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link docs to blogs
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Dale Mcdiarmid
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@ -22,8 +22,9 @@ functions in ClickHouse. The sample datasets include:
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- The [Cell Towers dataset](../getting-started/example-datasets/cell-towers.md) imports a CSV into ClickHouse
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- The [NYPD Complaint Data](../getting-started/example-datasets/nypd_complaint_data.md) demonstrates how to use data inference to simplify creating tables
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- The ["What's on the Menu?" dataset](../getting-started/example-datasets/menus.md) has an example of denormalizing data
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- The [Getting Data Into ClickHouse - Part 1](https://clickhouse.com/blog/getting-data-into-clickhouse-part-1) provides examples of defining a schema and loading a small Hacker News dataset
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- The [Getting Data Into ClickHouse - Part 2 - A JSON detour](https://clickhouse.com/blog/getting-data-into-clickhouse-part-2-json) shows how JSON data can be loaded
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- The [Getting Data Into ClickHouse - Part 3 - Using S3](https://clickhouse.com/blog/getting-data-into-clickhouse-part-3-s3) has examples of loading data from s3
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- [Getting Data Into ClickHouse - Part 1](https://clickhouse.com/blog/getting-data-into-clickhouse-part-1) provides examples of defining a schema and loading a small Hacker News dataset
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- [Getting Data Into ClickHouse - Part 2 - A JSON detour](https://clickhouse.com/blog/getting-data-into-clickhouse-part-2-json) shows how JSON data can be loaded
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- [Getting Data Into ClickHouse - Part 3 - Using S3](https://clickhouse.com/blog/getting-data-into-clickhouse-part-3-s3) has examples of loading data from s3
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- [Generating random data in ClickHouse](https://clickhouse.com/blog/generating-random-test-distribution-data-for-clickhouse) shows how to generate random data if none of the above fit your needs.
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View the **Tutorials and Datasets** menu for a complete list of sample datasets.
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@ -72,3 +72,10 @@ If procfs is supported and enabled on the system, ClickHouse server collects the
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- `OSWriteChars`
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- `OSReadBytes`
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- `OSWriteBytes`
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## Related content
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- Blog: [System Tables and a window into the internals of ClickHouse](https://clickhouse.com/blog/clickhouse-debugging-issues-with-system-tables)
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- Blog: [Essential monitoring queries - part 1 - INSERT queries](https://clickhouse.com/blog/monitoring-troubleshooting-insert-queries-clickhouse)
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- Blog: [Essential monitoring queries - part 2 - SELECT queries](https://clickhouse.com/blog/monitoring-troubleshooting-select-queries-clickhouse)
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@ -54,7 +54,9 @@ Functions:
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- [toLowCardinality](../../sql-reference/functions/type-conversion-functions.md#tolowcardinality)
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## See Also
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## Related content
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- [Reducing ClickHouse Storage Cost with the Low Cardinality Type – Lessons from an Instana Engineer](https://www.instana.com/blog/reducing-clickhouse-storage-cost-with-the-low-cardinality-type-lessons-from-an-instana-engineer/).
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- [String Optimization (video presentation in Russian)](https://youtu.be/rqf-ILRgBdY?list=PL0Z2YDlm0b3iwXCpEFiOOYmwXzVmjJfEt). [Slides in English](https://github.com/ClickHouse/clickhouse-presentations/raw/master/meetup19/string_optimization.pdf).
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- [Reducing ClickHouse Storage Cost with the Low Cardinality Type – Lessons from an Instana Engineer](https://www.instana.com/blog/reducing-clickhouse-storage-cost-with-the-low-cardinality-type-lessons-from-an-instana-engineer/)
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- [String Optimization (video presentation in Russian)](https://youtu.be/rqf-ILRgBdY?list=PL0Z2YDlm0b3iwXCpEFiOOYmwXzVmjJfEt). [Slides in English](https://github.com/ClickHouse/clickhouse-presentations/raw/master/meetup19/string_optimization.pdf)
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- Blog: [Optimizing ClickHouse with Schemas and Codecs](https://clickhouse.com/blog/optimize-clickhouse-codecs-compression-schema)
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- Blog: [Working with time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)
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@ -1582,3 +1582,8 @@ Result:
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│ 2020-01-01 │
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└────────────────────────────────────┘
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```
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## Related content
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- Blog: [Working with time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)
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@ -588,3 +588,6 @@ Result:
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│ aeca2A │
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└───────────────────────────────────────┘
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```
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## Related content
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- Blog: [Generating random data in ClickHouse](https://clickhouse.com/blog/generating-random-test-distribution-data-for-clickhouse)
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@ -115,3 +115,7 @@ Returns the exclusive upper bound of the corresponding hopping window.
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hopEnd(bounds_tuple);
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hopEnd(time_attr, hop_interval, window_interval [, timezone]);
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```
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## Related content
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- Blog: [Working with time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)
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@ -502,3 +502,9 @@ Result:
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│ t1 │ The temporary table │
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└──────┴─────────────────────┘
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```
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## Related content
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- Blog: [Optimizing ClickHouse with Schemas and Codecs](https://clickhouse.com/blog/optimize-clickhouse-codecs-compression-schema)
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- Blog: [Working with time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)
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@ -350,3 +350,7 @@ The window view is useful in the following scenarios:
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* **Monitoring**: Aggregate and calculate the metrics logs by time, and output the results to a target table. The dashboard can use the target table as a source table.
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* **Analyzing**: Automatically aggregate and preprocess data in the time window. This can be useful when analyzing a large number of logs. The preprocessing eliminates repeated calculations in multiple queries and reduces query latency.
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## Related Content
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- Blog: [Working with time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)
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@ -299,3 +299,8 @@ ARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num;
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## Implementation Details
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The query execution order is optimized when running `ARRAY JOIN`. Although `ARRAY JOIN` must always be specified before the [WHERE](../../../sql-reference/statements/select/where.md)/[PREWHERE](../../../sql-reference/statements/select/prewhere.md) clause in a query, technically they can be performed in any order, unless result of `ARRAY JOIN` is used for filtering. The processing order is controlled by the query optimizer.
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## Related content
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- Blog: [Working with time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)
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@ -543,3 +543,7 @@ Result:
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│ 7 │ original │ 7 │
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└─────┴──────────┴───────┘
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```
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## Related content
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- Blog: [Working with time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)
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@ -51,4 +51,7 @@ SELECT * FROM random;
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│ [] │ 68091.8197 │ ('2037-10-02 12:44:23.368','039ecab7-81c2-45ee-208c-844e5c6c5652') │
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│ [8,-83,0,-22,65,9,-30,28,64] │ -186233.4909 │ ('2062-01-11 00:06:04.124','69563ea1-5ad1-f870-16d8-67061da0df25') │
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└──────────────────────────────┴──────────────┴────────────────────────────────────────────────────────────────────┘
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```
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```
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## Related content
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- Blog: [Generating random data in ClickHouse](https://clickhouse.com/blog/generating-random-test-distribution-data-for-clickhouse)
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@ -131,3 +131,6 @@ CREATE TABLE pg_table_schema_with_dots (a UInt32)
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- [The PostgreSQL table engine](../../engines/table-engines/integrations/postgresql.md)
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- [Using PostgreSQL as a dictionary source](../../sql-reference/dictionaries/external-dictionaries/external-dicts-dict-sources.md#dicts-external_dicts_dict_sources-postgresql)
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## Related content
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- Blog: [ClickHouse and PostgreSQL - a match made in data heaven - part 1](https://clickhouse.com/blog/migrating-data-between-clickhouse-postgres)
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@ -590,5 +590,6 @@ ORDER BY
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## Related Content
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- [Window and array functions for Git commit sequences](https://clickhouse.com/blog/clickhouse-window-array-functions-git-commits)
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- [Getting Data Into ClickHouse - Part 3 - Using S3](https://clickhouse.com/blog/getting-data-into-clickhouse-part-3-s3)
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- Blog: [Working with time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)
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- Blog: [Window and array functions for Git commit sequences](https://clickhouse.com/blog/clickhouse-window-array-functions-git-commits)
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- Blog: [Getting Data Into ClickHouse - Part 3 - Using S3](https://clickhouse.com/blog/getting-data-into-clickhouse-part-3-s3)
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