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Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com> Co-authored-by: Charles Smith <techdocsmith@gmail.com>
160 lines
7.4 KiB
Markdown
160 lines
7.4 KiB
Markdown
---
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id: tutorial-batch-native
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title: "Load data with native batch ingestion"
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sidebar_label: Load data with native batch ingestion
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---
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~ Licensed to the Apache Software Foundation (ASF) under one
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This topic shows you how to load and query data files in Apache Druid using its native batch ingestion feature.
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## Prerequisites
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Install Druid, start up Druid services, and open the web console as described in the [Druid quickstart](index.md).
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## Load data
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Ingestion specs define the schema of the data Druid reads and stores. You can write ingestion specs by hand or using the _data loader_,
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as we'll do here to perform batch file loading with Druid's native batch ingestion.
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The Druid distribution bundles sample data we can use. The sample data located in `quickstart/tutorial/wikiticker-2015-09-12-sampled.json.gz`
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in the Druid root directory represents Wikipedia page edits for a given day.
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1. Click **Load data** from the web console header ().
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2. Select the **Local disk** tile and then click **Connect data**.
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3. Enter the following values:
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- **Base directory**: `quickstart/tutorial/`
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- **File filter**: `wikiticker-2015-09-12-sampled.json.gz`
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Entering the base directory and [wildcard file filter](https://commons.apache.org/proper/commons-io/apidocs/org/apache/commons/io/filefilter/WildcardFileFilter.html) separately, as afforded by the UI, allows you to specify multiple files for ingestion at once.
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4. Click **Apply**.
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The data loader displays the raw data, giving you a chance to verify that the data
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appears as expected.
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Notice that your position in the sequence of steps to load data, **Connect** in our case, appears at the top of the console, as shown below.
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You can click other steps to move forward or backward in the sequence at any time.
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5. Click **Next: Parse data**.
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The data loader tries to determine the parser appropriate for the data format automatically. In this case
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it identifies the data format as `json`, as shown in the **Input format** field at the bottom right.
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Feel free to select other **Input format** options to get a sense of their configuration settings
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and how Druid parses other types of data.
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6. With the JSON parser selected, click **Next: Parse time**. The **Parse time** settings are where you view and adjust the
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primary timestamp column for the data.
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Druid requires data to have a primary timestamp column (internally stored in a column called `__time`).
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If you do not have a timestamp in your data, select `Constant value`. In our example, the data loader
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determines that the `time` column is the only candidate that can be used as the primary time column.
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7. Click **Next: Transform**, **Next: Filter**, and then **Next: Configure schema**, skipping a few steps.
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You do not need to adjust transformation or filtering settings, as applying ingestion time transforms and
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filters are out of scope for this tutorial.
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8. The Configure schema settings are where you configure what [dimensions](../ingestion/schema-model.md#dimensions)
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and [metrics](../ingestion/schema-model.md#metrics) are ingested. The outcome of this configuration represents exactly how the
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data will appear in Druid after ingestion.
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Since our dataset is very small, you can turn off [rollup](../ingestion/rollup.md)
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by unsetting the **Rollup** switch and confirming the change when prompted.
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9. Click **Next: Partition** to configure how the data will be split into segments. In this case, choose `DAY` as the **Segment granularity**.
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Since this is a small dataset, we can have just a single segment, which is what selecting `DAY` as the
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segment granularity gives us.
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10. Click **Next: Tune** and **Next: Publish**.
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11. The Publish settings are where you specify the datasource name in Druid. Let's change the default name from `wikiticker-2015-09-12-sampled` to `wikipedia`.
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12. Click **Next: Edit spec** to review the ingestion spec we've constructed with the data loader.
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Feel free to go back and change settings from previous steps to see how doing so updates the spec.
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Similarly, you can edit the spec directly and see it reflected in the previous steps.
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For other ways to load ingestion specs in Druid, see [Tutorial: Loading a file](./tutorial-batch.md).
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13. Once you are satisfied with the spec, click **Submit**.
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The new task for our wikipedia datasource now appears in the Ingestion view.
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The task may take a minute or two to complete. When done, the task status should be "SUCCESS", with
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the duration of the task indicated. Note that the view is set to automatically
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refresh, so you do not need to refresh the browser to see the status change.
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A successful task means that one or more segments have been built and are now picked up by our data servers.
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## Query the data
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You can now see the data as a datasource in the console and try out a query, as follows:
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1. Click **Datasources** from the console header.
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If the wikipedia datasource doesn't appear, wait a few moments for the segment to finish loading. A datasource is
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queryable once it is shown to be "Fully available" in the **Availability** column.
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2. When the datasource is available, open the Actions menu () for that
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datasource and choose **Query with SQL**.
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:::info
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Notice the other actions you can perform for a datasource, including configuring retention rules, compaction, and more.
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:::
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3. Run the prepopulated query, `SELECT * FROM "wikipedia"` to see the results.
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