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准备 Roll-up 部分的翻译
2021-07-31 17:43:32 -04:00
# Roll-up
重新整理文件并且将官方的英文版内容拷贝部分
2021-07-19 16:14:08 -04:00
Apache Druid can summarize raw data at ingestion time using a process we refer to as "roll-up". Roll-up is a first-level aggregation operation over a selected set of columns that reduces the size of stored data.
This tutorial will demonstrate the effects of roll-up on an example dataset.
For this tutorial, we'll assume you've already downloaded Druid as described in
the [single-machine quickstart](index.html) and have it running on your local machine.
It will also be helpful to have finished [Tutorial: Loading a file](../tutorials/tutorial-batch.md) and [Tutorial: Querying data](../tutorials/tutorial-query.md).
## Example data
For this tutorial, we'll use a small sample of network flow event data, representing packet and byte counts for traffic from a source to a destination IP address that occurred within a particular second.
```json
{"timestamp":"2018-01-01T01:01:35Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":20,"bytes":9024}
{"timestamp":"2018-01-01T01:01:51Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":255,"bytes":21133}
{"timestamp":"2018-01-01T01:01:59Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":11,"bytes":5780}
{"timestamp":"2018-01-01T01:02:14Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":38,"bytes":6289}
{"timestamp":"2018-01-01T01:02:29Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":377,"bytes":359971}
{"timestamp":"2018-01-01T01:03:29Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":49,"bytes":10204}
{"timestamp":"2018-01-02T21:33:14Z","srcIP":"7.7.7.7", "dstIP":"8.8.8.8","packets":38,"bytes":6289}
{"timestamp":"2018-01-02T21:33:45Z","srcIP":"7.7.7.7", "dstIP":"8.8.8.8","packets":123,"bytes":93999}
{"timestamp":"2018-01-02T21:35:45Z","srcIP":"7.7.7.7", "dstIP":"8.8.8.8","packets":12,"bytes":2818}
```
A file containing this sample input data is located at `quickstart/tutorial/rollup-data.json`.
We'll ingest this data using the following ingestion task spec, located at `quickstart/tutorial/rollup-index.json`.
```json
{
"type" : "index_parallel",
"spec" : {
"dataSchema" : {
"dataSource" : "rollup-tutorial",
"dimensionsSpec" : {
"dimensions" : [
"srcIP",
"dstIP"
]
},
"timestampSpec": {
"column": "timestamp",
"format": "iso"
},
"metricsSpec" : [
{ "type" : "count", "name" : "count" },
{ "type" : "longSum", "name" : "packets", "fieldName" : "packets" },
{ "type" : "longSum", "name" : "bytes", "fieldName" : "bytes" }
],
"granularitySpec" : {
"type" : "uniform",
"segmentGranularity" : "week",
"queryGranularity" : "minute",
"intervals" : ["2018-01-01/2018-01-03"],
"rollup" : true
}
},
"ioConfig" : {
"type" : "index_parallel",
"inputSource" : {
"type" : "local",
"baseDir" : "quickstart/tutorial",
"filter" : "rollup-data.json"
},
"inputFormat" : {
"type" : "json"
},
"appendToExisting" : false
},
"tuningConfig" : {
"type" : "index_parallel",
"maxRowsPerSegment" : 5000000,
"maxRowsInMemory" : 25000
}
}
}
```
Roll-up has been enabled by setting `"rollup" : true` in the `granularitySpec`.
Note that we have `srcIP` and `dstIP` defined as dimensions, a longSum metric is defined for the `packets` and `bytes` columns, and the `queryGranularity` has been defined as `minute`.
We will see how these definitions are used after we load this data.
## Load the example data
更新下载的版本到 apache-druid-0.21.1
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From the apache-druid-apache-druid-0.21.1 package root, run the following command:
重新整理文件并且将官方的英文版内容拷贝部分
2021-07-19 16:14:08 -04:00
```bash
bin/post-index-task --file quickstart/tutorial/rollup-index.json --url http://localhost:8081
```
After the script completes, we will query the data.
## Query the example data
Let's run `bin/dsql` and issue a `select * from "rollup-tutorial";` query to see what data was ingested.
```bash
$ bin/dsql
Welcome to dsql, the command-line client for Druid SQL.
Type "\h" for help.
dsql> select * from "rollup-tutorial";
┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐
│ __time │ bytes │ count │ dstIP │ packets │ srcIP │
├──────────────────────────┼────────┼───────┼─────────┼─────────┼─────────┤
│ 2018-01-01T01:01:00.000Z │ 35937 │ 3 │ 2.2.2.2 │ 286 │ 1.1.1.1 │
│ 2018-01-01T01:02:00.000Z │ 366260 │ 2 │ 2.2.2.2 │ 415 │ 1.1.1.1 │
│ 2018-01-01T01:03:00.000Z │ 10204 │ 1 │ 2.2.2.2 │ 49 │ 1.1.1.1 │
│ 2018-01-02T21:33:00.000Z │ 100288 │ 2 │ 8.8.8.8 │ 161 │ 7.7.7.7 │
│ 2018-01-02T21:35:00.000Z │ 2818 │ 1 │ 8.8.8.8 │ 12 │ 7.7.7.7 │
└──────────────────────────┴────────┴───────┴─────────┴─────────┴─────────┘
Retrieved 5 rows in 1.18s.
dsql>
```
Let's look at the three events in the original input data that occurred during `2018-01-01T01:01`:
```json
{"timestamp":"2018-01-01T01:01:35Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":20,"bytes":9024}
{"timestamp":"2018-01-01T01:01:51Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":255,"bytes":21133}
{"timestamp":"2018-01-01T01:01:59Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":11,"bytes":5780}
```
These three rows have been "rolled up" into the following row:
```bash
┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐
│ __time │ bytes │ count │ dstIP │ packets │ srcIP │
├──────────────────────────┼────────┼───────┼─────────┼─────────┼─────────┤
│ 2018-01-01T01:01:00.000Z │ 35937 │ 3 │ 2.2.2.2 │ 286 │ 1.1.1.1 │
└──────────────────────────┴────────┴───────┴─────────┴─────────┴─────────┘
```
The input rows have been grouped by the timestamp and dimension columns `{timestamp, srcIP, dstIP}` with sum aggregations on the metric columns `packets` and `bytes`.
Before the grouping occurs, the timestamps of the original input data are bucketed/floored by minute, due to the `"queryGranularity":"minute"` setting in the ingestion spec.
Likewise, these two events that occurred during `2018-01-01T01:02` have been rolled up:
```json
{"timestamp":"2018-01-01T01:02:14Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":38,"bytes":6289}
{"timestamp":"2018-01-01T01:02:29Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":377,"bytes":359971}
```
```bash
┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐
│ __time │ bytes │ count │ dstIP │ packets │ srcIP │
├──────────────────────────┼────────┼───────┼─────────┼─────────┼─────────┤
│ 2018-01-01T01:02:00.000Z │ 366260 │ 2 │ 2.2.2.2 │ 415 │ 1.1.1.1 │
└──────────────────────────┴────────┴───────┴─────────┴─────────┴─────────┘
```
For the last event recording traffic between 1.1.1.1 and 2.2.2.2, no roll-up took place, because this was the only event that occurred during `2018-01-01T01:03`:
```json
{"timestamp":"2018-01-01T01:03:29Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":49,"bytes":10204}
```
```bash
┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐
│ __time │ bytes │ count │ dstIP │ packets │ srcIP │
├──────────────────────────┼────────┼───────┼─────────┼─────────┼─────────┤
│ 2018-01-01T01:03:00.000Z │ 10204 │ 1 │ 2.2.2.2 │ 49 │ 1.1.1.1 │
└──────────────────────────┴────────┴───────┴─────────┴─────────┴─────────┘
```
Note that the `count` metric shows how many rows in the original input data contributed to the final "rolled up" row.
准备 Roll-up 部分的翻译
2021-07-31 17:43:32 -04:00
## Roll-up
Apache Druid可以通过roll-up在数据摄取阶段对原始数据进行汇总。 Roll-up是对选定列集的一级聚合操作，它可以减小存储数据的大小。
本教程中将讨论在一个示例数据集上进行roll-up的结果。
本教程我们假设您已经按照[单服务器部署](../GettingStarted/chapter-3.md)中描述下载了Druid，并运行在本地机器上。
完成[加载本地文件](tutorial-batch.md)和[数据查询](./chapter-4.md)两部分内容也是非常有帮助的。
### 示例数据
对于本教程，我们将使用一个网络流事件数据的小样本，表示在特定时间内从源到目标IP地址的流量的数据包和字节计数。
```json
{"timestamp":"2018-01-01T01:01:35Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":20,"bytes":9024}
{"timestamp":"2018-01-01T01:01:51Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":255,"bytes":21133}
{"timestamp":"2018-01-01T01:01:59Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":11,"bytes":5780}
{"timestamp":"2018-01-01T01:02:14Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":38,"bytes":6289}
{"timestamp":"2018-01-01T01:02:29Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":377,"bytes":359971}
{"timestamp":"2018-01-01T01:03:29Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":49,"bytes":10204}
{"timestamp":"2018-01-02T21:33:14Z","srcIP":"7.7.7.7", "dstIP":"8.8.8.8","packets":38,"bytes":6289}
{"timestamp":"2018-01-02T21:33:45Z","srcIP":"7.7.7.7", "dstIP":"8.8.8.8","packets":123,"bytes":93999}
{"timestamp":"2018-01-02T21:35:45Z","srcIP":"7.7.7.7", "dstIP":"8.8.8.8","packets":12,"bytes":2818}
```
位于 `quickstart/tutorial/rollup-data.json` 的文件包含了样例输入数据
我们将使用 `quickstart/tutorial/rollup-index.json` 的摄入数据规范来摄取数据
```json
{
"type" : "index_parallel",
"spec" : {
"dataSchema" : {
"dataSource" : "rollup-tutorial",
"dimensionsSpec" : {
"dimensions" : [
"srcIP",
"dstIP"
]
},
"timestampSpec": {
"column": "timestamp",
"format": "iso"
},
"metricsSpec" : [
{ "type" : "count", "name" : "count" },
{ "type" : "longSum", "name" : "packets", "fieldName" : "packets" },
{ "type" : "longSum", "name" : "bytes", "fieldName" : "bytes" }
],
"granularitySpec" : {
"type" : "uniform",
"segmentGranularity" : "week",
"queryGranularity" : "minute",
"intervals" : ["2018-01-01/2018-01-03"],
"rollup" : true
}
},
"ioConfig" : {
"type" : "index_parallel",
"inputSource" : {
"type" : "local",
"baseDir" : "quickstart/tutorial",
"filter" : "rollup-data.json"
},
"inputFormat" : {
"type" : "json"
},
"appendToExisting" : false
},
"tuningConfig" : {
"type" : "index_parallel",
"maxRowsPerSegment" : 5000000,
"maxRowsInMemory" : 25000
}
}
}
```
通过在 `granularitySpec` 选项中设置 `rollup : true` 来启用Roll-up
注意，我们将`srcIP`和`dstIP`定义为**维度**，将`packets`和`bytes`列定义为了`longSum`类型的**指标**，并将 `queryGranularity` 配置定义为 `minute`。
加载这些数据后，我们将看到如何使用这些定义。
### 加载示例数据
在Druid的根目录下运行以下命令：
```json
bin/post-index-task --file quickstart/tutorial/rollup-index.json --url http://localhost:8081
```
脚本运行完成以后，我们将查询数据。
### 查询示例数据
现在运行 `bin/dsql` 然后执行查询 `select * from "rollup-tutorial";` 来查看已经被摄入的数据。
```json
$ bin/dsql
Welcome to dsql, the command-line client for Druid SQL.
Type "\h" for help.
dsql> select * from "rollup-tutorial";
┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐
│ __time │ bytes │ count │ dstIP │ packets │ srcIP │
├──────────────────────────┼────────┼───────┼─────────┼─────────┼─────────┤
│ 2018-01-01T01:01:00.000Z │ 35937 │ 3 │ 2.2.2.2 │ 286 │ 1.1.1.1 │
│ 2018-01-01T01:02:00.000Z │ 366260 │ 2 │ 2.2.2.2 │ 415 │ 1.1.1.1 │
│ 2018-01-01T01:03:00.000Z │ 10204 │ 1 │ 2.2.2.2 │ 49 │ 1.1.1.1 │
│ 2018-01-02T21:33:00.000Z │ 100288 │ 2 │ 8.8.8.8 │ 161 │ 7.7.7.7 │
│ 2018-01-02T21:35:00.000Z │ 2818 │ 1 │ 8.8.8.8 │ 12 │ 7.7.7.7 │
└──────────────────────────┴────────┴───────┴─────────┴─────────┴─────────┘
Retrieved 5 rows in 1.18s.
dsql>
```
我们来看发生在 `2018-01-01T01:01` 的三条原始数据：
```json
{"timestamp":"2018-01-01T01:01:35Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":20,"bytes":9024}
{"timestamp":"2018-01-01T01:01:51Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":255,"bytes":21133}
{"timestamp":"2018-01-01T01:01:59Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":11,"bytes":5780}
```
这三条数据已经被roll up为以下一行数据：
```json
┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐
│ __time │ bytes │ count │ dstIP │ packets │ srcIP │
├──────────────────────────┼────────┼───────┼─────────┼─────────┼─────────┤
│ 2018-01-01T01:01:00.000Z │ 35937 │ 3 │ 2.2.2.2 │ 286 │ 1.1.1.1 │
└──────────────────────────┴────────┴───────┴─────────┴─────────┴─────────┘
```
这输入的数据行已经被按照时间列和维度列 `{timestamp, srcIP, dstIP}` 在指标列 `{packages, bytes}` 上做求和聚合
在进行分组之前，原始输入数据的时间戳按分钟进行标记/布局，这是由于摄取规范中的 `"queryGranularity"："minute"` 设置造成的。
同样，`2018-01-01T01:02` 期间发生的这两起事件也已经汇总。
```json
{"timestamp":"2018-01-01T01:02:14Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":38,"bytes":6289}
{"timestamp":"2018-01-01T01:02:29Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":377,"bytes":359971}
```
```json
┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐
│ __time │ bytes │ count │ dstIP │ packets │ srcIP │
├──────────────────────────┼────────┼───────┼─────────┼─────────┼─────────┤
│ 2018-01-01T01:02:00.000Z │ 366260 │ 2 │ 2.2.2.2 │ 415 │ 1.1.1.1 │
└──────────────────────────┴────────┴───────┴─────────┴─────────┴─────────┘
```
对于记录1.1.1.1和2.2.2.2之间流量的最后一个事件没有发生汇总，因为这是 `2018-01-01T01:03` 期间发生的唯一事件
```json
{"timestamp":"2018-01-01T01:03:29Z","srcIP":"1.1.1.1", "dstIP":"2.2.2.2","packets":49,"bytes":10204}
```
```json
┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐
│ __time │ bytes │ count │ dstIP │ packets │ srcIP │
├──────────────────────────┼────────┼───────┼─────────┼─────────┼─────────┤
│ 2018-01-01T01:03:00.000Z │ 10204 │ 1 │ 2.2.2.2 │ 49 │ 1.1.1.1 │
└──────────────────────────┴────────┴───────┴─────────┴─────────┴─────────┘
```
请注意，`计数指标 count` 显示原始输入数据中有多少行贡献给最终的"roll up"行。
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