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Copy file name to clipboardExpand all lines: docs/getting-started/example-datasets/recipes.md
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10 rows in set. Elapsed: 0.215 sec. Processed 2.23 million rows, 1.48 GB (10.35 million rows/s., 6.86 GB/s.)
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```
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In this example, we involve [has](../../sql-reference/functions/array-functions.md#hasarr-elem) function to filter by array elements and sort by the number of directions.
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In this example, we involve [has](../../sql-reference/functions/array-functions.md#has) function to filter by array elements and sort by the number of directions.
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There is a wedding cake that requires the whole 126 steps to produce! Show that directions:
Copy file name to clipboardExpand all lines: docs/integrations/data-ingestion/data-formats/json/other.md
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@@ -643,7 +643,7 @@ FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/http/document
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0 rows in set. Elapsed: 12.121 sec. Processed 10.00 million rows, 107.30 MB (825.01 thousand rows/s., 8.85 MB/s.)
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```
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Querying this structure requires using the [`indexOf`](/sql-reference/functions/array-functions#indexofarr-x) function to identify the index of the required key (which should be consistent with the order of the values). This can be used to access the values array column i.e. `values[indexOf(keys, 'status')]`. We still require a JSON parsing method for the request column - in this case, `simpleJSONExtractString`.
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Querying this structure requires using the [`indexOf`](/sql-reference/functions/array-functions#indexOf) function to identify the index of the required key (which should be consistent with the order of the values). This can be used to access the values array column i.e. `values[indexOf(keys, 'status')]`. We still require a JSON parsing method for the request column - in this case, `simpleJSONExtractString`.
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```sql
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SELECT toUInt16(values[indexOf(keys, 'status')]) as status,
Copy file name to clipboardExpand all lines: docs/migrations/bigquery/equivalent-concepts.md
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@@ -159,17 +159,17 @@ Compared to BigQuery's 8 array functions, ClickHouse has over 80 [built-in array
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A typical design pattern in ClickHouse is to use the [`groupArray`](/sql-reference/aggregate-functions/reference/grouparray) aggregate function to (temporarily) transform specific row values of a table into an array. This then can be conveniently processed via array functions, and the result can be converted back into individual table rows via [`arrayJoin`](/sql-reference/functions/array-join) aggregate function.
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Because ClickHouse SQL supports [higher order lambda functions](/sql-reference/functions/overview#arrow-operator-and-lambda), many advanced array operations can be achieved by simply calling one of the higher order built-in array functions, instead of temporarily converting arrays back to tables, as it is often [required](https://cloud.google.com/bigquery/docs/arrays) in BigQuery, e.g. for [filtering](https://cloud.google.com/bigquery/docs/arrays#filtering_arrays) or [zipping](https://cloud.google.com/bigquery/docs/arrays#zipping_arrays) arrays. In ClickHouse these operations are just a simple function call of the higher order functions [`arrayFilter`](/sql-reference/functions/array-functions#arrayfilterfunc-arr1-), and [`arrayZip`](/sql-reference/functions/array-functions#arrayzip), respectively.
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Because ClickHouse SQL supports [higher order lambda functions](/sql-reference/functions/overview#arrow-operator-and-lambda), many advanced array operations can be achieved by simply calling one of the higher order built-in array functions, instead of temporarily converting arrays back to tables, as it is often [required](https://cloud.google.com/bigquery/docs/arrays) in BigQuery, e.g. for [filtering](https://cloud.google.com/bigquery/docs/arrays#filtering_arrays) or [zipping](https://cloud.google.com/bigquery/docs/arrays#zipping_arrays) arrays. In ClickHouse these operations are just a simple function call of the higher order functions [`arrayFilter`](/sql-reference/functions/array-functions#arrayFilter), and [`arrayZip`](/sql-reference/functions/array-functions#arrayZip), respectively.
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In the following, we provide a mapping of array operations from BigQuery to ClickHouse:
[arrayFilter](/sql-reference/functions/array-functions#arrayfilterfunc-arr1-) function
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[arrayFilter](/sql-reference/functions/array-functions#arrayFilter) function
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```sql
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WITH Sequences AS
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_ClickHouse_
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[arrayZip](/sql-reference/functions/array-functions#arrayzip) function
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[arrayZip](/sql-reference/functions/array-functions#arrayZip) function
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```sql
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WITH Combinations AS
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[arraySum](/sql-reference/functions/array-functions#arraysum), [arrayAvg](/sql-reference/functions/array-functions#arrayavg), ... function, or any of the over 90 existing aggregate function names as argument for the [arrayReduce](/sql-reference/functions/array-functions#arrayreduce) function
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[arraySum](/sql-reference/functions/array-functions#arraySum), [arrayAvg](/sql-reference/functions/array-functions#arrayAvg), ... function, or any of the over 90 existing aggregate function names as argument for the [arrayReduce](/sql-reference/functions/array-functions#arrayReduce) function
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