register this new data part in Keeper by allocating a unique block number and logging the new part's deta... | {"source_file": "appendix.md"} | [
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479ea928-93c0-42d1-9399-f43d33a1aefc | Conversely, PostgreSQL's streaming replication model typically can prevent dirty reads by employing synchronous replication options where the primary waits for at least one replica to confirm the receipt of data before committing transactions. This ensures that once a transaction is committed, a guarantee exists that t... | {"source_file": "appendix.md"} | [
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-0.0363... |
844c55e9-0f89-4894-94a0-aebb14d6f7cc | Contact support for access to sticky endpoints.
ClickHouse OSS {#clickhouse-oss}
To achieve this behavior in OSS depends on your shard and replica topology and if you are using a
Distributed table
for querying.
When you have only one shard and replicas (common since ClickHouse vertically scales), users select... | {"source_file": "appendix.md"} | [
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54aeef43-bf16-4812-809d-87b1951b55ec | Sync replicas manually
- If you write to one replica and read from another, you can use issue
SYSTEM SYNC REPLICA LIGHTWEIGHT
prior to reading.
Enable sequential consistency
- via the query setting
select_sequential_consistency = 1
. In OSS, the setting
insert_quorum = 'auto'
must also be specified.
See ... | {"source_file": "appendix.md"} | [
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107cc846-6bad-47f4-8f54-42f65bb420d2 | response title="Response"
┌─table───────┬─compressed_size─┐
│ posts │ 25.17 GiB │
│ users │ 846.57 MiB │
│ badges │ 513.13 MiB │
│ comments │ 7.11 GiB │
│ votes │ 1.28 GiB │
│ posthistory │ 40.44 GiB │
│ postlinks │ 79.22 MiB │
└─────────────┴───────... | {"source_file": "appendix.md"} | [
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0.032103974372148514,
... |
379bc8ef-8c74-4c51-94c2-488600adc351 | slug: /migrations/postgresql/rewriting-queries
title: 'Rewriting PostgreSQL Queries'
keywords: ['postgres', 'postgresql', 'rewriting queries']
description: 'Part 2 of a guide on migrating from PostgreSQL to ClickHouse'
sidebar_label: 'Part 2'
doc_type: 'guide'
This is
Part 2
of a guide on migrating from Postgre... | {"source_file": "02_migration_guide_part2.md"} | [
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0.009927603416144848,
-0.11... |
f95ec39d-03ee-4aa5-b754-b622d6da433c | ownerdisplayname | total_views
-------------------------+-------------
Joan Venge | 25520387
Ray Vega | 21576470
Tim | 18283579
J. Pablo Fernández | 12446818
Matt | 12298764
Time: 107620.508 ms (01:47.621)
```
... | {"source_file": "02_migration_guide_part2.md"} | [
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-... |
827bc178-ae96-4a92-906e-2c3bf6b33d36 | This is significantly simpler (and faster) than the equivalent Postgres query:
```sql
--Postgres
WITH yearly_views AS (
SELECT
EXTRACT(YEAR FROM CreationDate) AS Year,
Title,
ViewCount,
ROW_NUMBER() OVER (PARTITION BY EXTRACT(YEAR FROM CreationDate) ORDER BY ViewCount DESC) AS ... | {"source_file": "02_migration_guide_part2.md"} | [
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-0.04840996488928795,
-0.02086445689201355,
-0.0... |
2548c7de-36e5-4563-b362-2159ab63d8ef | 5 rows in set. Elapsed: 0.247 sec. Processed 5.08 million rows, 155.73 MB (20.58 million rows/s., 630.61 MB/s.)
Peak memory usage: 403.04 MiB.
```
```sql
--Postgres
SELECT
tag,
SUM(CASE WHEN year = 2023 THEN count ELSE 0 END) AS count_2023,
SUM(CASE WHEN year = 2022 THEN count ELSE 0 END) AS c... | {"source_file": "02_migration_guide_part2.md"} | [
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0.01790950819849968,
-0.059959... |
0851bae3-6f68-4251-870c-0c88c6a59ac3 | slug: /migrations/postgresql/data-modeling-techniques
title: 'Data modeling techniques'
description: 'Part 3 of a guide on migrating from PostgreSQL to ClickHouse'
keywords: ['postgres', 'postgresql']
show_related_blogs: true
sidebar_label: 'Part 3'
doc_type: 'guide'
import postgres_b_tree from '@site/static/images... | {"source_file": "03_migration_guide_part3.md"} | [
0.019291765987873077,
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0.040972139686346054,
-0.055... |
243ed0cd-0e82-4132-a79c-a222cd716594 | Sparse indexing
is possible because ClickHouse stores the rows for a part on disk ordered by a specified key. Instead of directly locating single rows (like a B-Tree-based index), the sparse primary index allows it to quickly (via a binary search over index entries) identify groups of rows that could possibly match th... | {"source_file": "03_migration_guide_part3.md"} | [
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0.03754081949591637,
-0.... |
aafaf0a2-1eb7-4306-8ec6-8600bfb28273 | In ClickHouse, partitioning is specified on a table when it is initially defined via the
PARTITION BY
clause. This clause can contain a SQL expression on any columns, the results of which will define which partition a row is sent to.
The data parts are logically associated with each partition on disk and can be q... | {"source_file": "03_migration_guide_part3.md"} | [
-0.03186561539769173,
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0.033294763416051865,
-0.04939156025648117,
0.06501320749521255,
... |
86c114e0-4c0d-4e08-a857-ecfa1788901e | 17 rows in set. Elapsed: 0.002 sec.
ALTER TABLE posts
(DROP PARTITION '2008')
Ok.
0 rows in set. Elapsed: 0.103 sec.
```
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... | {"source_file": "03_migration_guide_part3.md"} | [
-0.032774269580841064,
0.052155300974845886,
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-0.05052465200424194,
-0.06777766346931458,
-0.013319541700184345,
-0.006501175928860903,
-0.0129... |
1dfb4772-7405-462e-98ce-a1376f44e8d0 | Materialized views vs projections {#materialized-views-vs-projections}
Postgres allows for the creation of multiple indices on a single table, enabling optimization for a variety of access patterns. This flexibility allows administrators and developers to tailor database performance to specific queries and operationa... | {"source_file": "03_migration_guide_part3.md"} | [
-0.055786047130823135,
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0.05398562178015709,
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0.04424206167459488,
-0.049909837543964386,
0.08926092088222504,
0.07766064256429672,
-0.0... |
b3c519f4-b454-4101-9030-cb499c45b08d | ┌─parts_to_do─┬─is_done─┬─latest_fail_reason─┐
1. │ 1 │ 0 │ │
└─────────────┴─────────┴────────────────────┘
1 row in set. Elapsed: 0.003 sec.
```
If we repeat the above query, we can see performance has improved significantly at the expense of additional storage.
```sql
SELE... | {"source_file": "03_migration_guide_part3.md"} | [
-0.0408686101436615,
-0.07600858062505722,
-0.038990676403045654,
0.1353418380022049,
-0.005957781337201595,
-0.00975506380200386,
0.08423157036304474,
0.07078846544027328,
0.024243216961622238,
0.06647402793169022,
-0.01533757708966732,
0.01958363503217697,
0.10498765110969543,
-0.0137502... |
64d28ef4-dc14-4e0f-a239-a2ddf77a6970 | Users are comfortable with the associated increase in storage footprint and
overhead of writing data twice. Test the impact on insertion speed and
evaluate the storage overhead
.
:::note
Since version 25.5, ClickHouse supports the virtual column
_part_offset
in
projections. This unlocks a more space-effic... | {"source_file": "03_migration_guide_part3.md"} | [
-0.006047913804650307,
-0.026483528316020966,
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-0.018793392926454544,
-0.059061698615550995,
0.012263627722859383,
0.015199011191725731,
0.00693892827257514,
-0.01401819009333849,
0.07582350075244904,
-0.016605906188488007,
-0... |
6ad4e7ce-2c60-4330-953d-0b55ce2afa8d | slug: /migrations/postgresql/dataset
title: 'Migrating data'
description: 'Dataset example to migrate from PostgreSQL to ClickHouse'
keywords: ['Postgres']
show_related_blogs: true
sidebar_label: 'Part 1'
doc_type: 'guide'
import postgres_stackoverflow_schema from '@site/static/images/migrations/postgres-stackoverf... | {"source_file": "01_migration_guide_part1.md"} | [
-0.024915538728237152,
-0.10252857208251953,
-0.021951017901301384,
0.010082394815981388,
0.03727284446358681,
-0.0305637139827013,
-0.0029607100877910852,
-0.04973423480987549,
-0.06866820156574249,
0.014493543654680252,
0.05728641897439957,
0.013980424031615257,
0.0404694527387619,
-0.08... |
b929d357-45da-4912-ad4d-dc2256fcb2ba | While small for ClickHouse, this dataset is substantial for Postgres. The above represents a subset covering the first three months of 2024.
While our example results use the full dataset to show performance differences between Postgres and Clickhouse, all steps documented below are functionally identical with the ... | {"source_file": "01_migration_guide_part1.md"} | [
-0.03554532304406166,
-0.07207770645618439,
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-0.03497551381587982,
0.046682827174663544,
0.015444912016391754,
0.04044799134135246,
-0.019652115181088448,
-... |
d5b62020-1cba-4fee-bd59-194f167691fe | When loading data manually from PostgreSQL, you need to first create the tables in ClickHouse. Refer to this
Data Modeling documentation
to that also uses the Stack Overflow dataset to optimize the table schema in ClickHouse.
Data types between PostgreSQL and ClickHouse might differ. To establish the equivalent ty... | {"source_file": "01_migration_guide_part1.md"} | [
0.04391104355454445,
-0.10794541239738464,
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-0.01558037381619215,
0.017630139365792274,
-0.... |
514b472e-2c7e-440f-abd3-783bea93833c | ```sql
-- initial load
INSERT INTO stackoverflow.posts SELECT * FROM postgresql('
', 'postgres', 'posts', 'postgres', '<password')
INSERT INTO stackoverflow.posts SELECT * FROM postgresql('
', 'postgres', 'posts', 'postgres', '
( SELECT (max(CreationDate) FROM stackoverflow.posts)
```
ClickHouse will push down s... | {"source_file": "01_migration_guide_part1.md"} | [
-0.04824385419487953,
-0.0884239450097084,
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0.017461398616433144,
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0.0195328202098608,
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0.01638529822230339,
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0.0941142588853836,
0.04264460876584053,
-0.13269335... |
c6828b85-2484-4aa0-8630-0f831089e8ae | slug: /cloud/get-started/cloud/use-cases/real-time-analytics
title: 'Real-time analytics'
description: 'Learn how to build real-time analytics applications with ClickHouse Cloud for instant insights and data-driven decision making'
keywords: ['use cases', 'real-time analytics']
sidebar_label: 'Real-time analytics'
doc_... | {"source_file": "01_real-time-analytics.md"} | [
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0.04632817953824997,
0.014275740832090378,
0.01711765117943287,
0.050525989... |
98afdc13-9607-42eb-8b89-2aaf7d73da7d | We can now derive insights from events almost as soon as they’re generated. But
why is this useful?
Benefits of real-time analytics {#benefits-of-real-time-analytics}
In today's fast-paced world, organizations rely on real-time analytics to stay
agile and responsive to ever-changing conditions. A real-time analyt... | {"source_file": "01_real-time-analytics.md"} | [
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c27093c3-2d52-4bd6-bfd6-0897e85a879c | They identified the following characteristics of ClickHouse that enable real-time
fraud detection:
ClickHouse supports LSM-tree based MergeTree family engines.
These are optimized for writing which is suitable for ingesting large amounts
of data in real-time.
ClickHouse is designed and optimized explicitly for ... | {"source_file": "01_real-time-analytics.md"} | [
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c56d5b27-631a-476f-b31c-2c8e97af8444 | slug: /cloud/get-started/cloud/use-cases/data_lake_and_warehouse
title: 'Data Lakehouse'
description: 'Build modern data warehousing architectures with ClickHouse Cloud combining the flexibility of data lakes with database performance'
keywords: ['use cases', 'data lake and warehouse']
sidebar_label: 'Data warehousing'... | {"source_file": "03_data_warehousing.md"} | [
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b1a240f0-8c84-49be-957b-af15ae5d21d6 | Let's explore the core building blocks of a typical data lakehouse architecture
and how they interact to create a cohesive data management platform.
| Component | Description ... | {"source_file": "03_data_warehousing.md"} | [
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b2af204c-bb59-4605-be19-469e0035a7f6 | What are the benefits of the data lakehouse? {#benefits-of-the-data-lakehouse}
The data lakehouse architecture offers several significant advantages when compared
directly to both traditional data warehouses and data lakes:
Compared to traditional data warehouses {#compared-to-traditional-data-warehouses} | {"source_file": "03_data_warehousing.md"} | [
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e3d5e99d-f7bd-40b7-8e19-60981db0bd5d | | # | Benefit | Description ... | {"source_file": "03_data_warehousing.md"} | [
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77e39f80-1fd3-4e68-8f35-63878d2604d8 | | 5 |
Independent scaling
| Lakehouses separate storage from compute, allowing each to scale independently based on actual needs, unlike many data warehouses, where they scale together. ... | {"source_file": "03_data_warehousing.md"} | [
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b7edaa38-7fcc-4021-8cd8-385d814709f4 | Compared to data lakes {#compared-to-data-lakes}
| # | Benefit | Description |
|---|-----------------------------|... | {"source_file": "03_data_warehousing.md"} | [
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fec13f94-b268-4da7-af7d-b851d1eb3c17 | ClickHouse integrates with open table formats such as Apache Iceberg, Delta Lake,
or Apache Hudi for more sophisticated data management needs. This integration
enables ClickHouse to take advantage of these formats' advanced features, while
still delivering the exceptional query performance it's known for. Organization... | {"source_file": "03_data_warehousing.md"} | [
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... |
ef030a2d-826e-4e72-b446-56ec026331eb | slug: /cloud/get-started/cloud/use-cases/observability
title: 'Observability'
description: 'Use ClickHouse Cloud for observability, monitoring, logging, and system performance analysis in distributed applications'
keywords: ['use cases', 'observability']
sidebar_label: 'Observability'
doc_type: 'guide'
Modern sof... | {"source_file": "02_observability.md"} | [
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e7996ec9-f1f4-4083-afd1-e0d4976010af | The three pillars of observability {#three-pillars-of-observability}
To better understand how observability has evolved and works in practice, let's
examine the three pillars of observability - logs, metrics, and traces.
While modern observability has moved beyond treating these as separate concerns,
they remain ... | {"source_file": "02_observability.md"} | [
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2916ec7f-2056-4055-afd0-84b2d76db9b8 | Reduced Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR)
Improved query response times, enabling faster investigation
Quicker identification of performance bottlenecks
Reduced time spent on-call
Fewer resources wasted on unnecessary rollbacks
We see this in practice -
trip.com built their observa... | {"source_file": "02_observability.md"} | [
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b7480f27-b464-4f04-aaa3-0c39b7f3a86b | Challenges in implementing observability {#challenges-in-implementing-observability}
Implementing observability within an organization is a transformative step toward
gaining deeper insights into system performance and reliability. However, this
journey is not without its challenges. As organizations strive to harne... | {"source_file": "02_observability.md"} | [
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0.023485632613301277,... |
dc54560f-8229-4a80-a8f3-1e967f239bd1 | Security and compliance {#security-and-compliance}
Security and compliance considerations remain crucial, especially when handling
sensitive data within observability systems. Organizations must ensure that their
observability solutions adhere to relevant regulations and effectively protect
sensitive information.
... | {"source_file": "02_observability.md"} | [
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... |
baa50024-c3db-404d-b0ce-5252b4309b9f | slug: /cloud/get-started/cloud/use-cases/overview
title: 'Building on ClickHouse Cloud'
description: 'Explore ClickHouse Cloud use cases including real-time analytics, observability, data lake & warehouse, and machine learning applications'
keywords: ['use cases', 'Cloud']
sidebar_label: 'Overview'
doc_type: 'landing-p... | {"source_file": "00_overview.md"} | [
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b59eb2f7-9cb5-4d1f-b75a-78db6c5a8719 | | Use case | Description ... | {"source_file": "00_overview.md"} | [
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a1cb4ef4-774b-4431-8bf1-ea45e9e8f42a | |
Observability
| ClickHouse Cloud is well suited for observability workloads, featuring specialized engines and functions optimized for time-series data that can ingest and query terabytes of logs, metrics, and traces with ease. Through ClickStack, ClickHouse's comprehensive observability solu... | {"source_file": "00_overview.md"} | [
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104f4b9a-b851-4aa1-8a7d-4ae7bf11859e | slug: /cloud/get-started/cloud/use-cases/AI_ML
title: 'Machine learning'
description: 'Learn how ClickHouse powers machine learning applications across the ML pipeline.'
keywords: ['use cases', 'Machine Learning', 'Generative AI']
sidebar_label: 'Machine learning'
doc_type: 'guide'
import machine_learning_data_laye... | {"source_file": "01_machine_learning.md"} | [
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-... |
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