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1. Shard Calculation

Query:

Response:

Calculated Shard: 89 (from laksa031)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa089.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://clickhouse.com/docs/sql-reference/statements/select/sample\')), getAhrefsUnparsedNoserviceFromURL(\'https://clickhouse.com/docs/sql-reference/statements/select/sample\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample","crawl_time":1775603007,"first_indexed_time":1740096735,"http_code":200,"src_unparsed":"com,clickhouse!\/docs\/sql-reference\/statements\/select\/sample s443","src_root_hash":"12633450985039531489","history_drop_reason":null,"meta_title":"SAMPLE Clause | ClickHouse Docs","meta_descriptions":["Documentation for SAMPLE Clause"],"attrs_boilerpipe_text":"The\nSAMPLE\nclause allows for approximated\nSELECT\nquery processing.\nWhen data sampling is enabled, the query is not performed on all the data, but only on a certain fraction of data (sample). For example, if you need to calculate statistics for all the visits, it is enough to execute the query on the 1\/10 fraction of all the visits and then multiply the result by 10.\nApproximated query processing can be useful in the following cases:\nWhen you have strict latency requirements (like below 100ms) but you can't justify the cost of additional hardware resources to meet them.\nWhen your raw data is not accurate, so approximation does not noticeably degrade the quality.\nBusiness requirements target approximate results (for cost-effectiveness, or to market exact results to premium users).\nNote\nYou can only use sampling with the tables in the\nMergeTree\nfamily, and only if the sampling expression was specified during table creation (see\nMergeTree engine\n).\nThe features of data sampling are listed below:\nData sampling is a deterministic mechanism. The result of the same\nSELECT .. SAMPLE\nquery is always the same.\nSampling works consistently for different tables. For tables with a single sampling key, a sample with the same coefficient always selects the same subset of possible data. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This means that you can use the sample in subqueries in the\nIN\nclause. Also, you can join samples using the\nJOIN\nclause.\nSampling allows reading less data from a disk. Note that you must specify the sampling key correctly. For more information, see\nCreating a MergeTree Table\n.\nFor the\nSAMPLE\nclause the following syntax is supported:\nSAMPLE Clause Syntax\nDescription\nSAMPLE k\nHere\nk\nis the number from 0 to 1. The query is executed on\nk\nfraction of data. For example,\nSAMPLE 0.1\nruns the query on 10% of data.\nRead more\nSAMPLE n\nHere\nn\nis a sufficiently large integer. The query is executed on a sample of at least\nn\nrows (but not significantly more than this). For example,\nSAMPLE 10000000\nruns the query on a minimum of 10,000,000 rows.\nRead more\nSAMPLE k OFFSET m\nHere\nk\nand\nm\nare the numbers from 0 to 1. The query is executed on a sample of\nk\nfraction of the data. The data used for the sample is offset by\nm\nfraction.\nRead more\nSAMPLE K\n\nHere\nk\nis the number from 0 to 1 (both fractional and decimal notations are supported). For example,\nSAMPLE 1\/2\nor\nSAMPLE 0.5\n.\nIn a\nSAMPLE k\nclause, the sample is taken from the\nk\nfraction of data. The example is shown below:\nSELECT\nTitle\n,\ncount\n(\n)\n*\n10\nAS\nPageViews\nFROM\nhits_distributed\nSAMPLE\n0.1\nWHERE\nCounterID\n=\n34\nGROUP\nBY\nTitle\nORDER\nBY\nPageViews\nDESC\nLIMIT\n1000\nIn this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value\ncount()\nis manually multiplied by 10.\nSAMPLE N\n\nHere\nn\nis a sufficiently large integer. For example,\nSAMPLE 10000000\n.\nIn this case, the query is executed on a sample of at least\nn\nrows (but not significantly more than this). For example,\nSAMPLE 10000000\nruns the query on a minimum of 10,000,000 rows.\nSince the minimum unit for data reading is one granule (its size is set by the\nindex_granularity\nsetting), it makes sense to set a sample that is much larger than the size of the granule.\nWhen using the\nSAMPLE n\nclause, you do not know which relative percent of data was processed. So you do not know the coefficient the aggregate functions should be multiplied by. Use the\n_sample_factor\nvirtual column to get the approximate result.\nThe\n_sample_factor\ncolumn contains relative coefficients that are calculated dynamically. This column is created automatically when you\ncreate\na table with the specified sampling key. The usage examples of the\n_sample_factor\ncolumn are shown below.\nLet's consider the table\nvisits\n, which contains the statistics about site visits. The first example shows how to calculate the number of page views:\nSELECT\nsum\n(\nPageViews\n*\n_sample_factor\n)\nFROM\nvisits\nSAMPLE\n10000000\nThe next example shows how to calculate the total number of visits:\nSELECT\nsum\n(\n_sample_factor\n)\nFROM\nvisits\nSAMPLE\n10000000\nThe example below shows how to calculate the average session duration. Note that you do not need to use the relative coefficient to calculate the average values.\nSELECT\navg\n(\nDuration\n)\nFROM\nvisits\nSAMPLE\n10000000\nSAMPLE K OFFSET M\n\nHere\nk\nand\nm\nare numbers from 0 to 1. Examples are shown below.\nExample 1\nSAMPLE\n1\n\/\n10\nIn this example, the sample is 1\/10th of all data:\n[++------------]\nExample 2\nSAMPLE\n1\n\/\n10\nOFFSET\n1\n\/\n2\nHere, a sample of 10% is taken from the second half of the data.\n[------++------]","attrs_markdown":"[Skip to main content](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#__docusaurus_skipToContent_fallback)\n\n[![ClickHouse](https:\/\/clickhouse.com\/docs\/img\/ch_logo_docs.svg)](https:\/\/clickhouse.com\/)\n\n- [Products](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample)\n  \n  - [ClickHouse Cloud Best way to use ClickHouse. Available on AWS, GCP, and Azure.](https:\/\/clickhouse.com\/cloud)\n  - [BYOC (Bring Your Own Cloud) The fully managed ClickHouse Cloud service, Can be deployed in your AWS account.](https:\/\/clickhouse.com\/cloud\/bring-your-own-cloud)\n  - [ClickHouse Set up a database with open-source ClickHouse. 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For example, if you need to calculate statistics for all the visits, it is enough to execute the query on the 1\/10 fraction of all the visits and then multiply the result by 10.\n\nApproximated query processing can be useful in the following cases:\n\n- When you have strict latency requirements (like below 100ms) but you can't justify the cost of additional hardware resources to meet them.\n- When your raw data is not accurate, so approximation does not noticeably degrade the quality.\n- Business requirements target approximate results (for cost-effectiveness, or to market exact results to premium users).\n\nNote\n\nYou can only use sampling with the tables in the [MergeTree](https:\/\/clickhouse.com\/docs\/engines\/table-engines\/mergetree-family\/mergetree) family, and only if the sampling expression was specified during table creation (see [MergeTree engine](https:\/\/clickhouse.com\/docs\/engines\/table-engines\/mergetree-family\/mergetree#table_engine-mergetree-creating-a-table)).\n\nThe features of data sampling are listed below:\n\n- Data sampling is a deterministic mechanism. The result of the same `SELECT .. SAMPLE` query is always the same.\n- Sampling works consistently for different tables. For tables with a single sampling key, a sample with the same coefficient always selects the same subset of possible data. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This means that you can use the sample in subqueries in the [IN](https:\/\/clickhouse.com\/docs\/sql-reference\/operators\/in) clause. Also, you can join samples using the [JOIN](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/join) clause.\n- Sampling allows reading less data from a disk. Note that you must specify the sampling key correctly. For more information, see [Creating a MergeTree Table](https:\/\/clickhouse.com\/docs\/engines\/table-engines\/mergetree-family\/mergetree#table_engine-mergetree-creating-a-table).\n\nFor the `SAMPLE` clause the following syntax is supported:\n\n| SAMPLE Clause Syntax | Description |\n|---|---|\n| `SAMPLE k` | Here `k` is the number from 0 to 1. The query is executed on `k` fraction of data. For example, `SAMPLE 0.1` runs the query on 10% of data. [Read more](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-k) |\n| `SAMPLE n` | Here `n` is a sufficiently large integer. The query is executed on a sample of at least `n` rows (but not significantly more than this). For example, `SAMPLE 10000000` runs the query on a minimum of 10,000,000 rows. [Read more](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-n) |\n| `SAMPLE k OFFSET m` | Here `k` and `m` are the numbers from 0 to 1. The query is executed on a sample of `k` fraction of the data. The data used for the sample is offset by `m` fraction. [Read more](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-k-offset-m) |\n## SAMPLE K[](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-k \"Direct link to SAMPLE K\")\nHere `k` is the number from 0 to 1 (both fractional and decimal notations are supported). For example, `SAMPLE 1\/2` or `SAMPLE 0.5`.\n\nIn a `SAMPLE k` clause, the sample is taken from the `k` fraction of data. The example is shown below:\n```\nSELECT\n    Title,\n    count() * 10 AS PageViews\nFROM hits_distributed\nSAMPLE 0.1\nWHERE\n    CounterID = 34\nGROUP BY Title\nORDER BY PageViews DESC LIMIT 1000\n```\nIn this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value `count()` is manually multiplied by 10.\n\n## SAMPLE N[](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-n \"Direct link to SAMPLE N\")\nHere `n` is a sufficiently large integer. For example, `SAMPLE 10000000`.\n\nIn this case, the query is executed on a sample of at least `n` rows (but not significantly more than this). For example, `SAMPLE 10000000` runs the query on a minimum of 10,000,000 rows.\n\nSince the minimum unit for data reading is one granule (its size is set by the `index_granularity` setting), it makes sense to set a sample that is much larger than the size of the granule.\n\nWhen using the `SAMPLE n` clause, you do not know which relative percent of data was processed. So you do not know the coefficient the aggregate functions should be multiplied by. Use the `_sample_factor` virtual column to get the approximate result.\n\nThe `_sample_factor` column contains relative coefficients that are calculated dynamically. This column is created automatically when you [create](https:\/\/clickhouse.com\/docs\/engines\/table-engines\/mergetree-family\/mergetree#table_engine-mergetree-creating-a-table) a table with the specified sampling key. The usage examples of the `_sample_factor` column are shown below.\n\nLet's consider the table `visits`, which contains the statistics about site visits. The first example shows how to calculate the number of page views:\n```\nSELECT sum(PageViews * _sample_factor)\nFROM visits\nSAMPLE 10000000\n```\nThe next example shows how to calculate the total number of visits:\n```\nSELECT sum(_sample_factor)\nFROM visits\nSAMPLE 10000000\n```\nThe example below shows how to calculate the average session duration. Note that you do not need to use the relative coefficient to calculate the average values.\n```\nSELECT avg(Duration)\nFROM visits\nSAMPLE 10000000\n```\n## SAMPLE K OFFSET M[](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-k-offset-m \"Direct link to SAMPLE K OFFSET M\")\nHere `k` and `m` are numbers from 0 to 1. Examples are shown below.\n\n**Example 1**\n```\nSAMPLE 1\/10\n```\nIn this example, the sample is 1\/10th of all data:\n\n`[++------------]`\n\n**Example 2**\n```\nSAMPLE 1\/10 OFFSET 1\/2\n```\nHere, a sample of 10% is taken from the second half of the data.\n\n`[------++------]`\n\n[Previous REPLACE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/replace-modifier)\n\n[Next UNION](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/union)\n\n- [SAMPLE K](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-k)\n- [SAMPLE N](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-n)\n- [SAMPLE K OFFSET M](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-k-offset-m)\n\nWas this page helpful?\n\n###### Try ClickHouse Cloud for FREE\nSeparation of storage and compute, automatic scaling, built-in SQL console, and lots more. \\$300 in free credits when signing up.\n\n[Try it for Free](https:\/\/console.clickhouse.cloud\/signUp?loc=doc-card-banner&glxid=d9c1c271-d847-4188-bc4c-06053c07f641&pagePath=%2Fdocs%2Fsql-reference%2Fstatements%2Fselect%2Fsample&origPath=%2Fdocs%2Fsql-reference%2Fstatements%2Fselect%2Fsample&utm_ga=GA1.1.1296615451.1775603010)\n\n© 2016–2026 ClickHouse, Inc.\n\n[Trademark](https:\/\/clickhouse.com\/legal\/trademark-policy)·[Privacy](https:\/\/clickhouse.com\/legal\/privacy-policy)·[Security](https:\/\/trust.clickhouse.com\/)·[Terms of Service](https:\/\/clickhouse.com\/legal\/agreements\/terms-of-service)\n\n![](https:\/\/static.scarf.sh\/a.png?x-pxid=e6377503-591b-4886-9398-e69c7fee0b91)\n\n© 2016–2026 ClickHouse, Inc.\n\n[Trademark](https:\/\/clickhouse.com\/legal\/trademark-policy)·[Privacy](https:\/\/clickhouse.com\/legal\/privacy-policy)·[Security](https:\/\/trust.clickhouse.com\/)·[Terms of Service](https:\/\/clickhouse.com\/legal\/agreements\/terms-of-service)\n\n![](https:\/\/static.scarf.sh\/a.png?x-pxid=e6377503-591b-4886-9398-e69c7fee0b91)\n\n[![ClickHouse](https:\/\/clickhouse.com\/docs\/img\/ch_logo_docs.svg)](https:\/\/clickhouse.com\/)\n\nEN\n\n- Get started▼\n- Cloud▼\n- Manage data▼\n- Server admin▼\n- Reference▼\n- Integrations▼\n- ClickStack▼\n- chDB▼\n- About▼\n\n[![ClickHouse](https:\/\/clickhouse.com\/docs\/img\/ch_logo_docs.svg)](https:\/\/clickhouse.com\/)\n\nEN\n\nmain-menu\n\n- Introduction▼\n  - 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[HAVING](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/having)\n      - [INTERSECT](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/intersect)\n      - [INTO OUTFILE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/into-outfile)\n      - [JOIN](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/join)\n      - [LIMIT BY](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/limit-by)\n      - [LIMIT](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/limit)\n      - [OFFSET](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/offset)\n      - [ORDER BY](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/order-by)\n      - [PREWHERE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/prewhere)\n      - [QUALIFY](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/qualify)\n      - [REPLACE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/replace-modifier)\n      - [SAMPLE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample)\n      - [UNION](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/union)\n      - [WHERE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/where)\n      - [WITH](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/with)\n    - [INSERT INTO](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/insert-into)\n    - CREATE▼\n    - ALTER▼\n    - [DELETE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/delete)\n    - [SYSTEM](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/system)\n    - [SHOW](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/show)\n    - [GRANT](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/grant)\n    - [EXPLAIN](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/explain)\n    - [REVOKE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/revoke)\n    - [UPDATE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/update)\n    - [ATTACH](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/attach)\n    - [CHECK TABLE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/check-table)\n    - [DESCRIBE TABLE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/describe-table)\n    - [DETACH](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/detach)\n    - [DROP](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/drop)\n    - [EXISTS](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/exists)\n    - [KILL](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/kill)\n    - [OPTIMIZE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/optimize)\n    - [RENAME](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/rename)\n    - [EXCHANGE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/exchange)\n    - [SET](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/set)\n    - [SET ROLE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/set-role)\n    - [TRUNCATE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/truncate)\n    - [EXECUTE AS](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/execute_as)\n    - [PARALLEL WITH](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/parallel_with)\n    - [USE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/use)\n    - [WATCH](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/watch)\n    - [MOVE](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/move)\n    - [CHECK GRANT](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/check-grant)\n    - [UNDROP](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/undrop)\n  - Operators▼\n- Engines▼\n- Functions▼\n- Formats▼\n- [Data Lakes](https:\/\/clickhouse.com\/docs\/sql-reference\/datalakes)","attrs_readable_markdown":"The `SAMPLE` clause allows for approximated `SELECT` query processing.\n\nWhen data sampling is enabled, the query is not performed on all the data, but only on a certain fraction of data (sample). For example, if you need to calculate statistics for all the visits, it is enough to execute the query on the 1\/10 fraction of all the visits and then multiply the result by 10.\n\nApproximated query processing can be useful in the following cases:\n\n- When you have strict latency requirements (like below 100ms) but you can't justify the cost of additional hardware resources to meet them.\n- When your raw data is not accurate, so approximation does not noticeably degrade the quality.\n- Business requirements target approximate results (for cost-effectiveness, or to market exact results to premium users).\n\nNote\n\nYou can only use sampling with the tables in the [MergeTree](https:\/\/clickhouse.com\/docs\/engines\/table-engines\/mergetree-family\/mergetree) family, and only if the sampling expression was specified during table creation (see [MergeTree engine](https:\/\/clickhouse.com\/docs\/engines\/table-engines\/mergetree-family\/mergetree#table_engine-mergetree-creating-a-table)).\n\nThe features of data sampling are listed below:\n\n- Data sampling is a deterministic mechanism. The result of the same `SELECT .. SAMPLE` query is always the same.\n- Sampling works consistently for different tables. For tables with a single sampling key, a sample with the same coefficient always selects the same subset of possible data. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This means that you can use the sample in subqueries in the [IN](https:\/\/clickhouse.com\/docs\/sql-reference\/operators\/in) clause. Also, you can join samples using the [JOIN](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/join) clause.\n- Sampling allows reading less data from a disk. Note that you must specify the sampling key correctly. For more information, see [Creating a MergeTree Table](https:\/\/clickhouse.com\/docs\/engines\/table-engines\/mergetree-family\/mergetree#table_engine-mergetree-creating-a-table).\n\nFor the `SAMPLE` clause the following syntax is supported:\n\n| SAMPLE Clause Syntax | Description |\n|---|---|\n| `SAMPLE k` | Here `k` is the number from 0 to 1. The query is executed on `k` fraction of data. For example, `SAMPLE 0.1` runs the query on 10% of data. [Read more](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-k) |\n| `SAMPLE n` | Here `n` is a sufficiently large integer. The query is executed on a sample of at least `n` rows (but not significantly more than this). For example, `SAMPLE 10000000` runs the query on a minimum of 10,000,000 rows. [Read more](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-n) |\n| `SAMPLE k OFFSET m` | Here `k` and `m` are the numbers from 0 to 1. The query is executed on a sample of `k` fraction of the data. The data used for the sample is offset by `m` fraction. [Read more](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-k-offset-m) |\n## SAMPLE K[](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-k \"Direct link to SAMPLE K\")\nHere `k` is the number from 0 to 1 (both fractional and decimal notations are supported). For example, `SAMPLE 1\/2` or `SAMPLE 0.5`.\n\nIn a `SAMPLE k` clause, the sample is taken from the `k` fraction of data. The example is shown below:\n```\nSELECT\n    Title,\n    count() * 10 AS PageViews\nFROM hits_distributed\nSAMPLE 0.1\nWHERE\n    CounterID = 34\nGROUP BY Title\nORDER BY PageViews DESC LIMIT 1000\n```\nIn this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value `count()` is manually multiplied by 10.\n\n## SAMPLE N[](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-n \"Direct link to SAMPLE N\")\nHere `n` is a sufficiently large integer. For example, `SAMPLE 10000000`.\n\nIn this case, the query is executed on a sample of at least `n` rows (but not significantly more than this). For example, `SAMPLE 10000000` runs the query on a minimum of 10,000,000 rows.\n\nSince the minimum unit for data reading is one granule (its size is set by the `index_granularity` setting), it makes sense to set a sample that is much larger than the size of the granule.\n\nWhen using the `SAMPLE n` clause, you do not know which relative percent of data was processed. So you do not know the coefficient the aggregate functions should be multiplied by. Use the `_sample_factor` virtual column to get the approximate result.\n\nThe `_sample_factor` column contains relative coefficients that are calculated dynamically. This column is created automatically when you [create](https:\/\/clickhouse.com\/docs\/engines\/table-engines\/mergetree-family\/mergetree#table_engine-mergetree-creating-a-table) a table with the specified sampling key. The usage examples of the `_sample_factor` column are shown below.\n\nLet's consider the table `visits`, which contains the statistics about site visits. The first example shows how to calculate the number of page views:\n```\nSELECT sum(PageViews * _sample_factor)\nFROM visits\nSAMPLE 10000000\n```\nThe next example shows how to calculate the total number of visits:\n```\nSELECT sum(_sample_factor)\nFROM visits\nSAMPLE 10000000\n```\nThe example below shows how to calculate the average session duration. Note that you do not need to use the relative coefficient to calculate the average values.\n```\nSELECT avg(Duration)\nFROM visits\nSAMPLE 10000000\n```\n## SAMPLE K OFFSET M[](https:\/\/clickhouse.com\/docs\/sql-reference\/statements\/select\/sample#sample-k-offset-m \"Direct link to SAMPLE K OFFSET M\")\nHere `k` and `m` are numbers from 0 to 1. Examples are shown below.\n\n**Example 1**\n```\nSAMPLE 1\/10\n```\nIn this example, the sample is 1\/10th of all data:\n\n`[++------------]`\n\n**Example 2**\n```\nSAMPLE 1\/10 OFFSET 1\/2\n```\nHere, a sample of 10% is taken from the second half of the data.\n\n`[------++------]`","meta_canonical":null}

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Boilerpipe Text	The SAMPLE clause allows for approximated SELECT query processing. When data sampling is enabled, the query is not performed on all the data, but only on a certain fraction of data (sample). For example, if you need to calculate statistics for all the visits, it is enough to execute the query on the 1/10 fraction of all the visits and then multiply the result by 10. Approximated query processing can be useful in the following cases: When you have strict latency requirements (like below 100ms) but you can't justify the cost of additional hardware resources to meet them. When your raw data is not accurate, so approximation does not noticeably degrade the quality. Business requirements target approximate results (for cost-effectiveness, or to market exact results to premium users). Note You can only use sampling with the tables in the MergeTree family, and only if the sampling expression was specified during table creation (see MergeTree engine ). The features of data sampling are listed below: Data sampling is a deterministic mechanism. The result of the same SELECT .. SAMPLE query is always the same. Sampling works consistently for different tables. For tables with a single sampling key, a sample with the same coefficient always selects the same subset of possible data. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This means that you can use the sample in subqueries in the IN clause. Also, you can join samples using the JOIN clause. Sampling allows reading less data from a disk. Note that you must specify the sampling key correctly. For more information, see Creating a MergeTree Table . For the SAMPLE clause the following syntax is supported: SAMPLE Clause Syntax Description SAMPLE k Here k is the number from 0 to 1. The query is executed on k fraction of data. For example, SAMPLE 0.1 runs the query on 10% of data. Read more SAMPLE n Here n is a sufficiently large integer. The query is executed on a sample of at least n rows (but not significantly more than this). For example, SAMPLE 10000000 runs the query on a minimum of 10,000,000 rows. Read more SAMPLE k OFFSET m Here k and m are the numbers from 0 to 1. The query is executed on a sample of k fraction of the data. The data used for the sample is offset by m fraction. Read more SAMPLE K Here k is the number from 0 to 1 (both fractional and decimal notations are supported). For example, SAMPLE 1/2 or SAMPLE 0.5 . In a SAMPLE k clause, the sample is taken from the k fraction of data. The example is shown below: SELECT Title , count ( ) * 10 AS PageViews FROM hits_distributed SAMPLE 0.1 WHERE CounterID = 34 GROUP BY Title ORDER BY PageViews DESC LIMIT 1000 In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value count() is manually multiplied by 10. SAMPLE N Here n is a sufficiently large integer. For example, SAMPLE 10000000 . In this case, the query is executed on a sample of at least n rows (but not significantly more than this). For example, SAMPLE 10000000 runs the query on a minimum of 10,000,000 rows. Since the minimum unit for data reading is one granule (its size is set by the index_granularity setting), it makes sense to set a sample that is much larger than the size of the granule. When using the SAMPLE n clause, you do not know which relative percent of data was processed. So you do not know the coefficient the aggregate functions should be multiplied by. Use the _sample_factor virtual column to get the approximate result. The _sample_factor column contains relative coefficients that are calculated dynamically. This column is created automatically when you create a table with the specified sampling key. The usage examples of the _sample_factor column are shown below. Let's consider the table visits , which contains the statistics about site visits. The first example shows how to calculate the number of page views: SELECT sum ( PageViews * _sample_factor ) FROM visits SAMPLE 10000000 The next example shows how to calculate the total number of visits: SELECT sum ( _sample_factor ) FROM visits SAMPLE 10000000 The example below shows how to calculate the average session duration. Note that you do not need to use the relative coefficient to calculate the average values. SELECT avg ( Duration ) FROM visits SAMPLE 10000000 SAMPLE K OFFSET M Here k and m are numbers from 0 to 1. Examples are shown below. Example 1 SAMPLE 1 / 10 In this example, the sample is 1/10th of all data: [++------------] Example 2 SAMPLE 1 / 10 OFFSET 1 / 2 Here, a sample of 10% is taken from the second half of the data. [------++------]
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When data sampling is enabled, the query is not performed on all the data, but only on a certain fraction of data (sample). For example, if you need to calculate statistics for all the visits, it is enough to execute the query on the 1/10 fraction of all the visits and then multiply the result by 10. Approximated query processing can be useful in the following cases: - When you have strict latency requirements (like below 100ms) but you can't justify the cost of additional hardware resources to meet them. - When your raw data is not accurate, so approximation does not noticeably degrade the quality. - Business requirements target approximate results (for cost-effectiveness, or to market exact results to premium users). Note You can only use sampling with the tables in the [MergeTree](https://clickhouse.com/docs/engines/table-engines/mergetree-family/mergetree) family, and only if the sampling expression was specified during table creation (see [MergeTree engine](https://clickhouse.com/docs/engines/table-engines/mergetree-family/mergetree#table_engine-mergetree-creating-a-table)). The features of data sampling are listed below: - Data sampling is a deterministic mechanism. The result of the same `SELECT .. SAMPLE` query is always the same. - Sampling works consistently for different tables. For tables with a single sampling key, a sample with the same coefficient always selects the same subset of possible data. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This means that you can use the sample in subqueries in the [IN](https://clickhouse.com/docs/sql-reference/operators/in) clause. Also, you can join samples using the [JOIN](https://clickhouse.com/docs/sql-reference/statements/select/join) clause. - Sampling allows reading less data from a disk. Note that you must specify the sampling key correctly. For more information, see [Creating a MergeTree Table](https://clickhouse.com/docs/engines/table-engines/mergetree-family/mergetree#table_engine-mergetree-creating-a-table). For the `SAMPLE` clause the following syntax is supported: \| SAMPLE Clause Syntax \| Description \| \|---\|---\| \| `SAMPLE k` \| Here `k` is the number from 0 to 1. The query is executed on `k` fraction of data. For example, `SAMPLE 0.1` runs the query on 10% of data. [Read more](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-k) \| \| `SAMPLE n` \| Here `n` is a sufficiently large integer. The query is executed on a sample of at least `n` rows (but not significantly more than this). For example, `SAMPLE 10000000` runs the query on a minimum of 10,000,000 rows. [Read more](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-n) \| \| `SAMPLE k OFFSET m` \| Here `k` and `m` are the numbers from 0 to 1. The query is executed on a sample of `k` fraction of the data. The data used for the sample is offset by `m` fraction. [Read more](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-k-offset-m) \| ## SAMPLE K[](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-k "Direct link to SAMPLE K") Here `k` is the number from 0 to 1 (both fractional and decimal notations are supported). For example, `SAMPLE 1/2` or `SAMPLE 0.5`. In a `SAMPLE k` clause, the sample is taken from the `k` fraction of data. The example is shown below: ``` SELECT Title, count() * 10 AS PageViews FROM hits_distributed SAMPLE 0.1 WHERE CounterID = 34 GROUP BY Title ORDER BY PageViews DESC LIMIT 1000 ``` In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value `count()` is manually multiplied by 10. ## SAMPLE N[](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-n "Direct link to SAMPLE N") Here `n` is a sufficiently large integer. For example, `SAMPLE 10000000`. In this case, the query is executed on a sample of at least `n` rows (but not significantly more than this). For example, `SAMPLE 10000000` runs the query on a minimum of 10,000,000 rows. Since the minimum unit for data reading is one granule (its size is set by the `index_granularity` setting), it makes sense to set a sample that is much larger than the size of the granule. When using the `SAMPLE n` clause, you do not know which relative percent of data was processed. So you do not know the coefficient the aggregate functions should be multiplied by. Use the `_sample_factor` virtual column to get the approximate result. The `_sample_factor` column contains relative coefficients that are calculated dynamically. This column is created automatically when you [create](https://clickhouse.com/docs/engines/table-engines/mergetree-family/mergetree#table_engine-mergetree-creating-a-table) a table with the specified sampling key. The usage examples of the `_sample_factor` column are shown below. Let's consider the table `visits`, which contains the statistics about site visits. The first example shows how to calculate the number of page views: ``` SELECT sum(PageViews * _sample_factor) FROM visits SAMPLE 10000000 ``` The next example shows how to calculate the total number of visits: ``` SELECT sum(_sample_factor) FROM visits SAMPLE 10000000 ``` The example below shows how to calculate the average session duration. Note that you do not need to use the relative coefficient to calculate the average values. ``` SELECT avg(Duration) FROM visits SAMPLE 10000000 ``` ## SAMPLE K OFFSET M[](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-k-offset-m "Direct link to SAMPLE K OFFSET M") Here `k` and `m` are numbers from 0 to 1. Examples are shown below. Example 1 ``` SAMPLE 1/10 ``` In this example, the sample is 1/10th of all data: `[++------------]` Example 2 ``` SAMPLE 1/10 OFFSET 1/2 ``` Here, a sample of 10% is taken from the second half of the data. `[------++------]` [Previous REPLACE](https://clickhouse.com/docs/sql-reference/statements/select/replace-modifier) [Next UNION](https://clickhouse.com/docs/sql-reference/statements/select/union) - [SAMPLE K](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-k) - [SAMPLE N](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-n) - [SAMPLE K OFFSET M](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-k-offset-m) Was this page helpful? ###### Try ClickHouse Cloud for FREE Separation of storage and compute, automatic scaling, built-in SQL console, and lots more. \$300 in free credits when signing up. 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Readable Markdown	The `SAMPLE` clause allows for approximated `SELECT` query processing. When data sampling is enabled, the query is not performed on all the data, but only on a certain fraction of data (sample). For example, if you need to calculate statistics for all the visits, it is enough to execute the query on the 1/10 fraction of all the visits and then multiply the result by 10. Approximated query processing can be useful in the following cases: - When you have strict latency requirements (like below 100ms) but you can't justify the cost of additional hardware resources to meet them. - When your raw data is not accurate, so approximation does not noticeably degrade the quality. - Business requirements target approximate results (for cost-effectiveness, or to market exact results to premium users). Note You can only use sampling with the tables in the [MergeTree](https://clickhouse.com/docs/engines/table-engines/mergetree-family/mergetree) family, and only if the sampling expression was specified during table creation (see [MergeTree engine](https://clickhouse.com/docs/engines/table-engines/mergetree-family/mergetree#table_engine-mergetree-creating-a-table)). The features of data sampling are listed below: - Data sampling is a deterministic mechanism. The result of the same `SELECT .. SAMPLE` query is always the same. - Sampling works consistently for different tables. For tables with a single sampling key, a sample with the same coefficient always selects the same subset of possible data. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This means that you can use the sample in subqueries in the [IN](https://clickhouse.com/docs/sql-reference/operators/in) clause. Also, you can join samples using the [JOIN](https://clickhouse.com/docs/sql-reference/statements/select/join) clause. - Sampling allows reading less data from a disk. Note that you must specify the sampling key correctly. For more information, see [Creating a MergeTree Table](https://clickhouse.com/docs/engines/table-engines/mergetree-family/mergetree#table_engine-mergetree-creating-a-table). For the `SAMPLE` clause the following syntax is supported: \| SAMPLE Clause Syntax \| Description \| \|---\|---\| \| `SAMPLE k` \| Here `k` is the number from 0 to 1. The query is executed on `k` fraction of data. For example, `SAMPLE 0.1` runs the query on 10% of data. [Read more](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-k) \| \| `SAMPLE n` \| Here `n` is a sufficiently large integer. The query is executed on a sample of at least `n` rows (but not significantly more than this). For example, `SAMPLE 10000000` runs the query on a minimum of 10,000,000 rows. [Read more](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-n) \| \| `SAMPLE k OFFSET m` \| Here `k` and `m` are the numbers from 0 to 1. The query is executed on a sample of `k` fraction of the data. The data used for the sample is offset by `m` fraction. [Read more](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-k-offset-m) \| ## SAMPLE K[](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-k "Direct link to SAMPLE K") Here `k` is the number from 0 to 1 (both fractional and decimal notations are supported). For example, `SAMPLE 1/2` or `SAMPLE 0.5`. In a `SAMPLE k` clause, the sample is taken from the `k` fraction of data. The example is shown below: ``` SELECT Title, count() * 10 AS PageViews FROM hits_distributed SAMPLE 0.1 WHERE CounterID = 34 GROUP BY Title ORDER BY PageViews DESC LIMIT 1000 ``` In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value `count()` is manually multiplied by 10. ## SAMPLE N[](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-n "Direct link to SAMPLE N") Here `n` is a sufficiently large integer. For example, `SAMPLE 10000000`. In this case, the query is executed on a sample of at least `n` rows (but not significantly more than this). For example, `SAMPLE 10000000` runs the query on a minimum of 10,000,000 rows. Since the minimum unit for data reading is one granule (its size is set by the `index_granularity` setting), it makes sense to set a sample that is much larger than the size of the granule. When using the `SAMPLE n` clause, you do not know which relative percent of data was processed. So you do not know the coefficient the aggregate functions should be multiplied by. Use the `_sample_factor` virtual column to get the approximate result. The `_sample_factor` column contains relative coefficients that are calculated dynamically. This column is created automatically when you [create](https://clickhouse.com/docs/engines/table-engines/mergetree-family/mergetree#table_engine-mergetree-creating-a-table) a table with the specified sampling key. The usage examples of the `_sample_factor` column are shown below. Let's consider the table `visits`, which contains the statistics about site visits. The first example shows how to calculate the number of page views: ``` SELECT sum(PageViews * _sample_factor) FROM visits SAMPLE 10000000 ``` The next example shows how to calculate the total number of visits: ``` SELECT sum(_sample_factor) FROM visits SAMPLE 10000000 ``` The example below shows how to calculate the average session duration. Note that you do not need to use the relative coefficient to calculate the average values. ``` SELECT avg(Duration) FROM visits SAMPLE 10000000 ``` ## SAMPLE K OFFSET M[](https://clickhouse.com/docs/sql-reference/statements/select/sample#sample-k-offset-m "Direct link to SAMPLE K OFFSET M") Here `k` and `m` are numbers from 0 to 1. Examples are shown below. Example 1 ``` SAMPLE 1/10 ``` In this example, the sample is 1/10th of all data: `[++------------]` Example 2 ``` SAMPLE 1/10 OFFSET 1/2 ``` Here, a sample of 10% is taken from the second half of the data. `[------++------]`
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