vtlim commented on code in PR #16515: URL: https://github.com/apache/druid/pull/16515#discussion_r1626726781
########## docs/tutorials/tutorial-latest-by.md: ########## @@ -0,0 +1,233 @@ +--- +id: tutorial-latest-by +title: Query for latest values +sidebar_label: Query for latest data +description: How to use LATEST_BY or deltas for up-to-date values. +--- + +<!-- + ~ Licensed to the Apache Software Foundation (ASF) under one + ~ or more contributor license agreements. See the NOTICE file + ~ distributed with this work for additional information + ~ regarding copyright ownership. The ASF licenses this file + ~ to you under the Apache License, Version 2.0 (the + ~ "License"); you may not use this file except in compliance + ~ with the License. You may obtain a copy of the License at + ~ + ~ http://www.apache.org/licenses/LICENSE-2.0 + ~ + ~ Unless required by applicable law or agreed to in writing, + ~ software distributed under the License is distributed on an + ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + ~ KIND, either express or implied. See the License for the + ~ specific language governing permissions and limitations + ~ under the License. + --> + +This tutorial describes strategies in Apache Druid for use cases that might be handled by UPSERT in other databases. You can use the LATEST_BY aggregation at query time or "deltas" for numeric dimensions at insert time. + +The [Update data](./tutorial-update-data.md) tutorial demonstrates how to use batch operations to update data according to the timestamp, including UPSERT cases. However, with streaming data, you can potentially use LATEST_BY or deltas to satisfy requirements otherwise handled with updates. + +## Prerequisites + +Before you follow the steps in this tutorial, download Druid as described in the [Local quickstart](index.md) and have it running on your local machine. You don't need to load any data into the Druid cluster. + +You should be familiar with data querying in Druid. If you haven't already, go through the [Query data](../tutorials/tutorial-query.md) tutorial first. + +## Use LATEST_BY to retrieve updated values + +Sometimes, you want to read the latest value of one dimension or measure in relation to another dimension. In a transactional database, you might maintain dimensions or measures using UPSERT, but in Druid you can append all updates or changes during ingestion. The LATEST_BY function lets you get the most recent value for the dimension with the following type of query: + +```sql +SELECT dimension, + LATEST_BY(changed_dimension, updated_timestamp) +FROM my_table +GROUP BY 1 +``` + +In this example `update_timestamp` represents the reference timestamp to use to evalute the "latest" value. This could be `__time` or another timestamp. + +For example, consider the following table of events that log the total number of points for a user: + +| `__time` | `user_id`| `total_points`| +| --- | --- | --- | +| `2024-01-01T01:00:00.000Z`|`funny_bunny1`| 10 | +| `2024-01-01T01:05:00.000Z`|`funny_bunny1`| 30 | +| `2024-01-01T02:00:00.000Z`|`funny_bunny1`| 35 | +| `2024-01-01T02:00:00.000Z`|`silly_monkey2`| 30 | +| `2024-01-01T02:05:00.000Z`|`silly_monkey2`| 55 | +| `2024-01-01T03:00:00.000Z`|`funny_bunny1`| 40 | + +<details> +<summary>Insert sample data</summary> + +In the Druid web console, navigate to the **Query** view and run the following query to insert sample data: + +```sql +REPLACE INTO "latest_by_tutorial1" OVERWRITE ALL +WITH "ext" AS ( + SELECT * + FROM TABLE( + EXTERN( + '{"type":"inline","data":"{\"timestamp\":\"2024-01-01T01:00:00Z\",\"user_id\":\"funny_bunny1\", \"points\":10}\n{\"timestamp\":\"2024-01-01T01:05:00Z\",\"user_id\":\"funny_bunny1\", \"points\":30}\n{\"timestamp\": \"2024-01-01T02:00:00Z\",\"user_id\":\"funny_bunny1\", \"points\":35}\n{\"timestamp\":\"2024-01-01T02:00:00Z\",\"user_id\":\"silly_monkey2\", \"points\":30}\n{\"timestamp\":\"2024-01-01T02:05:00Z\",\"user_id\":\"silly_monkey2\", \"points\":55}\n{\"timestamp\":\"2024-01-01T03:00:00Z\",\"user_id\":\"funny_bunny1\", \"points\":40}"}', + '{"type":"json"}' + ) + ) EXTEND ("timestamp" VARCHAR, "user_id" VARCHAR, "points" BIGINT) +) +SELECT + TIME_PARSE("timestamp") AS "__time", + "user_id", + "points" +FROM "ext" +PARTITIONED BY DAY +``` +</details> + +Run the following query to retrieve the most recent `points` value for each `user_id`: + +```sql +SELECT user_id, + LATEST_BY("points", "__time") AS latest_points +FROM latest_by_tutorial1 +GROUP BY 1 +``` + +The results are as follows: + +|`user_id`|`total_points`| +| --- | --- | +|`silly_monkey2`| 55 | +|`funny_bunny1`| 40 | + +In the example, the values increase each time, but this method works even if the values fluctuate. + +You can use this query shape as a subquery for additional processing. However, if there are many values for `user_id`, the query can be expensive. + +If you want to track the latest value at different times within a larger granualarity time frame, you need an additional timestamp to record update times. This allows Druid to track the latest version. Consider the following data that represents points for various users updated within an hour time frame. `__time` is hour granularity, but `updated_timestamp` is minute granularity: + +| `__time` | `updated_timestamp` | `user_id`| `points`| +| --- | --- | --- | --- | +| `2024-01-01T01:00:00.000Z`| `2024-01-01T01:00:00.000Z`|`funny_bunny1`| 10 | +|`2024-01-01T01:00:00.000Z`| `2024-01-01T01:05:00.000Z`|`funny_bunny1`| 30 | +|`2024-01-01T02:00:00.000Z`| `2024-01-01T02:00:00.000Z`|`funny_bunny1`| 35 | +|`2024-01-01T02:00:00.000Z`|`2024-01-01T02:00:00.000Z`|`silly_monkey2`| 30 | +|`2024-01-01T02:00:00.000Z`| `2024-01-01T02:05:00.000Z`|`silly_monkey2`| 55 | +|`2024-01-01T03:00:00.000Z`| `2024-01-01T03:00:00.000Z`|`funny_bunny1`| 40 | + +<details> +<summary>Insert sample data</summary> + +Open a new tab in the **Query** view and run the following query to insert sample data: + +```sql +REPLACE INTO "latest_by_tutorial2" OVERWRITE ALL +WITH "ext" AS ( + SELECT * + FROM TABLE( + EXTERN( + '{"type":"inline","data":"{\"timestamp\":\"2024-01-01T01:00:00Z\",\"updated_timestamp\":\"2024-01-01T01:00:00Z\",\"user_id\":\"funny_bunny1\", \"points\":10}\n{\"timestamp\":\"2024-01-01T01:05:00Z\",\"updated_timestamp\":\"2024-01-01T01:05:00Z\",\"user_id\":\"funny_bunny1\", \"points\":30}\n{\"timestamp\": \"2024-01-01T02:00:00Z\",\"updated_timestamp\":\"2024-01-01T02:00:00Z\",\"user_id\":\"funny_bunny1\", \"points\":35}\n{\"timestamp\":\"2024-01-01T02:00:00Z\",\"updated_timestamp\":\"2024-01-01T02:00:00Z\",\"user_id\":\"silly_monkey2\", \"points\":30}\n{\"timestamp\":\"2024-01-01T02:00:00Z\",\"updated_timestamp\":\"2024-01-01T02:05:00Z\",\"user_id\":\"silly_monkey2\", \"points\":55}\n{\"timestamp\":\"2024-01-01T03:00:00Z\",\"updated_timestamp\":\"2024-01-01T03:00:00Z\",\"user_id\":\"funny_bunny1\", \"points\":40}"}', + '{"type":"json"}' + ) + ) EXTEND ("timestamp" VARCHAR, "updated_timestamp" VARCHAR, "user_id" VARCHAR, "points" BIGINT) +) +SELECT + TIME_PARSE("timestamp") AS "__time", + "updated_timestamp", + "user_id", + "points" +FROM "ext" +PARTITIONED BY DAY +``` +</details> + + +Run the following query to retrieve the latest points value by user for each hour: + +```sql +SELECT FLOOR("__time" TO HOUR) AS "hour_time", + "user_id", + LATEST_BY("points", TIME_PARSE(updated_timestamp)) AS "latest_points_hour" +FROM latest_by_tutorial2 +GROUP BY 1,2 +``` + +The results are as follows: + +| `hour_time` | `user_id` | `latest_points_hour`| +|---|---|---| +|`2024-01-01T01:00:00.000Z`|`funny_bunny1`|20| +|`2024-01-01T02:00:00.000Z`|`funny_bunny1`|5| +|`2024-01-01T02:00:00.000Z`|`silly_monkey2`|25| +|`2024-01-01T03:00:00.000Z`|`funny_bunny1`|10| + +LATEST_BY is an aggregation function. While it's very efficient when there are not many update rows matching a dimension, such as `user_id`, it scans all matching rows with the same dimension. For dimensions with numerous updates, such as when a user plays a game a million times, and the updates don't arrive in a timely order, Druid processes all rows matching the `user_id` to find the row with the max timestamp to provide the latest data. + +For instance, if updates constitute 1-5% of your data, you'll get good query performance. If updates constitute 50 percent or more of your data, your queries will be slow. Review Comment: ```suggestion For instance, if updates constitute 1-5 percent of your data, you'll get good query performance. If updates constitute 50 percent or more of your data, your queries will be slow. ``` ########## docs/tutorials/tutorial-latest-by.md: ########## @@ -0,0 +1,231 @@ +--- +id: tutorial-latest-by +title: Query for latest values +sidebar_label: Query for latest data +description: How to use LATEST_BY or deltas for up-to-date values. +--- + +<!-- + ~ Licensed to the Apache Software Foundation (ASF) under one + ~ or more contributor license agreements. See the NOTICE file + ~ distributed with this work for additional information + ~ regarding copyright ownership. The ASF licenses this file + ~ to you under the Apache License, Version 2.0 (the + ~ "License"); you may not use this file except in compliance + ~ with the License. You may obtain a copy of the License at + ~ + ~ http://www.apache.org/licenses/LICENSE-2.0 + ~ + ~ Unless required by applicable law or agreed to in writing, + ~ software distributed under the License is distributed on an + ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + ~ KIND, either express or implied. See the License for the + ~ specific language governing permissions and limitations + ~ under the License. + --> + +This tutorial describes strategies in Apache Druid for use cases that might be handled by UPSERT in other databases. You can use the LATEST_BY aggregation at query time or "deltas" for numeric dimensions at insert time. + +The [Update data](./tutorial-update-data.md) tutorial demonstrates how to use batch operations to update data according to the timestamp, including UPSERT cases. However, with streaming data, you can potentially use LATEST_BY or deltas to satisfy requirements otherwise handled with updates. + +## Prerequisites + +Before you follow the steps in this tutorial, download Druid as described in the [Local quickstart](index.md) and have it running on your local machine. You don't need to load any data into the Druid cluster. + +You should be familiar with data querying in Druid. If you haven't already, go through the [Query data](../tutorials/tutorial-query.md) tutorial first. + +## Use LATEST_BY to retrieve updated values + +Sometimes, you want to read the latest value of one dimension or measure in relation to another dimension. In a transactional database, you might maintain dimensions or measures using UPSERT, but in Druid you can append all updates or changes during ingestion. The LATEST_BY function lets you get the most recent value for the dimension with the following type of query: + +```sql +SELECT dimension, + LATEST_BY(changed_dimension, update_timestamp) +FROM my_table +GROUP BY 1 +``` + +For example, consider the following table of events that log the total number of points for a user: + +| `__time` | `user_id`| `total_points`| Review Comment: ```suggestion | `__time` | `user_id`| `points`| ``` ########## docs/tutorials/tutorial-latest-by.md: ########## @@ -0,0 +1,233 @@ +--- +id: tutorial-latest-by +title: Query for latest values +sidebar_label: Query for latest data +description: How to use LATEST_BY or deltas for up-to-date values. +--- + +<!-- + ~ Licensed to the Apache Software Foundation (ASF) under one + ~ or more contributor license agreements. See the NOTICE file + ~ distributed with this work for additional information + ~ regarding copyright ownership. The ASF licenses this file + ~ to you under the Apache License, Version 2.0 (the + ~ "License"); you may not use this file except in compliance + ~ with the License. You may obtain a copy of the License at + ~ + ~ http://www.apache.org/licenses/LICENSE-2.0 + ~ + ~ Unless required by applicable law or agreed to in writing, + ~ software distributed under the License is distributed on an + ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + ~ KIND, either express or implied. See the License for the + ~ specific language governing permissions and limitations + ~ under the License. + --> + +This tutorial describes strategies in Apache Druid for use cases that might be handled by UPSERT in other databases. You can use the LATEST_BY aggregation at query time or "deltas" for numeric dimensions at insert time. + +The [Update data](./tutorial-update-data.md) tutorial demonstrates how to use batch operations to update data according to the timestamp, including UPSERT cases. However, with streaming data, you can potentially use LATEST_BY or deltas to satisfy requirements otherwise handled with updates. + +## Prerequisites + +Before you follow the steps in this tutorial, download Druid as described in the [Local quickstart](index.md) and have it running on your local machine. You don't need to load any data into the Druid cluster. + +You should be familiar with data querying in Druid. If you haven't already, go through the [Query data](../tutorials/tutorial-query.md) tutorial first. + +## Use LATEST_BY to retrieve updated values + +Sometimes, you want to read the latest value of one dimension or measure in relation to another dimension. In a transactional database, you might maintain dimensions or measures using UPSERT, but in Druid you can append all updates or changes during ingestion. The LATEST_BY function lets you get the most recent value for the dimension with the following type of query: + +```sql +SELECT dimension, + LATEST_BY(changed_dimension, updated_timestamp) +FROM my_table +GROUP BY 1 +``` + +In this example `update_timestamp` represents the reference timestamp to use to evalute the "latest" value. This could be `__time` or another timestamp. Review Comment: ```suggestion In this example `update_timestamp` represents the reference timestamp to use to evaluate the "latest" value. This could be `__time` or another timestamp. ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. 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