mirror of https://github.com/apache/druid.git
279 lines
11 KiB
Markdown
279 lines
11 KiB
Markdown
---
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id: tutorial-kafka
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title: "Tutorial: Load streaming data from Apache Kafka"
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sidebar_label: "Load from Apache Kafka"
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---
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<!--
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~ Licensed to the Apache Software Foundation (ASF) under one
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## Getting started
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This tutorial demonstrates how to load data into Apache Druid from a Kafka stream, using Druid's Kafka indexing service.
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For this tutorial, we'll assume you've already downloaded Druid as described in
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the [quickstart](index.md) using the `micro-quickstart` single-machine configuration and have it
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running on your local machine. You don't need to have loaded any data yet.
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## Download and start Kafka
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[Apache Kafka](http://kafka.apache.org/) is a high throughput message bus that works well with
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Druid. For this tutorial, we will use Kafka 2.7.0. To download Kafka, issue the following
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commands in your terminal:
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```bash
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curl -O https://archive.apache.org/dist/kafka/2.7.0/kafka_2.13-2.7.0.tgz
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tar -xzf kafka_2.13-2.7.0.tgz
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cd kafka_2.13-2.7.0
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```
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Start a Kafka broker by running the following command in a new terminal:
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```bash
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./bin/kafka-server-start.sh config/server.properties
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```
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Run this command to create a Kafka topic called *wikipedia*, to which we'll send data:
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```bash
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./bin/kafka-topics.sh --create --topic wikipedia --bootstrap-server localhost:9092
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```
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## Load data into Kafka
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Let's launch a producer for our topic and send some data!
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In your Druid directory, run the following command:
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```bash
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cd quickstart/tutorial
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gunzip -c wikiticker-2015-09-12-sampled.json.gz > wikiticker-2015-09-12-sampled.json
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```
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In your Kafka directory, run the following command, where {PATH_TO_DRUID} is replaced by the path to the Druid directory:
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```bash
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export KAFKA_OPTS="-Dfile.encoding=UTF-8"
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./bin/kafka-console-producer.sh --broker-list localhost:9092 --topic wikipedia < {PATH_TO_DRUID}/quickstart/tutorial/wikiticker-2015-09-12-sampled.json
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```
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The previous command posted sample events to the *wikipedia* Kafka topic.
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Now we will use Druid's Kafka indexing service to ingest messages from our newly created topic.
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## Loading data with the data loader
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Navigate to [localhost:8888](http://localhost:8888) and click `Load data` in the console header.
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Select `Apache Kafka` and click `Connect data`.
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Enter `localhost:9092` as the bootstrap server and `wikipedia` as the topic.
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Click `Apply` and make sure that the data you are seeing is correct.
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Once the data is located, you can click "Next: Parse data" to go to the next step.
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The data loader will try to automatically determine the correct parser for the data.
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In this case it will successfully determine `json`.
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Feel free to play around with different parser options to get a preview of how Druid will parse your data.
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With the `json` parser selected, click `Next: Parse time` to get to the step centered around determining your primary timestamp column.
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Druid's architecture requires 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`.
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In our example, the data loader will determine that the `time` column in our raw data is the only candidate that can be used as the primary time column.
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Click `Next: ...` twice to go past the `Transform` and `Filter` steps.
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You do not need to enter anything in these steps as applying ingestion time transforms and filters are out of scope for this tutorial.
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In the `Configure schema` step, you can configure which [dimensions](../ingestion/data-model.md#dimensions) and [metrics](../ingestion/data-model.md#metrics) will be ingested into Druid.
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This is exactly what the data will appear like in Druid once it is ingested.
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Since our dataset is very small, go ahead and turn off [`Rollup`](../ingestion/rollup.md) by clicking on the switch and confirming the change.
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Once you are satisfied with the schema, click `Next` to go to the `Partition` step where you can fine tune how the data will be partitioned into segments.
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Here, you can adjust how the data will be split up into segments in Druid.
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Since this is a small dataset, there are no adjustments that need to be made in this step.
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Click `Next: Tune` to go to the tuning step.
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In the `Tune` step is it *very important* to set `Use earliest offset` to `True` since we want to consume the data from the start of the stream.
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There are no other changes that need to be made here, so click `Next: Publish` to go to the `Publish` step.
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Let's name this datasource `wikipedia-kafka`.
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Finally, click `Next` to review your spec.
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This is the spec you have constructed.
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Feel free to go back and make changes in previous steps to see how changes will update the spec.
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Similarly, you can also edit the spec directly and see it reflected in the previous steps.
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Once you are satisfied with the spec, click `Submit` and an ingestion task will be created.
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You will be taken to the task view with the focus on the newly created supervisor.
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The task view is set to auto refresh, wait until your supervisor launches a task.
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When a tasks starts running, it will also start serving the data that it is ingesting.
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Navigate to the `Datasources` view from the header.
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When the `wikipedia-kafka` datasource appears here it can be queried.
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*Note:* if the datasource does not appear after a minute you might have not set the supervisor to read from the start of the stream (in the `Tune` step).
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At this point, you can go to the `Query` view to run SQL queries against the datasource.
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Since this is a small dataset, you can simply run a `SELECT * FROM "wikipedia-kafka"` query to see your results.
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Check out the [query tutorial](../tutorials/tutorial-query.md) to run some example queries on the newly loaded data.
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### Submit a supervisor via the console
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In the console, click `Submit supervisor` to open the submit supervisor dialog.
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Paste in this spec and click `Submit`.
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```json
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{
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"type": "kafka",
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"spec" : {
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"dataSchema": {
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"dataSource": "wikipedia",
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"timestampSpec": {
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"column": "time",
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"format": "auto"
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},
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"dimensionsSpec": {
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"dimensions": [
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"channel",
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"cityName",
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"comment",
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"countryIsoCode",
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"countryName",
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"isAnonymous",
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"isMinor",
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"isNew",
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"isRobot",
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"isUnpatrolled",
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"metroCode",
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"namespace",
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"page",
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"regionIsoCode",
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"regionName",
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"user",
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{ "name": "added", "type": "long" },
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{ "name": "deleted", "type": "long" },
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{ "name": "delta", "type": "long" }
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]
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},
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"metricsSpec" : [],
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"granularitySpec": {
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"type": "uniform",
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"segmentGranularity": "DAY",
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"queryGranularity": "NONE",
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"rollup": false
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}
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},
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"tuningConfig": {
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"type": "kafka",
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"reportParseExceptions": false
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},
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"ioConfig": {
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"topic": "wikipedia",
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"inputFormat": {
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"type": "json"
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},
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"replicas": 2,
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"taskDuration": "PT10M",
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"completionTimeout": "PT20M",
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"consumerProperties": {
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"bootstrap.servers": "localhost:9092"
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}
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}
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}
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}
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```
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This will start the supervisor that will in turn spawn some tasks that will start listening for incoming data.
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### Submit a supervisor directly
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To start the service directly, we will need to submit a supervisor spec to the Druid overlord by running the following from the Druid package root:
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```bash
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curl -XPOST -H'Content-Type: application/json' -d @quickstart/tutorial/wikipedia-kafka-supervisor.json http://localhost:8081/druid/indexer/v1/supervisor
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```
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If the supervisor was successfully created, you will get a response containing the ID of the supervisor; in our case we should see `{"id":"wikipedia"}`.
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For more details about what's going on here, check out the
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[Druid Kafka indexing service documentation](../development/extensions-core/kafka-ingestion.md).
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You can view the current supervisors and tasks in the Druid Console: [http://localhost:8888/unified-console.md#tasks](http://localhost:8888/unified-console.html#tasks).
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## Querying your data
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After data is sent to the Kafka stream, it is immediately available for querying.
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Please follow the [query tutorial](../tutorials/tutorial-query.md) to run some example queries on the newly loaded data.
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## Cleanup
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To go through any of the other ingestion tutorials, you will need to shut down the cluster and reset the cluster state by removing the contents of the `var` directory in the Druid home, as the other tutorials will write to the same "wikipedia" datasource.
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You should additionally clear out any Kafka state. Do so by shutting down the Kafka broker with CTRL-C before stopping ZooKeeper and the Druid services, and then deleting the Kafka log directory at `/tmp/kafka-logs`:
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```bash
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rm -rf /tmp/kafka-logs
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```
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## Further reading
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For more information on loading data from Kafka streams, please see the [Druid Kafka indexing service documentation](../development/extensions-core/kafka-ingestion.md).
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