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87 lines
6.0 KiB
Markdown
87 lines
6.0 KiB
Markdown
---
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id: index
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title: "Ingestion"
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---
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Loading data in Druid is called _ingestion_ or _indexing_. When you ingest data into Druid, Druid reads the data from your source system and stores it in data files called _segments_. In general, segment files contain a few million rows.
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For most ingestion methods, the Druid [MiddleManager](../design/middlemanager.md) processes or the [Indexer](../design/indexer.md) processes load your source data. One exception is
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Hadoop-based ingestion, which uses a Hadoop MapReduce job on YARN MiddleManager or Indexer processes to start and monitor Hadoop jobs.
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During ingestion Druid creates segments and stores them in [deep storage](../dependencies/deep-storage.md). Historical nodes load the segments into memory to respond to queries. For streaming ingestion, the Middle Managers and indexers can respond to queries in real-time with arriving data. See the [Storage design](../design/architecture.md#storage-design) section of the Druid design documentation for more information.
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This topic introduces streaming and batch ingestion methods. The following topics describe ingestion concepts and information that apply to all [ingestion methods](#ingestion-methods):
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- [Druid data model](./data-model.md) introduces concepts of datasources, primary timestamp, dimensions, and metrics.
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- [Data rollup](./rollup.md) describes rollup as a concept and provides suggestions to maximize the benefits of rollup.
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- [Partitioning](./partitioning.md) describes time chunk and secondary partitioning in Druid.
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- [Ingestion spec reference](./ingestion-spec.md) provides a reference for the configuration options in the ingestion spec.
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For additional information about concepts and configurations that are unique to each ingestion method, see the topic for the ingestion method.
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## Ingestion methods
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The tables below list Druid's most common data ingestion methods, along with comparisons to help you choose
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the best one for your situation. Each ingestion method supports its own set of source systems to pull from. For details
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about how each method works, as well as configuration properties specific to that method, check out its documentation
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page.
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### Streaming
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The most recommended, and most popular, method of streaming ingestion is the
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[Kafka indexing service](../development/extensions-core/kafka-ingestion.md) that reads directly from Kafka. Alternatively, the Kinesis
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indexing service works with Amazon Kinesis Data Streams.
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Streaming ingestion uses an ongoing process called a supervisor that reads from the data stream to ingest data into Druid.
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This table compares the options:
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| **Method** | [Kafka](../development/extensions-core/kafka-ingestion.md) | [Kinesis](../development/extensions-core/kinesis-ingestion.md) |
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|---|-----|--------------|
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| **Supervisor type** | `kafka` | `kinesis`|
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| **How it works** | Druid reads directly from Apache Kafka. | Druid reads directly from Amazon Kinesis.|
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| **Can ingest late data?** | Yes | Yes |
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| **Exactly-once guarantees?** | Yes | Yes |
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### Batch
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When doing batch loads from files, you should use one-time [tasks](tasks.md), and you have three options: `index_parallel` (native batch; parallel), `index_hadoop` (Hadoop-based),
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or `index` (native batch; single-task).
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In general, we recommend native batch whenever it meets your needs, since the setup is simpler (it does not depend on
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an external Hadoop cluster). However, there are still scenarios where Hadoop-based batch ingestion might be a better choice,
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for example when you already have a running Hadoop cluster and want to
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use the cluster resource of the existing cluster for batch ingestion.
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This table compares the three available options:
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| **Method** | [Native batch (parallel)](./native-batch.md) | [Hadoop-based](hadoop.md) | [Native batch (simple)](./native-batch-simple-task.md) |
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|---|-----|--------------|------------|
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| **Task type** | `index_parallel` | `index_hadoop` | `index` |
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| **Parallel?** | Yes, if `inputFormat` is splittable and `maxNumConcurrentSubTasks` > 1 in `tuningConfig`. See [data format documentation](./data-formats.md) for details. | Yes, always. | No. Each task is single-threaded. |
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| **Can append or overwrite?** | Yes, both. | Overwrite only. | Yes, both. |
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| **External dependencies** | None. | Hadoop cluster (Druid submits Map/Reduce jobs). | None. |
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| **Input locations** | Any [`inputSource`](./native-batch-input-source.md). | Any Hadoop FileSystem or Druid datasource. | Any [`inputSource`](./native-batch-input-source.md). |
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| **File formats** | Any [`inputFormat`](./data-formats.md#input-format). | Any Hadoop InputFormat. | Any [`inputFormat`](./data-formats.md#input-format). |
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| **[Rollup modes](./rollup.md)** | Perfect if `forceGuaranteedRollup` = true in the [`tuningConfig`](native-batch.md#tuningconfig). | Always perfect. | Perfect if `forceGuaranteedRollup` = true in the [`tuningConfig`](native-batch.md#tuningconfig). |
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| **Partitioning options** | Dynamic, hash-based, and range-based partitioning methods are available. See [partitionsSpec](./native-batch.md#partitionsspec) for details.| Hash-based or range-based partitioning via [`partitionsSpec`](hadoop.md#partitionsspec). | Dynamic and hash-based partitioning methods are available. See [partitionsSpec](./native-batch.md#partitionsspec) for details. |
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