mirror of https://github.com/apache/druid.git
223 lines
7.3 KiB
Markdown
223 lines
7.3 KiB
Markdown
---
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id: parquet
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title: "Apache Parquet Extension"
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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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~ or more contributor license agreements. See the NOTICE file
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~ distributed with this work for additional information
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~ regarding copyright ownership. The ASF licenses this file
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~ to you under the Apache License, Version 2.0 (the
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~ "License"); you may not use this file except in compliance
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~ with the License. You may obtain a copy of the License at
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This Apache Druid (incubating) module extends [Druid Hadoop based indexing](../../ingestion/hadoop.md) to ingest data directly from offline
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Apache Parquet files.
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Note: `druid-parquet-extensions` depends on the `druid-avro-extensions` module, so be sure to
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[include both](../../development/extensions.md#loading-extensions).
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## Parquet Hadoop Parser
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This extension provides two ways to parse Parquet files:
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* `parquet` - using a simple conversion contained within this extension
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* `parquet-avro` - conversion to avro records with the `parquet-avro` library and using the `druid-avro-extensions`
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module to parse the avro data
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Selection of conversion method is controlled by parser type, and the correct hadoop input format must also be set in
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the `ioConfig`:
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* `org.apache.druid.data.input.parquet.DruidParquetInputFormat` for `parquet`
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* `org.apache.druid.data.input.parquet.DruidParquetAvroInputFormat` for `parquet-avro`
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Both parse options support auto field discovery and flattening if provided with a
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[`flattenSpec`](../../ingestion/index.md#flattenspec) with `parquet` or `avro` as the format. Parquet nested list and map
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[logical types](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md) _should_ operate correctly with
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json path expressions for all supported types. `parquet-avro` sets a hadoop job property
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`parquet.avro.add-list-element-records` to `false` (which normally defaults to `true`), in order to 'unwrap' primitive
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list elements into multi-value dimensions.
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The `parquet` parser supports `int96` Parquet values, while `parquet-avro` does not. There may also be some subtle
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differences in the behavior of json path expression evaluation of `flattenSpec`.
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We suggest using `parquet` over `parquet-avro` to allow ingesting data beyond the schema constraints of Avro conversion.
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However, `parquet-avro` was the original basis for this extension, and as such it is a bit more mature.
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|Field | Type | Description | Required|
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|----------|-------------|----------------------------------------------------------------------------------------|---------|
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| type | String | Choose `parquet` or `parquet-avro` to determine how Parquet files are parsed | yes |
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| parseSpec | JSON Object | Specifies the timestamp and dimensions of the data, and optionally, a flatten spec. Valid parseSpec formats are `timeAndDims`, `parquet`, `avro` (if used with avro conversion). | yes |
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| binaryAsString | Boolean | Specifies if the bytes parquet column which is not logically marked as a string or enum type should be converted to strings anyway. | no(default == false) |
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When the time dimension is a [DateType column](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md), a format should not be supplied. When the format is UTF8 (String), either `auto` or a explicitly defined [format](http://www.joda.org/joda-time/apidocs/org/joda/time/format/DateTimeFormat.html) is required.
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### Examples
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#### `parquet` parser, `parquet` parseSpec
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```json
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{
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"type": "index_hadoop",
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"spec": {
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"ioConfig": {
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"type": "hadoop",
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"inputSpec": {
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"type": "static",
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"inputFormat": "org.apache.druid.data.input.parquet.DruidParquetInputFormat",
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"paths": "path/to/file.parquet"
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},
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...
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},
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"dataSchema": {
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"dataSource": "example",
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"parser": {
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"type": "parquet",
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"parseSpec": {
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"format": "parquet",
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"flattenSpec": {
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"useFieldDiscovery": true,
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"fields": [
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{
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"type": "path",
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"name": "nestedDim",
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"expr": "$.nestedData.dim1"
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},
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{
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"type": "path",
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"name": "listDimFirstItem",
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"expr": "$.listDim[1]"
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}
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]
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},
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"timestampSpec": {
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"column": "timestamp",
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"format": "auto"
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},
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"dimensionsSpec": {
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"dimensions": [],
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"dimensionExclusions": [],
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"spatialDimensions": []
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}
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}
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},
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...
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},
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"tuningConfig": <hadoop-tuning-config>
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}
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}
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}
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```
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#### `parquet` parser, `timeAndDims` parseSpec
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```json
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{
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"type": "index_hadoop",
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"spec": {
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"ioConfig": {
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"type": "hadoop",
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"inputSpec": {
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"type": "static",
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"inputFormat": "org.apache.druid.data.input.parquet.DruidParquetInputFormat",
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"paths": "path/to/file.parquet"
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},
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...
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},
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"dataSchema": {
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"dataSource": "example",
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"parser": {
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"type": "parquet",
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"parseSpec": {
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"format": "timeAndDims",
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"timestampSpec": {
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"column": "timestamp",
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"format": "auto"
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},
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"dimensionsSpec": {
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"dimensions": [
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"dim1",
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"dim2",
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"dim3",
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"listDim"
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],
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"dimensionExclusions": [],
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"spatialDimensions": []
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}
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}
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},
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...
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},
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"tuningConfig": <hadoop-tuning-config>
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}
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}
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```
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#### `parquet-avro` parser, `avro` parseSpec
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```json
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{
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"type": "index_hadoop",
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"spec": {
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"ioConfig": {
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"type": "hadoop",
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"inputSpec": {
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"type": "static",
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"inputFormat": "org.apache.druid.data.input.parquet.DruidParquetAvroInputFormat",
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"paths": "path/to/file.parquet"
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},
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...
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},
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"dataSchema": {
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"dataSource": "example",
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"parser": {
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"type": "parquet-avro",
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"parseSpec": {
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"format": "avro",
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"flattenSpec": {
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"useFieldDiscovery": true,
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"fields": [
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{
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"type": "path",
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"name": "nestedDim",
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"expr": "$.nestedData.dim1"
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},
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{
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"type": "path",
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"name": "listDimFirstItem",
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"expr": "$.listDim[1]"
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}
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]
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},
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"timestampSpec": {
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"column": "timestamp",
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"format": "auto"
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},
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"dimensionsSpec": {
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"dimensions": [],
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"dimensionExclusions": [],
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"spatialDimensions": []
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}
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}
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},
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...
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},
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"tuningConfig": <hadoop-tuning-config>
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}
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}
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}
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```
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For additional details see [Hadoop ingestion](../../ingestion/hadoop.md) and [general ingestion spec](../../ingestion/index.md) documentation.
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