Druid.io系列（九）：数据摄入

1. 概述

Druid的数据摄入主要包括两大类： 
1. 实时输入摄入：包括Pull,Push两种 
- Pull:需要启动一个RealtimeNode节点，通过不同的Firehose摄取不同种类的数据源。 
- Push:需要启动Tranquility或是Kafka索引服务。通过HTTP调用的方式进行数据摄入 
2. 离线数据摄入:可以通过Realtime节点摄入，也可以通过索引节点启动任务摄入

本文演示环节主要基于上一章部署的集群来进行

2. 实时数据摄入

2.1 Pull

由于Realtime Node 没有提供高可用，可伸缩等特性，对于比较重要的场景推荐使用 Tranquility Server or 或是Tranquility Kafka索引服务

2.2 Push

Indexing service在前文已经介绍过了，Tranquility 是一个Scala库，它通过索引服务实现数据实时的摄入。它之所以存在，是因为Indexing service API属于低层面的。Tranquility是对索引服务进行抽象封装， 对使用者屏蔽了 创建任务，处理分区、复制、服务发现和shema rollover等环节。

通过Tranquility 的数据摄入，可以分为两种方式

• Tranquility Server：发送方可以通过Tranquility Server 提供的HTTP接口，向Druid发送数据。
• Tranquility Kafka：发送发可以先将数据发送到Kafka,Tranquility Kafka会根据配置从Kafka获取数据，并写到Druid中。

2.2.1 Tranquility Server配置

配置流程如下 
1. 开启Tranquility Server，在数据节点上编辑conf/supervise/data-with-query.conf 文件，将Tranquility Server注释放开

# Uncomment to use Tranquility Server
!p95 tranquility-server bin/tranquility server -configFile conf/tranquility/server.json 

2. 拷贝quick里面的server.json

root@druid:~/imply-2.3.# cp conf-quickstart/tranquility/server.json conf/tranquility/

3. 启动服务

root@druid:~/imply-2.3.# bin/supervise -c conf/supervise/data-with-query.conf

启动信息如下：

[Fri Dec   :: ] Running command[tranquility-server], logging to[/root/imply-2.3./var/sv/tranquility-server.log]: bin/tranquility server -configFile conf/tranquility/server.json

4. 发送数据

bin/generate-example-metrics | curl -XPOST -H'Content-Type: application/json' --data-binary @- http://localhost:8200/v1/post/tutorial-tranquility-server

如果成功会打印出,表名产生了25条数据到druid里

{"result":{"received":,"sent":}}

5. 查询数据

root@druid:~/imply-2.3./bin#./plyql -h localhost -p  -q "SELECT server, SUM("count") AS "events", COUNT(*) AS "rows" FROM "tutorial-tranquility-server" GROUP BY server;"

┌──────────────────┬────────┬──────┐
│ server           │ events │ rows │
├──────────────────┼────────┼──────┤
│ www1.example.com │       │     │
│ www2.example.com │       │     │
│ www3.example.com │       │     │
│ www4.example.com │       │     │
│ www5.example.com │       │     │
└──────────────────┴────────┴──────┘

6. 重启Tranquility Server:

bin/service –restart tranquility-server

2.2.2 Tranquility Kafka配置

配置流程如下 
1. 开启Tranquility Kafka，在数据节点上编辑conf/supervise/data-with-query.conf 文件，将Tranquility Kafka注释放开

# Uncomment to use Tranquility Server
!p95 tranquility-server bin/tranquility server -configFile conf/tranquility/server.json 

2. 拷贝quick里面的kafka.json

root@druid:~/imply-2.3.# cp conf-quickstart/tranquility/kafka.json conf/tranquility/

详细配置可参考：http://druid.io/docs/0.12.1/tutorials/tutorial-kafka.html

3. 在kafa集群中创建topic

root@druid:/opt/PaaS/Talas/lib/Kafka/bin#./kafka-topics.sh --create --zookeeper native-lufanfeng----:,native-lufanfeng----:,native-lufanfeng----: --replication-factor  --partitions  --topic tutorial-tranquility-kafka

4. 启动服务

root@druid:~/imply-2.3.# bin/supervise -c conf/supervise/data-with-query.conf

启动信息如下：

[Tue Dec  :: ] Running command[tranquility-kafka], logging to[/root/imply-2.3./var/sv/tranquility-kafka.log]: bin/tranquility kafka -configFile conf/tranquility/kafka.json

5. 使用kafka自带的工具发送数据

root@druid:/opt/PaaS/Talas/lib/Kafka/bin# ./kafka-console-producer.sh --broker-list native-lufanfeng----:,native-lufanfeng----:,native-lufanfeng----: --topic tutorial-tranquility-kafka
{"unit": "milliseconds", "http_method": "GET", "value": , "timestamp": "2017-12-12T05:55:59Z", "http_code": "", "page": "/list", "metricType": "request/latency", "server": "www1.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": , "timestamp": "2017-12-12T05:55:59Z", "http_code": "", "page": "/list", "metricType": "request/latency", "server": "www1.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": , "timestamp": "2017-12-12T05:55:59Z", "http_code": "", "page": "/list", "metricType": "request/latency", "server": "www5.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": , "timestamp": "2017-12-12T05:55:59Z", "http_code": "", "page": "/list", "metricType": "request/latency", "server": "www4.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": , "timestamp": "2017-12-12T05:55:59Z", "http_code": "", "page": "/", "metricType": "request/latency", "server": "www3.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": , "timestamp": "2017-12-12T05:55:59Z", "http_code": "", "page": "/list", "metricType": "request/latency", "server": "www2.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": , "timestamp": "2017-12-12T05:55:59Z", "http_code": "", "page": "/", "metricType": "request/latency", "server": "www5.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": , "timestamp": "2017-12-12T05:55:59Z", "http_code": "", "page": "/", "metricType": "request/latency", "server": "www3.example.com"}

此时观察kafka-server.log的日志会发现类似于如下输出

-- ::, [KafkaConsumer-CommitThread] INFO  c.m.tranquility.kafka.KafkaConsumer - Flushed {tutorial-tranquility-kafka={receivedCount=, sentCount=,droppedCount=, unparseableCount=}} pending messages in 0ms and committed offsets in 0ms. 

在datasource中,windowPeriod设置成了P10M,timestamp不在当前时间10M内的数据都会被过滤，由于上面的数据的timestamp和执行时间相差了大概26分钟左右，所以都会被drop调，为了达到演示效果，可以对bin/generate-example-metrics-main 的脚本进行调整。代码如下：

# Copyright 2017 Imply Data, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import json
import random
import sys
from datetime import datetime
from kafka import KafkaProducer
from kafka import KafkaClient

hosts="native-lufanfeng-2-5-24-138:9092,native-lufanfeng-3-5-24-139:9092,native-lufanfeng-4-5-24-140:9092"
# hosts="10.48.253.104:9092"
topic='tutorial-tranquility-kafka'

class KafkaSender():

    def __init__(self):
        self.client=KafkaClient(hosts)
        self.producer=KafkaProducer(bootstrap_servers=hosts)
        self.client.ensure_topic_exists(topic)
    def send_messages(self,msg):
        self.producer.send(topic,msg)
        self.producer.r

def main():
  parser = argparse.ArgumentParser(description='Generate example page request latency metrics.')
  parser.add_argument('--count', '-c', type=int, default=25, help='Number of events to generate (negative for unlimited)')
  args = parser.parse_args()

  count = 0
  sender = KafkaSender()
  while args.count < 0 or count < args.count:
    timestamp = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")

    r = random.randint(1, 4)
    if r == 1 or r == 2:
      page = '/'
    elif r == 3:
      page = '/list'
    else:
      page = '/get/' + str(random.randint(1, 99))

    server = 'www' + str(random.randint(1, 5)) + '.example.com'

    latency = max(1, random.gauss(80, 40))

    record = json.dumps({
      'timestamp': timestamp,
      'metricType': 'request/latency',
      'value': int(latency),

      # Additional dimensions
      'page': page,
      'server': server,
      'http_method': 'GET',
      'http_code': '',
      'unit': 'milliseconds'
    })
    sender.send_messages(record)
    print 'Send:%s Successful!' % record
    count += 1

try:
  main()
except KeyboardInterrupt:
  sys.exit(1)

3. 离线数据摄入

3.1 静态文件摄入

使用自带的摄入机制，可以在数据节点摄入本地文件，方法如下：

bin/post-index-task --file quickstart/wikiticker-index.json

wikiticker-index.json 文件中既包括datasource的定义，也包括数据文件位置的配置

3.2 HDFS文件摄入

配置过程可参考：http://druid.io/docs/0.12.1/ingestion/batch-ingestion.html

4. 配置参考

通用配置：https://github.com/druid-io/tranquility/blob/master/docs/configuration.md 
数据摄入通用配置:http://druid.io/docs/latest/ingestion/index.html 
Tranquility Kafka:https://github.com/druid-io/tranquility/blob/master/docs/kafka.md

5. 其他注意事项

5.1 数据分片

Druid的分片基本都是通过配置tunningConfig来配置的，实时，批量配置的方式会存在一定的差异

实时加载包括下面两种类型 
- Linear分片： 
- 添加新节点时，原节点的配置不需要调整 
- 当存在分片时数据也能被查询 
- Numbered分片 
- 所有分片存在时，才能查询 
- 需要制定分片总数

本地文件加载包括下面两种类型 
- 按照Partition大小分片 
- 设置总的分片数

Hadoop文件加载包括下面两种类型 
- 哈希分片 
- 范围分片

5.2 高基数维度优化

对于需要统计维度基数的需求，如果某个维度的基数很大，可能会存在下列问题。维度基数统计主要包括下面两种类型 
- Cardinality: 基于HyperLogLog算法，只在查询阶段做了优化，不能减少存储容量，基数大时，效率可能会有问题 
- HyperUnique: 在摄入阶段进行优化，对于不需要对高基数维度进行过滤，分组的业务场景可以使用该类型