mirror of https://github.com/apache/druid.git
148 lines
6.9 KiB
Markdown
148 lines
6.9 KiB
Markdown
---
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id: tutorial-msq-extern
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title: Load files with SQL-based ingestion
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sidebar_label: Load files using SQL
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description: How to generate a query that references externally hosted data
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---
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~ Licensed to the Apache Software Foundation (ASF) under one
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> This page describes SQL-based batch ingestion using the [`druid-multi-stage-query`](../multi-stage-query/index.md)
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> extension, new in Druid 24.0. Refer to the [ingestion methods](../ingestion/index.md#batch) table to determine which
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> ingestion method is right for you.
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This tutorial demonstrates how to generate a query that references externally hosted data using the **Connect external data** wizard.
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The following example uses EXTERN to query a JSON file located at https://druid.apache.org/data/wikipedia.json.gz.
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Although you can manually create a query in the UI, you can use Druid to generate a base query for you that you can modify to meet your requirements.
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To generate a query from external data, do the following:
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1. In the **Query** view of the web console, click **Connect external data**.
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2. On the **Select input type** screen, choose **HTTP(s)** and enter the following value in the **URIs** field: `https://druid.apache.org/data/wikipedia.json.gz`. Leave the HTTP auth username and password blank.
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3. Click **Connect data**.
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4. On the **Parse** screen, you can perform additional actions before you load the data into Druid:
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- Expand a row to see what data it corresponds to from the source.
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- Customize how Druid handles the data by selecting the **Input format** and its related options, such as adding **JSON parser features** for JSON files.
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5. When you're ready, click **Done**. You're returned to the **Query** view where you can see the starter query that will insert the data from the external source into a table named `wikipedia`.
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<details><summary>Show the query</summary>
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```sql
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REPLACE INTO "wikipedia" OVERWRITE ALL
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WITH ext AS (SELECT *
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FROM TABLE(
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EXTERN(
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'{"type":"http","uris":["https://druid.apache.org/data/wikipedia.json.gz"]}',
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'{"type":"json"}',
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'[{"name":"isRobot","type":"string"},{"name":"channel","type":"string"},{"name":"timestamp","type":"string"},{"name":"flags","type":"string"},{"name":"isUnpatrolled","type":"string"},{"name":"page","type":"string"},{"name":"diffUrl","type":"string"},{"name":"added","type":"long"},{"name":"comment","type":"string"},{"name":"commentLength","type":"long"},{"name":"isNew","type":"string"},{"name":"isMinor","type":"string"},{"name":"delta","type":"long"},{"name":"isAnonymous","type":"string"},{"name":"user","type":"string"},{"name":"deltaBucket","type":"long"},{"name":"deleted","type":"long"},{"name":"namespace","type":"string"},{"name":"cityName","type":"string"},{"name":"countryName","type":"string"},{"name":"regionIsoCode","type":"string"},{"name":"metroCode","type":"long"},{"name":"countryIsoCode","type":"string"},{"name":"regionName","type":"string"}]'
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)
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))
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SELECT
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TIME_PARSE("timestamp") AS __time,
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isRobot,
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channel,
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flags,
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isUnpatrolled,
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page,
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diffUrl,
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added,
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comment,
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commentLength,
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isNew,
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isMinor,
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delta,
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isAnonymous,
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user,
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deltaBucket,
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deleted,
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namespace,
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cityName,
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countryName,
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regionIsoCode,
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metroCode,
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countryIsoCode,
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regionName
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FROM ext
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PARTITIONED BY DAY
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```
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</details>
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6. Review and modify the query to meet your needs. For example, you can rename the table or change segment granularity. To partition by something other than ALL, include `TIME_PARSE("timestamp") AS __time` in your SELECT statement.
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For example, to specify day-based segment granularity, change the partitioning to `PARTITIONED BY DAY`:
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```sql
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INSERT INTO ...
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SELECT
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TIME_PARSE("timestamp") AS __time,
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...
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...
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PARTITIONED BY DAY
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```
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1. Optionally, select **Preview** to review the data before you ingest it. A preview runs the query without the REPLACE INTO clause and with an added LIMIT.
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You can see the general shape of the data before you commit to inserting it.
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The LIMITs make the query run faster but can cause incomplete results.
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2. Click **Run** to launch your query. The query returns information including its duration and the number of rows inserted into the table.
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## Query the data
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You can query the `wikipedia` table after the ingestion completes.
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For example, you can analyze the data in the table to produce a list of top channels:
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```sql
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SELECT
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channel,
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COUNT(*)
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FROM "wikipedia"
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GROUP BY channel
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ORDER BY COUNT(*) DESC
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```
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With the EXTERN function, you could run the same query on the external data directly without ingesting it first:
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<details><summary>Show the query</summary>
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```sql
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SELECT
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channel,
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COUNT(*)
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FROM TABLE(
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EXTERN(
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'{"type": "http", "uris": ["https://druid.apache.org/data/wikipedia.json.gz"]}',
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'{"type": "json"}',
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'[{"name": "added", "type": "long"}, {"name": "channel", "type": "string"}, {"name": "cityName", "type": "string"}, {"name": "comment", "type": "string"}, {"name": "commentLength", "type": "long"}, {"name": "countryIsoCode", "type": "string"}, {"name": "countryName", "type": "string"}, {"name": "deleted", "type": "long"}, {"name": "delta", "type": "long"}, {"name": "deltaBucket", "type": "string"}, {"name": "diffUrl", "type": "string"}, {"name": "flags", "type": "string"}, {"name": "isAnonymous", "type": "string"}, {"name": "isMinor", "type": "string"}, {"name": "isNew", "type": "string"}, {"name": "isRobot", "type": "string"}, {"name": "isUnpatrolled", "type": "string"}, {"name": "metroCode", "type": "string"}, {"name": "namespace", "type": "string"}, {"name": "page", "type": "string"}, {"name": "regionIsoCode", "type": "string"}, {"name": "regionName", "type": "string"}, {"name": "timestamp", "type": "string"}, {"name": "user", "type": "string"}]'
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)
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)
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GROUP BY channel
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ORDER BY COUNT(*) DESC
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```
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</details>
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## Further reading
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See the following topics to learn more:
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* [SQL-based ingestion overview](../multi-stage-query/index.md) to further explore SQL-based ingestion.
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* [SQL-based ingestion reference](../multi-stage-query/reference.md) for reference on context parameters, functions, and error codes.
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